From 8e53c6bc9f0c1b107eb5b48149a9c6ec427b1e71 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 9 Dec 2024 21:42:46 +0530 Subject: [PATCH 001/394] new swapper --- .flake8 | 2 +- .gitmodules | 3 + face_swapper/LICENSE.md | 3 + face_swapper/LivePortrait | 1 + face_swapper/config.ini | 61 +++++++++ face_swapper/src/augmentations.py | 27 ++++ face_swapper/src/data_loader.py | 82 ++++++++++++ face_swapper/src/discriminator.py | 85 ++++++++++++ face_swapper/src/generator.py | 212 ++++++++++++++++++++++++++++++ face_swapper/src/model_loader.py | 50 +++++++ face_swapper/src/sub_typing.py | 12 ++ face_swapper/src/training.py | 5 + face_swapper/train.py | 7 + mypy.ini | 1 + 14 files changed, 550 insertions(+), 1 deletion(-) create mode 100644 .gitmodules create mode 100644 face_swapper/LICENSE.md create mode 160000 face_swapper/LivePortrait create mode 100644 face_swapper/config.ini create mode 100644 face_swapper/src/augmentations.py create mode 100644 face_swapper/src/data_loader.py create mode 100644 face_swapper/src/discriminator.py create mode 100644 face_swapper/src/generator.py create mode 100644 face_swapper/src/model_loader.py create mode 100644 face_swapper/src/sub_typing.py create mode 100644 face_swapper/src/training.py create mode 100644 face_swapper/train.py diff --git a/.flake8 b/.flake8 index a840286..bd09e59 100644 --- a/.flake8 +++ b/.flake8 @@ -4,4 +4,4 @@ plugins = flake8-import-order application_import_names = arcface_converter import-order-style = pycharm per-file-ignores = preparing.py:E402 - +exclude = LivePortrait diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..79022b3 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "face_swapper/LivePortrait"] + path = face_swapper/LivePortrait + url = https://github.com/KwaiVGI/LivePortrait diff --git a/face_swapper/LICENSE.md b/face_swapper/LICENSE.md new file mode 100644 index 0000000..158be07 --- /dev/null +++ b/face_swapper/LICENSE.md @@ -0,0 +1,3 @@ +Non-Commercial license + +Copyright (c) 2024 Henry Ruhs diff --git a/face_swapper/LivePortrait b/face_swapper/LivePortrait new file mode 160000 index 0000000..632da74 --- /dev/null +++ b/face_swapper/LivePortrait @@ -0,0 +1 @@ +Subproject commit 632da7486d2c3fb86663fc44190a09aca4e1a8de diff --git a/face_swapper/config.ini b/face_swapper/config.ini new file mode 100644 index 0000000..0651a02 --- /dev/null +++ b/face_swapper/config.ini @@ -0,0 +1,61 @@ +[preparing.dataset] +dataset_path = + +[preparing.dataloader] +same_person_probability = 0.2 + +[preparing.augmentation] +expression_augmentation = false + +[training.loader] +batch_size = 6 +num_workers = 8 + +[training.generator] +num_blocks = 2 +id_channels = 512 + +[training.discriminator] +input_channels = 3 +num_filters = 64 +num_layers = 5 +num_discriminators = 3 + +[auxiliary_models.paths] +arcface_path = +landmarker_path = +motion_extractor_path = /home/hari/Documents/Github/Phantom/assets/pretrained_models/liveportrait_motion_extractor.pth +feature_extractor_path = +warping_netwrk_path = +spade_generator_path = + +[training.losses] +weight_adversarial = 1 +weight_identity = 20 +weight_attribute = 10 +weight_reconstruction = 10 +weight_tsr = 0 +weight_expression = 0 + +[training.optimizers] +scheduler_step = 5000 +scheduler_gamma = 0.2 +generator_learning_rate = 0.0004 +discriminator_learning_rate = 0.0004 + +[training.trainer] +epochs = 50 +disable_discriminator = false + +[training.output] +directory_path = +file_pattern = + +[exporting] +directory_path = +source_path = +target_path = +opset_version = + +[execution] +providers = diff --git a/face_swapper/src/augmentations.py b/face_swapper/src/augmentations.py new file mode 100644 index 0000000..5ed35d2 --- /dev/null +++ b/face_swapper/src/augmentations.py @@ -0,0 +1,27 @@ +import torch +from torch import Tensor + + +def apply_random_motion_blur(tensor_image : Tensor) -> Tensor: + kernel_size = 9 + kernel = torch.zeros((kernel_size, kernel_size), dtype=torch.float32) + random_angle = torch.empty(1).uniform_(-2 * torch.pi, 2 * torch.pi) + dx = torch.cos(random_angle) + dy = torch.sin(random_angle) + center = kernel_size // 2 + + for i in range(kernel_size): + x = int(center + (i - center) * dx) + y = int(center + (i - center) * dy) + if 0 <= x < kernel_size and 0 <= y < kernel_size: + kernel[y, x] = 1 + kernel /= kernel.sum() + kernel = kernel.unsqueeze(0).unsqueeze(0) + blurred_channels = [] + + for channel in tensor_image: + channel = channel.unsqueeze(0).unsqueeze(0) + channel = torch.nn.functional.conv2d(channel, kernel, padding=kernel_size // 2) + channel = channel.squeeze(0).squeeze(0) + blurred_channels.append(channel) + return torch.stack(blurred_channels) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py new file mode 100644 index 0000000..6019747 --- /dev/null +++ b/face_swapper/src/data_loader.py @@ -0,0 +1,82 @@ +import configparser +import glob +import random + +import cv2 +import torchvision.transforms as transforms +import tqdm +from PIL import Image +from torch.utils.data import TensorDataset + +from .augmentations import apply_random_motion_blur +from .sub_typing import Batch + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +def read_image(image_path: str) -> Image.Image: + image = cv2.imread(image_path)[:, :, ::-1] + pil_image = Image.fromarray(image) + return pil_image + + +class DataLoaderVGG(TensorDataset): + def __init__(self, dataset_path : str) -> None: + self.same_person_probability = float(CONFIG.get('preparing.dataloader', 'same_person_probability')) + self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) + self.folder_paths = glob.glob('{}/*'.format(dataset_path)) + self.image_path_dict = {} + + for folder_path in tqdm.tqdm(self.folder_paths): + image_paths = glob.glob('{}/*'.format(folder_path)) + self.image_path_dict[folder_path] = image_paths + self.dataset_total = len(self.image_paths) + self.transforms_basic = transforms.Compose( + [ + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + self.transforms_moderate = transforms.Compose( + [ + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), + transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1), fill = 0), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + self.transforms_complex = transforms.Compose( + [ + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.RandomHorizontalFlip(p = 0.5), + transforms.RandomApply([ apply_random_motion_blur ], p = 0.3), + transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation = 0.2, hue = 0.1), + transforms.RandomAffine(8, translate = (0.02, 0.02), scale = (0.98, 1.02), shear = (1, 1), fill = 0), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + + def __getitem__(self, item : int) -> Batch: + source_image_path = self.image_paths[item] + source = read_image(source_image_path) + + if random.random() > self.same_person_probability: + is_same_person = 0 + target_image_path = random.choice(self.image_paths) + target = read_image(target_image_path) + source_transform = self.transforms_moderate(source) + target_transform = self.transforms_complex(target) + else: + is_same_person = 1 + source_folder_path = '/'.join(source_image_path.split('/')[:-1]) + target_image_path = random.choice(self.image_path_dict[source_folder_path]) + target = read_image(target_image_path) + source_transform = self.transforms_basic(source) + target_transform = self.transforms_basic(target) + + return source_transform, target_transform, is_same_person + + def __len__(self) -> int: + return self.dataset_total diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py new file mode 100644 index 0000000..b1b3938 --- /dev/null +++ b/face_swapper/src/discriminator.py @@ -0,0 +1,85 @@ +from typing import List + +import numpy +import torch.nn as nn + +from .sub_typing import Tensor + + +class NLayerDiscriminator(nn.Module): + def __init__(self, input_channels : int, num_filters : int, num_layers : int) -> None: + super(NLayerDiscriminator, self).__init__() + self.num_layers = num_layers + kernel_size = 4 + padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) + model_layers = [ + [ + nn.Conv2d(input_channels, num_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), + nn.LeakyReLU(0.2, True) + ]] + current_filters = num_filters + + for layer_index in range(1, num_layers): + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + model_layers += [ + [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), + nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) + ]] + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + model_layers += [ + [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 1, padding = padding_size), + nn.InstanceNorm2d(current_filters), + nn.LeakyReLU(0.2, True) + ]] + model_layers += [ + [ + nn.Conv2d(current_filters, 1, kernel_size = kernel_size, stride = 1, padding = padding_size) + ]] + combined_layers = [] + + for layer in model_layers: + combined_layers += layer + self.model = nn.Sequential(*combined_layers) + + def forward(self, input_tensor : Tensor) -> Tensor: + return self.model(input_tensor) + + +# input_channels=3, num_filters=64, num_layers=5, num_discriminators=3 +class MultiscaleDiscriminator(nn.Module): + def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int): + super(MultiscaleDiscriminator, self).__init__() + self.num_discriminators = num_discriminators + self.num_layers = num_layers + + for discriminator_index in range(num_discriminators): + single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers) + setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) + self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] + + def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: + + if self.return_intermediate_features: + feature_maps = [ input_tensor ] + + for layer in model_layers: + feature_maps.append(layer(feature_maps[-1])) + return feature_maps[1:] + else: + return [ model_layers(input_tensor) ] + + def forward(self, input_tensor : Tensor) -> List[Tensor]: + discriminator_outputs = [] + downsampled_input = input_tensor + + for discriminator_index in range(self.num_discriminators): + model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) + discriminator_outputs.append(self.single_discriminator_forward(model_layers, downsampled_input)) + + if discriminator_index != (self.num_discriminators - 1): + downsampled_input = self.downsample(downsampled_input) + return discriminator_outputs diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py new file mode 100644 index 0000000..8b8bff2 --- /dev/null +++ b/face_swapper/src/generator.py @@ -0,0 +1,212 @@ +from typing import Tuple + +import torch +import torch.nn as nn + +from .sub_typing import Tensor, UNetAttributes + + +class AdaptiveEmbeddingIntegrationNetwork(nn.Module): + def __init__(self, id_channels : int, num_blocks : int) -> None: + super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() + self.encoder = UNet() + self.generator = AdaptiveAttentionalDenorm_Generator(id_channels, num_blocks) + + def forward(self, target : Tensor, source_embedding : Tensor) -> Tuple[Tensor, UNetAttributes]: + target_attributes = self.get_attributes(target) + swap = self.generator(target_attributes, source_embedding) + return swap, target_attributes + + def get_attributes(self, target : Tensor) -> UNetAttributes: + return self.encoder(target) + + +class AdaptiveAttentionalDenorm_Generator(nn.Module): + def __init__(self, id_channels : int, num_blocks : int) -> None: + super(AdaptiveAttentionalDenorm_Generator, self).__init__() + self.upsample = Upsample(id_channels, 1024 * 4) + self.block_1 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 1024, id_channels, num_blocks) + self.block_2 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 2048, id_channels, num_blocks) + self.block_3 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 1024, id_channels, num_blocks) + self.block_4 = AdaptiveAttentionalDenorm_ResBlock(1024, 512, 512, id_channels, num_blocks) + self.block_5 = AdaptiveAttentionalDenorm_ResBlock(512, 256, 256, id_channels, num_blocks) + self.block_6 = AdaptiveAttentionalDenorm_ResBlock(256, 128, 128, id_channels, num_blocks) + self.block_7 = AdaptiveAttentionalDenorm_ResBlock(128, 64, 64, id_channels, num_blocks) + self.block_7 = AdaptiveAttentionalDenorm_ResBlock(64, 3, 64, id_channels, num_blocks) + self.apply(initialize_weight) + + def forward(self, target_attributes : UNetAttributes, source_embedding : Tensor) -> Tensor: + feature_map = self.upsample(source_embedding) + feature_map_1 = nn.functional.interpolate(self.block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_2 = nn.functional.interpolate(self.block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_3 = nn.functional.interpolate(self.block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_4 = nn.functional.interpolate(self.block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_5 = nn.functional.interpolate(self.block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_6 = nn.functional.interpolate(self.block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + feature_map_7 = nn.functional.interpolate(self.block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) + output = self.block_7(feature_map_7, target_attributes[7], source_embedding) + return torch.tanh(output) + + +class UNet(nn.Module): + def __init__(self) -> None: + super(UNet, self).__init__() + self.downsampler_1 = Conv4x4(3, 32) + self.downsampler_2 = Conv4x4(32, 64) + self.downsampler_3 = Conv4x4(64, 128) + self.downsampler_4 = Conv4x4(128, 256) + self.downsampler_5 = Conv4x4(256, 512) + self.downsampler_6 = Conv4x4(512, 1024) + self.bottleneck = Conv4x4(1024, 1024) + self.upsampler_1 = DeConv4x4(1024, 1024) + self.upsampler_2 = DeConv4x4(2048, 512) + self.upsampler_3 = DeConv4x4(1024, 256) + self.upsampler_4 = DeConv4x4(512, 128) + self.upsampler_5 = DeConv4x4(256, 64) + self.upsampler_6 = DeConv4x4(128, 32) + self.apply(initialize_weight) + + def forward(self, input_tensor : Tensor) -> UNetAttributes: + downsample_feature_1 = self.downsampler_1(input_tensor) + downsample_feature_2 = self.downsampler_2(downsample_feature_1) + downsample_feature_3 = self.downsampler_3(downsample_feature_2) + downsample_feature_4 = self.downsampler_4(downsample_feature_3) + downsample_feature_5 = self.downsampler_5(downsample_feature_4) + downsample_feature_6 = self.downsampler_6(downsample_feature_5) + bottleneck_output = self.bottleneck(downsample_feature_6) + upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) + upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) + upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) + upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) + upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) + upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) + output = nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) + return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output + + +class AdaptiveAttentionalDenorm_Layer(nn.Module): + def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: + super(AdaptiveAttentionalDenorm_Layer, self).__init__() + self.attr_channels = attr_channels + self.id_channels = id_channels + self.input_channels = input_channels + self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) + self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) + self.fc_gamma = nn.Linear(id_channels, input_channels) + self.fc_beta = nn.Linear(id_channels, input_channels) + self.instance_norm = nn.InstanceNorm2d(input_channels, affine=False) + self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1, stride = 1, padding = 0, bias = True) + + def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : Tensor) -> Tensor: + feature_map = self.instance_norm(feature_map) + attr_gamma = self.conv_gamma(attr_embedding) + attr_beta = self.conv_beta(attr_embedding) + attr_modulation = attr_gamma * feature_map + attr_beta + id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + id_modulation = id_gamma * feature_map + id_beta + feature_mask = torch.sigmoid(self.conv_mask(feature_map)) + feature_blend = (1 - feature_mask) * attr_modulation + feature_mask * id_modulation + return feature_blend + + +class AddBlocksSequential(nn.Sequential): + def forward(self, *inputs : Tuple[Tensor, ...]) -> Tensor: + feature_map, attr_embedding, id_embedding = inputs + + for index, module in enumerate(self._modules.values()): + if index % 3 == 0 and index > 0: + inputs = (inputs, attr_embedding, id_embedding) + if type(inputs) == tuple: + inputs = module(*inputs) + else: + inputs = module(inputs) + return inputs + + +class AdaptiveAttentionalDenorm_ResBlock(nn.Module): + def __init__(self, in_channels : int, out_channels : int, attr_channels : int, id_channels : int, num_blocks : int) -> None: + super(AdaptiveAttentionalDenorm_ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + primary_add_blocks = [] + + for i in range(num_blocks): + intermediate_channels = in_channels if i < (num_blocks - 1) else out_channels + primary_add_blocks.extend( + [ + AdaptiveAttentionalDenorm_Layer(in_channels, attr_channels, id_channels), + nn.ReLU(inplace = True), + nn.Conv2d(in_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + ]) + self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) + + if in_channels != out_channels: + auxiliary_add_blocks = \ + [ + AdaptiveAttentionalDenorm_Layer(in_channels, attr_channels, id_channels), + nn.ReLU(inplace = True), + nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + ] + self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) + + def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : Tensor) -> Tensor: + primary_feature = self.primary_add_blocks(feature_map, attr_embedding, id_embedding) + + if self.in_channels != self.out_channels: + feature_map = self.auxiliary_add_blocks(feature_map, attr_embedding, id_embedding) + output_feature = primary_feature + feature_map + return output_feature + + +class Conv4x4(nn.Module): + def __init__(self, in_channels : int, out_channels : int) -> None: + super(Conv4x4, self).__init__() + self.conv = nn.Conv2d(in_channels=in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(out_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, input : Tensor) -> Tensor: + output = self.conv(input) + output = self.batch_norm(output) + output = self.leaky_relu(output) + return output + + +class DeConv4x4(nn.Module): + def __init__(self, in_channels : int, out_channels : int) -> None: + super(DeConv4x4, self).__init__() + self.deconv = nn.ConvTranspose2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(out_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, input : Tensor, skip_connection : Tensor) -> Tensor: + output = self.deconv(input) + output = self.batch_norm(output) + output = self.leaky_relu(output) + output = torch.cat((output, skip_connection), dim = 1) + return output + + +class Upsample(nn.Module): + def __init__(self, in_channels : int, out_channels : int): + super(Upsample, self).__init__() + self.initial_conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1) + self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) + + def forward(self, input : Tensor) -> Tensor: + output = self.initial_conv(input.view(input.shape[0], -1, 1, 1)) + output = self.pixel_shuffle(output) + return output + + +def initialize_weight(module : nn.Module) -> None: + if isinstance(module, nn.Linear): + module.weight.data.normal_(0, 0.001) + module.bias.data.zero_() + + if isinstance(module, nn.Conv2d): + nn.init.xavier_normal_(module.weight.data) + + if isinstance(module, nn.ConvTranspose2d): + nn.init.xavier_normal_(module.weight.data) diff --git a/face_swapper/src/model_loader.py b/face_swapper/src/model_loader.py new file mode 100644 index 0000000..db83a1a --- /dev/null +++ b/face_swapper/src/model_loader.py @@ -0,0 +1,50 @@ +import configparser + +import torch +import torch.nn as nn + +from .discriminator import MultiscaleDiscriminator +from .generator import AdaptiveEmbeddingIntegrationNetwork + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +def load_generator() -> nn.Module: + id_channels = CONFIG.getint('training.generator', 'id_channels') + num_blocks = CONFIG.getint('training.generator', 'num_blocks') + generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) + return generator + + +def load_discriminator() -> nn.Module: + input_channels = CONFIG.getint('training.discriminator', 'input_channels') + num_filters = CONFIG.getint('training.discriminator', 'num_filters') + num_layers = CONFIG.getint('training.discriminator', 'num_layers') + num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') + discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) + return discriminator + + +def load_arcface() -> nn.Module: + model_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') + arcface = torch.load(model_path, map_location = 'cpu', weights_only = False) + arcface.eval() + return arcface + + +def load_landmarker() -> nn.Module: + model_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') + landmarker = torch.load(model_path, map_location = 'cpu', weights_only = False) + landmarker.eval() + return landmarker + + +def load_motion_extractor() -> nn.Module: + from LivePortrait.src.modules.motion_extractor import MotionExtractor + + model_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') + motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') + motion_extractor.load_state_dict(torch.load(model_path, map_location = 'cpu', weights_only = True)) + motion_extractor.eval() + return motion_extractor diff --git a/face_swapper/src/sub_typing.py b/face_swapper/src/sub_typing.py new file mode 100644 index 0000000..4297b40 --- /dev/null +++ b/face_swapper/src/sub_typing.py @@ -0,0 +1,12 @@ +from typing import Any, Tuple + +from numpy.typing import NDArray +from torch import Tensor +from torch.utils.data import DataLoader + +Batch = Tuple[Tensor, Tensor, int] +Loader = DataLoader[Tuple[Tensor, ...]] +UNetAttributes = Tuple[Tensor, ...] + +Embedding = NDArray[Any] +VisionFrame = NDArray[Any] diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py new file mode 100644 index 0000000..1482236 --- /dev/null +++ b/face_swapper/src/training.py @@ -0,0 +1,5 @@ +from .model_loader import load_motion_extractor + + +def train(): + return print(load_motion_extractor()) diff --git a/face_swapper/train.py b/face_swapper/train.py new file mode 100644 index 0000000..bbff847 --- /dev/null +++ b/face_swapper/train.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python3 + +from src.training import train + + +if __name__ == '__main__': + train() diff --git a/mypy.ini b/mypy.ini index 64218bc..d144335 100644 --- a/mypy.ini +++ b/mypy.ini @@ -5,3 +5,4 @@ disallow_untyped_calls = True disallow_untyped_defs = True ignore_missing_imports = True strict_optional = False +exclude = ^LivePortrait From 7bef17b55115b4a4667a830b6f8071e56979f80c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 9 Dec 2024 17:45:57 +0100 Subject: [PATCH 002/394] Fix mypy --- mypy.ini | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/mypy.ini b/mypy.ini index d144335..081e7bf 100644 --- a/mypy.ini +++ b/mypy.ini @@ -5,4 +5,5 @@ disallow_untyped_calls = True disallow_untyped_defs = True ignore_missing_imports = True strict_optional = False -exclude = ^LivePortrait +explicit_package_bases = True +exclude = face_swapper/LivePortrait From e6c2a64256b77a120066127edcee0b6e9ea536ec Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 10 Dec 2024 22:19:03 +0530 Subject: [PATCH 003/394] ugly training code --- .flake8 | 2 +- face_swapper/config.ini | 48 ++- face_swapper/src/data_loader.py | 15 +- face_swapper/src/discriminator.py | 15 +- face_swapper/src/generator.py | 166 ++++---- face_swapper/src/helper.py | 128 ++++++ face_swapper/src/model_loader.py | 50 --- face_swapper/src/training.py | 388 +++++++++++++++++- face_swapper/src/{sub_typing.py => typing.py} | 8 +- mypy.ini | 2 +- 10 files changed, 655 insertions(+), 167 deletions(-) create mode 100644 face_swapper/src/helper.py delete mode 100644 face_swapper/src/model_loader.py rename face_swapper/src/{sub_typing.py => typing.py} (50%) diff --git a/.flake8 b/.flake8 index bd09e59..8103c45 100644 --- a/.flake8 +++ b/.flake8 @@ -4,4 +4,4 @@ plugins = flake8-import-order application_import_names = arcface_converter import-order-style = pycharm per-file-ignores = preparing.py:E402 -exclude = LivePortrait +exclude = face_swapper diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 0651a02..b94f925 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,55 +1,65 @@ [preparing.dataset] -dataset_path = +dataset_path = /assets/VGGface2_None_norm_512_true_bygfpgan [preparing.dataloader] same_person_probability = 0.2 [preparing.augmentation] -expression_augmentation = false +expression = false [training.loader] -batch_size = 6 +batch_size = 4 num_workers = 8 [training.generator] num_blocks = 2 id_channels = 512 +learning_rate = 0.0004 [training.discriminator] input_channels = 3 num_filters = 64 num_layers = 5 num_discriminators = 3 +learning_rate = 0.0004 +disable = false [auxiliary_models.paths] -arcface_path = -landmarker_path = -motion_extractor_path = /home/hari/Documents/Github/Phantom/assets/pretrained_models/liveportrait_motion_extractor.pth -feature_extractor_path = -warping_netwrk_path = -spade_generator_path = +arcface_path = /assets/pretrained_models/arcface_w600k_r50.pt +landmarker_path = /assets/pretrained_models/landmark_203.pt +motion_extractor_path = /assets/pretrained_models/liveportrait_motion_extractor.pth +feature_extractor_path = /assets/pretrained_models/liveportrait_feature_extractor.pth +warping_network_path = /assets/pretrained_models/liveportrait_warping_model.pth +spade_generator_path = /assets/pretrained_models/liveportrait_spade_generator.pth [training.losses] weight_adversarial = 1 weight_identity = 20 weight_attribute = 10 weight_reconstruction = 10 -weight_tsr = 0 -weight_expression = 0 +weight_tsr = 100 +weight_eye_gaze = 5 +weight_eye_open = 5 +weight_lip_open = 5 -[training.optimizers] -scheduler_step = 5000 -scheduler_gamma = 0.2 -generator_learning_rate = 0.0004 -discriminator_learning_rate = 0.0004 +[training.schedulers] +step = 5000 +gamma = 0.2 [training.trainer] -epochs = 50 +max_epochs = 50 disable_discriminator = false [training.output] -directory_path = -file_pattern = +checkpoint_path = checkpoints/last.ckpt +directory_path = checkpoints +file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' +preview_frequency = 250 +validation_frequency = 1000 + +[training.validation] +sources = assets/test/front/sources +targets = assets/test/front/targets [exporting] directory_path = diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 6019747..412a970 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -9,7 +9,7 @@ from PIL import Image from torch.utils.data import TensorDataset from .augmentations import apply_random_motion_blur -from .sub_typing import Batch +from .typing import Batch CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -27,6 +27,7 @@ class DataLoaderVGG(TensorDataset): self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) self.folder_paths = glob.glob('{}/*'.format(dataset_path)) self.image_path_dict = {} + self._current_index = 0 for folder_path in tqdm.tqdm(self.folder_paths): image_paths = glob.glob('{}/*'.format(folder_path)) @@ -50,12 +51,12 @@ class DataLoaderVGG(TensorDataset): [ transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), transforms.RandomHorizontalFlip(p = 0.5), transforms.RandomApply([ apply_random_motion_blur ], p = 0.3), transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(8, translate = (0.02, 0.02), scale = (0.98, 1.02), shear = (1, 1), fill = 0), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), ]) def __getitem__(self, item : int) -> Batch: @@ -80,3 +81,11 @@ class DataLoaderVGG(TensorDataset): def __len__(self) -> int: return self.dataset_total + + + def state_dict(self): + return {'current_index': self._current_index} + + + def load_state_dict(self, state_dict): + self._current_index = state_dict['current_index'] diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index b1b3938..b38ca9e 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -3,7 +3,7 @@ from typing import List import numpy import torch.nn as nn -from .sub_typing import Tensor +from .typing import Tensor, DiscriminatorOutputs class NLayerDiscriminator(nn.Module): @@ -49,7 +49,6 @@ class NLayerDiscriminator(nn.Module): return self.model(input_tensor) -# input_channels=3, num_filters=64, num_layers=5, num_discriminators=3 class MultiscaleDiscriminator(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int): super(MultiscaleDiscriminator, self).__init__() @@ -61,18 +60,12 @@ class MultiscaleDiscriminator(nn.Module): setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] + def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: + return [ model_layers(input_tensor) ] - if self.return_intermediate_features: - feature_maps = [ input_tensor ] - for layer in model_layers: - feature_maps.append(layer(feature_maps[-1])) - return feature_maps[1:] - else: - return [ model_layers(input_tensor) ] - - def forward(self, input_tensor : Tensor) -> List[Tensor]: + def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: discriminator_outputs = [] downsampled_input = input_tensor diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index 8b8bff2..3dd18c9 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -1,55 +1,52 @@ -from typing import Tuple - import torch import torch.nn as nn - -from .sub_typing import Tensor, UNetAttributes +import torch.nn.functional as F class AdaptiveEmbeddingIntegrationNetwork(nn.Module): - def __init__(self, id_channels : int, num_blocks : int) -> None: + def __init__(self, id_channels=512, num_blocks=2): super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() self.encoder = UNet() - self.generator = AdaptiveAttentionalDenorm_Generator(id_channels, num_blocks) + self.generator = AADGenerator(id_channels, num_blocks) - def forward(self, target : Tensor, source_embedding : Tensor) -> Tuple[Tensor, UNetAttributes]: + def forward(self, target, source_embedding): target_attributes = self.get_attributes(target) swap = self.generator(target_attributes, source_embedding) return swap, target_attributes - def get_attributes(self, target : Tensor) -> UNetAttributes: + def get_attributes(self, target): return self.encoder(target) -class AdaptiveAttentionalDenorm_Generator(nn.Module): - def __init__(self, id_channels : int, num_blocks : int) -> None: - super(AdaptiveAttentionalDenorm_Generator, self).__init__() +class AADGenerator(nn.Module): + def __init__(self, id_channels=512, num_blocks=2): + super(AADGenerator, self).__init__() self.upsample = Upsample(id_channels, 1024 * 4) - self.block_1 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 1024, id_channels, num_blocks) - self.block_2 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 2048, id_channels, num_blocks) - self.block_3 = AdaptiveAttentionalDenorm_ResBlock(1024, 1024, 1024, id_channels, num_blocks) - self.block_4 = AdaptiveAttentionalDenorm_ResBlock(1024, 512, 512, id_channels, num_blocks) - self.block_5 = AdaptiveAttentionalDenorm_ResBlock(512, 256, 256, id_channels, num_blocks) - self.block_6 = AdaptiveAttentionalDenorm_ResBlock(256, 128, 128, id_channels, num_blocks) - self.block_7 = AdaptiveAttentionalDenorm_ResBlock(128, 64, 64, id_channels, num_blocks) - self.block_7 = AdaptiveAttentionalDenorm_ResBlock(64, 3, 64, id_channels, num_blocks) + self.AADBlk1 = AAD_ResBlk(1024, 1024, 1024, id_channels, num_blocks) + self.AADBlk2 = AAD_ResBlk(1024, 1024, 2048, id_channels, num_blocks) + self.AADBlk3 = AAD_ResBlk(1024, 1024, 1024, id_channels, num_blocks) + self.AADBlk4 = AAD_ResBlk(1024, 512, 512, id_channels, num_blocks) + self.AADBlk5 = AAD_ResBlk(512, 256, 256, id_channels, num_blocks) + self.AADBlk6 = AAD_ResBlk(256, 128, 128, id_channels, num_blocks) + self.AADBlk7 = AAD_ResBlk(128, 64, 64, id_channels, num_blocks) + self.AADBlk8 = AAD_ResBlk(64, 3, 64, id_channels, num_blocks) self.apply(initialize_weight) - def forward(self, target_attributes : UNetAttributes, source_embedding : Tensor) -> Tensor: + def forward(self, target_attributes, source_embedding): feature_map = self.upsample(source_embedding) - feature_map_1 = nn.functional.interpolate(self.block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_2 = nn.functional.interpolate(self.block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_3 = nn.functional.interpolate(self.block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_4 = nn.functional.interpolate(self.block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_5 = nn.functional.interpolate(self.block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_6 = nn.functional.interpolate(self.block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - feature_map_7 = nn.functional.interpolate(self.block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode ='bilinear', align_corners = False) - output = self.block_7(feature_map_7, target_attributes[7], source_embedding) + feature_map_1 = F.interpolate(self.AADBlk1(feature_map, target_attributes[0], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_2 = F.interpolate(self.AADBlk2(feature_map_1, target_attributes[1], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_3 = F.interpolate(self.AADBlk3(feature_map_2, target_attributes[2], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_4 = F.interpolate(self.AADBlk4(feature_map_3, target_attributes[3], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_5 = F.interpolate(self.AADBlk5(feature_map_4, target_attributes[4], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_6 = F.interpolate(self.AADBlk6(feature_map_5, target_attributes[5], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + feature_map_7 = F.interpolate(self.AADBlk7(feature_map_6, target_attributes[6], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) + output = self.AADBlk8(feature_map_7, target_attributes[7], source_embedding) return torch.tanh(output) class UNet(nn.Module): - def __init__(self) -> None: + def __init__(self): super(UNet, self).__init__() self.downsampler_1 = Conv4x4(3, 32) self.downsampler_2 = Conv4x4(32, 64) @@ -57,7 +54,9 @@ class UNet(nn.Module): self.downsampler_4 = Conv4x4(128, 256) self.downsampler_5 = Conv4x4(256, 512) self.downsampler_6 = Conv4x4(512, 1024) + self.bottleneck = Conv4x4(1024, 1024) + self.upsampler_1 = DeConv4x4(1024, 1024) self.upsampler_2 = DeConv4x4(2048, 512) self.upsampler_3 = DeConv4x4(1024, 256) @@ -66,53 +65,64 @@ class UNet(nn.Module): self.upsampler_6 = DeConv4x4(128, 32) self.apply(initialize_weight) - def forward(self, input_tensor : Tensor) -> UNetAttributes: + def forward(self, input_tensor): downsample_feature_1 = self.downsampler_1(input_tensor) downsample_feature_2 = self.downsampler_2(downsample_feature_1) downsample_feature_3 = self.downsampler_3(downsample_feature_2) downsample_feature_4 = self.downsampler_4(downsample_feature_3) downsample_feature_5 = self.downsampler_5(downsample_feature_4) downsample_feature_6 = self.downsampler_6(downsample_feature_5) + bottleneck_output = self.bottleneck(downsample_feature_6) + upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) - output = nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) + + output = F.interpolate(upsample_feature_6, scale_factor=2, mode='bilinear', align_corners=False) + return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output -class AdaptiveAttentionalDenorm_Layer(nn.Module): - def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: - super(AdaptiveAttentionalDenorm_Layer, self).__init__() +class AADLayer(nn.Module): + def __init__(self, input_channels, attr_channels, id_channels): + super(AADLayer, self).__init__() self.attr_channels = attr_channels self.id_channels = id_channels self.input_channels = input_channels - self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) - self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) + + self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=True) + self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=True) self.fc_gamma = nn.Linear(id_channels, input_channels) self.fc_beta = nn.Linear(id_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels, affine=False) - self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1, stride = 1, padding = 0, bias = True) - def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : Tensor) -> Tensor: + self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, feature_map, attr_embedding, id_embedding): feature_map = self.instance_norm(feature_map) + attr_gamma = self.conv_gamma(attr_embedding) attr_beta = self.conv_beta(attr_embedding) attr_modulation = attr_gamma * feature_map + attr_beta - id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + + id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as( + feature_map) + id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as( + feature_map) id_modulation = id_gamma * feature_map + id_beta + feature_mask = torch.sigmoid(self.conv_mask(feature_map)) feature_blend = (1 - feature_mask) * attr_modulation + feature_mask * id_modulation return feature_blend class AddBlocksSequential(nn.Sequential): - def forward(self, *inputs : Tuple[Tensor, ...]) -> Tensor: - feature_map, attr_embedding, id_embedding = inputs + def forward(self, *inputs): + h, attr_embedding, id_embedding = inputs for index, module in enumerate(self._modules.values()): if index % 3 == 0 and index > 0: @@ -124,9 +134,9 @@ class AddBlocksSequential(nn.Sequential): return inputs -class AdaptiveAttentionalDenorm_ResBlock(nn.Module): - def __init__(self, in_channels : int, out_channels : int, attr_channels : int, id_channels : int, num_blocks : int) -> None: - super(AdaptiveAttentionalDenorm_ResBlock, self).__init__() +class AAD_ResBlk(nn.Module): + def __init__(self, in_channels, out_channels, attr_channels, id_channels, num_blocks): + super(AAD_ResBlk, self).__init__() self.in_channels = in_channels self.out_channels = out_channels primary_add_blocks = [] @@ -135,22 +145,22 @@ class AdaptiveAttentionalDenorm_ResBlock(nn.Module): intermediate_channels = in_channels if i < (num_blocks - 1) else out_channels primary_add_blocks.extend( [ - AdaptiveAttentionalDenorm_Layer(in_channels, attr_channels, id_channels), - nn.ReLU(inplace = True), - nn.Conv2d(in_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + AADLayer(in_channels, attr_channels, id_channels), + nn.ReLU(inplace=True), + nn.Conv2d(in_channels, intermediate_channels, kernel_size=3, stride=1, padding=1, bias=False) ]) self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) if in_channels != out_channels: auxiliary_add_blocks = \ [ - AdaptiveAttentionalDenorm_Layer(in_channels, attr_channels, id_channels), - nn.ReLU(inplace = True), - nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + AADLayer(in_channels, attr_channels, id_channels), + nn.ReLU(inplace=True), + nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) ] self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) - def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : Tensor) -> Tensor: + def forward(self, feature_map, attr_embedding, id_embedding): primary_feature = self.primary_add_blocks(feature_map, attr_embedding, id_embedding) if self.in_channels != self.out_channels: @@ -160,47 +170,49 @@ class AdaptiveAttentionalDenorm_ResBlock(nn.Module): class Conv4x4(nn.Module): - def __init__(self, in_channels : int, out_channels : int) -> None: + def __init__(self, in_channels, out_channels): super(Conv4x4, self).__init__() - self.conv = nn.Conv2d(in_channels=in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=4, stride=2, padding=1, + bias=False) self.batch_norm = nn.BatchNorm2d(out_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + self.leaky_relu = nn.LeakyReLU(0.1, inplace=True) - def forward(self, input : Tensor) -> Tensor: - output = self.conv(input) - output = self.batch_norm(output) - output = self.leaky_relu(output) - return output + def forward(self, x): + x = self.conv(x) + x = self.batch_norm(x) + x = self.leaky_relu(x) + return x class DeConv4x4(nn.Module): - def __init__(self, in_channels : int, out_channels : int) -> None: + def __init__(self, in_channels, out_channels): super(DeConv4x4, self).__init__() - self.deconv = nn.ConvTranspose2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.deconv = nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=4, stride=2, + padding=1, bias=False) self.batch_norm = nn.BatchNorm2d(out_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + self.leaky_relu = nn.LeakyReLU(0.1, inplace=True) - def forward(self, input : Tensor, skip_connection : Tensor) -> Tensor: - output = self.deconv(input) - output = self.batch_norm(output) - output = self.leaky_relu(output) - output = torch.cat((output, skip_connection), dim = 1) - return output + def forward(self, x, skip): + x = self.deconv(x) + x = self.batch_norm(x) + x = self.leaky_relu(x) + return torch.cat((x, skip), dim=1) class Upsample(nn.Module): - def __init__(self, in_channels : int, out_channels : int): + def __init__(self, in_channels, out_channels): super(Upsample, self).__init__() - self.initial_conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1) - self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) + self.initial_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, + padding=1) + self.pixel_shuffle = nn.PixelShuffle(upscale_factor=2) - def forward(self, input : Tensor) -> Tensor: - output = self.initial_conv(input.view(input.shape[0], -1, 1, 1)) - output = self.pixel_shuffle(output) - return output + def forward(self, x): + x = self.initial_conv(x.view(x.shape[0], -1, 1, 1)) + x = self.pixel_shuffle(x) + return x -def initialize_weight(module : nn.Module) -> None: +def initialize_weight(module): if isinstance(module, nn.Linear): module.weight.data.normal_(0, 0.001) module.bias.data.zero_() diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py new file mode 100644 index 0000000..4a6b2bd --- /dev/null +++ b/face_swapper/src/helper.py @@ -0,0 +1,128 @@ +import configparser +from typing import Tuple + +import torch +from .typing import Tensor +import numpy +import torch.nn.functional as F + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + +if CONFIG.getboolean('preparing.augmentation', 'expression'): + from LivePortrait.src.utils.camera import headpose_pred_to_degree, get_rotation_matrix + +L2_loss = torch.nn.MSELoss() +EXPRESSION_MIN = numpy.array( +[ + [ + [-2.88067125e-02, -8.12731311e-02, -1.70541159e-03], + [-4.88598682e-02, -3.32196616e-02, -1.67431499e-04], + [-6.75425082e-02, -4.28681746e-02, -1.98950816e-04], + [-7.23103955e-02, -3.28503326e-02, -7.31324719e-04], + [-3.87073644e-02, -6.01546466e-02, -5.50269964e-04], + [-6.38048723e-02, -2.23840728e-01, -7.13261834e-04], + [-3.02710701e-02, -3.93195450e-02, -8.24086510e-06], + [-2.95799859e-02, -5.39318882e-02, -1.74219604e-04], + [-2.92359516e-02, -1.53050944e-02, -6.30460854e-05], + [-5.56493877e-03, -2.34344602e-02, -1.26858242e-04], + [-4.37593013e-02, -2.77768299e-02, -2.70503685e-02], + [-1.76926646e-02, -1.91676542e-02, -1.15090821e-04], + [-8.34268332e-03, -3.99775570e-03, -3.27481248e-05], + [-3.40162888e-02, -2.81868968e-02, -1.96679524e-04], + [-2.91855410e-02, -3.97511162e-02, -2.81230678e-05], + [-1.50395725e-02, -2.49494594e-02, -9.42573533e-05], + [-1.67938769e-02, -2.00953931e-02, -4.00750607e-04], + [-1.86435618e-02, -2.48535164e-02, -2.74416432e-02], + [-4.61211195e-03, -1.21660791e-02, -2.93173041e-04], + [-4.10017073e-02, -7.43824020e-02, -4.42762971e-02], + [-1.90370996e-02, -3.74363363e-02, -1.34740388e-02] + ] +]).astype(numpy.float32) +EXPRESSION_MAX = numpy.array( +[ + [ + [4.46682945e-02, 7.08772913e-02, 4.08344204e-04], + [2.14308221e-02, 6.15894832e-02, 4.85319615e-05], + [3.02363783e-02, 4.45043296e-02, 1.28298725e-05], + [3.05869691e-02, 3.79812494e-02, 6.57040102e-04], + [4.45670523e-02, 3.97259220e-02, 7.10966764e-04], + [9.43699256e-02, 9.85926315e-02, 2.02551950e-04], + [1.61131397e-02, 2.92906128e-02, 3.44733417e-06], + [5.23825921e-02, 1.07065082e-01, 6.61510974e-04], + [2.85718683e-03, 8.32320191e-03, 2.39314613e-04], + [2.57947259e-02, 1.60935968e-02, 2.41853559e-05], + [4.90833223e-02, 3.43903080e-02, 3.22353356e-02], + [1.44766076e-02, 3.39248963e-02, 1.42291479e-04], + [8.75749043e-04, 6.82212645e-03, 2.76097053e-05], + [1.86958015e-02, 3.84016186e-02, 7.33085908e-05], + [2.01714113e-02, 4.90544215e-02, 2.34028921e-05], + [2.46518422e-02, 3.29151377e-02, 3.48571630e-05], + [2.22457591e-02, 1.21796541e-02, 1.56396593e-04], + [1.72109623e-02, 3.01626958e-02, 1.36556877e-02], + [1.83460284e-02, 1.61141958e-02, 2.87440169e-04], + [3.57594155e-02, 1.80554688e-01, 2.75554154e-02], + [2.17450950e-02, 8.66811201e-02, 3.34241726e-02] + ] +]).astype(numpy.float32) + + +def randomize_expression(face_tensor, feature_extractor, motion_extractor, warping_network, spade_generator): + with torch.no_grad(): + face_tensor_norm = (face_tensor + 1) * 0.5 + input_device = face_tensor.device + feature_volume = feature_extractor(face_tensor_norm) + motion_extractor_dict = motion_extractor(face_tensor_norm) + + translation = motion_extractor_dict.get('t') + expression = motion_extractor_dict.get('exp') + scale = motion_extractor_dict.get('scale') + points = motion_extractor_dict.get('kp') + + pitch = headpose_pred_to_degree(motion_extractor_dict.get('pitch'))[:, None] + yaw = headpose_pred_to_degree(motion_extractor_dict.get('yaw'))[:, None] + roll = headpose_pred_to_degree(motion_extractor_dict.get('roll'))[:, None] + rotation_matrix = get_rotation_matrix(pitch, yaw, roll) + random_expression = get_random_expression_blend(expression) + + points_transformed = transform_points(points, rotation_matrix, expression, scale, translation) + points_driv = transform_points(points, rotation_matrix, random_expression, scale, translation) + + data = warping_network(feature_volume, points_driv, points_transformed).get('out') + output = spade_generator(data) + output = output.to(input_device) + output = F.interpolate(output.clamp(0, 1), [256, 256], mode='bilinear', align_corners=False) + output = (output - 0.5) * 2 + return output + + +def get_random_expression_blend(expression : Tensor) -> Tensor: + blend = 0.35 + expression = expression.view(-1, 21, 3) + min_array = torch.from_numpy(EXPRESSION_MIN).to(expression.device).to(expression.dtype).expand(expression.shape[0], -1, -1) + max_array = torch.from_numpy(EXPRESSION_MAX).to(expression.device).to(expression.dtype).expand(expression.shape[0], -1, -1) + random_batch = torch.rand_like(min_array).to(expression.device) * (max_array - min_array) + min_array + random_batch[:, [0, 1, 8, 6, 9, 4, 5, 10]] = expression[:, [0, 1, 8, 6, 9, 4, 5, 10]] + random_batch[:, [3, 7]] = random_batch[:, [13, 16]] * 0.1 + expression[:, [13, 16]] * 0.9 + random_batch[:, [3, 7]] = random_batch[:, [3, 7]] * 0.5 + expression[:, [3, 7]] * 0.5 + return random_batch * 0.8 * blend + expression * (1 - blend) + + +def transform_points(points : Tensor, rotation_matrix : Tensor, expression : Tensor, scale : Tensor, translation : Tensor): + points_transformed = points.view(-1, 21, 3) @ rotation_matrix + expression.view(-1, 21, 3) + points_transformed *= scale[..., None] + points_transformed[:, :, 0:2] += translation[:, None, 0:2] + return points_transformed + + +def hinge_loss(tensor : Tensor, is_positive : bool) -> Tensor: + if is_positive: + return torch.relu(1 - tensor) + else: + return torch.relu(tensor + 1) + + +def calc_distance_ratio(landmarks : Tensor, indices : Tuple[int, int, int, int]) -> Tensor: + distance_horizontal = torch.norm(landmarks[:, indices[0]] - landmarks[:, indices[1]], p = 2, dim = 1, keepdim = True) + distance_vertical = torch.norm(landmarks[:, indices[2]] - landmarks[:, indices[3]], p=2, dim = 1, keepdim = True) + return distance_horizontal / (distance_vertical + 1e-4) diff --git a/face_swapper/src/model_loader.py b/face_swapper/src/model_loader.py deleted file mode 100644 index db83a1a..0000000 --- a/face_swapper/src/model_loader.py +++ /dev/null @@ -1,50 +0,0 @@ -import configparser - -import torch -import torch.nn as nn - -from .discriminator import MultiscaleDiscriminator -from .generator import AdaptiveEmbeddingIntegrationNetwork - -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - - -def load_generator() -> nn.Module: - id_channels = CONFIG.getint('training.generator', 'id_channels') - num_blocks = CONFIG.getint('training.generator', 'num_blocks') - generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) - return generator - - -def load_discriminator() -> nn.Module: - input_channels = CONFIG.getint('training.discriminator', 'input_channels') - num_filters = CONFIG.getint('training.discriminator', 'num_filters') - num_layers = CONFIG.getint('training.discriminator', 'num_layers') - num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') - discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) - return discriminator - - -def load_arcface() -> nn.Module: - model_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') - arcface = torch.load(model_path, map_location = 'cpu', weights_only = False) - arcface.eval() - return arcface - - -def load_landmarker() -> nn.Module: - model_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') - landmarker = torch.load(model_path, map_location = 'cpu', weights_only = False) - landmarker.eval() - return landmarker - - -def load_motion_extractor() -> nn.Module: - from LivePortrait.src.modules.motion_extractor import MotionExtractor - - model_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') - motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') - motion_extractor.load_state_dict(torch.load(model_path, map_location = 'cpu', weights_only = True)) - motion_extractor.eval() - return motion_extractor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1482236..182fb58 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,5 +1,389 @@ -from .model_loader import load_motion_extractor +import configparser +import random + +from sympy.stats.sampling.sample_numpy import numpy + +from typing import Tuple +import os +import cv2 +import torchvision + +import pytorch_lightning +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import ModelCheckpoint +from torch.utils.data import DataLoader +from pytorch_lightning.utilities.types import OptimizerLRScheduler +import torch + +from .discriminator import MultiscaleDiscriminator +from .generator import AdaptiveEmbeddingIntegrationNetwork +from .data_loader import DataLoaderVGG, read_image + +from .typing import Tensor, LossDict, TargetAttributes, DiscriminatorOutputs, Batch +from .helper import hinge_loss, calc_distance_ratio, L2_loss, randomize_expression +from pytorch_msssim import ssim + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +def load_models(): + id_channels = CONFIG.getint('training.generator', 'id_channels') + num_blocks = CONFIG.getint('training.generator', 'num_blocks') + generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) + + input_channels = CONFIG.getint('training.discriminator', 'input_channels') + num_filters = CONFIG.getint('training.discriminator', 'num_filters') + num_layers = CONFIG.getint('training.discriminator', 'num_layers') + num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') + discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) + + model_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') + arcface = torch.load(model_path, map_location = 'cpu', weights_only = False) + arcface.eval() + + if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0 or CONFIG.getfloat('training.losses', 'weight_eye_open') > 0 or CONFIG.getfloat('training.losses', 'weight_lip_open') > 0: + model_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') + landmarker = torch.load(model_path, map_location = 'cpu', weights_only = False) + landmarker.eval() + else: + landmarker = None + + if CONFIG.getfloat('training.losses', 'weight_tsr') > 0 or CONFIG.getboolean('preparing.augmentation', 'expression'): + from LivePortrait.src.modules.motion_extractor import MotionExtractor + + model_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') + motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') + motion_extractor.load_state_dict(torch.load(model_path, map_location = 'cpu', weights_only = True)) + motion_extractor.eval() + else: + motion_extractor = None + + if CONFIG.getboolean('preparing.augmentation', 'expression'): + from LivePortrait.src.modules.appearance_feature_extractor import AppearanceFeatureExtractor + from LivePortrait.src.modules.warping_network import WarpingNetwork + from LivePortrait.src.modules.spade_generator import SPADEDecoder + + feature_extractor_path = CONFIG.get('auxiliary_models.paths', 'feature_extractor_path') + feature_extractor = AppearanceFeatureExtractor(3, 64, 2, 512, 32, 16, 6) + feature_extractor.load_state_dict(torch.load(feature_extractor_path, map_location = 'cpu', weights_only = True)) + feature_extractor.eval() + + warping_network_path = CONFIG.get('auxiliary_models.paths', 'warping_network_path') + dense_motion_params = { + 'block_expansion': 32, + 'max_features': 1024, + 'num_blocks': 5, + 'reshape_depth': 16, + 'compress': 4 + } + warping_network = WarpingNetwork(num_kp = 21, block_expansion = 64, max_features = 512, num_down_blocks = 2, reshape_channel = 32, estimate_occlusion_map = True, dense_motion_params = dense_motion_params) + warping_network.load_state_dict(torch.load(warping_network_path, map_location='cpu', weights_only=True)) + warping_network.eval() + + spade_generator_path = CONFIG.get('auxiliary_models.paths', 'spade_generator_path') + spade_generator = SPADEDecoder(upscale = 2, block_expansion = 64, max_features = 512, num_down_blocks = 2) + spade_generator.load_state_dict(torch.load(spade_generator_path, map_location = 'cpu', weights_only = True)) + spade_generator.eval() + else: + feature_extractor = None + warping_network = None + spade_generator = None + return generator, discriminator, arcface, landmarker, motion_extractor, feature_extractor, warping_network, spade_generator + + +def create_trainer() -> Trainer: + trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') + output_directory_path = CONFIG.get('training.output', 'directory_path') + output_file_pattern = CONFIG.get('training.output', 'file_pattern') + os.makedirs(output_directory_path, exist_ok = True) + + return Trainer( + max_epochs = trainer_max_epochs, + precision = '16-mixed', + callbacks = + [ + ModelCheckpoint( + monitor = 'l_G', + dirpath = output_directory_path, + filename = output_file_pattern, + # every_n_epochs = 1, + every_n_train_steps = 1000, + save_top_k = 5, + mode = 'min', + save_last = True + ) + ], + log_every_n_steps = 10, + accumulate_grad_batches = 1, + ) def train(): - return print(load_motion_extractor()) + batch_size = CONFIG.getint('training.loader', 'batch_size') + num_workers = CONFIG.getint('training.loader', 'num_workers') + checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') + dataset = DataLoaderVGG(CONFIG.get('preparing.dataset', 'dataset_path')) + + if not (checkpoint_path and os.path.exists(checkpoint_path)): + checkpoint_path = None + data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + face_swap_model = FaceSwapper(*load_models()) + trainer = create_trainer() + trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path) + + +class FaceSwapper(pytorch_lightning.LightningModule): + def __init__(self, generator, discriminator, arcface, landmarker, motion_extractor, feature_extractor, warping_network, spade_generator) -> None: + super().__init__() + self.generator = generator + self.discriminator = discriminator + self.arcface = arcface + self.landmarker = landmarker + self.motion_extractor = motion_extractor + self.feature_extractor = feature_extractor + self.warping_network = warping_network + self.spade_generator = spade_generator + + self.loss_adversarial_accumulated = 20 + self.automatic_optimization = False + self.batch_size = CONFIG.getint('training.loader', 'batch_size') + + + def forward(self, target_tensor : Tensor, source_embedding : Tensor) -> Tensor: + output = self.generator(target_tensor, source_embedding) + return output + + + def state_dict(self, *args, **kwargs): + return { + "generator": self.generator.state_dict(), + "discriminator": self.discriminator.state_dict(), + } + + def load_state_dict(self, state_dict, strict: bool = True): + if "generator" in state_dict: + self.generator.load_state_dict(state_dict["generator"], strict = strict) + if "discriminator" in state_dict: + self.discriminator.load_state_dict(state_dict["discriminator"], strict = strict) + + + def configure_optimizers(self) -> OptimizerLRScheduler: + generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = CONFIG.getfloat('training.generator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = CONFIG.getfloat('training.discriminator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) + generator_scheduler = torch.optim.lr_scheduler.StepLR(generator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) + discriminator_scheduler = torch.optim.lr_scheduler.StepLR(discriminator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) + return ( + { + "optimizer": generator_optimizer, + "lr_scheduler": generator_scheduler + }, + { + "optimizer": discriminator_optimizer, + "lr_scheduler": discriminator_scheduler + }) + + + def training_step(self, batch : Batch, batch_index : int) -> Tensor: + source_tensor, target_tensor, is_same_person = batch + generator_optimizer, discriminator_optimizer = self.optimizers() + source_embedding = self.get_arcface_embedding(source_tensor, (0, 0, 0, 0)) + + if random.random() > 0.5 and CONFIG.getboolean('preparing.augmentation', 'expression'): + target_tensor = randomize_expression(target_tensor, self.feature_extractor, self.motion_extractor, self.warping_network, self.spade_generator) + + swap_tensor, target_attributes = self(target_tensor, source_embedding) + discriminator_outputs = self.discriminator(swap_tensor) + + generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, discriminator_outputs, batch) + generator_optimizer.zero_grad() + self.manual_backward(generator_losses.get('loss_generator')) + generator_optimizer.step() + + discriminator_losses = self.calc_discriminator_loss(swap_tensor, source_tensor) + discriminator_optimizer.zero_grad() + self.manual_backward(discriminator_losses.get('loss_discriminator')) + + if not CONFIG.getboolean('training.discriminator', 'disable') or self.loss_adversarial_accumulated < 0.4: + discriminator_optimizer.step() + + if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: + self.log_generator_preview(source_tensor, target_tensor, swap_tensor) + + if self.global_step % CONFIG.getint('training.output', 'validation_frequency') == 0: + self.log_validation_preview() + self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) + self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) + self.log('l_ADV_A', self.loss_adversarial_accumulated, prog_bar = True) + self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = False) + self.log('l_id', generator_losses.get('loss_identity'), prog_bar = True) + self.log('l_attr', generator_losses.get('loss_attribute'), prog_bar = True) + self.log('l_rec', generator_losses.get('loss_reconstruction'), prog_bar = True) + return generator_losses.get('loss_generator') + + + def calc_generator_loss(self, swap_tensor : Tensor, target_attributes : TargetAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> LossDict: + source_tensor, target_tensor, is_same_person = batch + generator_losses = {} + # adversarial loss + loss_adversarial = 0 + + for discriminator_output in discriminator_outputs: + loss_adversarial += hinge_loss(discriminator_output[0], True).mean(dim = [ 1, 2, 3 ]) + loss_adversarial = torch.mean(loss_adversarial) + generator_losses['loss_adversarial'] = loss_adversarial + generator_losses['loss_generator'] = loss_adversarial * CONFIG.getfloat('training.losses', 'weight_adversarial') + self.loss_adversarial_accumulated = self.loss_adversarial_accumulated * 0.98 + loss_adversarial.item() * 0.02 + + # identity loss + swap_embedding = self.get_arcface_embedding(swap_tensor, (30, 0, 10, 10)) + source_embedding = self.get_arcface_embedding(source_tensor, (30, 0, 10, 10)) + loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() + generator_losses['loss_identity'] = loss_identity + generator_losses['loss_generator'] += loss_identity * CONFIG.getfloat('training.losses', 'weight_identity') + + # attribute loss + loss_attribute = 0 + swap_attributes = self.generator.get_attributes(swap_tensor) + + for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): + loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() + loss_attribute *= 0.5 + generator_losses['loss_attribute'] = loss_attribute + generator_losses['loss_generator'] += loss_attribute * CONFIG.getfloat('training.losses', 'weight_attribute') + + # reconstruction loss + loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) + loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() + loss_reconstruction = loss_reconstruction * 0.3 + loss_ssim * 0.7 + generator_losses['loss_reconstruction'] = loss_reconstruction + generator_losses['loss_generator'] += CONFIG.getfloat('training.losses', 'weight_reconstruction') + + if CONFIG.getfloat('training.losses', 'weight_tsr') > 0: + # tsr loss + swap_motion_features = self.get_motion_features(swap_tensor) + target_motion_features = self.get_motion_features(target_tensor) + loss_tsr = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): + loss_tsr += L2_loss(swap_motion_feature, target_motion_feature) + generator_losses['loss_tsr'] = loss_tsr + generator_losses['loss_generator'] += loss_tsr * CONFIG.getfloat('training.losses', 'weight_tsr') + + + if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0 or CONFIG.getfloat('training.losses', 'weight_eye_open') > 0 or CONFIG.getfloat('training.losses', 'weight_lip_open') > 0: + swap_landmark_features = self.get_landmark_features(swap_tensor) + target_landmark_features = self.get_landmark_features(target_tensor) + + # eye gaze loss + loss_left_eye_gaze = L2_loss(swap_landmark_features[3], target_landmark_features[3]) + loss_right_eye_gaze = L2_loss(swap_landmark_features[4], target_landmark_features[4]) + loss_eye_gaze = loss_left_eye_gaze + loss_right_eye_gaze + generator_losses['loss_eye_gaze'] = loss_eye_gaze + generator_losses['loss_generator'] += loss_eye_gaze * CONFIG.getfloat('training.losses', 'weight_eye_gaze') + + # eye open loss + loss_left_eye_open = L2_loss(swap_landmark_features[0], target_landmark_features[0]) + loss_right_eye_open = L2_loss(swap_landmark_features[1], target_landmark_features[1]) + loss_eye_open = loss_left_eye_open + loss_right_eye_open + generator_losses['loss_eye_open'] = loss_eye_open * CONFIG.getfloat('training.losses', 'weight_eye_open') + generator_losses['loss_generator'] += loss_eye_open + + # lip open loss + loss_lip_open = L2_loss(swap_landmark_features[2], target_landmark_features[2]) + generator_losses['loss_lip_open'] = loss_lip_open * CONFIG.getfloat('training.losses', 'weight_lip_open') + generator_losses['loss_generator'] += loss_lip_open + return generator_losses + + + def calc_discriminator_loss(self, swap_tensor : Tensor, source_tensor : Tensor) -> LossDict: + discriminator_losses = {} + fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) + loss_fake = 0 + + for fake_discriminator_output in fake_discriminator_outputs: + loss_fake += torch.mean(hinge_loss(fake_discriminator_output[0], False).mean(dim=[1, 2, 3])) + true_discriminator_outputs = self.discriminator(source_tensor) + loss_true = 0 + + for true_discriminator_output in true_discriminator_outputs: + loss_true += torch.mean(hinge_loss(true_discriminator_output[0], True).mean(dim=[1, 2, 3])) + discriminator_losses['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 + return discriminator_losses + + + def get_arcface_embedding(self, vision_tensor : Tensor, padding : Tuple[int, int, int, int]) -> Tensor: + _, _, height, width = vision_tensor.shape + crop_height = int(height * 0.0586) + crop_width = int(width * 0.0586) + crop_vision_tensor = vision_tensor[:, :, crop_height : height - crop_height, crop_width : width - crop_width] + crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'bilinear') + crop_vision_tensor[:, :, :padding[0], :] = 0 + crop_vision_tensor[:, :, -padding[1]:, :] = 0 + crop_vision_tensor[:, :, :, :padding[2]] = 0 + crop_vision_tensor[:, :, :, -padding[3]:] = 0 + embedding = self.arcface(crop_vision_tensor) + embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) + return embedding + + + def get_landmark_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') + landmarks = self.landmarker(vision_tensor_norm)[2] + landmarks = landmarks.view(-1, 203, 2) * 256 + left_eye_open_ratio = calc_distance_ratio(landmarks, (6, 18, 0, 12)) + right_eye_open_ratio = calc_distance_ratio(landmarks, (30, 42, 24, 36)) + lip_open_ratio = calc_distance_ratio(landmarks, (90, 102, 48, 66)) + left_eye_gaze = landmarks[:, 198] + right_eye_gaze = landmarks[:, 197] + return left_eye_open_ratio, right_eye_open_ratio, lip_open_ratio, left_eye_gaze, right_eye_gaze + + + def get_motion_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + motion_dict = self.motion_extractor(vision_tensor_norm) + translation = motion_dict.get('t') + scale = motion_dict.get('scale') + rotation = torch.cat([ motion_dict.get('pitch'), motion_dict.get('yaw'), motion_dict.get('roll') ], dim = 1) + return translation, scale, rotation + + + def log_generator_preview(self, source_tensor, target_tensor, swap_tensor): + max_preview = 8 + source_tensor = source_tensor[:max_preview] + target_tensor = target_tensor[:max_preview] + swap_tensor = swap_tensor[:max_preview] + rows = [torch.cat([src, tgt, swp], dim=2) for src, tgt, swp in zip(source_tensor, target_tensor, swap_tensor)] + grid = torchvision.utils.make_grid(torch.cat(rows, dim=1).unsqueeze(0), nrow=1, normalize=True, scale_each=True) + os.makedirs("previews", exist_ok=True) + torchvision.utils.save_image(grid, f"previews/step_{self.global_step}.jpg") + self.logger.experiment.add_image("Generator Preview", grid, self.global_step) + + def log_validation_preview(self): + validation_source_path = CONFIG.get('training.validation', 'sources') + validation_target_path = CONFIG.get('training.validation', 'targets') + sources = [read_image(os.path.join(validation_source_path, f)) for f in os.listdir(validation_source_path) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + targets = [read_image(os.path.join(validation_target_path, f)) for f in os.listdir(validation_target_path) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + transforms = torchvision.transforms.Compose( + [ + torchvision.transforms.Resize((256, 256), interpolation = torchvision.transforms.InterpolationMode.BICUBIC), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:,:,::-1] + 1) * 127.5 + self.generator.eval() + results = [] + + for source, target in zip(sources, targets): + source_tensor = transforms(source).unsqueeze(0).to(self.device).half() + target_tensor = transforms(target).unsqueeze(0).to(self.device).half() + source_embedding = self.get_arcface_embedding(source_tensor, (0, 0, 0, 0)) + + with torch.no_grad(): + output, _ = self.generator(target_tensor, source_embedding) + results.append(numpy.hstack([to_numpy(source_tensor), to_numpy(target_tensor), to_numpy(output)])) + preview = numpy.vstack(results) + os.makedirs("validation_previews", exist_ok=True) + cv2.imwrite(f"validation_previews/step_{self.global_step}.jpg", preview) + self.generator.train() diff --git a/face_swapper/src/sub_typing.py b/face_swapper/src/typing.py similarity index 50% rename from face_swapper/src/sub_typing.py rename to face_swapper/src/typing.py index 4297b40..529cb5f 100644 --- a/face_swapper/src/sub_typing.py +++ b/face_swapper/src/typing.py @@ -1,12 +1,14 @@ -from typing import Any, Tuple +from typing import Any, Tuple, List, Dict, Optional from numpy.typing import NDArray from torch import Tensor from torch.utils.data import DataLoader -Batch = Tuple[Tensor, Tensor, int] +Batch = Tuple[Any, Any, Any] Loader = DataLoader[Tuple[Tensor, ...]] -UNetAttributes = Tuple[Tensor, ...] +TargetAttributes = Tuple[Tensor, ...] +DiscriminatorOutputs = List[List[Tensor]] +LossDict = Dict[str, Tensor] Embedding = NDArray[Any] VisionFrame = NDArray[Any] diff --git a/mypy.ini b/mypy.ini index 081e7bf..75ef9f3 100644 --- a/mypy.ini +++ b/mypy.ini @@ -6,4 +6,4 @@ disallow_untyped_defs = True ignore_missing_imports = True strict_optional = False explicit_package_bases = True -exclude = face_swapper/LivePortrait +exclude = face_swapper From fe28b6fffebe57162db494c084839130893291d4 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 10 Dec 2024 22:26:40 +0530 Subject: [PATCH 004/394] ugly training code --- face_swapper/src/training.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 182fb58..c2d512b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,7 +1,6 @@ import configparser import random - -from sympy.stats.sampling.sample_numpy import numpy +import numpy from typing import Tuple import os From 650268c06ba56222e7413d19a358c79ef7cf04f5 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 10 Dec 2024 22:45:00 +0530 Subject: [PATCH 005/394] ugly training code --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index c2d512b..f7d1c37 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -318,9 +318,9 @@ class FaceSwapper(pytorch_lightning.LightningModule): crop_vision_tensor = vision_tensor[:, :, crop_height : height - crop_height, crop_width : width - crop_width] crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'bilinear') crop_vision_tensor[:, :, :padding[0], :] = 0 - crop_vision_tensor[:, :, -padding[1]:, :] = 0 + crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 crop_vision_tensor[:, :, :, :padding[2]] = 0 - crop_vision_tensor[:, :, :, -padding[3]:] = 0 + crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 embedding = self.arcface(crop_vision_tensor) embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) return embedding From a1fd382659fde3a33cfaceee6ac356b3082ac7e1 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 11 Dec 2024 14:28:13 +0530 Subject: [PATCH 006/394] ugly training code --- face_swapper/src/training.py | 87 ++++++++++++++++++++++++------------ 1 file changed, 59 insertions(+), 28 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f7d1c37..342db36 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -170,17 +170,19 @@ class FaceSwapper(pytorch_lightning.LightningModule): def configure_optimizers(self) -> OptimizerLRScheduler: generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = CONFIG.getfloat('training.generator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = CONFIG.getfloat('training.discriminator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) - generator_scheduler = torch.optim.lr_scheduler.StepLR(generator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) - discriminator_scheduler = torch.optim.lr_scheduler.StepLR(discriminator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) - return ( - { - "optimizer": generator_optimizer, - "lr_scheduler": generator_scheduler - }, - { - "optimizer": discriminator_optimizer, - "lr_scheduler": discriminator_scheduler - }) + if CONFIG.getboolean('training.schedulers', 'enable'): + generator_scheduler = torch.optim.lr_scheduler.StepLR(generator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) + discriminator_scheduler = torch.optim.lr_scheduler.StepLR(discriminator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) + return ( + { + "optimizer": generator_optimizer, + "lr_scheduler": generator_scheduler + }, + { + "optimizer": discriminator_optimizer, + "lr_scheduler": discriminator_scheduler + }) + return generator_optimizer, discriminator_optimizer def training_step(self, batch : Batch, batch_index : int) -> Tensor: @@ -256,7 +258,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() loss_reconstruction = loss_reconstruction * 0.3 + loss_ssim * 0.7 generator_losses['loss_reconstruction'] = loss_reconstruction - generator_losses['loss_generator'] += CONFIG.getfloat('training.losses', 'weight_reconstruction') + generator_losses['loss_generator'] += loss_reconstruction * CONFIG.getfloat('training.losses', 'weight_reconstruction') if CONFIG.getfloat('training.losses', 'weight_tsr') > 0: # tsr loss @@ -285,13 +287,13 @@ class FaceSwapper(pytorch_lightning.LightningModule): loss_left_eye_open = L2_loss(swap_landmark_features[0], target_landmark_features[0]) loss_right_eye_open = L2_loss(swap_landmark_features[1], target_landmark_features[1]) loss_eye_open = loss_left_eye_open + loss_right_eye_open - generator_losses['loss_eye_open'] = loss_eye_open * CONFIG.getfloat('training.losses', 'weight_eye_open') - generator_losses['loss_generator'] += loss_eye_open + generator_losses['loss_eye_open'] = loss_eye_open + generator_losses['loss_generator'] += loss_eye_open * CONFIG.getfloat('training.losses', 'weight_eye_open') # lip open loss loss_lip_open = L2_loss(swap_landmark_features[2], target_landmark_features[2]) - generator_losses['loss_lip_open'] = loss_lip_open * CONFIG.getfloat('training.losses', 'weight_lip_open') - generator_losses['loss_generator'] += loss_lip_open + generator_losses['loss_lip_open'] = loss_lip_open + generator_losses['loss_generator'] += loss_lip_open * CONFIG.getfloat('training.losses', 'weight_lip_open') return generator_losses @@ -359,30 +361,59 @@ class FaceSwapper(pytorch_lightning.LightningModule): torchvision.utils.save_image(grid, f"previews/step_{self.global_step}.jpg") self.logger.experiment.add_image("Generator Preview", grid, self.global_step) + def log_validation_preview(self): - validation_source_path = CONFIG.get('training.validation', 'sources') - validation_target_path = CONFIG.get('training.validation', 'targets') - sources = [read_image(os.path.join(validation_source_path, f)) for f in os.listdir(validation_source_path) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] - targets = [read_image(os.path.join(validation_target_path, f)) for f in os.listdir(validation_target_path) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + read_images = lambda path: [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:, :, ::-1] + 1) * 127.5 transforms = torchvision.transforms.Compose( [ torchvision.transforms.Resize((256, 256), interpolation = torchvision.transforms.InterpolationMode.BICUBIC), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) - to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:,:,::-1] + 1) * 127.5 - self.generator.eval() - results = [] + sources = read_images(CONFIG.get('training.validation', 'sources')) + targets_front = read_images(CONFIG.get('training.validation', 'targets_front')) + targets_side = read_images(CONFIG.get('training.validation', 'targets_side')) + targets_makeup = read_images(CONFIG.get('training.validation', 'targets_makeup')) + targets_occlusion = read_images(CONFIG.get('training.validation', 'targets_occlusion')) - for source, target in zip(sources, targets): + + self.generator.eval() + + results_source = [] + results_front = [] + results_side = [] + results_makeup = [] + results_occlusion = [] + + for source, target_front, target_side, target_makeup, target_occlusion in zip(sources, targets_front, targets_side, targets_makeup, targets_occlusion): source_tensor = transforms(source).unsqueeze(0).to(self.device).half() - target_tensor = transforms(target).unsqueeze(0).to(self.device).half() source_embedding = self.get_arcface_embedding(source_tensor, (0, 0, 0, 0)) + target_front_tensor = transforms(target_front).unsqueeze(0).to(self.device).half() + target_side_tensor = transforms(target_side).unsqueeze(0).to(self.device).half() + target_makeup_tensor = transforms(target_makeup).unsqueeze(0).to(self.device).half() + target_occlusion_tensor = transforms(target_occlusion).unsqueeze(0).to(self.device).half() with torch.no_grad(): - output, _ = self.generator(target_tensor, source_embedding) - results.append(numpy.hstack([to_numpy(source_tensor), to_numpy(target_tensor), to_numpy(output)])) - preview = numpy.vstack(results) + output_front, _ = self.generator(target_front_tensor, source_embedding) + output_side, _ = self.generator(target_side_tensor, source_embedding) + output_makeup, _ = self.generator(target_makeup_tensor, source_embedding) + output_occlusion, _ = self.generator(target_occlusion_tensor, source_embedding) + + results_source.append(to_numpy(source_tensor)) + results_front.append(numpy.hstack([to_numpy(target_front_tensor), to_numpy(output_front)])) + results_side.append(numpy.hstack([to_numpy(target_side_tensor), to_numpy(output_side)])) + results_makeup.append(numpy.hstack([to_numpy(target_makeup_tensor), to_numpy(output_makeup)])) + results_occlusion.append(numpy.hstack([to_numpy(target_occlusion_tensor), to_numpy(output_occlusion)])) + + sources_vertical = numpy.vstack(results_source) + results_front_vertical = numpy.vstack(results_front) + results_side_vertical = numpy.vstack(results_side) + results_makeup_vertical = numpy.vstack(results_makeup) + results_occlusion_vertical = numpy.vstack(results_occlusion) + pad = numpy.zeros((sources_vertical.shape[0], 10, 3), dtype = sources_vertical.dtype) + preview = numpy.hstack([sources_vertical, pad, results_front_vertical, pad, results_side_vertical, pad, results_makeup_vertical, pad, results_occlusion_vertical]) + os.makedirs("validation_previews", exist_ok=True) cv2.imwrite(f"validation_previews/step_{self.global_step}.jpg", preview) self.generator.train() From ef313042c601c496f36e8507087febbed8fc1e25 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 13 Jan 2025 12:25:02 +0530 Subject: [PATCH 007/394] clean generator, discriminator and typing --- .flake8 | 2 +- face_swapper/src/discriminator.py | 6 +- face_swapper/src/generator.py | 211 ++++++++++++++---------------- face_swapper/src/typing.py | 3 +- mypy.ini | 2 +- 5 files changed, 106 insertions(+), 118 deletions(-) diff --git a/.flake8 b/.flake8 index 8103c45..0ed7d05 100644 --- a/.flake8 +++ b/.flake8 @@ -4,4 +4,4 @@ plugins = flake8-import-order application_import_names = arcface_converter import-order-style = pycharm per-file-ignores = preparing.py:E402 -exclude = face_swapper +exclude = face_swapper/LivePortrait diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index b38ca9e..e73f458 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -1,9 +1,7 @@ -from typing import List - import numpy import torch.nn as nn -from .typing import Tensor, DiscriminatorOutputs +from .typing import DiscriminatorOutputs, List, Tensor class NLayerDiscriminator(nn.Module): @@ -60,11 +58,9 @@ class MultiscaleDiscriminator(nn.Module): setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] - def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: return [ model_layers(input_tensor) ] - def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: discriminator_outputs = [] downsampled_input = input_tensor diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index 3dd18c9..e93a668 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -1,132 +1,118 @@ import torch import torch.nn as nn -import torch.nn.functional as F + +from .typing import IDEmbedding, TargetAttributes, Tensor, Tuple class AdaptiveEmbeddingIntegrationNetwork(nn.Module): - def __init__(self, id_channels=512, num_blocks=2): + def __init__(self, id_channels : int, num_blocks : int) -> None: super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() self.encoder = UNet() self.generator = AADGenerator(id_channels, num_blocks) - def forward(self, target, source_embedding): + def forward(self, target : Tensor, source_embedding : IDEmbedding) -> Tuple[Tensor, TargetAttributes]: target_attributes = self.get_attributes(target) swap = self.generator(target_attributes, source_embedding) return swap, target_attributes - def get_attributes(self, target): + def get_attributes(self, target : Tensor) -> TargetAttributes: return self.encoder(target) class AADGenerator(nn.Module): - def __init__(self, id_channels=512, num_blocks=2): + def __init__(self, id_channels : int, num_blocks : int) -> None: super(AADGenerator, self).__init__() - self.upsample = Upsample(id_channels, 1024 * 4) - self.AADBlk1 = AAD_ResBlk(1024, 1024, 1024, id_channels, num_blocks) - self.AADBlk2 = AAD_ResBlk(1024, 1024, 2048, id_channels, num_blocks) - self.AADBlk3 = AAD_ResBlk(1024, 1024, 1024, id_channels, num_blocks) - self.AADBlk4 = AAD_ResBlk(1024, 512, 512, id_channels, num_blocks) - self.AADBlk5 = AAD_ResBlk(512, 256, 256, id_channels, num_blocks) - self.AADBlk6 = AAD_ResBlk(256, 128, 128, id_channels, num_blocks) - self.AADBlk7 = AAD_ResBlk(128, 64, 64, id_channels, num_blocks) - self.AADBlk8 = AAD_ResBlk(64, 3, 64, id_channels, num_blocks) + self.upsample = PixelShuffleUpsample(id_channels, 1024 * 4) + self.res_block_1 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks) + self.res_block_2 = AADResBlock(1024, 1024, 2048, id_channels, num_blocks) + self.res_block_3 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks) + self.res_block_4 = AADResBlock(1024, 512, 512, id_channels, num_blocks) + self.res_block_5 = AADResBlock(512, 256, 256, id_channels, num_blocks) + self.res_block_6 = AADResBlock(256, 128, 128, id_channels, num_blocks) + self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) + self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) self.apply(initialize_weight) - def forward(self, target_attributes, source_embedding): + def forward(self, target_attributes : TargetAttributes, source_embedding : IDEmbedding) -> Tensor: feature_map = self.upsample(source_embedding) - feature_map_1 = F.interpolate(self.AADBlk1(feature_map, target_attributes[0], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_2 = F.interpolate(self.AADBlk2(feature_map_1, target_attributes[1], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_3 = F.interpolate(self.AADBlk3(feature_map_2, target_attributes[2], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_4 = F.interpolate(self.AADBlk4(feature_map_3, target_attributes[3], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_5 = F.interpolate(self.AADBlk5(feature_map_4, target_attributes[4], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_6 = F.interpolate(self.AADBlk6(feature_map_5, target_attributes[5], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - feature_map_7 = F.interpolate(self.AADBlk7(feature_map_6, target_attributes[6], source_embedding), scale_factor=2, mode='bilinear', align_corners=False) - output = self.AADBlk8(feature_map_7, target_attributes[7], source_embedding) + feature_map_1 = torch.nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_2 = torch.nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_3 = torch.nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_4 = torch.nn.functional.interpolate(self.res_block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_5 = torch.nn.functional.interpolate(self.res_block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_6 = torch.nn.functional.interpolate(self.res_block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_7 = torch.nn.functional.interpolate(self.res_block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + output = self.res_block_8(feature_map_7, target_attributes[7], source_embedding) return torch.tanh(output) class UNet(nn.Module): - def __init__(self): + def __init__(self) -> None: super(UNet, self).__init__() - self.downsampler_1 = Conv4x4(3, 32) - self.downsampler_2 = Conv4x4(32, 64) - self.downsampler_3 = Conv4x4(64, 128) - self.downsampler_4 = Conv4x4(128, 256) - self.downsampler_5 = Conv4x4(256, 512) - self.downsampler_6 = Conv4x4(512, 1024) - - self.bottleneck = Conv4x4(1024, 1024) - - self.upsampler_1 = DeConv4x4(1024, 1024) - self.upsampler_2 = DeConv4x4(2048, 512) - self.upsampler_3 = DeConv4x4(1024, 256) - self.upsampler_4 = DeConv4x4(512, 128) - self.upsampler_5 = DeConv4x4(256, 64) - self.upsampler_6 = DeConv4x4(128, 32) + self.downsampler_1 = DownSample(3, 32) + self.downsampler_2 = DownSample(32, 64) + self.downsampler_3 = DownSample(64, 128) + self.downsampler_4 = DownSample(128, 256) + self.downsampler_5 = DownSample(256, 512) + self.downsampler_6 = DownSample(512, 1024) + self.bottleneck = DownSample(1024, 1024) + self.upsampler_1 = Upsample(1024, 1024) + self.upsampler_2 = Upsample(2048, 512) + self.upsampler_3 = Upsample(1024, 256) + self.upsampler_4 = Upsample(512, 128) + self.upsampler_5 = Upsample(256, 64) + self.upsampler_6 = Upsample(128, 32) self.apply(initialize_weight) - def forward(self, input_tensor): - downsample_feature_1 = self.downsampler_1(input_tensor) + def forward(self, target : Tensor) -> TargetAttributes: + downsample_feature_1 = self.downsampler_1(target) downsample_feature_2 = self.downsampler_2(downsample_feature_1) downsample_feature_3 = self.downsampler_3(downsample_feature_2) downsample_feature_4 = self.downsampler_4(downsample_feature_3) downsample_feature_5 = self.downsampler_5(downsample_feature_4) downsample_feature_6 = self.downsampler_6(downsample_feature_5) - bottleneck_output = self.bottleneck(downsample_feature_6) - upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) - - output = F.interpolate(upsample_feature_6, scale_factor=2, mode='bilinear', align_corners=False) - + output = torch.nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output class AADLayer(nn.Module): - def __init__(self, input_channels, attr_channels, id_channels): + def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: super(AADLayer, self).__init__() - self.attr_channels = attr_channels - self.id_channels = id_channels self.input_channels = input_channels - - self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=True) - self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size=1, stride=1, padding=0, bias=True) - self.fc_gamma = nn.Linear(id_channels, input_channels) + self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) + self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) self.fc_beta = nn.Linear(id_channels, input_channels) - self.instance_norm = nn.InstanceNorm2d(input_channels, affine=False) + self.fc_gamma = nn.Linear(id_channels, input_channels) + self.instance_norm = nn.InstanceNorm2d(input_channels, affine = False) + self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1, stride = 1, padding = 0, bias = True) - self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size=1, stride=1, padding=0, bias=True) - - def forward(self, feature_map, attr_embedding, id_embedding): + def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : IDEmbedding) -> Tensor: feature_map = self.instance_norm(feature_map) - attr_gamma = self.conv_gamma(attr_embedding) attr_beta = self.conv_beta(attr_embedding) attr_modulation = attr_gamma * feature_map + attr_beta - - id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as( - feature_map) - id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as( - feature_map) + id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) id_modulation = id_gamma * feature_map + id_beta - feature_mask = torch.sigmoid(self.conv_mask(feature_map)) feature_blend = (1 - feature_mask) * attr_modulation + feature_mask * id_modulation return feature_blend class AddBlocksSequential(nn.Sequential): - def forward(self, *inputs): - h, attr_embedding, id_embedding = inputs + def forward(self, *inputs : Tuple[Tensor, Tensor, IDEmbedding]) -> Tuple[Tuple[Tensor, Tensor, IDEmbedding], ...]: + _, attr_embedding, id_embedding = inputs for index, module in enumerate(self._modules.values()): if index % 3 == 0 and index > 0: - inputs = (inputs, attr_embedding, id_embedding) + inputs = (inputs, attr_embedding, id_embedding) # type:ignore[assignment] if type(inputs) == tuple: inputs = module(*inputs) else: @@ -134,9 +120,9 @@ class AddBlocksSequential(nn.Sequential): return inputs -class AAD_ResBlk(nn.Module): - def __init__(self, in_channels, out_channels, attr_channels, id_channels, num_blocks): - super(AAD_ResBlk, self).__init__() +class AADResBlock(nn.Module): + def __init__(self, in_channels : int, out_channels : int, attr_channels : int, id_channels : int, num_blocks : int) -> None: + super(AADResBlock, self).__init__() self.in_channels = in_channels self.out_channels = out_channels primary_add_blocks = [] @@ -146,8 +132,8 @@ class AAD_ResBlk(nn.Module): primary_add_blocks.extend( [ AADLayer(in_channels, attr_channels, id_channels), - nn.ReLU(inplace=True), - nn.Conv2d(in_channels, intermediate_channels, kernel_size=3, stride=1, padding=1, bias=False) + nn.ReLU(inplace = True), + nn.Conv2d(in_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) ]) self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) @@ -155,12 +141,12 @@ class AAD_ResBlk(nn.Module): auxiliary_add_blocks = \ [ AADLayer(in_channels, attr_channels, id_channels), - nn.ReLU(inplace=True), - nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) + nn.ReLU(inplace = True), + nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) ] self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) - def forward(self, feature_map, attr_embedding, id_embedding): + def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : IDEmbedding) -> Tensor: primary_feature = self.primary_add_blocks(feature_map, attr_embedding, id_embedding) if self.in_channels != self.out_channels: @@ -169,50 +155,47 @@ class AAD_ResBlk(nn.Module): return output_feature -class Conv4x4(nn.Module): - def __init__(self, in_channels, out_channels): - super(Conv4x4, self).__init__() - self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=4, stride=2, padding=1, - bias=False) +class DownSample(nn.Module): + def __init__(self, in_channels : int, out_channels : int) -> None: + super(DownSample, self).__init__() + self.conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(out_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace=True) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - def forward(self, x): - x = self.conv(x) - x = self.batch_norm(x) - x = self.leaky_relu(x) - return x - - -class DeConv4x4(nn.Module): - def __init__(self, in_channels, out_channels): - super(DeConv4x4, self).__init__() - self.deconv = nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels, kernel_size=4, stride=2, - padding=1, bias=False) - self.batch_norm = nn.BatchNorm2d(out_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace=True) - - def forward(self, x, skip): - x = self.deconv(x) - x = self.batch_norm(x) - x = self.leaky_relu(x) - return torch.cat((x, skip), dim=1) + def forward(self, temp : Tensor) -> Tensor: + temp = self.conv(temp) + temp = self.batch_norm(temp) + temp = self.leaky_relu(temp) + return temp class Upsample(nn.Module): - def __init__(self, in_channels, out_channels): + def __init__(self, in_channels : int, out_channels : int) -> None: super(Upsample, self).__init__() - self.initial_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=1, - padding=1) - self.pixel_shuffle = nn.PixelShuffle(upscale_factor=2) + self.deconv = nn.ConvTranspose2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(out_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - def forward(self, x): - x = self.initial_conv(x.view(x.shape[0], -1, 1, 1)) - x = self.pixel_shuffle(x) - return x + def forward(self, temp : Tensor, skip_tensor : Tensor) -> Tensor: + temp = self.deconv(temp) + temp = self.batch_norm(temp) + temp = self.leaky_relu(temp) + return torch.cat((temp, skip_tensor), dim = 1) -def initialize_weight(module): +class PixelShuffleUpsample(nn.Module): + def __init__(self, in_channels : int, out_channels : int) -> None: + super(PixelShuffleUpsample, self).__init__() + self.conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1) + self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) + + def forward(self, temp : Tensor) -> Tensor: + temp = self.conv(temp.view(temp.shape[0], -1, 1, 1)) + temp = self.pixel_shuffle(temp) + return temp + + +def initialize_weight(module : nn.Module) -> None: if isinstance(module, nn.Linear): module.weight.data.normal_(0, 0.001) module.bias.data.zero_() @@ -222,3 +205,11 @@ def initialize_weight(module): if isinstance(module, nn.ConvTranspose2d): nn.init.xavier_normal_(module.weight.data) + + +if __name__ == '__main__': + model = AdaptiveEmbeddingIntegrationNetwork(512, 2) + src = torch.randn(1, 512) + trg = torch.randn(1, 3, 256, 256) + out = model(trg, src) + print(out[0].shape) diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index 529cb5f..b99e58b 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -1,4 +1,4 @@ -from typing import Any, Tuple, List, Dict, Optional +from typing import Any, Dict, List, Tuple from numpy.typing import NDArray from torch import Tensor @@ -9,6 +9,7 @@ Loader = DataLoader[Tuple[Tensor, ...]] TargetAttributes = Tuple[Tensor, ...] DiscriminatorOutputs = List[List[Tensor]] LossDict = Dict[str, Tensor] +IDEmbedding = Tensor Embedding = NDArray[Any] VisionFrame = NDArray[Any] diff --git a/mypy.ini b/mypy.ini index 75ef9f3..081e7bf 100644 --- a/mypy.ini +++ b/mypy.ini @@ -6,4 +6,4 @@ disallow_untyped_defs = True ignore_missing_imports = True strict_optional = False explicit_package_bases = True -exclude = face_swapper +exclude = face_swapper/LivePortrait From 9e1c71b498ca51c57d4d1ce22540d8c600d81af7 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 14 Jan 2025 16:06:09 +0530 Subject: [PATCH 008/394] clean --- face_swapper/src/data_loader.py | 10 -- face_swapper/src/helper.py | 104 +------------- face_swapper/src/training.py | 236 +++++++------------------------- face_swapper/src/typing.py | 3 +- 4 files changed, 55 insertions(+), 298 deletions(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 412a970..34c7dca 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -8,7 +8,6 @@ import tqdm from PIL import Image from torch.utils.data import TensorDataset -from .augmentations import apply_random_motion_blur from .typing import Batch CONFIG = configparser.ConfigParser() @@ -52,7 +51,6 @@ class DataLoaderVGG(TensorDataset): transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.RandomHorizontalFlip(p = 0.5), - transforms.RandomApply([ apply_random_motion_blur ], p = 0.3), transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(8, translate = (0.02, 0.02), scale = (0.98, 1.02), shear = (1, 1), fill = 0), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), @@ -81,11 +79,3 @@ class DataLoaderVGG(TensorDataset): def __len__(self) -> int: return self.dataset_total - - - def state_dict(self): - return {'current_index': self._current_index} - - - def load_state_dict(self, state_dict): - self._current_index = state_dict['current_index'] diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 4a6b2bd..6f42cd2 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,114 +1,16 @@ import configparser -from typing import Tuple import torch -from .typing import Tensor -import numpy -import torch.nn.functional as F + +from .typing import Tensor, Tuple CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -if CONFIG.getboolean('preparing.augmentation', 'expression'): - from LivePortrait.src.utils.camera import headpose_pred_to_degree, get_rotation_matrix - L2_loss = torch.nn.MSELoss() -EXPRESSION_MIN = numpy.array( -[ - [ - [-2.88067125e-02, -8.12731311e-02, -1.70541159e-03], - [-4.88598682e-02, -3.32196616e-02, -1.67431499e-04], - [-6.75425082e-02, -4.28681746e-02, -1.98950816e-04], - [-7.23103955e-02, -3.28503326e-02, -7.31324719e-04], - [-3.87073644e-02, -6.01546466e-02, -5.50269964e-04], - [-6.38048723e-02, -2.23840728e-01, -7.13261834e-04], - [-3.02710701e-02, -3.93195450e-02, -8.24086510e-06], - [-2.95799859e-02, -5.39318882e-02, -1.74219604e-04], - [-2.92359516e-02, -1.53050944e-02, -6.30460854e-05], - [-5.56493877e-03, -2.34344602e-02, -1.26858242e-04], - [-4.37593013e-02, -2.77768299e-02, -2.70503685e-02], - [-1.76926646e-02, -1.91676542e-02, -1.15090821e-04], - [-8.34268332e-03, -3.99775570e-03, -3.27481248e-05], - [-3.40162888e-02, -2.81868968e-02, -1.96679524e-04], - [-2.91855410e-02, -3.97511162e-02, -2.81230678e-05], - [-1.50395725e-02, -2.49494594e-02, -9.42573533e-05], - [-1.67938769e-02, -2.00953931e-02, -4.00750607e-04], - [-1.86435618e-02, -2.48535164e-02, -2.74416432e-02], - [-4.61211195e-03, -1.21660791e-02, -2.93173041e-04], - [-4.10017073e-02, -7.43824020e-02, -4.42762971e-02], - [-1.90370996e-02, -3.74363363e-02, -1.34740388e-02] - ] -]).astype(numpy.float32) -EXPRESSION_MAX = numpy.array( -[ - [ - [4.46682945e-02, 7.08772913e-02, 4.08344204e-04], - [2.14308221e-02, 6.15894832e-02, 4.85319615e-05], - [3.02363783e-02, 4.45043296e-02, 1.28298725e-05], - [3.05869691e-02, 3.79812494e-02, 6.57040102e-04], - [4.45670523e-02, 3.97259220e-02, 7.10966764e-04], - [9.43699256e-02, 9.85926315e-02, 2.02551950e-04], - [1.61131397e-02, 2.92906128e-02, 3.44733417e-06], - [5.23825921e-02, 1.07065082e-01, 6.61510974e-04], - [2.85718683e-03, 8.32320191e-03, 2.39314613e-04], - [2.57947259e-02, 1.60935968e-02, 2.41853559e-05], - [4.90833223e-02, 3.43903080e-02, 3.22353356e-02], - [1.44766076e-02, 3.39248963e-02, 1.42291479e-04], - [8.75749043e-04, 6.82212645e-03, 2.76097053e-05], - [1.86958015e-02, 3.84016186e-02, 7.33085908e-05], - [2.01714113e-02, 4.90544215e-02, 2.34028921e-05], - [2.46518422e-02, 3.29151377e-02, 3.48571630e-05], - [2.22457591e-02, 1.21796541e-02, 1.56396593e-04], - [1.72109623e-02, 3.01626958e-02, 1.36556877e-02], - [1.83460284e-02, 1.61141958e-02, 2.87440169e-04], - [3.57594155e-02, 1.80554688e-01, 2.75554154e-02], - [2.17450950e-02, 8.66811201e-02, 3.34241726e-02] - ] -]).astype(numpy.float32) -def randomize_expression(face_tensor, feature_extractor, motion_extractor, warping_network, spade_generator): - with torch.no_grad(): - face_tensor_norm = (face_tensor + 1) * 0.5 - input_device = face_tensor.device - feature_volume = feature_extractor(face_tensor_norm) - motion_extractor_dict = motion_extractor(face_tensor_norm) - - translation = motion_extractor_dict.get('t') - expression = motion_extractor_dict.get('exp') - scale = motion_extractor_dict.get('scale') - points = motion_extractor_dict.get('kp') - - pitch = headpose_pred_to_degree(motion_extractor_dict.get('pitch'))[:, None] - yaw = headpose_pred_to_degree(motion_extractor_dict.get('yaw'))[:, None] - roll = headpose_pred_to_degree(motion_extractor_dict.get('roll'))[:, None] - rotation_matrix = get_rotation_matrix(pitch, yaw, roll) - random_expression = get_random_expression_blend(expression) - - points_transformed = transform_points(points, rotation_matrix, expression, scale, translation) - points_driv = transform_points(points, rotation_matrix, random_expression, scale, translation) - - data = warping_network(feature_volume, points_driv, points_transformed).get('out') - output = spade_generator(data) - output = output.to(input_device) - output = F.interpolate(output.clamp(0, 1), [256, 256], mode='bilinear', align_corners=False) - output = (output - 0.5) * 2 - return output - - -def get_random_expression_blend(expression : Tensor) -> Tensor: - blend = 0.35 - expression = expression.view(-1, 21, 3) - min_array = torch.from_numpy(EXPRESSION_MIN).to(expression.device).to(expression.dtype).expand(expression.shape[0], -1, -1) - max_array = torch.from_numpy(EXPRESSION_MAX).to(expression.device).to(expression.dtype).expand(expression.shape[0], -1, -1) - random_batch = torch.rand_like(min_array).to(expression.device) * (max_array - min_array) + min_array - random_batch[:, [0, 1, 8, 6, 9, 4, 5, 10]] = expression[:, [0, 1, 8, 6, 9, 4, 5, 10]] - random_batch[:, [3, 7]] = random_batch[:, [13, 16]] * 0.1 + expression[:, [13, 16]] * 0.9 - random_batch[:, [3, 7]] = random_batch[:, [3, 7]] * 0.5 + expression[:, [3, 7]] * 0.5 - return random_batch * 0.8 * blend + expression * (1 - blend) - - -def transform_points(points : Tensor, rotation_matrix : Tensor, expression : Tensor, scale : Tensor, translation : Tensor): +def transform_points(points : Tensor, rotation_matrix : Tensor, expression : Tensor, scale : Tensor, translation : Tensor) -> Tensor: points_transformed = points.view(-1, 21, 3) @ rotation_matrix + expression.view(-1, 21, 3) points_transformed *= scale[..., None] points_transformed[:, :, 0:2] += translation[:, None, 0:2] diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 342db36..12d740b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,96 +1,28 @@ import configparser -import random -import numpy - -from typing import Tuple import os -import cv2 -import torchvision +import cv2 +import numpy import pytorch_lightning +import torch +import torchvision +from LivePortrait.src.modules.motion_extractor import MotionExtractor from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint +from pytorch_lightning.utilities.types import Optimizer +from pytorch_msssim import ssim from torch.utils.data import DataLoader -from pytorch_lightning.utilities.types import OptimizerLRScheduler -import torch +from .data_loader import DataLoaderVGG, read_image from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork -from .data_loader import DataLoaderVGG, read_image - -from .typing import Tensor, LossDict, TargetAttributes, DiscriminatorOutputs, Batch -from .helper import hinge_loss, calc_distance_ratio, L2_loss, randomize_expression -from pytorch_msssim import ssim +from .helper import L2_loss, hinge_loss +from .typing import Batch, DiscriminatorOutputs, IDEmbedding, LossDict, TargetAttributes, Tensor, Tuple CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def load_models(): - id_channels = CONFIG.getint('training.generator', 'id_channels') - num_blocks = CONFIG.getint('training.generator', 'num_blocks') - generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) - - input_channels = CONFIG.getint('training.discriminator', 'input_channels') - num_filters = CONFIG.getint('training.discriminator', 'num_filters') - num_layers = CONFIG.getint('training.discriminator', 'num_layers') - num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') - discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) - - model_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') - arcface = torch.load(model_path, map_location = 'cpu', weights_only = False) - arcface.eval() - - if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0 or CONFIG.getfloat('training.losses', 'weight_eye_open') > 0 or CONFIG.getfloat('training.losses', 'weight_lip_open') > 0: - model_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') - landmarker = torch.load(model_path, map_location = 'cpu', weights_only = False) - landmarker.eval() - else: - landmarker = None - - if CONFIG.getfloat('training.losses', 'weight_tsr') > 0 or CONFIG.getboolean('preparing.augmentation', 'expression'): - from LivePortrait.src.modules.motion_extractor import MotionExtractor - - model_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') - motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') - motion_extractor.load_state_dict(torch.load(model_path, map_location = 'cpu', weights_only = True)) - motion_extractor.eval() - else: - motion_extractor = None - - if CONFIG.getboolean('preparing.augmentation', 'expression'): - from LivePortrait.src.modules.appearance_feature_extractor import AppearanceFeatureExtractor - from LivePortrait.src.modules.warping_network import WarpingNetwork - from LivePortrait.src.modules.spade_generator import SPADEDecoder - - feature_extractor_path = CONFIG.get('auxiliary_models.paths', 'feature_extractor_path') - feature_extractor = AppearanceFeatureExtractor(3, 64, 2, 512, 32, 16, 6) - feature_extractor.load_state_dict(torch.load(feature_extractor_path, map_location = 'cpu', weights_only = True)) - feature_extractor.eval() - - warping_network_path = CONFIG.get('auxiliary_models.paths', 'warping_network_path') - dense_motion_params = { - 'block_expansion': 32, - 'max_features': 1024, - 'num_blocks': 5, - 'reshape_depth': 16, - 'compress': 4 - } - warping_network = WarpingNetwork(num_kp = 21, block_expansion = 64, max_features = 512, num_down_blocks = 2, reshape_channel = 32, estimate_occlusion_map = True, dense_motion_params = dense_motion_params) - warping_network.load_state_dict(torch.load(warping_network_path, map_location='cpu', weights_only=True)) - warping_network.eval() - - spade_generator_path = CONFIG.get('auxiliary_models.paths', 'spade_generator_path') - spade_generator = SPADEDecoder(upscale = 2, block_expansion = 64, max_features = 512, num_down_blocks = 2) - spade_generator.load_state_dict(torch.load(spade_generator_path, map_location = 'cpu', weights_only = True)) - spade_generator.eval() - else: - feature_extractor = None - warping_network = None - spade_generator = None - return generator, discriminator, arcface, landmarker, motion_extractor, feature_extractor, warping_network, spade_generator - - def create_trainer() -> Trainer: trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') output_directory_path = CONFIG.get('training.output', 'directory_path') @@ -106,7 +38,6 @@ def create_trainer() -> Trainer: monitor = 'l_G', dirpath = output_directory_path, filename = output_file_pattern, - # every_n_epochs = 1, every_n_train_steps = 1000, save_top_k = 5, mode = 'min', @@ -118,7 +49,7 @@ def create_trainer() -> Trainer: ) -def train(): +def train() -> None: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') @@ -127,85 +58,50 @@ def train(): if not (checkpoint_path and os.path.exists(checkpoint_path)): checkpoint_path = None data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - face_swap_model = FaceSwapper(*load_models()) + face_swap_model = FaceSwapper() trainer = create_trainer() trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path) class FaceSwapper(pytorch_lightning.LightningModule): - def __init__(self, generator, discriminator, arcface, landmarker, motion_extractor, feature_extractor, warping_network, spade_generator) -> None: + def __init__(self) -> None: super().__init__() - self.generator = generator - self.discriminator = discriminator - self.arcface = arcface - self.landmarker = landmarker - self.motion_extractor = motion_extractor - self.feature_extractor = feature_extractor - self.warping_network = warping_network - self.spade_generator = spade_generator - - self.loss_adversarial_accumulated = 20 + self.generator = AdaptiveEmbeddingIntegrationNetwork(CONFIG.getint('training.generator', 'id_channels'), CONFIG.getint('training.generator', 'num_blocks')) + self.discriminator = MultiscaleDiscriminator(CONFIG.getint('training.discriminator', 'input_channels'), CONFIG.getint('training.discriminator', 'num_filters'), CONFIG.getint('training.discriminator', 'num_layers'), CONFIG.getint('training.discriminator', 'num_discriminators')) + self.arcface = torch.load(CONFIG.get('auxiliary_models.paths', 'arcface_path'), map_location = 'cpu', weights_only = False) + self.landmarker = torch.load(CONFIG.get('auxiliary_models.paths', 'landmarker_path'), map_location = 'cpu', weights_only = False) + self.motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') + self.motion_extractor.load_state_dict(torch.load(CONFIG.get('auxiliary_models.paths', 'motion_extractor_path'), map_location = 'cpu', weights_only = True)) + self.arcface.eval() + self.landmarker.eval() + self.motion_extractor.eval() self.automatic_optimization = False self.batch_size = CONFIG.getint('training.loader', 'batch_size') - - def forward(self, target_tensor : Tensor, source_embedding : Tensor) -> Tensor: + def forward(self, target_tensor : Tensor, source_embedding : IDEmbedding) -> Tuple[Tensor, TargetAttributes]: output = self.generator(target_tensor, source_embedding) return output - - def state_dict(self, *args, **kwargs): - return { - "generator": self.generator.state_dict(), - "discriminator": self.discriminator.state_dict(), - } - - def load_state_dict(self, state_dict, strict: bool = True): - if "generator" in state_dict: - self.generator.load_state_dict(state_dict["generator"], strict = strict) - if "discriminator" in state_dict: - self.discriminator.load_state_dict(state_dict["discriminator"], strict = strict) - - - def configure_optimizers(self) -> OptimizerLRScheduler: + def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = CONFIG.getfloat('training.generator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = CONFIG.getfloat('training.discriminator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) - if CONFIG.getboolean('training.schedulers', 'enable'): - generator_scheduler = torch.optim.lr_scheduler.StepLR(generator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) - discriminator_scheduler = torch.optim.lr_scheduler.StepLR(discriminator_optimizer, step_size = CONFIG.getint('training.schedulers', 'step'), gamma = CONFIG.getfloat('training.schedulers', 'gamma')) - return ( - { - "optimizer": generator_optimizer, - "lr_scheduler": generator_scheduler - }, - { - "optimizer": discriminator_optimizer, - "lr_scheduler": discriminator_scheduler - }) return generator_optimizer, discriminator_optimizer - def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor, is_same_person = batch - generator_optimizer, discriminator_optimizer = self.optimizers() - source_embedding = self.get_arcface_embedding(source_tensor, (0, 0, 0, 0)) - - if random.random() > 0.5 and CONFIG.getboolean('preparing.augmentation', 'expression'): - target_tensor = randomize_expression(target_tensor, self.feature_extractor, self.motion_extractor, self.warping_network, self.spade_generator) - - swap_tensor, target_attributes = self(target_tensor, source_embedding) + generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] + source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) + swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) discriminator_outputs = self.discriminator(swap_tensor) - generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, discriminator_outputs, batch) generator_optimizer.zero_grad() self.manual_backward(generator_losses.get('loss_generator')) generator_optimizer.step() - discriminator_losses = self.calc_discriminator_loss(swap_tensor, source_tensor) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_losses.get('loss_discriminator')) - if not CONFIG.getboolean('training.discriminator', 'disable') or self.loss_adversarial_accumulated < 0.4: + if not CONFIG.getboolean('training.discriminator', 'disable'): discriminator_optimizer.step() if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: @@ -215,36 +111,33 @@ class FaceSwapper(pytorch_lightning.LightningModule): self.log_validation_preview() self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) - self.log('l_ADV_A', self.loss_adversarial_accumulated, prog_bar = True) self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = False) - self.log('l_id', generator_losses.get('loss_identity'), prog_bar = True) - self.log('l_attr', generator_losses.get('loss_attribute'), prog_bar = True) - self.log('l_rec', generator_losses.get('loss_reconstruction'), prog_bar = True) + self.log('l_ATTR', generator_losses.get('loss_attribute'), prog_bar = True) + self.log('l_ID', generator_losses.get('loss_identity'), prog_bar=True) + self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) return generator_losses.get('loss_generator') - def calc_generator_loss(self, swap_tensor : Tensor, target_attributes : TargetAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> LossDict: source_tensor, target_tensor, is_same_person = batch generator_losses = {} # adversarial loss - loss_adversarial = 0 + loss_adversarial = torch.Tensor(0) for discriminator_output in discriminator_outputs: loss_adversarial += hinge_loss(discriminator_output[0], True).mean(dim = [ 1, 2, 3 ]) loss_adversarial = torch.mean(loss_adversarial) generator_losses['loss_adversarial'] = loss_adversarial generator_losses['loss_generator'] = loss_adversarial * CONFIG.getfloat('training.losses', 'weight_adversarial') - self.loss_adversarial_accumulated = self.loss_adversarial_accumulated * 0.98 + loss_adversarial.item() * 0.02 # identity loss - swap_embedding = self.get_arcface_embedding(swap_tensor, (30, 0, 10, 10)) - source_embedding = self.get_arcface_embedding(source_tensor, (30, 0, 10, 10)) + swap_embedding = self.get_id_embedding(swap_tensor, (30, 0, 10, 10)) + source_embedding = self.get_id_embedding(source_tensor, (30, 0, 10, 10)) loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() generator_losses['loss_identity'] = loss_identity generator_losses['loss_generator'] += loss_identity * CONFIG.getfloat('training.losses', 'weight_identity') # attribute loss - loss_attribute = 0 + loss_attribute = torch.Tensor(0) swap_attributes = self.generator.get_attributes(swap_tensor) for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): @@ -256,7 +149,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): # reconstruction loss loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = loss_reconstruction * 0.3 + loss_ssim * 0.7 + loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 generator_losses['loss_reconstruction'] = loss_reconstruction generator_losses['loss_generator'] += loss_reconstruction * CONFIG.getfloat('training.losses', 'weight_reconstruction') @@ -271,49 +164,32 @@ class FaceSwapper(pytorch_lightning.LightningModule): generator_losses['loss_tsr'] = loss_tsr generator_losses['loss_generator'] += loss_tsr * CONFIG.getfloat('training.losses', 'weight_tsr') - - if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0 or CONFIG.getfloat('training.losses', 'weight_eye_open') > 0 or CONFIG.getfloat('training.losses', 'weight_lip_open') > 0: + if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0: swap_landmark_features = self.get_landmark_features(swap_tensor) target_landmark_features = self.get_landmark_features(target_tensor) - - # eye gaze loss - loss_left_eye_gaze = L2_loss(swap_landmark_features[3], target_landmark_features[3]) - loss_right_eye_gaze = L2_loss(swap_landmark_features[4], target_landmark_features[4]) + loss_left_eye_gaze = L2_loss(swap_landmark_features[0], target_landmark_features[1]) + loss_right_eye_gaze = L2_loss(swap_landmark_features[0], target_landmark_features[1]) loss_eye_gaze = loss_left_eye_gaze + loss_right_eye_gaze generator_losses['loss_eye_gaze'] = loss_eye_gaze generator_losses['loss_generator'] += loss_eye_gaze * CONFIG.getfloat('training.losses', 'weight_eye_gaze') - - # eye open loss - loss_left_eye_open = L2_loss(swap_landmark_features[0], target_landmark_features[0]) - loss_right_eye_open = L2_loss(swap_landmark_features[1], target_landmark_features[1]) - loss_eye_open = loss_left_eye_open + loss_right_eye_open - generator_losses['loss_eye_open'] = loss_eye_open - generator_losses['loss_generator'] += loss_eye_open * CONFIG.getfloat('training.losses', 'weight_eye_open') - - # lip open loss - loss_lip_open = L2_loss(swap_landmark_features[2], target_landmark_features[2]) - generator_losses['loss_lip_open'] = loss_lip_open - generator_losses['loss_generator'] += loss_lip_open * CONFIG.getfloat('training.losses', 'weight_lip_open') return generator_losses - def calc_discriminator_loss(self, swap_tensor : Tensor, source_tensor : Tensor) -> LossDict: discriminator_losses = {} fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) - loss_fake = 0 + loss_fake = torch.Tensor(0) for fake_discriminator_output in fake_discriminator_outputs: loss_fake += torch.mean(hinge_loss(fake_discriminator_output[0], False).mean(dim=[1, 2, 3])) true_discriminator_outputs = self.discriminator(source_tensor) - loss_true = 0 + loss_true = torch.Tensor(0) for true_discriminator_output in true_discriminator_outputs: loss_true += torch.mean(hinge_loss(true_discriminator_output[0], True).mean(dim=[1, 2, 3])) discriminator_losses['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 return discriminator_losses - - def get_arcface_embedding(self, vision_tensor : Tensor, padding : Tuple[int, int, int, int]) -> Tensor: + def get_id_embedding(self, vision_tensor : Tensor, padding : Tuple[int, int, int, int]) -> Tensor: _, _, height, width = vision_tensor.shape crop_height = int(height * 0.0586) crop_width = int(width * 0.0586) @@ -327,19 +203,12 @@ class FaceSwapper(pytorch_lightning.LightningModule): embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) return embedding - - def get_landmark_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + def get_landmark_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor]: vision_tensor_norm = (vision_tensor + 1) * 0.5 vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') landmarks = self.landmarker(vision_tensor_norm)[2] landmarks = landmarks.view(-1, 203, 2) * 256 - left_eye_open_ratio = calc_distance_ratio(landmarks, (6, 18, 0, 12)) - right_eye_open_ratio = calc_distance_ratio(landmarks, (30, 42, 24, 36)) - lip_open_ratio = calc_distance_ratio(landmarks, (90, 102, 48, 66)) - left_eye_gaze = landmarks[:, 198] - right_eye_gaze = landmarks[:, 197] - return left_eye_open_ratio, right_eye_open_ratio, lip_open_ratio, left_eye_gaze, right_eye_gaze - + return landmarks[:, 198], landmarks[:, 197] def get_motion_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: vision_tensor_norm = (vision_tensor + 1) * 0.5 @@ -349,21 +218,17 @@ class FaceSwapper(pytorch_lightning.LightningModule): rotation = torch.cat([ motion_dict.get('pitch'), motion_dict.get('yaw'), motion_dict.get('roll') ], dim = 1) return translation, scale, rotation - - def log_generator_preview(self, source_tensor, target_tensor, swap_tensor): + def log_generator_preview(self, source_tensor : Tensor, target_tensor : Tensor, swap_tensor : Tensor) -> None: max_preview = 8 source_tensor = source_tensor[:max_preview] target_tensor = target_tensor[:max_preview] swap_tensor = swap_tensor[:max_preview] - rows = [torch.cat([src, tgt, swp], dim=2) for src, tgt, swp in zip(source_tensor, target_tensor, swap_tensor)] - grid = torchvision.utils.make_grid(torch.cat(rows, dim=1).unsqueeze(0), nrow=1, normalize=True, scale_each=True) - os.makedirs("previews", exist_ok=True) - torchvision.utils.save_image(grid, f"previews/step_{self.global_step}.jpg") + rows = [torch.cat([src, tgt, swp], dim = 2) for src, tgt, swp in zip(source_tensor, target_tensor, swap_tensor)] + grid = torchvision.utils.make_grid(torch.cat(rows, dim = 1).unsqueeze(0), nrow = 1, normalize = True, scale_each = True) self.logger.experiment.add_image("Generator Preview", grid, self.global_step) - - def log_validation_preview(self): - read_images = lambda path: [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + def log_validation_preview(self) -> None: + read_images = lambda path : [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:, :, ::-1] + 1) * 127.5 transforms = torchvision.transforms.Compose( [ @@ -377,7 +242,6 @@ class FaceSwapper(pytorch_lightning.LightningModule): targets_makeup = read_images(CONFIG.get('training.validation', 'targets_makeup')) targets_occlusion = read_images(CONFIG.get('training.validation', 'targets_occlusion')) - self.generator.eval() results_source = [] @@ -388,7 +252,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): for source, target_front, target_side, target_makeup, target_occlusion in zip(sources, targets_front, targets_side, targets_makeup, targets_occlusion): source_tensor = transforms(source).unsqueeze(0).to(self.device).half() - source_embedding = self.get_arcface_embedding(source_tensor, (0, 0, 0, 0)) + source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) target_front_tensor = transforms(target_front).unsqueeze(0).to(self.device).half() target_side_tensor = transforms(target_side).unsqueeze(0).to(self.device).half() target_makeup_tensor = transforms(target_makeup).unsqueeze(0).to(self.device).half() diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index b99e58b..29ab130 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -1,3 +1,4 @@ +from collections import OrderedDict from typing import Any, Dict, List, Tuple from numpy.typing import NDArray @@ -10,6 +11,6 @@ TargetAttributes = Tuple[Tensor, ...] DiscriminatorOutputs = List[List[Tensor]] LossDict = Dict[str, Tensor] IDEmbedding = Tensor - +StateDict = OrderedDict[str, Any] Embedding = NDArray[Any] VisionFrame = NDArray[Any] From e3273221c954f57a63f18706b3f92a42fe31d060 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 14 Jan 2025 12:04:28 +0100 Subject: [PATCH 009/394] Delete unused file --- face_swapper/src/augmentations.py | 27 --------------------------- 1 file changed, 27 deletions(-) delete mode 100644 face_swapper/src/augmentations.py diff --git a/face_swapper/src/augmentations.py b/face_swapper/src/augmentations.py deleted file mode 100644 index 5ed35d2..0000000 --- a/face_swapper/src/augmentations.py +++ /dev/null @@ -1,27 +0,0 @@ -import torch -from torch import Tensor - - -def apply_random_motion_blur(tensor_image : Tensor) -> Tensor: - kernel_size = 9 - kernel = torch.zeros((kernel_size, kernel_size), dtype=torch.float32) - random_angle = torch.empty(1).uniform_(-2 * torch.pi, 2 * torch.pi) - dx = torch.cos(random_angle) - dy = torch.sin(random_angle) - center = kernel_size // 2 - - for i in range(kernel_size): - x = int(center + (i - center) * dx) - y = int(center + (i - center) * dy) - if 0 <= x < kernel_size and 0 <= y < kernel_size: - kernel[y, x] = 1 - kernel /= kernel.sum() - kernel = kernel.unsqueeze(0).unsqueeze(0) - blurred_channels = [] - - for channel in tensor_image: - channel = channel.unsqueeze(0).unsqueeze(0) - channel = torch.nn.functional.conv2d(channel, kernel, padding=kernel_size // 2) - channel = channel.squeeze(0).squeeze(0) - blurred_channels.append(channel) - return torch.stack(blurred_channels) From 2bbba3563b5fd07536e91c64233609a726143a70 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 14 Jan 2025 12:40:13 +0100 Subject: [PATCH 010/394] Use mypy and flake8 for face swapper --- .github/workflows/ci.yml | 4 ++-- face_swapper/src/discriminator.py | 4 +++- face_swapper/train.py | 1 - 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b73fedb..6b75de0 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,5 +15,5 @@ jobs: - run: pip install flake8 - run: pip install flake8-import-order - run: pip install mypy - - run: flake8 arcface_converter - - run: mypy arcface_converter + - run: flake8 arcface_converter face_swapper + - run: mypy arcface_converter face_swapper diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index e73f458..ccf3077 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -1,7 +1,9 @@ +from typing import List + import numpy import torch.nn as nn -from .typing import DiscriminatorOutputs, List, Tensor +from .typing import DiscriminatorOutputs, Tensor class NLayerDiscriminator(nn.Module): diff --git a/face_swapper/train.py b/face_swapper/train.py index bbff847..3591cc8 100644 --- a/face_swapper/train.py +++ b/face_swapper/train.py @@ -2,6 +2,5 @@ from src.training import train - if __name__ == '__main__': train() From 23ac63d55b2021bf1053fcd33b5e98482079455f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 14 Jan 2025 12:48:43 +0100 Subject: [PATCH 011/394] Fix more typing --- face_swapper/src/generator.py | 4 +++- face_swapper/src/helper.py | 3 ++- face_swapper/src/training.py | 3 ++- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index e93a668..2f92bc3 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -1,7 +1,9 @@ +from typing import Tuple + import torch import torch.nn as nn -from .typing import IDEmbedding, TargetAttributes, Tensor, Tuple +from .typing import IDEmbedding, TargetAttributes, Tensor class AdaptiveEmbeddingIntegrationNetwork(nn.Module): diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 6f42cd2..954e0b6 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,8 +1,9 @@ import configparser +from typing import Tuple import torch -from .typing import Tensor, Tuple +from .typing import Tensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 12d740b..6070d3c 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,5 +1,6 @@ import configparser import os +from typing import Tuple import cv2 import numpy @@ -17,7 +18,7 @@ from .data_loader import DataLoaderVGG, read_image from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork from .helper import L2_loss, hinge_loss -from .typing import Batch, DiscriminatorOutputs, IDEmbedding, LossDict, TargetAttributes, Tensor, Tuple +from .typing import Batch, DiscriminatorOutputs, IDEmbedding, LossDict, TargetAttributes, Tensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From 10826558b40d9b21d1212e94ad4a589541838a1f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 14 Jan 2025 12:55:48 +0100 Subject: [PATCH 012/394] Fix indent to tabs --- face_swapper/src/discriminator.py | 94 +++++++++++++++---------------- face_swapper/src/helper.py | 8 +-- face_swapper/src/training.py | 2 +- 3 files changed, 52 insertions(+), 52 deletions(-) diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index ccf3077..e328b69 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -7,70 +7,70 @@ from .typing import DiscriminatorOutputs, Tensor class NLayerDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int) -> None: - super(NLayerDiscriminator, self).__init__() - self.num_layers = num_layers - kernel_size = 4 - padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) - model_layers = [ + def __init__(self, input_channels : int, num_filters : int, num_layers : int) -> None: + super(NLayerDiscriminator, self).__init__() + self.num_layers = num_layers + kernel_size = 4 + padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) + model_layers = [ [ nn.Conv2d(input_channels, num_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), nn.LeakyReLU(0.2, True) ]] - current_filters = num_filters + current_filters = num_filters - for layer_index in range(1, num_layers): - previous_filters = current_filters - current_filters = min(current_filters * 2, 512) - model_layers += [ + for layer_index in range(1, num_layers): + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + model_layers += [ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), - nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ]] - previous_filters = current_filters - current_filters = min(current_filters * 2, 512) - model_layers += [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), + nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) + ]] + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + model_layers += [ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 1, padding = padding_size), - nn.InstanceNorm2d(current_filters), - nn.LeakyReLU(0.2, True) - ]] - model_layers += [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 1, padding = padding_size), + nn.InstanceNorm2d(current_filters), + nn.LeakyReLU(0.2, True) + ]] + model_layers += [ [ nn.Conv2d(current_filters, 1, kernel_size = kernel_size, stride = 1, padding = padding_size) ]] - combined_layers = [] + combined_layers = [] - for layer in model_layers: - combined_layers += layer - self.model = nn.Sequential(*combined_layers) + for layer in model_layers: + combined_layers += layer + self.model = nn.Sequential(*combined_layers) - def forward(self, input_tensor : Tensor) -> Tensor: - return self.model(input_tensor) + def forward(self, input_tensor : Tensor) -> Tensor: + return self.model(input_tensor) class MultiscaleDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int): - super(MultiscaleDiscriminator, self).__init__() - self.num_discriminators = num_discriminators - self.num_layers = num_layers + def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int): + super(MultiscaleDiscriminator, self).__init__() + self.num_discriminators = num_discriminators + self.num_layers = num_layers - for discriminator_index in range(num_discriminators): - single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers) - setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) - self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] + for discriminator_index in range(num_discriminators): + single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers) + setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) + self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] - def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: - return [ model_layers(input_tensor) ] + def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: + return [ model_layers(input_tensor) ] - def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: - discriminator_outputs = [] - downsampled_input = input_tensor + def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: + discriminator_outputs = [] + downsampled_input = input_tensor - for discriminator_index in range(self.num_discriminators): - model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) - discriminator_outputs.append(self.single_discriminator_forward(model_layers, downsampled_input)) + for discriminator_index in range(self.num_discriminators): + model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) + discriminator_outputs.append(self.single_discriminator_forward(model_layers, downsampled_input)) - if discriminator_index != (self.num_discriminators - 1): - downsampled_input = self.downsample(downsampled_input) - return discriminator_outputs + if discriminator_index != (self.num_discriminators - 1): + downsampled_input = self.downsample(downsampled_input) + return discriminator_outputs diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 954e0b6..3358c5a 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -19,10 +19,10 @@ def transform_points(points : Tensor, rotation_matrix : Tensor, expression : Ten def hinge_loss(tensor : Tensor, is_positive : bool) -> Tensor: - if is_positive: - return torch.relu(1 - tensor) - else: - return torch.relu(tensor + 1) + if is_positive: + return torch.relu(1 - tensor) + else: + return torch.relu(tensor + 1) def calc_distance_ratio(landmarks : Tensor, indices : Tuple[int, int, int, int]) -> Tensor: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 6070d3c..a2c6d65 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -229,7 +229,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): self.logger.experiment.add_image("Generator Preview", grid, self.global_step) def log_validation_preview(self) -> None: - read_images = lambda path : [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] + read_images = lambda path : [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:, :, ::-1] + 1) * 127.5 transforms = torchvision.transforms.Compose( [ From 008a221f5514e494b330f1cbc26cd64aba9e1bff Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 17 Jan 2025 16:09:16 +0530 Subject: [PATCH 013/394] cleaning --- face_swapper/config.ini | 7 +- face_swapper/src/data_loader.py | 16 +- face_swapper/src/discriminator.py | 44 ++-- face_swapper/src/generator.py | 97 ++++--- face_swapper/src/helper.py | 28 +- face_swapper/src/training.py | 424 ++++++++++++++---------------- face_swapper/src/typing.py | 13 +- 7 files changed, 288 insertions(+), 341 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index b94f925..c20f445 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -24,7 +24,7 @@ num_discriminators = 3 learning_rate = 0.0004 disable = false -[auxiliary_models.paths] +[auxiliary_models.paths] #just model.trainer ... try to match the config of the arcface converter arcface_path = /assets/pretrained_models/arcface_w600k_r50.pt landmarker_path = /assets/pretrained_models/landmark_203.pt motion_extractor_path = /assets/pretrained_models/liveportrait_motion_extractor.pth @@ -34,13 +34,10 @@ spade_generator_path = /assets/pretrained_models/liveportrait_spade_generator.pt [training.losses] weight_adversarial = 1 -weight_identity = 20 +weight_id = 20 weight_attribute = 10 weight_reconstruction = 10 weight_tsr = 100 -weight_eye_gaze = 5 -weight_eye_open = 5 -weight_lip_open = 5 [training.schedulers] step = 5000 diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 34c7dca..cceae06 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -16,20 +16,20 @@ CONFIG.read('config.ini') def read_image(image_path: str) -> Image.Image: image = cv2.imread(image_path)[:, :, ::-1] - pil_image = Image.fromarray(image) + pil_image = Image.fromarray(image) # @todo like said, use the PIL transformator return pil_image class DataLoaderVGG(TensorDataset): def __init__(self, dataset_path : str) -> None: - self.same_person_probability = float(CONFIG.get('preparing.dataloader', 'same_person_probability')) - self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) + self.same_person_probability = float(CONFIG.get('preparing.dataloader', 'same_person_probability')) # @todo use CONFIG.getfloat() - also config block at the top + self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) # @todo globs belong to the config self.folder_paths = glob.glob('{}/*'.format(dataset_path)) - self.image_path_dict = {} + self.image_path_dict = {} # @todo we are not using dict as suffix... this image_path_set? self._current_index = 0 for folder_path in tqdm.tqdm(self.folder_paths): - image_paths = glob.glob('{}/*'.format(folder_path)) + image_paths = glob.glob('{}/*'.format(folder_path)) # @todo not sure about alls this globs being used here :-) self.image_path_dict[folder_path] = image_paths self.dataset_total = len(self.image_paths) self.transforms_basic = transforms.Compose( @@ -61,15 +61,15 @@ class DataLoaderVGG(TensorDataset): source_image_path = self.image_paths[item] source = read_image(source_image_path) - if random.random() > self.same_person_probability: + if random.random() > self.same_person_probability: # @todo if -> we_call_a_method_that_explains_what_we_do() is_same_person = 0 target_image_path = random.choice(self.image_paths) target = read_image(target_image_path) source_transform = self.transforms_moderate(source) target_transform = self.transforms_complex(target) - else: + else: # @todo else -> we_do_some_alternative_action() - in other words, move it to speaking methods :-) is_same_person = 1 - source_folder_path = '/'.join(source_image_path.split('/')[:-1]) + source_folder_path = '/'.join(source_image_path.split('/')[:-1]) # @todo use os.path.join() target_image_path = random.choice(self.image_path_dict[source_folder_path]) target = read_image(target_image_path) source_transform = self.transforms_basic(source) diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index e328b69..114f377 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -1,9 +1,8 @@ -from typing import List - import numpy import torch.nn as nn +from torch import Tensor -from .typing import DiscriminatorOutputs, Tensor +from .typing import DiscriminatorOutputs class NLayerDiscriminator(nn.Module): @@ -12,37 +11,45 @@ class NLayerDiscriminator(nn.Module): self.num_layers = num_layers kernel_size = 4 padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) - model_layers = [ + model_layers =\ + [ [ nn.Conv2d(input_channels, num_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), nn.LeakyReLU(0.2, True) - ]] + ] + ] current_filters = num_filters for layer_index in range(1, num_layers): previous_filters = current_filters current_filters = min(current_filters * 2, 512) - model_layers += [ + model_layers +=\ + [ [ nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ]] + ] + ] previous_filters = current_filters current_filters = min(current_filters * 2, 512) - model_layers += [ + model_layers +=\ + [ [ nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 1, padding = padding_size), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ]] - model_layers += [ + ] + ] + model_layers +=\ + [ [ nn.Conv2d(current_filters, 1, kernel_size = kernel_size, stride = 1, padding = padding_size) - ]] + ] + ] combined_layers = [] - for layer in model_layers: - combined_layers += layer + for model_layer in model_layers: + combined_layers += model_layer self.model = nn.Sequential(*combined_layers) def forward(self, input_tensor : Tensor) -> Tensor: @@ -60,17 +67,14 @@ class MultiscaleDiscriminator(nn.Module): setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] - def single_discriminator_forward(self, model_layers : nn.Sequential, input_tensor : Tensor) -> List[Tensor]: - return [ model_layers(input_tensor) ] - def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: discriminator_outputs = [] - downsampled_input = input_tensor + temp_downsampled_input = input_tensor for discriminator_index in range(self.num_discriminators): model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) - discriminator_outputs.append(self.single_discriminator_forward(model_layers, downsampled_input)) + discriminator_outputs.append([ model_layers(temp_downsampled_input) ]) - if discriminator_index != (self.num_discriminators - 1): - downsampled_input = self.downsample(downsampled_input) + if discriminator_index < (self.num_discriminators - 1): + temp_downsampled_input = self.downsample(temp_downsampled_input) return discriminator_outputs diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index 2f92bc3..fca082b 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -2,8 +2,9 @@ from typing import Tuple import torch import torch.nn as nn +from torch import Tensor -from .typing import IDEmbedding, TargetAttributes, Tensor +from .typing import SourceEmbedding, TargetAttributes, VisionTensor class AdaptiveEmbeddingIntegrationNetwork(nn.Module): @@ -12,12 +13,12 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): self.encoder = UNet() self.generator = AADGenerator(id_channels, num_blocks) - def forward(self, target : Tensor, source_embedding : IDEmbedding) -> Tuple[Tensor, TargetAttributes]: + def forward(self, target : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: target_attributes = self.get_attributes(target) - swap = self.generator(target_attributes, source_embedding) - return swap, target_attributes + swap_tensor = self.generator(target_attributes, source_embedding) + return swap_tensor, target_attributes - def get_attributes(self, target : Tensor) -> TargetAttributes: + def get_attributes(self, target : VisionTensor) -> TargetAttributes: return self.encoder(target) @@ -35,7 +36,7 @@ class AADGenerator(nn.Module): self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) self.apply(initialize_weight) - def forward(self, target_attributes : TargetAttributes, source_embedding : IDEmbedding) -> Tensor: + def forward(self, target_attributes : TargetAttributes, source_embedding : SourceEmbedding) -> Tensor: feature_map = self.upsample(source_embedding) feature_map_1 = torch.nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) feature_map_2 = torch.nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) @@ -66,7 +67,7 @@ class UNet(nn.Module): self.upsampler_6 = Upsample(128, 32) self.apply(initialize_weight) - def forward(self, target : Tensor) -> TargetAttributes: + def forward(self, target : VisionTensor) -> TargetAttributes: downsample_feature_1 = self.downsampler_1(target) downsample_feature_2 = self.downsampler_2(downsample_feature_1) downsample_feature_3 = self.downsampler_3(downsample_feature_2) @@ -88,34 +89,34 @@ class AADLayer(nn.Module): def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: super(AADLayer, self).__init__() self.input_channels = input_channels - self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) - self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1, stride = 1, padding = 0, bias = True) + self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1) + self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1) self.fc_beta = nn.Linear(id_channels, input_channels) self.fc_gamma = nn.Linear(id_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels, affine = False) - self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1, stride = 1, padding = 0, bias = True) + self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) - def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : IDEmbedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: feature_map = self.instance_norm(feature_map) - attr_gamma = self.conv_gamma(attr_embedding) - attr_beta = self.conv_beta(attr_embedding) - attr_modulation = attr_gamma * feature_map + attr_beta + gamma_attribute = self.conv_gamma(attribute_embedding) + beta_attribute = self.conv_beta(attribute_embedding) + attribute_modulation = gamma_attribute * feature_map + beta_attribute id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) id_modulation = id_gamma * feature_map + id_beta feature_mask = torch.sigmoid(self.conv_mask(feature_map)) - feature_blend = (1 - feature_mask) * attr_modulation + feature_mask * id_modulation + feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * id_modulation return feature_blend class AddBlocksSequential(nn.Sequential): - def forward(self, *inputs : Tuple[Tensor, Tensor, IDEmbedding]) -> Tuple[Tuple[Tensor, Tensor, IDEmbedding], ...]: - _, attr_embedding, id_embedding = inputs + def forward(self, *inputs : Tuple[Tensor, Tensor, SourceEmbedding]) -> Tuple[Tuple[Tensor, Tensor, SourceEmbedding], ...]: + _, attr_embedding, id_embedding = inputs #@todo we are not using shortcuts, it is attribute_embedding - for index, module in enumerate(self._modules.values()): + for index, module in enumerate(self._modules.values()): #@todo refactor this to return values if index % 3 == 0 and index > 0: inputs = (inputs, attr_embedding, id_embedding) # type:ignore[assignment] - if type(inputs) == tuple: + if type(inputs) == tuple: #@todo my IDE complains about the type check inputs = module(*inputs) else: inputs = module(inputs) @@ -123,45 +124,45 @@ class AddBlocksSequential(nn.Sequential): class AADResBlock(nn.Module): - def __init__(self, in_channels : int, out_channels : int, attr_channels : int, id_channels : int, num_blocks : int) -> None: + def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, id_channels : int, num_blocks : int) -> None: super(AADResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels + self.input_channels = input_channels + self.output_channels = output_channels primary_add_blocks = [] for i in range(num_blocks): - intermediate_channels = in_channels if i < (num_blocks - 1) else out_channels + intermediate_channels = input_channels if i < (num_blocks - 1) else output_channels primary_add_blocks.extend( - [ - AADLayer(in_channels, attr_channels, id_channels), + [ #@todo indent + AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), - nn.Conv2d(in_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) ]) self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) - if in_channels != out_channels: - auxiliary_add_blocks = \ - [ - AADLayer(in_channels, attr_channels, id_channels), + if input_channels != output_channels: + auxiliary_add_blocks =\ + [ #@todo indent + AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), - nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + nn.Conv2d(input_channels, output_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) ] self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) - def forward(self, feature_map : Tensor, attr_embedding : Tensor, id_embedding : IDEmbedding) -> Tensor: - primary_feature = self.primary_add_blocks(feature_map, attr_embedding, id_embedding) + def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: + primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding) - if self.in_channels != self.out_channels: - feature_map = self.auxiliary_add_blocks(feature_map, attr_embedding, id_embedding) + if self.input_channels != self.output_channels: + feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, id_embedding) output_feature = primary_feature + feature_map return output_feature class DownSample(nn.Module): - def __init__(self, in_channels : int, out_channels : int) -> None: + def __init__(self, input_channels : int, output_channels : int) -> None: super(DownSample, self).__init__() - self.conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(out_channels) + self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) def forward(self, temp : Tensor) -> Tensor: @@ -172,10 +173,10 @@ class DownSample(nn.Module): class Upsample(nn.Module): - def __init__(self, in_channels : int, out_channels : int) -> None: + def __init__(self, input_channels : int, output_channels : int) -> None: super(Upsample, self).__init__() - self.deconv = nn.ConvTranspose2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(out_channels) + self.deconv = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) def forward(self, temp : Tensor, skip_tensor : Tensor) -> Tensor: @@ -186,9 +187,9 @@ class Upsample(nn.Module): class PixelShuffleUpsample(nn.Module): - def __init__(self, in_channels : int, out_channels : int) -> None: + def __init__(self, input_channels : int, output_channels : int) -> None: super(PixelShuffleUpsample, self).__init__() - self.conv = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1) + self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 3, padding = 1) self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) def forward(self, temp : Tensor) -> Tensor: @@ -199,7 +200,7 @@ class PixelShuffleUpsample(nn.Module): def initialize_weight(module : nn.Module) -> None: if isinstance(module, nn.Linear): - module.weight.data.normal_(0, 0.001) + module.weight.data.normal_(std = 0.001) module.bias.data.zero_() if isinstance(module, nn.Conv2d): @@ -207,11 +208,3 @@ def initialize_weight(module : nn.Module) -> None: if isinstance(module, nn.ConvTranspose2d): nn.init.xavier_normal_(module.weight.data) - - -if __name__ == '__main__': - model = AdaptiveEmbeddingIntegrationNetwork(512, 2) - src = torch.randn(1, 512) - trg = torch.randn(1, 3, 256, 256) - out = model(trg, src) - print(out[0].shape) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 3358c5a..5abdb41 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,31 +1,11 @@ -import configparser -from typing import Tuple - import torch from .typing import Tensor -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') -L2_loss = torch.nn.MSELoss() +def hinge_real_loss(tensor : Tensor) -> Tensor: + return torch.relu(1 - tensor) -def transform_points(points : Tensor, rotation_matrix : Tensor, expression : Tensor, scale : Tensor, translation : Tensor) -> Tensor: - points_transformed = points.view(-1, 21, 3) @ rotation_matrix + expression.view(-1, 21, 3) - points_transformed *= scale[..., None] - points_transformed[:, :, 0:2] += translation[:, None, 0:2] - return points_transformed - - -def hinge_loss(tensor : Tensor, is_positive : bool) -> Tensor: - if is_positive: - return torch.relu(1 - tensor) - else: - return torch.relu(tensor + 1) - - -def calc_distance_ratio(landmarks : Tensor, indices : Tuple[int, int, int, int]) -> Tensor: - distance_horizontal = torch.norm(landmarks[:, indices[0]] - landmarks[:, indices[1]], p = 2, dim = 1, keepdim = True) - distance_vertical = torch.norm(landmarks[:, indices[2]] - landmarks[:, indices[3]], p=2, dim = 1, keepdim = True) - return distance_horizontal / (distance_vertical + 1e-4) +def hinge_fake_loss(tensor : Tensor) -> Tensor: + return torch.relu(tensor + 1) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a2c6d65..87e151d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -2,8 +2,6 @@ import configparser import os from typing import Tuple -import cv2 -import numpy import pytorch_lightning import torch import torchvision @@ -12,18 +10,212 @@ from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.utilities.types import Optimizer from pytorch_msssim import ssim +from torch import Tensor from torch.utils.data import DataLoader -from .data_loader import DataLoaderVGG, read_image +from .data_loader import DataLoaderVGG from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork -from .helper import L2_loss, hinge_loss -from .typing import Batch, DiscriminatorOutputs, IDEmbedding, LossDict, TargetAttributes, Tensor +from .helper import hinge_fake_loss, hinge_real_loss +from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, IdEmbedding, Loss, Padding, SourceEmbedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') +class FaceSwapper(pytorch_lightning.LightningModule): + def __init__(self) -> None: + super().__init__() + id_channels = CONFIG.getint('training.generator', 'id_channels') + num_blocks = CONFIG.getint('training.generator', 'num_blocks') + input_channels = CONFIG.getint('training.discriminator', 'input_channels') + num_filters = CONFIG.getint('training.discriminator', 'num_filters') + num_layers = CONFIG.getint('training.discriminator', 'num_layers') + num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') + arcface_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') + landmarker_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') + motion_extractor_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') + + self.generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) + self.discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) + self.arcface = torch.load(arcface_path, map_location = 'cpu', weights_only = False) + self.landmarker = torch.load(landmarker_path, map_location = 'cpu', weights_only = False) + self.motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') + self.motion_extractor.load_state_dict(torch.load(motion_extractor_path, map_location = 'cpu', weights_only = True)) + self.arcface.eval() + self.landmarker.eval() + self.motion_extractor.eval() + self.automatic_optimization = False + self.mse_loss = torch.nn.MSELoss() + self.batch_size = CONFIG.getint('training.loader', 'batch_size') + + def forward(self, target_tensor : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: + output = self.generator(target_tensor, source_embedding) + return output + + def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: + generator_learning_rate = CONFIG.getfloat('training.generator', 'learning_rate') + discriminator_learning_rate = CONFIG.getfloat('training.discriminator', 'learning_rate') + generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = generator_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = discriminator_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + return generator_optimizer, discriminator_optimizer + + def training_step(self, batch : Batch, batch_index : int) -> Tensor: + source_tensor, target_tensor, is_same_person = batch + generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] + source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) + swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) + discriminator_outputs = self.discriminator(swap_tensor) + + generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, discriminator_outputs, batch) + generator_optimizer.zero_grad() + self.manual_backward(generator_losses.get('loss_generator')) + generator_optimizer.step() + + discriminator_losses = self.calc_discriminator_loss(swap_tensor, source_tensor) + discriminator_optimizer.zero_grad() + self.manual_backward(discriminator_losses.get('loss_discriminator')) + discriminator_optimizer.step() + + if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: + self.log_generator_preview(source_tensor, target_tensor, swap_tensor) + + self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) + self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) + self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = True) + self.log('l_ATTR', generator_losses.get('loss_attribute'), prog_bar = True) + self.log('l_ID', generator_losses.get('loss_id'), prog_bar=True) + self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) + return generator_losses.get('loss_generator') + + def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> Loss: + loss_adversarial = torch.Tensor(0) + + for discriminator_output in discriminator_outputs: + loss_adversarial += hinge_real_loss(discriminator_output[0]).mean(dim = [ 1, 2, 3 ]) + loss_adversarial = torch.mean(loss_adversarial) + return loss_adversarial + + def calc_attribute_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes) -> Loss: + loss_attribute = torch.Tensor(0) + swap_attributes = self.generator.get_attributes(swap_tensor) + + for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): + loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() + loss_attribute *= 0.5 + return loss_attribute + + def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> Loss: + loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) + loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() + loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 + return loss_reconstruction + + def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> Loss: + swap_embedding = self.get_id_embedding(swap_tensor, (30, 0, 10, 10)) + source_embedding = self.get_id_embedding(source_tensor, (30, 0, 10, 10)) + loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() + return loss_id + + def calc_tsr_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Loss: + swap_motion_features = self.get_pose_features(swap_tensor) + target_motion_features = self.get_pose_features(target_tensor) + loss_tsr = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): + loss_tsr += self.mse_loss(swap_motion_feature, target_motion_feature) + return loss_tsr + + def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Loss: + swap_landmark = self.get_face_landmarks(swap_tensor) + target_landmark = self.get_face_landmarks(target_tensor) + left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) + right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) + gaze_loss = left_gaze_loss + right_gaze_loss + return gaze_loss + + def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: + source_tensor, target_tensor, is_same_person = batch + weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') + weight_id = CONFIG.getfloat('training.losses', 'weight_id') + weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') + weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') + weight_tsr = CONFIG.getfloat('training.losses', 'weight_tsr') + weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') + + generator_loss_set = {} + generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) + generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) + generator_loss_set['loss_attribute'] = self.calc_attribute_loss(swap_tensor, target_attributes) + generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + + if weight_tsr > 0: + generator_loss_set['loss_tsr'] = self.calc_tsr_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_tsr'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + if weight_gaze > 0: + generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_tsr') * weight_tsr + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze + return generator_loss_set + + def calc_discriminator_loss(self, swap_tensor : VisionTensor, source_tensor : VisionTensor) -> DiscriminatorLossSet: + discriminator_loss_set = {} + fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) + loss_fake = torch.Tensor(0) + + for fake_discriminator_output in fake_discriminator_outputs: + loss_fake += torch.mean(hinge_fake_loss(fake_discriminator_output[0]).mean(dim = [ 1, 2, 3 ])) + true_discriminator_outputs = self.discriminator(source_tensor) + loss_true = torch.Tensor(0) + + for true_discriminator_output in true_discriminator_outputs: + loss_true += torch.mean(hinge_real_loss(true_discriminator_output[0]).mean(dim = [ 1, 2, 3 ])) + discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 + return discriminator_loss_set + + def get_id_embedding(self, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: + crop_vision_tensor = torch.nn.functional.interpolate(vision_tensor, size = (112, 112), mode = 'area') + crop_vision_tensor = crop_vision_tensor[:, :, 0:112, 8:128] + crop_vision_tensor[:, :, :padding[0], :] = 0 + crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 + crop_vision_tensor[:, :, :, :padding[2]] = 0 + crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 + embedding = self.arcface(crop_vision_tensor) + embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) + return embedding + + def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') + landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) + return landmarks + + def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + motion_dict = self.motion_extractor(vision_tensor_norm) + translation = motion_dict.get('t') + scale = motion_dict.get('scale') + rotation = torch.cat([ motion_dict.get('pitch'), motion_dict.get('yaw'), motion_dict.get('roll') ], dim = 1) + return translation, scale, rotation + + def log_generator_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: + max_preview = 8 + source_tensors = source_tensor[:max_preview] + target_tensors = target_tensor[:max_preview] + swap_tensors = swap_tensor[:max_preview] + rows = [ torch.cat([ source_tensor, target_tensor, swap_tensor ], dim = 2) for source_tensor, target_tensor, swap_tensor in zip(source_tensors, target_tensors, swap_tensors) ] + grid = torchvision.utils.make_grid(torch.cat(rows, dim = 1).unsqueeze(0), nrow = 1, normalize = True, scale_each = True) + self.logger.experiment.add_image("Generator Preview", grid, self.global_step) + + def create_trainer() -> Trainer: trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') output_directory_path = CONFIG.get('training.output', 'directory_path') @@ -56,229 +248,7 @@ def train() -> None: checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') dataset = DataLoaderVGG(CONFIG.get('preparing.dataset', 'dataset_path')) - if not (checkpoint_path and os.path.exists(checkpoint_path)): - checkpoint_path = None data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapper() trainer = create_trainer() trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path) - - -class FaceSwapper(pytorch_lightning.LightningModule): - def __init__(self) -> None: - super().__init__() - self.generator = AdaptiveEmbeddingIntegrationNetwork(CONFIG.getint('training.generator', 'id_channels'), CONFIG.getint('training.generator', 'num_blocks')) - self.discriminator = MultiscaleDiscriminator(CONFIG.getint('training.discriminator', 'input_channels'), CONFIG.getint('training.discriminator', 'num_filters'), CONFIG.getint('training.discriminator', 'num_layers'), CONFIG.getint('training.discriminator', 'num_discriminators')) - self.arcface = torch.load(CONFIG.get('auxiliary_models.paths', 'arcface_path'), map_location = 'cpu', weights_only = False) - self.landmarker = torch.load(CONFIG.get('auxiliary_models.paths', 'landmarker_path'), map_location = 'cpu', weights_only = False) - self.motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') - self.motion_extractor.load_state_dict(torch.load(CONFIG.get('auxiliary_models.paths', 'motion_extractor_path'), map_location = 'cpu', weights_only = True)) - self.arcface.eval() - self.landmarker.eval() - self.motion_extractor.eval() - self.automatic_optimization = False - self.batch_size = CONFIG.getint('training.loader', 'batch_size') - - def forward(self, target_tensor : Tensor, source_embedding : IDEmbedding) -> Tuple[Tensor, TargetAttributes]: - output = self.generator(target_tensor, source_embedding) - return output - - def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: - generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = CONFIG.getfloat('training.generator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) - discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = CONFIG.getfloat('training.discriminator', 'learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) - return generator_optimizer, discriminator_optimizer - - def training_step(self, batch : Batch, batch_index : int) -> Tensor: - source_tensor, target_tensor, is_same_person = batch - generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] - source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) - swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) - discriminator_outputs = self.discriminator(swap_tensor) - generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, discriminator_outputs, batch) - generator_optimizer.zero_grad() - self.manual_backward(generator_losses.get('loss_generator')) - generator_optimizer.step() - discriminator_losses = self.calc_discriminator_loss(swap_tensor, source_tensor) - discriminator_optimizer.zero_grad() - self.manual_backward(discriminator_losses.get('loss_discriminator')) - - if not CONFIG.getboolean('training.discriminator', 'disable'): - discriminator_optimizer.step() - - if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: - self.log_generator_preview(source_tensor, target_tensor, swap_tensor) - - if self.global_step % CONFIG.getint('training.output', 'validation_frequency') == 0: - self.log_validation_preview() - self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) - self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) - self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = False) - self.log('l_ATTR', generator_losses.get('loss_attribute'), prog_bar = True) - self.log('l_ID', generator_losses.get('loss_identity'), prog_bar=True) - self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) - return generator_losses.get('loss_generator') - - def calc_generator_loss(self, swap_tensor : Tensor, target_attributes : TargetAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> LossDict: - source_tensor, target_tensor, is_same_person = batch - generator_losses = {} - # adversarial loss - loss_adversarial = torch.Tensor(0) - - for discriminator_output in discriminator_outputs: - loss_adversarial += hinge_loss(discriminator_output[0], True).mean(dim = [ 1, 2, 3 ]) - loss_adversarial = torch.mean(loss_adversarial) - generator_losses['loss_adversarial'] = loss_adversarial - generator_losses['loss_generator'] = loss_adversarial * CONFIG.getfloat('training.losses', 'weight_adversarial') - - # identity loss - swap_embedding = self.get_id_embedding(swap_tensor, (30, 0, 10, 10)) - source_embedding = self.get_id_embedding(source_tensor, (30, 0, 10, 10)) - loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() - generator_losses['loss_identity'] = loss_identity - generator_losses['loss_generator'] += loss_identity * CONFIG.getfloat('training.losses', 'weight_identity') - - # attribute loss - loss_attribute = torch.Tensor(0) - swap_attributes = self.generator.get_attributes(swap_tensor) - - for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): - loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() - loss_attribute *= 0.5 - generator_losses['loss_attribute'] = loss_attribute - generator_losses['loss_generator'] += loss_attribute * CONFIG.getfloat('training.losses', 'weight_attribute') - - # reconstruction loss - loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) - loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 - generator_losses['loss_reconstruction'] = loss_reconstruction - generator_losses['loss_generator'] += loss_reconstruction * CONFIG.getfloat('training.losses', 'weight_reconstruction') - - if CONFIG.getfloat('training.losses', 'weight_tsr') > 0: - # tsr loss - swap_motion_features = self.get_motion_features(swap_tensor) - target_motion_features = self.get_motion_features(target_tensor) - loss_tsr = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): - loss_tsr += L2_loss(swap_motion_feature, target_motion_feature) - generator_losses['loss_tsr'] = loss_tsr - generator_losses['loss_generator'] += loss_tsr * CONFIG.getfloat('training.losses', 'weight_tsr') - - if CONFIG.getfloat('training.losses', 'weight_eye_gaze') > 0: - swap_landmark_features = self.get_landmark_features(swap_tensor) - target_landmark_features = self.get_landmark_features(target_tensor) - loss_left_eye_gaze = L2_loss(swap_landmark_features[0], target_landmark_features[1]) - loss_right_eye_gaze = L2_loss(swap_landmark_features[0], target_landmark_features[1]) - loss_eye_gaze = loss_left_eye_gaze + loss_right_eye_gaze - generator_losses['loss_eye_gaze'] = loss_eye_gaze - generator_losses['loss_generator'] += loss_eye_gaze * CONFIG.getfloat('training.losses', 'weight_eye_gaze') - return generator_losses - - def calc_discriminator_loss(self, swap_tensor : Tensor, source_tensor : Tensor) -> LossDict: - discriminator_losses = {} - fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) - loss_fake = torch.Tensor(0) - - for fake_discriminator_output in fake_discriminator_outputs: - loss_fake += torch.mean(hinge_loss(fake_discriminator_output[0], False).mean(dim=[1, 2, 3])) - true_discriminator_outputs = self.discriminator(source_tensor) - loss_true = torch.Tensor(0) - - for true_discriminator_output in true_discriminator_outputs: - loss_true += torch.mean(hinge_loss(true_discriminator_output[0], True).mean(dim=[1, 2, 3])) - discriminator_losses['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 - return discriminator_losses - - def get_id_embedding(self, vision_tensor : Tensor, padding : Tuple[int, int, int, int]) -> Tensor: - _, _, height, width = vision_tensor.shape - crop_height = int(height * 0.0586) - crop_width = int(width * 0.0586) - crop_vision_tensor = vision_tensor[:, :, crop_height : height - crop_height, crop_width : width - crop_width] - crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'bilinear') - crop_vision_tensor[:, :, :padding[0], :] = 0 - crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 - crop_vision_tensor[:, :, :, :padding[2]] = 0 - crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 - embedding = self.arcface(crop_vision_tensor) - embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) - return embedding - - def get_landmark_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor]: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') - landmarks = self.landmarker(vision_tensor_norm)[2] - landmarks = landmarks.view(-1, 203, 2) * 256 - return landmarks[:, 198], landmarks[:, 197] - - def get_motion_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - motion_dict = self.motion_extractor(vision_tensor_norm) - translation = motion_dict.get('t') - scale = motion_dict.get('scale') - rotation = torch.cat([ motion_dict.get('pitch'), motion_dict.get('yaw'), motion_dict.get('roll') ], dim = 1) - return translation, scale, rotation - - def log_generator_preview(self, source_tensor : Tensor, target_tensor : Tensor, swap_tensor : Tensor) -> None: - max_preview = 8 - source_tensor = source_tensor[:max_preview] - target_tensor = target_tensor[:max_preview] - swap_tensor = swap_tensor[:max_preview] - rows = [torch.cat([src, tgt, swp], dim = 2) for src, tgt, swp in zip(source_tensor, target_tensor, swap_tensor)] - grid = torchvision.utils.make_grid(torch.cat(rows, dim = 1).unsqueeze(0), nrow = 1, normalize = True, scale_each = True) - self.logger.experiment.add_image("Generator Preview", grid, self.global_step) - - def log_validation_preview(self) -> None: - read_images = lambda path : [read_image(os.path.join(path, f)) for f in sorted(os.listdir(path)) if f.lower().endswith('.jpg') or f.lower().endswith('.png')] - to_numpy = lambda x: (x.cpu().detach().numpy()[0].transpose(1, 2, 0).clip(-1, 1)[:, :, ::-1] + 1) * 127.5 - transforms = torchvision.transforms.Compose( - [ - torchvision.transforms.Resize((256, 256), interpolation = torchvision.transforms.InterpolationMode.BICUBIC), - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), - ]) - sources = read_images(CONFIG.get('training.validation', 'sources')) - targets_front = read_images(CONFIG.get('training.validation', 'targets_front')) - targets_side = read_images(CONFIG.get('training.validation', 'targets_side')) - targets_makeup = read_images(CONFIG.get('training.validation', 'targets_makeup')) - targets_occlusion = read_images(CONFIG.get('training.validation', 'targets_occlusion')) - - self.generator.eval() - - results_source = [] - results_front = [] - results_side = [] - results_makeup = [] - results_occlusion = [] - - for source, target_front, target_side, target_makeup, target_occlusion in zip(sources, targets_front, targets_side, targets_makeup, targets_occlusion): - source_tensor = transforms(source).unsqueeze(0).to(self.device).half() - source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) - target_front_tensor = transforms(target_front).unsqueeze(0).to(self.device).half() - target_side_tensor = transforms(target_side).unsqueeze(0).to(self.device).half() - target_makeup_tensor = transforms(target_makeup).unsqueeze(0).to(self.device).half() - target_occlusion_tensor = transforms(target_occlusion).unsqueeze(0).to(self.device).half() - - with torch.no_grad(): - output_front, _ = self.generator(target_front_tensor, source_embedding) - output_side, _ = self.generator(target_side_tensor, source_embedding) - output_makeup, _ = self.generator(target_makeup_tensor, source_embedding) - output_occlusion, _ = self.generator(target_occlusion_tensor, source_embedding) - - results_source.append(to_numpy(source_tensor)) - results_front.append(numpy.hstack([to_numpy(target_front_tensor), to_numpy(output_front)])) - results_side.append(numpy.hstack([to_numpy(target_side_tensor), to_numpy(output_side)])) - results_makeup.append(numpy.hstack([to_numpy(target_makeup_tensor), to_numpy(output_makeup)])) - results_occlusion.append(numpy.hstack([to_numpy(target_occlusion_tensor), to_numpy(output_occlusion)])) - - sources_vertical = numpy.vstack(results_source) - results_front_vertical = numpy.vstack(results_front) - results_side_vertical = numpy.vstack(results_side) - results_makeup_vertical = numpy.vstack(results_makeup) - results_occlusion_vertical = numpy.vstack(results_occlusion) - pad = numpy.zeros((sources_vertical.shape[0], 10, 3), dtype = sources_vertical.dtype) - preview = numpy.hstack([sources_vertical, pad, results_front_vertical, pad, results_side_vertical, pad, results_makeup_vertical, pad, results_occlusion_vertical]) - - os.makedirs("validation_previews", exist_ok=True) - cv2.imwrite(f"validation_previews/step_{self.global_step}.jpg", preview) - self.generator.train() diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index 29ab130..f1366b5 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -1,7 +1,6 @@ from collections import OrderedDict from typing import Any, Dict, List, Tuple -from numpy.typing import NDArray from torch import Tensor from torch.utils.data import DataLoader @@ -9,8 +8,12 @@ Batch = Tuple[Any, Any, Any] Loader = DataLoader[Tuple[Tensor, ...]] TargetAttributes = Tuple[Tensor, ...] DiscriminatorOutputs = List[List[Tensor]] -LossDict = Dict[str, Tensor] -IDEmbedding = Tensor +IdEmbedding = Tensor +SourceEmbedding = IdEmbedding StateDict = OrderedDict[str, Any] -Embedding = NDArray[Any] -VisionFrame = NDArray[Any] +Padding = Tuple[int, int, int, int] +FaceLandmark203 = Tensor +VisionTensor = Tensor +Loss = Tensor +GeneratorLossSet = Dict[str, Loss] +DiscriminatorLossSet = Dict[str, Loss] From e45f46d3552905e90a2401734b2142784d1d7a52 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 17 Jan 2025 16:14:57 +0530 Subject: [PATCH 014/394] cleaning --- face_swapper/src/training.py | 14 +++++++------- face_swapper/src/typing.py | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 87e151d..9416543 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,7 @@ from .data_loader import DataLoaderVGG from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork from .helper import hinge_fake_loss, hinge_real_loss -from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, IdEmbedding, Loss, Padding, SourceEmbedding, TargetAttributes, VisionTensor +from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, IdEmbedding, LossTensor, Padding, SourceEmbedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -88,7 +88,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) return generator_losses.get('loss_generator') - def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> Loss: + def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: loss_adversarial = torch.Tensor(0) for discriminator_output in discriminator_outputs: @@ -96,7 +96,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): loss_adversarial = torch.mean(loss_adversarial) return loss_adversarial - def calc_attribute_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes) -> Loss: + def calc_attribute_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes) -> LossTensor: loss_attribute = torch.Tensor(0) swap_attributes = self.generator.get_attributes(swap_tensor) @@ -105,19 +105,19 @@ class FaceSwapper(pytorch_lightning.LightningModule): loss_attribute *= 0.5 return loss_attribute - def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> Loss: + def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 return loss_reconstruction - def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> Loss: + def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: swap_embedding = self.get_id_embedding(swap_tensor, (30, 0, 10, 10)) source_embedding = self.get_id_embedding(source_tensor, (30, 0, 10, 10)) loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() return loss_id - def calc_tsr_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Loss: + def calc_tsr_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: swap_motion_features = self.get_pose_features(swap_tensor) target_motion_features = self.get_pose_features(target_tensor) loss_tsr = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) @@ -126,7 +126,7 @@ class FaceSwapper(pytorch_lightning.LightningModule): loss_tsr += self.mse_loss(swap_motion_feature, target_motion_feature) return loss_tsr - def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Loss: + def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: swap_landmark = self.get_face_landmarks(swap_tensor) target_landmark = self.get_face_landmarks(target_tensor) left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index f1366b5..da89355 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -14,6 +14,6 @@ StateDict = OrderedDict[str, Any] Padding = Tuple[int, int, int, int] FaceLandmark203 = Tensor VisionTensor = Tensor -Loss = Tensor -GeneratorLossSet = Dict[str, Loss] -DiscriminatorLossSet = Dict[str, Loss] +LossTensor = Tensor +GeneratorLossSet = Dict[str, LossTensor] +DiscriminatorLossSet = Dict[str, LossTensor] From 798ba48a52790210154de197c57187bd9ca70b47 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 21 Jan 2025 21:43:42 +0530 Subject: [PATCH 015/394] clean data_loader --- face_swapper/src/data_loader.py | 77 ++++++++++++++------------------- face_swapper/src/typing.py | 3 ++ 2 files changed, 36 insertions(+), 44 deletions(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index cceae06..d203054 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -1,81 +1,70 @@ import configparser import glob +import os.path import random import cv2 +import torch import torchvision.transforms as transforms -import tqdm -from PIL import Image from torch.utils.data import TensorDataset -from .typing import Batch +from .typing import Batch, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def read_image(image_path: str) -> Image.Image: +def read_image(image_path: str) -> VisionFrame: image = cv2.imread(image_path)[:, :, ::-1] - pil_image = Image.fromarray(image) # @todo like said, use the PIL transformator - return pil_image + return image class DataLoaderVGG(TensorDataset): def __init__(self, dataset_path : str) -> None: - self.same_person_probability = float(CONFIG.get('preparing.dataloader', 'same_person_probability')) # @todo use CONFIG.getfloat() - also config block at the top + self.same_person_probability = CONFIG.getfloat('preparing.dataloader', 'same_person_probability') self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) # @todo globs belong to the config self.folder_paths = glob.glob('{}/*'.format(dataset_path)) - self.image_path_dict = {} # @todo we are not using dict as suffix... this image_path_set? + self.image_path_set = {} self._current_index = 0 - for folder_path in tqdm.tqdm(self.folder_paths): + for folder_path in self.folder_paths: image_paths = glob.glob('{}/*'.format(folder_path)) # @todo not sure about alls this globs being used here :-) - self.image_path_dict[folder_path] = image_paths + self.image_path_set[folder_path] = image_paths self.dataset_total = len(self.image_paths) - self.transforms_basic = transforms.Compose( + self.transforms = transforms.Compose( [ - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), - transforms.ToTensor(), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - self.transforms_moderate = transforms.Compose( - [ - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), - transforms.ToTensor(), + transforms.ToPILImage(), + transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1), fill = 0), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) - ]) - self.transforms_complex = transforms.Compose( - [ - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), - transforms.RandomHorizontalFlip(p = 0.5), - transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation = 0.2, hue = 0.1), - transforms.RandomAffine(8, translate = (0.02, 0.02), scale = (0.98, 1.02), shear = (1, 1), fill = 0), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) def __getitem__(self, item : int) -> Batch: source_image_path = self.image_paths[item] - source = read_image(source_image_path) - if random.random() > self.same_person_probability: # @todo if -> we_call_a_method_that_explains_what_we_do() - is_same_person = 0 - target_image_path = random.choice(self.image_paths) - target = read_image(target_image_path) - source_transform = self.transforms_moderate(source) - target_transform = self.transforms_complex(target) - else: # @todo else -> we_do_some_alternative_action() - in other words, move it to speaking methods :-) - is_same_person = 1 - source_folder_path = '/'.join(source_image_path.split('/')[:-1]) # @todo use os.path.join() - target_image_path = random.choice(self.image_path_dict[source_folder_path]) - target = read_image(target_image_path) - source_transform = self.transforms_basic(source) - target_transform = self.transforms_basic(target) + if random.random() > self.same_person_probability: + return self.prepare_same_person(source_image_path) + return self.prepare_different_person(source_image_path) - return source_transform, target_transform, is_same_person + def prepare_different_person(self, source_image_path : str) -> Batch: + is_same_person = torch.tensor(0) + target_image_path = random.choice(self.image_paths) + source_vision_frame = read_image(source_image_path) + target_vision_frame = read_image(target_image_path) + source_tensor = self.transforms(source_vision_frame) + target_tensor = self.transforms(target_vision_frame) + return source_tensor, target_tensor, is_same_person + + def prepare_same_person(self, source_image_path : str) -> Batch: + is_same_person = torch.tensor(1) + target_image_path = random.choice(self.image_path_set.get(os.path.dirname(source_image_path))) + source_vision_frame = read_image(source_image_path) + target_vision_frame = read_image(target_image_path) + source_tensor = self.transforms(source_vision_frame) + target_tensor = self.transforms(target_vision_frame) + return source_tensor, target_tensor, is_same_person def __len__(self) -> int: return self.dataset_total diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index da89355..d72df42 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -1,9 +1,11 @@ from collections import OrderedDict from typing import Any, Dict, List, Tuple +from numpy.typing import NDArray from torch import Tensor from torch.utils.data import DataLoader + Batch = Tuple[Any, Any, Any] Loader = DataLoader[Tuple[Tensor, ...]] TargetAttributes = Tuple[Tensor, ...] @@ -17,3 +19,4 @@ VisionTensor = Tensor LossTensor = Tensor GeneratorLossSet = Dict[str, LossTensor] DiscriminatorLossSet = Dict[str, LossTensor] +VisionFrame = NDArray[Any] From d018dd06332ed78a3d0935ef5e61e04de32e2fdc Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 21 Jan 2025 23:13:38 +0530 Subject: [PATCH 016/394] some cleaning and add exporting --- face_swapper/config.ini | 4 ---- face_swapper/export.py | 6 ++++++ face_swapper/src/exporting.py | 25 +++++++++++++++++++++++++ face_swapper/src/generator.py | 31 +++++++++++++++++-------------- 4 files changed, 48 insertions(+), 18 deletions(-) create mode 100644 face_swapper/export.py create mode 100644 face_swapper/src/exporting.py diff --git a/face_swapper/config.ini b/face_swapper/config.ini index c20f445..f9c40b3 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -28,9 +28,6 @@ disable = false arcface_path = /assets/pretrained_models/arcface_w600k_r50.pt landmarker_path = /assets/pretrained_models/landmark_203.pt motion_extractor_path = /assets/pretrained_models/liveportrait_motion_extractor.pth -feature_extractor_path = /assets/pretrained_models/liveportrait_feature_extractor.pth -warping_network_path = /assets/pretrained_models/liveportrait_warping_model.pth -spade_generator_path = /assets/pretrained_models/liveportrait_spade_generator.pth [training.losses] weight_adversarial = 1 @@ -45,7 +42,6 @@ gamma = 0.2 [training.trainer] max_epochs = 50 -disable_discriminator = false [training.output] checkpoint_path = checkpoints/last.ckpt diff --git a/face_swapper/export.py b/face_swapper/export.py new file mode 100644 index 0000000..d560240 --- /dev/null +++ b/face_swapper/export.py @@ -0,0 +1,6 @@ +#!/usr/bin/env python3 + +from src.exporting import export + +if __name__ == '__main__': + export() diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py new file mode 100644 index 0000000..ce954de --- /dev/null +++ b/face_swapper/src/exporting.py @@ -0,0 +1,25 @@ +import configparser +from os import makedirs + +import torch + +from .generator import AdaptiveEmbeddingIntegrationNetwork + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +def export() -> None: + directory_path = CONFIG.get('exporting', 'directory_path') + source_path = CONFIG.get('exporting', 'source_path') + target_path = CONFIG.get('exporting', 'target_path') + opset_version = CONFIG.getint('exporting', 'opset_version') + + makedirs(directory_path, exist_ok = True) + state_dict = torch.load(source_path, map_location = 'cpu')['state_dict']['generator'] + model = AdaptiveEmbeddingIntegrationNetwork(512, 2) + model.load_state_dict(state_dict) + model.eval() + source_tensor = torch.randn(1, 512) + target_tensor = torch.randn(1, 3, 256, 256) + torch.onnx.export(model, (target_tensor, source_tensor), target_path, input_names = [ 'target', 'source' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index fca082b..d2aef72 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -111,15 +111,17 @@ class AADLayer(nn.Module): class AddBlocksSequential(nn.Sequential): def forward(self, *inputs : Tuple[Tensor, Tensor, SourceEmbedding]) -> Tuple[Tuple[Tensor, Tensor, SourceEmbedding], ...]: - _, attr_embedding, id_embedding = inputs #@todo we are not using shortcuts, it is attribute_embedding + _, attribute_embedding, id_embedding = inputs + modules = self._modules.values() - for index, module in enumerate(self._modules.values()): #@todo refactor this to return values - if index % 3 == 0 and index > 0: - inputs = (inputs, attr_embedding, id_embedding) # type:ignore[assignment] - if type(inputs) == tuple: #@todo my IDE complains about the type check - inputs = module(*inputs) - else: + for module_index, module in enumerate(modules): + if module_index % 3 == 0 and module_index > 0: + inputs = (inputs, attribute_embedding, id_embedding) # type:ignore[assignment] + + if isinstance(inputs, torch.Tensor): inputs = module(inputs) + else: + inputs = module(*inputs) return inputs @@ -133,20 +135,21 @@ class AADResBlock(nn.Module): for i in range(num_blocks): intermediate_channels = input_channels if i < (num_blocks - 1) else output_channels primary_add_blocks.extend( - [ #@todo indent + [ AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) - ]) + ] + ) self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) if input_channels != output_channels: auxiliary_add_blocks =\ - [ #@todo indent - AADLayer(input_channels, attribute_channels, id_channels), - nn.ReLU(inplace = True), - nn.Conv2d(input_channels, output_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) - ] + [ + AADLayer(input_channels, attribute_channels, id_channels), + nn.ReLU(inplace = True), + nn.Conv2d(input_channels, output_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + ] self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: From 4260bd6c289f1b02091429c9d36500d7d64c1019 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 21 Jan 2025 23:17:13 +0530 Subject: [PATCH 017/394] fix type --- face_swapper/src/typing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index d72df42..0154b09 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -17,6 +17,6 @@ Padding = Tuple[int, int, int, int] FaceLandmark203 = Tensor VisionTensor = Tensor LossTensor = Tensor -GeneratorLossSet = Dict[str, LossTensor] -DiscriminatorLossSet = Dict[str, LossTensor] +GeneratorLossSet = Dict[str, Tensor] +DiscriminatorLossSet = Dict[str, Tensor] VisionFrame = NDArray[Any] From cfcf0ee2bdb9f57744ebb31f0a067451a49a446f Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 21 Jan 2025 23:36:06 +0530 Subject: [PATCH 018/394] add glob image pattern to config --- face_swapper/config.ini | 2 ++ face_swapper/src/data_loader.py | 10 ++++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index f9c40b3..116c6ec 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,5 +1,7 @@ [preparing.dataset] dataset_path = /assets/VGGface2_None_norm_512_true_bygfpgan +folder_pattern = {}/* +image_pattern = {}/*.*g [preparing.dataloader] same_person_probability = 0.2 diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index d203054..c4f8a9f 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -22,13 +22,15 @@ def read_image(image_path: str) -> VisionFrame: class DataLoaderVGG(TensorDataset): def __init__(self, dataset_path : str) -> None: self.same_person_probability = CONFIG.getfloat('preparing.dataloader', 'same_person_probability') - self.image_paths = glob.glob('{}/*/*.*g'.format(dataset_path)) # @todo globs belong to the config - self.folder_paths = glob.glob('{}/*'.format(dataset_path)) + image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') + folder_pattern = CONFIG.get('preparing.dataset', 'folder_pattern') + self.folder_paths = glob.glob(folder_pattern.format(dataset_path)) + self.image_paths = [] self.image_path_set = {} - self._current_index = 0 for folder_path in self.folder_paths: - image_paths = glob.glob('{}/*'.format(folder_path)) # @todo not sure about alls this globs being used here :-) + image_paths = glob.glob(image_pattern.format(folder_path)) + self.image_paths.extend(image_paths) self.image_path_set[folder_path] = image_paths self.dataset_total = len(self.image_paths) self.transforms = transforms.Compose( From 3b8b6442fc1f09189c38660a7e8d69fd858770ff Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 26 Jan 2025 22:03:34 +0530 Subject: [PATCH 019/394] add infer and some cleaning --- face_swapper/config.ini | 42 +++++++++----------- face_swapper/infer.py | 29 ++++++++++++++ face_swapper/src/data_loader.py | 24 ++++-------- face_swapper/src/helper.py | 47 ++++++++++++++++++++++- face_swapper/src/training.py | 68 ++++++++++++++------------------- 5 files changed, 127 insertions(+), 83 deletions(-) create mode 100644 face_swapper/infer.py diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 116c6ec..8422a1f 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -2,34 +2,26 @@ dataset_path = /assets/VGGface2_None_norm_512_true_bygfpgan folder_pattern = {}/* image_pattern = {}/*.*g - -[preparing.dataloader] same_person_probability = 0.2 -[preparing.augmentation] -expression = false - [training.loader] -batch_size = 4 -num_workers = 8 +batch_size = 24 +num_workers = 12 -[training.generator] +[training.model] +id_embedder_path = +landmarker_path = +motion_extractor_path = + +[training.model.generator] num_blocks = 2 id_channels = 512 -learning_rate = 0.0004 -[training.discriminator] +[training.model.discriminator] input_channels = 3 num_filters = 64 num_layers = 5 num_discriminators = 3 -learning_rate = 0.0004 -disable = false - -[auxiliary_models.paths] #just model.trainer ... try to match the config of the arcface converter -arcface_path = /assets/pretrained_models/arcface_w600k_r50.pt -landmarker_path = /assets/pretrained_models/landmark_203.pt -motion_extractor_path = /assets/pretrained_models/liveportrait_motion_extractor.pth [training.losses] weight_adversarial = 1 @@ -38,12 +30,9 @@ weight_attribute = 10 weight_reconstruction = 10 weight_tsr = 100 -[training.schedulers] -step = 5000 -gamma = 0.2 - [training.trainer] max_epochs = 50 +learning_rate = 0.0004 [training.output] checkpoint_path = checkpoints/last.ckpt @@ -52,10 +41,6 @@ file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' preview_frequency = 250 validation_frequency = 1000 -[training.validation] -sources = assets/test/front/sources -targets = assets/test/front/targets - [exporting] directory_path = source_path = @@ -64,3 +49,10 @@ opset_version = [execution] providers = + +[inference] +generator_path = +id_embedder_path = +source_path = +target_path = +output_path = diff --git a/face_swapper/infer.py b/face_swapper/infer.py new file mode 100644 index 0000000..1a2ac35 --- /dev/null +++ b/face_swapper/infer.py @@ -0,0 +1,29 @@ +import configparser + +import cv2 +import torch +from src.generator import AdaptiveEmbeddingIntegrationNetwork +from src.helper import infer, read_image + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +if __name__ == '__main__': + generator_path = CONFIG.get('inference', 'generator_path') + id_embedder_path = CONFIG.get('inference', 'id_embedder_path') + source_path = CONFIG.get('inference', 'source_path') + target_path = CONFIG.get('inference', 'target_path') + output_path = CONFIG.get('inference', 'output_path') + + state_dict = torch.load(generator_path, map_location = 'cpu')['state_dict']['generator'] + generator = AdaptiveEmbeddingIntegrationNetwork(512, 2) + generator.load_state_dict(state_dict) + generator.eval() + id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') #type:ignore[no-untyped-call] + id_embedder.eval() + + source_vision_frame = read_image(source_path) + target_vision_frame = read_image(target_path) + output_vision_frame = infer(generator, id_embedder, source_vision_frame, target_vision_frame) + cv2.imwrite(output_path, output_vision_frame) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index c4f8a9f..c8b6784 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -1,35 +1,24 @@ -import configparser import glob import os.path import random -import cv2 import torch import torchvision.transforms as transforms from torch.utils.data import TensorDataset -from .typing import Batch, VisionFrame - -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - - -def read_image(image_path: str) -> VisionFrame: - image = cv2.imread(image_path)[:, :, ::-1] - return image +from .helper import read_image +from .typing import Batch class DataLoaderVGG(TensorDataset): - def __init__(self, dataset_path : str) -> None: - self.same_person_probability = CONFIG.getfloat('preparing.dataloader', 'same_person_probability') - image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') - folder_pattern = CONFIG.get('preparing.dataset', 'folder_pattern') - self.folder_paths = glob.glob(folder_pattern.format(dataset_path)) + def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_folder_pattern : str, same_person_probability : float) -> None: + self.same_person_probability = same_person_probability + self.folder_paths = glob.glob(dataset_folder_pattern.format(dataset_path)) self.image_paths = [] self.image_path_set = {} for folder_path in self.folder_paths: - image_paths = glob.glob(image_pattern.format(folder_path)) + image_paths = glob.glob(dataset_image_pattern.format(folder_path)) self.image_paths.extend(image_paths) self.image_path_set[folder_path] = image_paths self.dataset_total = len(self.image_paths) @@ -40,6 +29,7 @@ class DataLoaderVGG(TensorDataset): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1), fill = 0), transforms.ToTensor(), + transforms.Lambda(lambda img: img[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 5abdb41..438e273 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,6 +1,30 @@ +import cv2 +import numpy import torch -from .typing import Tensor +from .typing import IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor + + +def read_image(image_path : str) -> VisionFrame: + image = cv2.imread(image_path) + return image + + +def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor: + vision_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) + vision_tensor = vision_tensor / 255 + vision_tensor = (vision_tensor - 0.5) * 2 + vision_tensor = vision_tensor.unsqueeze(0) + return vision_tensor + + +def convert_to_vision_frame(vision_tensor : VisionTensor) -> VisionFrame: + vision_frame = vision_tensor.detach().cpu().numpy()[0] + vision_frame = vision_frame.transpose(1, 2, 0) + vision_frame = (vision_frame + 1) * 127.5 + vision_frame = vision_frame.clip(0, 255).astype(numpy.uint8) + vision_frame = vision_frame[:, :, ::-1] + return vision_frame def hinge_real_loss(tensor : Tensor) -> Tensor: @@ -9,3 +33,24 @@ def hinge_real_loss(tensor : Tensor) -> Tensor: def hinge_fake_loss(tensor : Tensor) -> Tensor: return torch.relu(tensor + 1) + + +def calc_id_embedding(id_embedder : torch.nn.Module, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: + crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] + crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') + crop_vision_tensor[:, :, :padding[0], :] = 0 + crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 + crop_vision_tensor[:, :, :, :padding[2]] = 0 + crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 + source_embedding = id_embedder(crop_vision_tensor) + source_embedding = torch.nn.functional.normalize(source_embedding, p = 2, dim = 1) + return source_embedding + + +def infer(generator : torch.nn.Module, id_embedder : torch.nn.Module, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: + source_vision_tensor = convert_to_vision_tensor(source_vision_frame) + target_vision_tensor = convert_to_vision_tensor(target_vision_frame) + source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) + output_vision_tensor = generator(source_embedding, target_vision_tensor)[0] + output_vision_frame = convert_to_vision_frame(output_vision_tensor) + return output_vision_frame diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 9416543..e248ee3 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -5,7 +5,6 @@ from typing import Tuple import pytorch_lightning import torch import torchvision -from LivePortrait.src.modules.motion_extractor import MotionExtractor from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.utilities.types import Optimizer @@ -16,8 +15,8 @@ from torch.utils.data import DataLoader from .data_loader import DataLoaderVGG from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork -from .helper import hinge_fake_loss, hinge_real_loss -from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, IdEmbedding, LossTensor, Padding, SourceEmbedding, TargetAttributes, VisionTensor +from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -26,23 +25,22 @@ CONFIG.read('config.ini') class FaceSwapper(pytorch_lightning.LightningModule): def __init__(self) -> None: super().__init__() - id_channels = CONFIG.getint('training.generator', 'id_channels') - num_blocks = CONFIG.getint('training.generator', 'num_blocks') - input_channels = CONFIG.getint('training.discriminator', 'input_channels') - num_filters = CONFIG.getint('training.discriminator', 'num_filters') - num_layers = CONFIG.getint('training.discriminator', 'num_layers') - num_discriminators = CONFIG.getint('training.discriminator', 'num_discriminators') - arcface_path = CONFIG.get('auxiliary_models.paths', 'arcface_path') - landmarker_path = CONFIG.get('auxiliary_models.paths', 'landmarker_path') - motion_extractor_path = CONFIG.get('auxiliary_models.paths', 'motion_extractor_path') + id_channels = CONFIG.getint('training.model.generator', 'id_channels') + num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') + input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') + num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') + num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') + num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') + id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') + landmarker_path = CONFIG.get('training.model', 'landmarker_path') + motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) self.discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) - self.arcface = torch.load(arcface_path, map_location = 'cpu', weights_only = False) - self.landmarker = torch.load(landmarker_path, map_location = 'cpu', weights_only = False) - self.motion_extractor = MotionExtractor(num_kp = 21, backbone = 'convnextv2_tiny') - self.motion_extractor.load_state_dict(torch.load(motion_extractor_path, map_location = 'cpu', weights_only = True)) - self.arcface.eval() + self.id_embedder = torch.jit.load(id_embedder_path, map_location ='cpu') #type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') #type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') #type:ignore[no-untyped-call] + self.id_embedder.eval() self.landmarker.eval() self.motion_extractor.eval() self.automatic_optimization = False @@ -54,16 +52,15 @@ class FaceSwapper(pytorch_lightning.LightningModule): return output def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: - generator_learning_rate = CONFIG.getfloat('training.generator', 'learning_rate') - discriminator_learning_rate = CONFIG.getfloat('training.discriminator', 'learning_rate') - generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = generator_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = discriminator_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') + generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) return generator_optimizer, discriminator_optimizer def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor, is_same_person = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] - source_embedding = self.get_id_embedding(source_tensor, (0, 0, 0, 0)) + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) discriminator_outputs = self.discriminator(swap_tensor) @@ -112,8 +109,8 @@ class FaceSwapper(pytorch_lightning.LightningModule): return loss_reconstruction def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - swap_embedding = self.get_id_embedding(swap_tensor, (30, 0, 10, 10)) - source_embedding = self.get_id_embedding(source_tensor, (30, 0, 10, 10)) + swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() return loss_id @@ -181,17 +178,6 @@ class FaceSwapper(pytorch_lightning.LightningModule): discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 return discriminator_loss_set - def get_id_embedding(self, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: - crop_vision_tensor = torch.nn.functional.interpolate(vision_tensor, size = (112, 112), mode = 'area') - crop_vision_tensor = crop_vision_tensor[:, :, 0:112, 8:128] - crop_vision_tensor[:, :, :padding[0], :] = 0 - crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 - crop_vision_tensor[:, :, :, :padding[2]] = 0 - crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 - embedding = self.arcface(crop_vision_tensor) - embedding = torch.nn.functional.normalize(embedding, p = 2, dim = 1) - return embedding - def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: vision_tensor_norm = (vision_tensor + 1) * 0.5 vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') @@ -200,10 +186,8 @@ class FaceSwapper(pytorch_lightning.LightningModule): def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: vision_tensor_norm = (vision_tensor + 1) * 0.5 - motion_dict = self.motion_extractor(vision_tensor_norm) - translation = motion_dict.get('t') - scale = motion_dict.get('scale') - rotation = torch.cat([ motion_dict.get('pitch'), motion_dict.get('yaw'), motion_dict.get('roll') ], dim = 1) + pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) return translation, scale, rotation def log_generator_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: @@ -246,7 +230,11 @@ def train() -> None: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') - dataset = DataLoaderVGG(CONFIG.get('preparing.dataset', 'dataset_path')) + dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') + dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') + dataset_folder_pattern = CONFIG.get('preparing.dataset', 'folder_pattern') + same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') + dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_folder_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapper() From fcb3390796ba2458caa28d55dd4220c98a53b4cd Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 29 Jan 2025 20:41:54 +0530 Subject: [PATCH 020/394] again again cleaning --- face_swapper/README.md | 107 ++++++++++++ face_swapper/config.ini | 54 +++--- face_swapper/infer.py | 28 +-- face_swapper/src/data_loader.py | 42 +++-- face_swapper/src/discriminator.py | 34 ++-- face_swapper/src/exporting.py | 2 +- face_swapper/src/generator.py | 39 +++-- face_swapper/src/helper.py | 35 ++-- face_swapper/src/inferencing.py | 40 +++++ face_swapper/src/training.py | 278 ++++++++++++++++-------------- face_swapper/src/typing.py | 21 ++- 11 files changed, 426 insertions(+), 254 deletions(-) create mode 100644 face_swapper/README.md create mode 100644 face_swapper/src/inferencing.py diff --git a/face_swapper/README.md b/face_swapper/README.md new file mode 100644 index 0000000..541ca3b --- /dev/null +++ b/face_swapper/README.md @@ -0,0 +1,107 @@ +Face Swapper +================= + +> Swap one face over another face. + +![License](https://img.shields.io/badge/license-MIT-green) + + +Preview +------- + +![Preview]() + + +Installation +------------ + +``` +pip install -r requirements.txt +``` + + +Example +------- + +This example utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap. + +``` +[preparing.dataset] +dataset_path = datasets/train +folder_pattern = {}/* +image_pattern = {}/*.*g +same_person_probability = 0.2 + +[training.loader] +batch_size = 24 +num_workers = 12 + +[training.model] +id_embedder_path = assets/models/id_embedder.pt +landmarker_path = assets/models/landmarker.pt +motion_extractor_path = assets/models/motion_extractor.pt + +[training.model.generator] +num_blocks = 2 +id_channels = 512 + +[training.model.discriminator] +input_channels = 3 +num_filters = 64 +num_layers = 5 +num_discriminators = 3 +kernel_size = 4 + +[training.losses] +weight_adversarial = 1 +weight_id = 20 +weight_attribute = 10 +weight_reconstruction = 10 +weight_pose = 100 + +[training.trainer] +max_epochs = 50 +learning_rate = 0.0004 +precision = 16-mixed +automatic_optimization = false + +[training.output] +checkpoint_path = outputs/last.ckpt +directory_path = outputs +file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' +preview_frequency = 250 +validation_frequency = 1000 + +[exporting] +directory_path = export +source_path = outputs/last.ckpt +target_path = export/face_swapper.onnx +opset_version = 15 + +[inference] +generator_path = outputs/last.ckpt +id_embedder_path = assets/models/id_embedder.pt +source_path = assets/images/source.jpg +target_path = assets/models/target.jpg +output_path = outputs/output.jpg +``` + + +Training +-------- + +Train the Face swapper model. + +``` +python train.py +``` + + +Exporting +--------- + +Export the model to ONNX. + +``` +python export.py +``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 8422a1f..66dcdc1 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,12 +1,12 @@ [preparing.dataset] -dataset_path = /assets/VGGface2_None_norm_512_true_bygfpgan -folder_pattern = {}/* -image_pattern = {}/*.*g -same_person_probability = 0.2 +dataset_path = +directory_pattern = +image_pattern = +same_person_probability = [training.loader] -batch_size = 24 -num_workers = 12 +batch_size = +num_workers = [training.model] id_embedder_path = @@ -14,32 +14,35 @@ landmarker_path = motion_extractor_path = [training.model.generator] -num_blocks = 2 -id_channels = 512 +num_blocks = +id_channels = [training.model.discriminator] -input_channels = 3 -num_filters = 64 -num_layers = 5 -num_discriminators = 3 +input_channels = +num_filters = +num_layers = +num_discriminators = +kernel_size = [training.losses] -weight_adversarial = 1 -weight_id = 20 -weight_attribute = 10 -weight_reconstruction = 10 -weight_tsr = 100 +weight_adversarial = +weight_id = +weight_attribute = +weight_reconstruction = +weight_pose = [training.trainer] -max_epochs = 50 -learning_rate = 0.0004 +max_epochs = +learning_rate = +precision = +automatic_optimization = [training.output] -checkpoint_path = checkpoints/last.ckpt -directory_path = checkpoints -file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' -preview_frequency = 250 -validation_frequency = 1000 +checkpoint_path = +directory_path = +file_pattern = +preview_frequency = +validation_frequency = [exporting] directory_path = @@ -47,9 +50,6 @@ source_path = target_path = opset_version = -[execution] -providers = - [inference] generator_path = id_embedder_path = diff --git a/face_swapper/infer.py b/face_swapper/infer.py index 1a2ac35..fc3eb29 100644 --- a/face_swapper/infer.py +++ b/face_swapper/infer.py @@ -1,29 +1,7 @@ -import configparser +#!/usr/bin/env python3 -import cv2 -import torch -from src.generator import AdaptiveEmbeddingIntegrationNetwork -from src.helper import infer, read_image - -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +from face_swapper.src.inferencing import infer if __name__ == '__main__': - generator_path = CONFIG.get('inference', 'generator_path') - id_embedder_path = CONFIG.get('inference', 'id_embedder_path') - source_path = CONFIG.get('inference', 'source_path') - target_path = CONFIG.get('inference', 'target_path') - output_path = CONFIG.get('inference', 'output_path') - - state_dict = torch.load(generator_path, map_location = 'cpu')['state_dict']['generator'] - generator = AdaptiveEmbeddingIntegrationNetwork(512, 2) - generator.load_state_dict(state_dict) - generator.eval() - id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') #type:ignore[no-untyped-call] - id_embedder.eval() - - source_vision_frame = read_image(source_path) - target_vision_frame = read_image(target_path) - output_vision_frame = infer(generator, id_embedder, source_vision_frame, target_vision_frame) - cv2.imwrite(output_path, output_vision_frame) + infer() diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index c8b6784..31df9ae 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -1,43 +1,53 @@ import glob import os.path import random +from typing import Tuple import torch import torchvision.transforms as transforms from torch.utils.data import TensorDataset from .helper import read_image -from .typing import Batch +from .typing import Batch, ImagePathList, ImagePathSet class DataLoaderVGG(TensorDataset): def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_folder_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability - self.folder_paths = glob.glob(dataset_folder_pattern.format(dataset_path)) - self.image_paths = [] - self.image_path_set = {} - - for folder_path in self.folder_paths: - image_paths = glob.glob(dataset_image_pattern.format(folder_path)) - self.image_paths.extend(image_paths) - self.image_path_set[folder_path] = image_paths + self.directory_paths = glob.glob(dataset_folder_pattern.format(dataset_path)) + self.image_paths, self.image_path_set = self.prepare_image_paths(dataset_image_pattern) self.dataset_total = len(self.image_paths) - self.transforms = transforms.Compose( + self.transforms = self.compose_transforms() + + def prepare_image_paths(self, dataset_image_pattern : str) -> Tuple[ImagePathList, ImagePathSet]: + image_paths = [] + image_path_set = {} + + for directory_path in self.directory_paths: + image_paths = glob.glob(dataset_image_pattern.format(directory_path)) + image_paths.extend(image_paths) + image_path_set[directory_path] = image_paths + return image_paths, image_path_set + + def compose_transforms(self) -> transforms: + transform = transforms.Compose( [ transforms.ToPILImage(), - transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), - transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), - transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1), fill = 0), + transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), + transforms.RandomAffine(4, translate=(0.01, 0.01), scale=(0.98, 1.02), shear=(1, 1), fill=0), transforms.ToTensor(), - transforms.Lambda(lambda img: img[[2, 1, 0], :, :]), + transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) + return transform - def __getitem__(self, item : int) -> Batch: - source_image_path = self.image_paths[item] + def __getitem__(self, index : int) -> Batch: + source_image_path = self.image_paths[index] if random.random() > self.same_person_probability: return self.prepare_same_person(source_image_path) + return self.prepare_different_person(source_image_path) def prepare_different_person(self, source_image_path : str) -> Batch: diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index 114f377..9e15f37 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -1,4 +1,8 @@ +from itertools import chain +from typing import List + import numpy +import torch.nn import torch.nn as nn from torch import Tensor @@ -6,11 +10,15 @@ from .typing import DiscriminatorOutputs class NLayerDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int) -> None: + def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super(NLayerDiscriminator, self).__init__() self.num_layers = num_layers - kernel_size = 4 + model_layers = self.prepare_model_layers(input_channels, num_filters, num_layers, kernel_size) + self.model = nn.Sequential(*list(chain(*model_layers))) + + def prepare_model_layers(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> List[List[torch.nn.Module]]: padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) + model_layers =\ [ [ @@ -35,7 +43,7 @@ class NLayerDiscriminator(nn.Module): model_layers +=\ [ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 1, padding = padding_size), + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding_size), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) ] @@ -43,38 +51,36 @@ class NLayerDiscriminator(nn.Module): model_layers +=\ [ [ - nn.Conv2d(current_filters, 1, kernel_size = kernel_size, stride = 1, padding = padding_size) + nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding_size) ] ] - combined_layers = [] - - for model_layer in model_layers: - combined_layers += model_layer - self.model = nn.Sequential(*combined_layers) + return model_layers def forward(self, input_tensor : Tensor) -> Tensor: return self.model(input_tensor) class MultiscaleDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int): + def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int, kernel_size : int): super(MultiscaleDiscriminator, self).__init__() self.num_discriminators = num_discriminators self.num_layers = num_layers for discriminator_index in range(num_discriminators): - single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers) + single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers, kernel_size) setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) + self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: discriminator_outputs = [] - temp_downsampled_input = input_tensor + temp_tensor = input_tensor for discriminator_index in range(self.num_discriminators): model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) - discriminator_outputs.append([ model_layers(temp_downsampled_input) ]) + discriminator_outputs.append([ model_layers(temp_tensor) ]) if discriminator_index < (self.num_discriminators - 1): - temp_downsampled_input = self.downsample(temp_downsampled_input) + temp_tensor = self.downsample(temp_tensor) + return discriminator_outputs diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index ce954de..033e26d 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -16,7 +16,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - state_dict = torch.load(source_path, map_location = 'cpu')['state_dict']['generator'] + state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') model = AdaptiveEmbeddingIntegrationNetwork(512, 2) model.load_state_dict(state_dict) model.eval() diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index d2aef72..d48b5e1 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -34,7 +34,7 @@ class AADGenerator(nn.Module): self.res_block_6 = AADResBlock(256, 128, 128, id_channels, num_blocks) self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) - self.apply(initialize_weight) + self.apply(init_weight) def forward(self, target_attributes : TargetAttributes, source_embedding : SourceEmbedding) -> Tensor: feature_map = self.upsample(source_embedding) @@ -65,7 +65,7 @@ class UNet(nn.Module): self.upsampler_4 = Upsample(512, 128) self.upsampler_5 = Upsample(256, 64) self.upsampler_6 = Upsample(128, 32) - self.apply(initialize_weight) + self.apply(init_weight) def forward(self, target : VisionTensor) -> TargetAttributes: downsample_feature_1 = self.downsampler_1(target) @@ -93,7 +93,7 @@ class AADLayer(nn.Module): self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1) self.fc_beta = nn.Linear(id_channels, input_channels) self.fc_gamma = nn.Linear(id_channels, input_channels) - self.instance_norm = nn.InstanceNorm2d(input_channels, affine = False) + self.instance_norm = nn.InstanceNorm2d(input_channels) self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: @@ -110,9 +110,10 @@ class AADLayer(nn.Module): class AddBlocksSequential(nn.Sequential): + #todo: what are inputs? improve the name def forward(self, *inputs : Tuple[Tensor, Tensor, SourceEmbedding]) -> Tuple[Tuple[Tensor, Tensor, SourceEmbedding], ...]: _, attribute_embedding, id_embedding = inputs - modules = self._modules.values() + modules = self._modules.values() #todo: what kind of modules? for module_index, module in enumerate(modules): if module_index % 3 == 0 and module_index > 0: @@ -122,7 +123,8 @@ class AddBlocksSequential(nn.Sequential): inputs = module(inputs) else: inputs = module(*inputs) - return inputs + + return inputs #todo: would be easier to read when you just return xxx_inputs, attribute_embedding, id_embedding ? class AADResBlock(nn.Module): @@ -130,33 +132,38 @@ class AADResBlock(nn.Module): super(AADResBlock, self).__init__() self.input_channels = input_channels self.output_channels = output_channels + self.prepare_primary_add_blocks(input_channels, attribute_channels, id_channels, output_channels, num_blocks) + self.prepare_auxiliary_add_blocks(input_channels, attribute_channels, id_channels, output_channels) + + def prepare_primary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int, num_blocks : int) -> None: primary_add_blocks = [] - for i in range(num_blocks): - intermediate_channels = input_channels if i < (num_blocks - 1) else output_channels + for index in range(num_blocks): + intermediate_channels = input_channels if index < (num_blocks - 1) else output_channels primary_add_blocks.extend( [ AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), - nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) + nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False) ] ) self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) - if input_channels != output_channels: - auxiliary_add_blocks =\ - [ + def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int) -> None: + if input_channels > output_channels: + auxiliary_add_blocks = AddBlocksSequential( AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), - nn.Conv2d(input_channels, output_channels, kernel_size = 3, stride = 1, padding = 1, bias = False) - ] - self.auxiliary_add_blocks = AddBlocksSequential(*auxiliary_add_blocks) + nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) + ) + self.auxiliary_add_blocks = auxiliary_add_blocks def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding) - if self.input_channels != self.output_channels: + if self.input_channels > self.output_channels: feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, id_embedding) + output_feature = primary_feature + feature_map return output_feature @@ -201,7 +208,7 @@ class PixelShuffleUpsample(nn.Module): return temp -def initialize_weight(module : nn.Module) -> None: +def init_weight(module : nn.Module) -> None: if isinstance(module, nn.Linear): module.weight.data.normal_(std = 0.001) module.bias.data.zero_() diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 438e273..ce77c42 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,13 +1,21 @@ +import platform + import cv2 import numpy import torch -from .typing import IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor +from .typing import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor + + +def is_windows() -> bool: + return platform.system().lower() == 'windows' def read_image(image_path : str) -> VisionFrame: - image = cv2.imread(image_path) - return image + if is_windows(): + image_buffer = numpy.fromfile(image_path, dtype = numpy.uint8) + return cv2.imdecode(image_buffer, cv2.IMREAD_COLOR) + return cv2.imread(image_path) def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor: @@ -28,14 +36,18 @@ def convert_to_vision_frame(vision_tensor : VisionTensor) -> VisionFrame: def hinge_real_loss(tensor : Tensor) -> Tensor: - return torch.relu(1 - tensor) + real_loss = torch.relu(1 - tensor) + real_loss = real_loss.mean(dim = [ 1, 2, 3 ]) + return real_loss def hinge_fake_loss(tensor : Tensor) -> Tensor: - return torch.relu(tensor + 1) + fake_loss = torch.relu(tensor + 1) + fake_loss = fake_loss.mean(dim = [ 1, 2, 3 ]) + return fake_loss -def calc_id_embedding(id_embedder : torch.nn.Module, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: +def calc_id_embedding(id_embedder : IdEmbedder, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') crop_vision_tensor[:, :, :padding[0], :] = 0 @@ -43,14 +55,5 @@ def calc_id_embedding(id_embedder : torch.nn.Module, vision_tensor : VisionTenso crop_vision_tensor[:, :, :, :padding[2]] = 0 crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 source_embedding = id_embedder(crop_vision_tensor) - source_embedding = torch.nn.functional.normalize(source_embedding, p = 2, dim = 1) + source_embedding = torch.nn.functional.normalize(source_embedding, p = 2) return source_embedding - - -def infer(generator : torch.nn.Module, id_embedder : torch.nn.Module, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: - source_vision_tensor = convert_to_vision_tensor(source_vision_frame) - target_vision_tensor = convert_to_vision_tensor(target_vision_frame) - source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) - output_vision_tensor = generator(source_embedding, target_vision_tensor)[0] - output_vision_frame = convert_to_vision_frame(output_vision_tensor) - return output_vision_frame diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py new file mode 100644 index 0000000..56ae4bc --- /dev/null +++ b/face_swapper/src/inferencing.py @@ -0,0 +1,40 @@ +import configparser + +import cv2 +import torch + +from .generator import AdaptiveEmbeddingIntegrationNetwork +from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image +from .typing import Generator, IdEmbedder, VisionFrame + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +def run_swap(generator : Generator, id_embedder : IdEmbedder, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: + source_vision_tensor = convert_to_vision_tensor(source_vision_frame) + target_vision_tensor = convert_to_vision_tensor(target_vision_frame) + source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) + output_vision_tensor = generator(source_embedding, target_vision_tensor)[0] + output_vision_frame = convert_to_vision_frame(output_vision_tensor) + return output_vision_frame + + +def infer() -> None: + generator_path = CONFIG.get('inference', 'generator_path') + id_embedder_path = CONFIG.get('inference', 'id_embedder_path') + source_path = CONFIG.get('inference', 'source_path') + target_path = CONFIG.get('inference', 'target_path') + output_path = CONFIG.get('inference', 'output_path') + + state_dict = torch.load(generator_path, map_location='cpu').get('state_dict').get('generator') + generator = AdaptiveEmbeddingIntegrationNetwork(512, 2) + generator.load_state_dict(state_dict) + generator.eval() + id_embedder = torch.jit.load(id_embedder_path, map_location='cpu') # type:ignore[no-untyped-call] + id_embedder.eval() + + source_vision_frame = read_image(source_path) + target_vision_frame = read_image(target_path) + output_vision_frame = run_swap(generator, id_embedder, source_vision_frame, target_vision_frame) + cv2.imwrite(output_path, output_vision_frame) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e248ee3..33534aa 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,13 +16,138 @@ from .data_loader import DataLoaderVGG from .discriminator import MultiscaleDiscriminator from .generator import AdaptiveEmbeddingIntegrationNetwork from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss -from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, TargetAttributes, VisionTensor +from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapper(pytorch_lightning.LightningModule): +class FaceSwapperLoss: + def __init__(self) -> None: + id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') + landmarker_path = CONFIG.get('training.model', 'landmarker_path') + motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') + self.batch_size = CONFIG.getint('training.loader', 'batch_size') + self.mse_loss = torch.nn.MSELoss() + self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.id_embedder.eval() + self.landmarker.eval() + self.motion_extractor.eval() + + def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: + source_tensor, target_tensor, is_same_person = batch + weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') + weight_id = CONFIG.getfloat('training.losses', 'weight_id') + weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') + weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') + weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') + weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') + generator_loss_set = {} + + generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) + generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) + generator_loss_set['loss_attribute'] = self.calc_attribute_loss(target_attributes, swap_attributes) + generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + + if weight_pose > 0: + generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_pose'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + if weight_gaze > 0: + generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_pose') * weight_pose + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze + return generator_loss_set + + def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: + discriminator_loss_set = {} + loss_fake = torch.Tensor(0) + + for fake_discriminator_output in fake_discriminator_outputs: + loss_fake += hinge_fake_loss(fake_discriminator_output[0]).mean() + + loss_true = torch.Tensor(0) + + for true_discriminator_output in real_discriminator_outputs: + loss_true += hinge_real_loss(true_discriminator_output[0]).mean() + + discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 + return discriminator_loss_set + + def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: + loss_adversarial = torch.Tensor(0) + + for discriminator_output in discriminator_outputs: + loss_adversarial += hinge_real_loss(discriminator_output[0]) + + loss_adversarial = torch.mean(loss_adversarial) + return loss_adversarial + + def calc_attribute_loss(self, target_attributes : TargetAttributes, swap_attributes : SwapAttributes) -> LossTensor: + loss_attribute = torch.Tensor(0) + + for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): + loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() + + loss_attribute *= 0.5 + return loss_attribute + + def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: + loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) + loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 + loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) + loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() + loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 + return loss_reconstruction + + def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: + swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) + loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() + return loss_id + + def calc_pose_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: + swap_motion_features = self.get_pose_features(swap_tensor) + target_motion_features = self.get_pose_features(target_tensor) + loss_pose = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): + loss_pose += self.mse_loss(swap_motion_feature, target_motion_feature) + + return loss_pose + + def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: + swap_landmark = self.get_face_landmarks(swap_tensor) + target_landmark = self.get_face_landmarks(target_tensor) + left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) + right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) + gaze_loss = left_gaze_loss + right_gaze_loss + return gaze_loss + + def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') + landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) + return landmarks + + def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) + return translation, scale, rotation + + +class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() id_channels = CONFIG.getint('training.model.generator', 'id_channels') @@ -31,21 +156,10 @@ class FaceSwapper(pytorch_lightning.LightningModule): num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') - id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') - landmarker_path = CONFIG.get('training.model', 'landmarker_path') - motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') - + kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') self.generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) - self.discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators) - self.id_embedder = torch.jit.load(id_embedder_path, map_location ='cpu') #type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') #type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') #type:ignore[no-untyped-call] - self.id_embedder.eval() - self.landmarker.eval() - self.motion_extractor.eval() - self.automatic_optimization = False - self.mse_loss = torch.nn.MSELoss() - self.batch_size = CONFIG.getint('training.loader', 'batch_size') + self.discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators, kernel_size) + self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') def forward(self, target_tensor : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: output = self.generator(target_tensor, source_embedding) @@ -62,135 +176,32 @@ class FaceSwapper(pytorch_lightning.LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) - discriminator_outputs = self.discriminator(swap_tensor) + swap_attributes = self.generator.get_attributes(swap_tensor) + real_discriminator_outputs = self.discriminator(source_tensor.detach()) + fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) - generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, discriminator_outputs, batch) + generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, swap_attributes, fake_discriminator_outputs, batch) generator_optimizer.zero_grad() self.manual_backward(generator_losses.get('loss_generator')) generator_optimizer.step() - discriminator_losses = self.calc_discriminator_loss(swap_tensor, source_tensor) + discriminator_losses = self.calc_discriminator_loss(real_discriminator_outputs, fake_discriminator_outputs) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_losses.get('loss_discriminator')) discriminator_optimizer.step() if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: - self.log_generator_preview(source_tensor, target_tensor, swap_tensor) + self.generate_preview(source_tensor, target_tensor, swap_tensor) self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = True) self.log('l_ATTR', generator_losses.get('loss_attribute'), prog_bar = True) - self.log('l_ID', generator_losses.get('loss_id'), prog_bar=True) + self.log('l_ID', generator_losses.get('loss_id'), prog_bar = True) self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) return generator_losses.get('loss_generator') - def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: - loss_adversarial = torch.Tensor(0) - - for discriminator_output in discriminator_outputs: - loss_adversarial += hinge_real_loss(discriminator_output[0]).mean(dim = [ 1, 2, 3 ]) - loss_adversarial = torch.mean(loss_adversarial) - return loss_adversarial - - def calc_attribute_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes) -> LossTensor: - loss_attribute = torch.Tensor(0) - swap_attributes = self.generator.get_attributes(swap_tensor) - - for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): - loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() - loss_attribute *= 0.5 - return loss_attribute - - def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: - loss_reconstruction = torch.sum(0.5 * torch.mean(torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1), dim = 1) * is_same_person) / (is_same_person.sum() + 1e-4) - loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 - return loss_reconstruction - - def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) - loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding, dim = 1)).mean() - return loss_id - - def calc_tsr_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_motion_features = self.get_pose_features(swap_tensor) - target_motion_features = self.get_pose_features(target_tensor) - loss_tsr = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): - loss_tsr += self.mse_loss(swap_motion_feature, target_motion_feature) - return loss_tsr - - def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_landmark = self.get_face_landmarks(swap_tensor) - target_landmark = self.get_face_landmarks(target_tensor) - left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) - right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) - gaze_loss = left_gaze_loss + right_gaze_loss - return gaze_loss - - def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - source_tensor, target_tensor, is_same_person = batch - weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') - weight_id = CONFIG.getfloat('training.losses', 'weight_id') - weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') - weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') - weight_tsr = CONFIG.getfloat('training.losses', 'weight_tsr') - weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') - - generator_loss_set = {} - generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) - generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) - generator_loss_set['loss_attribute'] = self.calc_attribute_loss(swap_tensor, target_attributes) - generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) - - if weight_tsr > 0: - generator_loss_set['loss_tsr'] = self.calc_tsr_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_tsr'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - if weight_gaze > 0: - generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_tsr') * weight_tsr - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze - return generator_loss_set - - def calc_discriminator_loss(self, swap_tensor : VisionTensor, source_tensor : VisionTensor) -> DiscriminatorLossSet: - discriminator_loss_set = {} - fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) - loss_fake = torch.Tensor(0) - - for fake_discriminator_output in fake_discriminator_outputs: - loss_fake += torch.mean(hinge_fake_loss(fake_discriminator_output[0]).mean(dim = [ 1, 2, 3 ])) - true_discriminator_outputs = self.discriminator(source_tensor) - loss_true = torch.Tensor(0) - - for true_discriminator_output in true_discriminator_outputs: - loss_true += torch.mean(hinge_real_loss(true_discriminator_output[0]).mean(dim = [ 1, 2, 3 ])) - discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 - return discriminator_loss_set - - def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') - landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) - return landmarks - - def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - return translation, scale, rotation - - def log_generator_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: + def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: max_preview = 8 source_tensors = source_tensor[:max_preview] target_tensors = target_tensor[:max_preview] @@ -204,11 +215,12 @@ def create_trainer() -> Trainer: trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') output_directory_path = CONFIG.get('training.output', 'directory_path') output_file_pattern = CONFIG.get('training.output', 'file_pattern') + trainer_precision = CONFIG.get('training.trainer', 'precision') os.makedirs(output_directory_path, exist_ok = True) return Trainer( max_epochs = trainer_max_epochs, - precision = '16-mixed', + precision = trainer_precision, callbacks = [ ModelCheckpoint( @@ -217,12 +229,10 @@ def create_trainer() -> Trainer: filename = output_file_pattern, every_n_train_steps = 1000, save_top_k = 5, - mode = 'min', save_last = True ) ], - log_every_n_steps = 10, - accumulate_grad_batches = 1, + log_every_n_steps = 10 ) @@ -232,11 +242,11 @@ def train() -> None: checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') - dataset_folder_pattern = CONFIG.get('preparing.dataset', 'folder_pattern') + dataset_directory_pattern = CONFIG.get('preparing.dataset', 'directory_pattern') same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') - dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_folder_pattern, same_person_probability) + dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - face_swap_model = FaceSwapper() + face_swap_model = FaceSwapperTrain() trainer = create_trainer() trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path) diff --git a/face_swapper/src/typing.py b/face_swapper/src/typing.py index 0154b09..96de434 100644 --- a/face_swapper/src/typing.py +++ b/face_swapper/src/typing.py @@ -1,22 +1,33 @@ from collections import OrderedDict from typing import Any, Dict, List, Tuple +import torch.nn from numpy.typing import NDArray from torch import Tensor from torch.utils.data import DataLoader - Batch = Tuple[Any, Any, Any] Loader = DataLoader[Tuple[Tensor, ...]] +ImagePathList = List[str] +ImagePathSet = Dict[str, ImagePathList] + +SwapAttributes = Tuple[Tensor, ...] TargetAttributes = Tuple[Tensor, ...] DiscriminatorOutputs = List[List[Tensor]] + IdEmbedding = Tensor SourceEmbedding = IdEmbedding -StateDict = OrderedDict[str, Any] -Padding = Tuple[int, int, int, int] FaceLandmark203 = Tensor -VisionTensor = Tensor + +StateSet = OrderedDict[str, Any] +Padding = Tuple[int, int, int, int] + LossTensor = Tensor +VisionTensor = Tensor +VisionFrame = NDArray[Any] + GeneratorLossSet = Dict[str, Tensor] DiscriminatorLossSet = Dict[str, Tensor] -VisionFrame = NDArray[Any] + +Generator = torch.nn.Module +IdEmbedder = torch.nn.Module From bb0e3b4a8ab8cfa660cffcec81c549f472133cb4 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 29 Jan 2025 20:46:56 +0530 Subject: [PATCH 021/394] remove liveportrait and update requirements --- .flake8 | 1 - .gitmodules | 3 --- mypy.ini | 1 - requirements.txt | 1 + 4 files changed, 1 insertion(+), 5 deletions(-) diff --git a/.flake8 b/.flake8 index 0ed7d05..400be93 100644 --- a/.flake8 +++ b/.flake8 @@ -4,4 +4,3 @@ plugins = flake8-import-order application_import_names = arcface_converter import-order-style = pycharm per-file-ignores = preparing.py:E402 -exclude = face_swapper/LivePortrait diff --git a/.gitmodules b/.gitmodules index 79022b3..e69de29 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +0,0 @@ -[submodule "face_swapper/LivePortrait"] - path = face_swapper/LivePortrait - url = https://github.com/KwaiVGI/LivePortrait diff --git a/mypy.ini b/mypy.ini index 081e7bf..182b87b 100644 --- a/mypy.ini +++ b/mypy.ini @@ -6,4 +6,3 @@ disallow_untyped_defs = True ignore_missing_imports = True strict_optional = False explicit_package_bases = True -exclude = face_swapper/LivePortrait diff --git a/requirements.txt b/requirements.txt index ec67d61..b04959e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,4 @@ onnx==1.17.0 onnxruntime==1.20.0 opencv-python==4.10.0.84 mxnet==1.9.1 +pytorch-msssim==1.0.0 From 5892460c3d9660a8f6a0595f6b7b1a529b26ad91 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Feb 2025 17:56:08 +0100 Subject: [PATCH 022/394] Add the final license --- face_swapper/LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/LICENSE.md b/face_swapper/LICENSE.md index 158be07..5da6fcd 100644 --- a/face_swapper/LICENSE.md +++ b/face_swapper/LICENSE.md @@ -1,3 +1,3 @@ -Non-Commercial license +ResearchRAIL-M license Copyright (c) 2024 Henry Ruhs From 9c15f584aa67cd60d0e278d421798489043fe91f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Feb 2025 19:00:09 +0100 Subject: [PATCH 023/394] Mixed bag of cleanups --- .gitignore | 5 ++ arcface_converter/README.md | 40 ++++++---- arcface_converter/src/exporting.py | 6 +- .../arcface_converter.py} | 0 arcface_converter/src/preparing.py | 4 +- arcface_converter/src/training.py | 14 ++-- arcface_converter/src/{typing.py => types.py} | 0 face_swapper/LivePortrait | 2 +- face_swapper/README.md | 74 ++++++++++++------- face_swapper/config.ini | 4 +- face_swapper/src/data_loader.py | 10 +-- face_swapper/src/discriminator.py | 2 +- face_swapper/src/generator.py | 2 +- face_swapper/src/helper.py | 4 +- face_swapper/src/inferencing.py | 20 ++--- .../src/losses/.gitkeep | 0 face_swapper/src/networks/.gitkeep | 0 face_swapper/src/training.py | 18 ++--- face_swapper/src/{typing.py => types.py} | 0 19 files changed, 123 insertions(+), 82 deletions(-) rename arcface_converter/src/{model.py => networks/arcface_converter.py} (100%) rename arcface_converter/src/{typing.py => types.py} (100%) rename arcface_converter/src/__init__.py => face_swapper/src/losses/.gitkeep (100%) create mode 100644 face_swapper/src/networks/.gitkeep rename face_swapper/src/{typing.py => types.py} (100%) diff --git a/.gitignore b/.gitignore index 8ee9a7e..52d5fa1 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,8 @@ __pycache__ +.assets +.datasets .idea +.inputs +.exports +.outputs .vscode diff --git a/arcface_converter/README.md b/arcface_converter/README.md index dbc3e1a..49b33d5 100644 --- a/arcface_converter/README.md +++ b/arcface_converter/README.md @@ -20,44 +20,58 @@ pip install -r requirements.txt ``` -Example -------- +Setup +----- -This example utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap. +This `config.ini` utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap. ``` [preparing.dataset] -dataset_path = datasets/megaface/train.rec +dataset_path = .datasets/megaface/train.rec crop_size = 112 process_limit = 650000 +``` +``` [preparing.model] -source_path = models/arcface_w600k_r50.onnx -target_path = models/arcface_simswap.onnx +source_path = .models/arcface_w600k_r50.onnx +target_path = .models/arcface_simswap.onnx +``` +``` [preparing.input] -directory_path = inputs -source_path = inputs/arcface_w600k_r50.npy -target_path = inputs/arcface_simswap.npy +directory_path = .inputs +source_path = .inputs/arcface_w600k_r50.npy +target_path = .inputs/arcface_simswap.npy +``` +``` [training.loader] split_ratio = 0.8 batch_size = 51200 num_workers = 8 +``` +``` [training.trainer] max_epochs = 4096 +``` +``` [training.output] -directory_path = outputs +directory_path = .outputs file_pattern = arcface_converter_simswap_{epoch:02d}_{val_loss:.4f} +``` +``` [exporting] -directory_path = exports -source_path = outputs/last.ckpt -target_path = exports/arcface_converter_simswap.onnx +directory_path = .exports +source_path = .outputs/last.ckpt +target_path = .exports/arcface_converter_simswap.onnx opset_version = 15 +``` +``` [execution] providers = CUDAExecutionProvider ``` diff --git a/arcface_converter/src/exporting.py b/arcface_converter/src/exporting.py index c5d9693..de551c8 100644 --- a/arcface_converter/src/exporting.py +++ b/arcface_converter/src/exporting.py @@ -16,7 +16,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - model = ArcFaceConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') - model.eval() + arcface_converter_trainer = ArcFaceConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + arcface_converter_trainer.eval() input_tensor = torch.randn(1, 512) - torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) + torch.onnx.export(arcface_converter_trainer, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/arcface_converter/src/model.py b/arcface_converter/src/networks/arcface_converter.py similarity index 100% rename from arcface_converter/src/model.py rename to arcface_converter/src/networks/arcface_converter.py diff --git a/arcface_converter/src/preparing.py b/arcface_converter/src/preparing.py index 60151d0..6722f74 100644 --- a/arcface_converter/src/preparing.py +++ b/arcface_converter/src/preparing.py @@ -9,14 +9,14 @@ from mxnet.io import ImageRecordIter from onnxruntime import InferenceSession from tqdm import tqdm -from .typing import Embedding, EmbeddingPairs, VisionFrame +from types import Embedding, EmbeddingPairs, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') def prepare_crop_vision_frame(crop_vision_frame : VisionFrame) -> VisionFrame: - crop_vision_frame = crop_vision_frame.astype(numpy.float32) / 255 + crop_vision_frame = crop_vision_frame.astype(numpy.float32) / 255.0 crop_vision_frame = (crop_vision_frame - 0.5) * 2 return crop_vision_frame diff --git a/arcface_converter/src/training.py b/arcface_converter/src/training.py index 149bf43..2f5cb74 100644 --- a/arcface_converter/src/training.py +++ b/arcface_converter/src/training.py @@ -10,8 +10,8 @@ from pytorch_lightning.tuner.tuning import Tuner from torch import Tensor from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split -from .model import ArcFaceConverter -from .typing import Batch, Loader +from networks.arcface_converter import ArcFaceConverter +from types import Batch, Loader CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -20,12 +20,12 @@ CONFIG.read('config.ini') class ArcFaceConverterTrainer(pytorch_lightning.LightningModule): def __init__(self) -> None: super(ArcFaceConverterTrainer, self).__init__() - self.model = ArcFaceConverter() + self.arcface_converter = ArcFaceConverter() self.loss_fn = torch.nn.MSELoss() self.lr = 0.001 def forward(self, source_embedding : Tensor) -> Tensor: - return self.model(source_embedding) + return self.arcface_converter(source_embedding) def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_embedding, target_embedding = batch @@ -110,7 +110,7 @@ def create_trainer() -> Trainer: def train() -> None: trainer = create_trainer() training_loader, validation_loader = create_loaders() - model = ArcFaceConverterTrainer() + arcface_converter = ArcFaceConverterTrainer() tuner = Tuner(trainer) - tuner.lr_find(model, training_loader, validation_loader) - trainer.fit(model, training_loader, validation_loader) + tuner.lr_find(arcface_converter, training_loader, validation_loader) + trainer.fit(arcface_converter, training_loader, validation_loader) diff --git a/arcface_converter/src/typing.py b/arcface_converter/src/types.py similarity index 100% rename from arcface_converter/src/typing.py rename to arcface_converter/src/types.py diff --git a/face_swapper/LivePortrait b/face_swapper/LivePortrait index 632da74..1b22029 160000 --- a/face_swapper/LivePortrait +++ b/face_swapper/LivePortrait @@ -1 +1 @@ -Subproject commit 632da7486d2c3fb86663fc44190a09aca4e1a8de +Subproject commit 1b22029ec07a827b48db2a82e2ced36393556c4a diff --git a/face_swapper/README.md b/face_swapper/README.md index 541ca3b..3125040 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -1,15 +1,9 @@ Face Swapper -================= +============ -> Swap one face over another face. +> Face shape and feature aware identity transfer. -![License](https://img.shields.io/badge/license-MIT-green) - - -Preview -------- - -![Preview]() +![License](https://img.shields.io/badge/license-ResearchRAIL--M-red) Installation @@ -23,74 +17,92 @@ pip install -r requirements.txt Example ------- -This example utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap. +This example utilizes the MegaFace dataset to train the Face Swapper model. ``` [preparing.dataset] -dataset_path = datasets/train +dataset_path = .datasets/train folder_pattern = {}/* image_pattern = {}/*.*g same_person_probability = 0.2 +``` +``` [training.loader] batch_size = 24 num_workers = 12 +``` +``` [training.model] -id_embedder_path = assets/models/id_embedder.pt -landmarker_path = assets/models/landmarker.pt -motion_extractor_path = assets/models/motion_extractor.pt +id_embedder_path = .models/id_embedder.pt +landmarker_path = .models/landmarker.pt +motion_extractor_path = .models/motion_extractor.pt +``` +``` [training.model.generator] num_blocks = 2 id_channels = 512 +``` +``` [training.model.discriminator] input_channels = 3 num_filters = 64 num_layers = 5 num_discriminators = 3 kernel_size = 4 +``` +``` [training.losses] weight_adversarial = 1 weight_id = 20 weight_attribute = 10 weight_reconstruction = 10 weight_pose = 100 +``` +``` [training.trainer] max_epochs = 50 learning_rate = 0.0004 precision = 16-mixed automatic_optimization = false +``` +``` [training.output] -checkpoint_path = outputs/last.ckpt -directory_path = outputs +directory_path = .outputs +file_path = .outputs/last.ckpt file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' preview_frequency = 250 validation_frequency = 1000 +``` +``` [exporting] -directory_path = export -source_path = outputs/last.ckpt -target_path = export/face_swapper.onnx +directory_path = .exports +source_path = .outputs/last.ckpt +target_path = .exports/face_swapper.onnx opset_version = 15 +``` -[inference] -generator_path = outputs/last.ckpt -id_embedder_path = assets/models/id_embedder.pt -source_path = assets/images/source.jpg -target_path = assets/models/target.jpg -output_path = outputs/output.jpg +``` +[inferencing] +generator_path = .outputs/last.ckpt +id_embedder_path = .models/id_embedder.pt +source_path = .assets/source.jpg +target_path = .assets/target.jpg +output_path = .outputs/output.jpg ``` Training -------- -Train the Face swapper model. +Train the Face Swapper model. ``` python train.py @@ -105,3 +117,13 @@ Export the model to ONNX. ``` python export.py ``` + + +Inferencing +----------- + +Inference the model. + +``` +python infer.py +``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 66dcdc1..6211385 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -38,8 +38,8 @@ precision = automatic_optimization = [training.output] -checkpoint_path = directory_path = +file_path = file_pattern = preview_frequency = validation_frequency = @@ -50,7 +50,7 @@ source_path = target_path = opset_version = -[inference] +[inferencing] generator_path = id_embedder_path = source_path = diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 31df9ae..d648bdd 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -7,8 +7,8 @@ import torch import torchvision.transforms as transforms from torch.utils.data import TensorDataset -from .helper import read_image -from .typing import Batch, ImagePathList, ImagePathSet +from helper import read_image +from types import Batch, ImagePathList, ImagePathSet class DataLoaderVGG(TensorDataset): @@ -33,9 +33,9 @@ class DataLoaderVGG(TensorDataset): transform = transforms.Compose( [ transforms.ToPILImage(), - transforms.Resize((256, 256), interpolation=transforms.InterpolationMode.BICUBIC), - transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1), - transforms.RandomAffine(4, translate=(0.01, 0.01), scale=(0.98, 1.02), shear=(1, 1), fill=0), + transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), + transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index 9e15f37..47c8967 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -6,7 +6,7 @@ import torch.nn import torch.nn as nn from torch import Tensor -from .typing import DiscriminatorOutputs +from types import DiscriminatorOutputs class NLayerDiscriminator(nn.Module): diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index d48b5e1..db312ab 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -4,7 +4,7 @@ import torch import torch.nn as nn from torch import Tensor -from .typing import SourceEmbedding, TargetAttributes, VisionTensor +from types import SourceEmbedding, TargetAttributes, VisionTensor class AdaptiveEmbeddingIntegrationNetwork(nn.Module): diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index ce77c42..b8a93c6 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -4,7 +4,7 @@ import cv2 import numpy import torch -from .typing import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor +from types import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor def is_windows() -> bool: @@ -20,7 +20,7 @@ def read_image(image_path : str) -> VisionFrame: def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor: vision_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) - vision_tensor = vision_tensor / 255 + vision_tensor = vision_tensor / 255.0 vision_tensor = (vision_tensor - 0.5) * 2 vision_tensor = vision_tensor.unsqueeze(0) return vision_tensor diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 56ae4bc..8284f37 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,9 +3,9 @@ import configparser import cv2 import torch -from .generator import AdaptiveEmbeddingIntegrationNetwork -from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image -from .typing import Generator, IdEmbedder, VisionFrame +from generator import AdaptiveEmbeddingIntegrationNetwork +from helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image +from types import Generator, IdEmbedder, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -21,17 +21,17 @@ def run_swap(generator : Generator, id_embedder : IdEmbedder, source_vision_fram def infer() -> None: - generator_path = CONFIG.get('inference', 'generator_path') - id_embedder_path = CONFIG.get('inference', 'id_embedder_path') - source_path = CONFIG.get('inference', 'source_path') - target_path = CONFIG.get('inference', 'target_path') - output_path = CONFIG.get('inference', 'output_path') + generator_path = CONFIG.get('inferencing', 'generator_path') + id_embedder_path = CONFIG.get('inferencing', 'id_embedder_path') + source_path = CONFIG.get('inferencing', 'source_path') + target_path = CONFIG.get('inferencing', 'target_path') + output_path = CONFIG.get('inferencing', 'output_path') - state_dict = torch.load(generator_path, map_location='cpu').get('state_dict').get('generator') + state_dict = torch.load(generator_path, map_location = 'cpu').get('state_dict').get('generator') generator = AdaptiveEmbeddingIntegrationNetwork(512, 2) generator.load_state_dict(state_dict) generator.eval() - id_embedder = torch.jit.load(id_embedder_path, map_location='cpu') # type:ignore[no-untyped-call] + id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] id_embedder.eval() source_vision_frame = read_image(source_path) diff --git a/arcface_converter/src/__init__.py b/face_swapper/src/losses/.gitkeep similarity index 100% rename from arcface_converter/src/__init__.py rename to face_swapper/src/losses/.gitkeep diff --git a/face_swapper/src/networks/.gitkeep b/face_swapper/src/networks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 33534aa..1c218f7 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -12,11 +12,11 @@ from pytorch_msssim import ssim from torch import Tensor from torch.utils.data import DataLoader -from .data_loader import DataLoaderVGG -from .discriminator import MultiscaleDiscriminator -from .generator import AdaptiveEmbeddingIntegrationNetwork -from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss -from .typing import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor +from data_loader import DataLoaderVGG +from discriminator import MultiscaleDiscriminator +from generator import AdaptiveEmbeddingIntegrationNetwork +from helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -237,16 +237,16 @@ def create_trainer() -> Trainer: def train() -> None: - batch_size = CONFIG.getint('training.loader', 'batch_size') - num_workers = CONFIG.getint('training.loader', 'num_workers') - checkpoint_path = CONFIG.get('training.output', 'checkpoint_path') dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') dataset_directory_pattern = CONFIG.get('preparing.dataset', 'directory_pattern') same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') + batch_size = CONFIG.getint('training.loader', 'batch_size') + num_workers = CONFIG.getint('training.loader', 'num_workers') + file_path = CONFIG.get('training.output', 'file_path') dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapperTrain() trainer = create_trainer() - trainer.fit(face_swap_model, data_loader, ckpt_path = checkpoint_path) + trainer.fit(face_swap_model, data_loader, ckpt_path = file_path) diff --git a/face_swapper/src/typing.py b/face_swapper/src/types.py similarity index 100% rename from face_swapper/src/typing.py rename to face_swapper/src/types.py From a1cd025f81ce21c0fbd1d3c75c46761cf60684db Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Feb 2025 19:02:58 +0100 Subject: [PATCH 024/394] Mixed bag of cleanups --- arcface_converter/src/preparing.py | 1 + face_swapper/README.md | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/arcface_converter/src/preparing.py b/arcface_converter/src/preparing.py index 6722f74..36a88a7 100644 --- a/arcface_converter/src/preparing.py +++ b/arcface_converter/src/preparing.py @@ -4,6 +4,7 @@ from os.path import isfile from typing import List import numpy + numpy.bool = numpy.bool_ from mxnet.io import ImageRecordIter from onnxruntime import InferenceSession diff --git a/face_swapper/README.md b/face_swapper/README.md index 3125040..77250be 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -14,10 +14,10 @@ pip install -r requirements.txt ``` -Example -------- +Setup +----- -This example utilizes the MegaFace dataset to train the Face Swapper model. +This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [preparing.dataset] From 989a81c751a7bccdc583c38f1460b6a9c185c712 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Feb 2025 19:08:50 +0100 Subject: [PATCH 025/394] Mixed bag of cleanups --- .flake8 | 4 ++-- arcface_converter/src/preparing.py | 3 +-- arcface_converter/src/training.py | 4 ++-- face_swapper/src/data_loader.py | 4 ++-- face_swapper/src/discriminator.py | 2 +- face_swapper/src/generator.py | 2 +- face_swapper/src/helper.py | 2 +- face_swapper/src/inferencing.py | 6 +++--- face_swapper/src/training.py | 10 +++++----- 9 files changed, 18 insertions(+), 19 deletions(-) diff --git a/.flake8 b/.flake8 index 400be93..8a4f145 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,6 @@ [flake8] -select = E3, E4, F, I1, I2 +select = E22, E23, E24, E27, E3, E4, E7, F, I1, I2 plugins = flake8-import-order -application_import_names = arcface_converter +application_import_names = arcface_converter, face_swapper import-order-style = pycharm per-file-ignores = preparing.py:E402 diff --git a/arcface_converter/src/preparing.py b/arcface_converter/src/preparing.py index 36a88a7..92676d7 100644 --- a/arcface_converter/src/preparing.py +++ b/arcface_converter/src/preparing.py @@ -4,13 +4,12 @@ from os.path import isfile from typing import List import numpy - numpy.bool = numpy.bool_ from mxnet.io import ImageRecordIter from onnxruntime import InferenceSession from tqdm import tqdm -from types import Embedding, EmbeddingPairs, VisionFrame +from .types import Embedding, EmbeddingPairs, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/arcface_converter/src/training.py b/arcface_converter/src/training.py index 2f5cb74..07f8e4d 100644 --- a/arcface_converter/src/training.py +++ b/arcface_converter/src/training.py @@ -10,8 +10,8 @@ from pytorch_lightning.tuner.tuning import Tuner from torch import Tensor from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split -from networks.arcface_converter import ArcFaceConverter -from types import Batch, Loader +from .networks.arcface_converter import ArcFaceConverter +from .types import Batch, Loader CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index d648bdd..c7a5c6c 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -7,8 +7,8 @@ import torch import torchvision.transforms as transforms from torch.utils.data import TensorDataset -from helper import read_image -from types import Batch, ImagePathList, ImagePathSet +from .helper import read_image +from .types import Batch, ImagePathList, ImagePathSet class DataLoaderVGG(TensorDataset): diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/discriminator.py index 47c8967..8d1a2ab 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/discriminator.py @@ -6,7 +6,7 @@ import torch.nn import torch.nn as nn from torch import Tensor -from types import DiscriminatorOutputs +from .types import DiscriminatorOutputs class NLayerDiscriminator(nn.Module): diff --git a/face_swapper/src/generator.py b/face_swapper/src/generator.py index db312ab..12f82e6 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/generator.py @@ -4,7 +4,7 @@ import torch import torch.nn as nn from torch import Tensor -from types import SourceEmbedding, TargetAttributes, VisionTensor +from .types import SourceEmbedding, TargetAttributes, VisionTensor class AdaptiveEmbeddingIntegrationNetwork(nn.Module): diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index b8a93c6..c9fce5f 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -4,7 +4,7 @@ import cv2 import numpy import torch -from types import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor +from .types import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor def is_windows() -> bool: diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 8284f37..570c277 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,9 +3,9 @@ import configparser import cv2 import torch -from generator import AdaptiveEmbeddingIntegrationNetwork -from helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image -from types import Generator, IdEmbedder, VisionFrame +from .generator import AdaptiveEmbeddingIntegrationNetwork +from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image +from .types import Generator, IdEmbedder, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1c218f7..e6ad370 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -12,11 +12,11 @@ from pytorch_msssim import ssim from torch import Tensor from torch.utils.data import DataLoader -from data_loader import DataLoaderVGG -from discriminator import MultiscaleDiscriminator -from generator import AdaptiveEmbeddingIntegrationNetwork -from helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss -from types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor +from .data_loader import DataLoaderVGG +from .discriminator import MultiscaleDiscriminator +from .generator import AdaptiveEmbeddingIntegrationNetwork +from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from .types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From a17d0506485bc7ff46fcdfd8fc516165c6b2276f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Feb 2025 19:18:41 +0100 Subject: [PATCH 026/394] Mixed bag of cleanups --- .../src/losses/.gitkeep => arcface_converter/src/__init__.py | 0 .../.gitkeep => arcface_converter/src/networks/__init__.py | 0 face_swapper/src/__init__.py | 0 face_swapper/src/losses/__init__.py | 0 face_swapper/src/networks/__init__.py | 0 5 files changed, 0 insertions(+), 0 deletions(-) rename face_swapper/src/losses/.gitkeep => arcface_converter/src/__init__.py (100%) rename face_swapper/src/networks/.gitkeep => arcface_converter/src/networks/__init__.py (100%) create mode 100644 face_swapper/src/__init__.py create mode 100644 face_swapper/src/losses/__init__.py create mode 100644 face_swapper/src/networks/__init__.py diff --git a/face_swapper/src/losses/.gitkeep b/arcface_converter/src/__init__.py similarity index 100% rename from face_swapper/src/losses/.gitkeep rename to arcface_converter/src/__init__.py diff --git a/face_swapper/src/networks/.gitkeep b/arcface_converter/src/networks/__init__.py similarity index 100% rename from face_swapper/src/networks/.gitkeep rename to arcface_converter/src/networks/__init__.py diff --git a/face_swapper/src/__init__.py b/face_swapper/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/face_swapper/src/losses/__init__.py b/face_swapper/src/losses/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/face_swapper/src/networks/__init__.py b/face_swapper/src/networks/__init__.py new file mode 100644 index 0000000..e69de29 From dc0ef536681aede1c3b43cad80f9c82d3924c79d Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 6 Feb 2025 13:34:03 +0530 Subject: [PATCH 027/394] add face swapper preview --- .github/preview_face_swapper.png | Bin 0 -> 5480141 bytes face_swapper/README.md | 6 ++++++ 2 files changed, 6 insertions(+) create mode 100644 .github/preview_face_swapper.png diff --git a/.github/preview_face_swapper.png b/.github/preview_face_swapper.png new file 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zXo=GXVFgM_aQJCe9oGX+OuK|Mmrjn7`3J?bh$~36`9MORKlgs7ifeBvP3PC!Vs1=n(?U+-Q**%1}lo zQY|tg0qa7KXf5aLkxD_y*0p4|z|qlW1_bqpgv^QnNR%?+#j^K|R3Ma@At4h0X%JY5 z(Y!!LFq5!^Orj+4mLao8^)if}tru5;xue#hz>VzDQO@33DZ4YAx$phf-^`ai~KX zXjo#2=v0SO4r^|2v7(ItM#`@&c;1Az_u)o>1d2X#49Y7g|akUz!X_;H^tqi63Q0s7fc2>$%2A_A! z_5SLex4!=1ov;1GPyapAl)^2707wE5MSLFxKmb8e_&!1a1VA(zOcE%P03-k+KteKr zq(mSEh!81Af+*0;XwjDs9=!7hU;5oIe&M%lcmB~A?|$;7dut0Uhk`g7>o8PzCkc{3 zM9h5#rEWd4DK4*e^PKC=X1$rrd?@B#+`JYyb1w#`yO|7TPBRwL%;}}L84YxEy1@XW z2f$PWGa3}V_vkGmdft+dibz3(A~G|~7`oMEo=wUOAtD83sz+qc=o#6wN8XZpkKX&z zFfVOsy+tfNmQYBMDJc_?3Z+s>DJ0yYfFz}mm;_Q1ZY4p8Vo0Ts0A(~6X+{Zd)>8na z6ig5Sq-dall2Qcebaw-3D$eRw=vJ(ljfP?xN-aYjidC~=u%q?3S#QR1EM}q@T}rtr zg-p^!1HGd!T Date: Thu, 6 Feb 2025 13:44:25 +0530 Subject: [PATCH 028/394] add face swapper preview --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index f6da434..c58332d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -9,7 +9,7 @@ Face Swapper Preview ------- -![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/master/.github/preview_face_swapper.png?sanitize=true) +![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/next/.github/preview_face_swapper.png?sanitize=true) Installation From b7e2d3ccd7c957ce75606fbbc4d6e05573aa97c0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 8 Feb 2025 02:54:29 +0100 Subject: [PATCH 029/394] It should be models and networks --- arcface_converter/src/{networks => models}/__init__.py | 0 arcface_converter/src/{networks => models}/arcface_converter.py | 0 arcface_converter/src/training.py | 2 +- face_swapper/src/{losses => models}/__init__.py | 0 4 files changed, 1 insertion(+), 1 deletion(-) rename arcface_converter/src/{networks => models}/__init__.py (100%) rename arcface_converter/src/{networks => models}/arcface_converter.py (100%) rename face_swapper/src/{losses => models}/__init__.py (100%) diff --git a/arcface_converter/src/networks/__init__.py b/arcface_converter/src/models/__init__.py similarity index 100% rename from arcface_converter/src/networks/__init__.py rename to arcface_converter/src/models/__init__.py diff --git a/arcface_converter/src/networks/arcface_converter.py b/arcface_converter/src/models/arcface_converter.py similarity index 100% rename from arcface_converter/src/networks/arcface_converter.py rename to arcface_converter/src/models/arcface_converter.py diff --git a/arcface_converter/src/training.py b/arcface_converter/src/training.py index 07f8e4d..f843a52 100644 --- a/arcface_converter/src/training.py +++ b/arcface_converter/src/training.py @@ -10,7 +10,7 @@ from pytorch_lightning.tuner.tuning import Tuner from torch import Tensor from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split -from .networks.arcface_converter import ArcFaceConverter +from .models.arcface_converter import ArcFaceConverter from .types import Batch, Loader CONFIG = configparser.ConfigParser() diff --git a/face_swapper/src/losses/__init__.py b/face_swapper/src/models/__init__.py similarity index 100% rename from face_swapper/src/losses/__init__.py rename to face_swapper/src/models/__init__.py From 2ed558a873b756a1eea56d52acccbbcd119422e2 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Feb 2025 22:43:15 +0530 Subject: [PATCH 030/394] cleanup --- face_swapper/__init__.py | 0 face_swapper/src/data_loader.py | 3 +- face_swapper/src/exporting.py | 4 +- face_swapper/src/inferencing.py | 4 +- .../src/{ => models}/discriminator.py | 63 ++++---- face_swapper/src/models/generator.py | 43 +++++ face_swapper/src/models/loss.py | 137 ++++++++++++++++ .../attribute_modulator.py} | 127 ++------------- face_swapper/src/networks/encoder.py | 67 ++++++++ face_swapper/src/training.py | 148 +----------------- 10 files changed, 310 insertions(+), 286 deletions(-) create mode 100644 face_swapper/__init__.py rename face_swapper/src/{ => models}/discriminator.py (75%) create mode 100644 face_swapper/src/models/generator.py create mode 100644 face_swapper/src/models/loss.py rename face_swapper/src/{generator.py => networks/attribute_modulator.py} (54%) create mode 100644 face_swapper/src/networks/encoder.py diff --git a/face_swapper/__init__.py b/face_swapper/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index c7a5c6c..49f1c21 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -24,8 +24,7 @@ class DataLoaderVGG(TensorDataset): image_path_set = {} for directory_path in self.directory_paths: - image_paths = glob.glob(dataset_image_pattern.format(directory_path)) - image_paths.extend(image_paths) + image_paths.extend(glob.glob(dataset_image_pattern.format(directory_path))) image_path_set[directory_path] = image_paths return image_paths, image_path_set diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 033e26d..311fdc8 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -3,7 +3,7 @@ from os import makedirs import torch -from .generator import AdaptiveEmbeddingIntegrationNetwork +from .models.generator import AdaptiveEmbeddingIntegrationNetwork CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -17,7 +17,7 @@ def export() -> None: makedirs(directory_path, exist_ok = True) state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') - model = AdaptiveEmbeddingIntegrationNetwork(512, 2) + model = AdaptiveEmbeddingIntegrationNetwork() model.load_state_dict(state_dict) model.eval() source_tensor = torch.randn(1, 512) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 570c277..3da1573 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,8 +3,8 @@ import configparser import cv2 import torch -from .generator import AdaptiveEmbeddingIntegrationNetwork from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image +from .models.generator import AdaptiveEmbeddingIntegrationNetwork from .types import Generator, IdEmbedder, VisionFrame CONFIG = configparser.ConfigParser() @@ -28,7 +28,7 @@ def infer() -> None: output_path = CONFIG.get('inferencing', 'output_path') state_dict = torch.load(generator_path, map_location = 'cpu').get('state_dict').get('generator') - generator = AdaptiveEmbeddingIntegrationNetwork(512, 2) + generator = AdaptiveEmbeddingIntegrationNetwork() generator.load_state_dict(state_dict) generator.eval() id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] diff --git a/face_swapper/src/discriminator.py b/face_swapper/src/models/discriminator.py similarity index 75% rename from face_swapper/src/discriminator.py rename to face_swapper/src/models/discriminator.py index 8d1a2ab..475d66f 100644 --- a/face_swapper/src/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -1,3 +1,4 @@ +import configparser from itertools import chain from typing import List @@ -6,7 +7,41 @@ import torch.nn import torch.nn as nn from torch import Tensor -from .types import DiscriminatorOutputs +from face_swapper.src.types import DiscriminatorOutputs + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +class MultiscaleDiscriminator(nn.Module): + def __init__(self) -> None: + super(MultiscaleDiscriminator, self).__init__() + self.input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') + self.num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') + self.kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') + self.num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') + self.num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') + + self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] + self.prepare_discriminators() + + def prepare_discriminators(self) -> None: + for discriminator_index in range(self.num_discriminators): + single_discriminator = NLayerDiscriminator(self.input_channels, self.num_filters, self.num_layers, self.kernel_size) + setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) + + def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: + discriminator_outputs = [] + temp_tensor = input_tensor + + for discriminator_index in range(self.num_discriminators): + model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) + discriminator_outputs.append([ model_layers(temp_tensor) ]) + + if discriminator_index < (self.num_discriminators - 1): + temp_tensor = self.downsample(temp_tensor) + + return discriminator_outputs class NLayerDiscriminator(nn.Module): @@ -58,29 +93,3 @@ class NLayerDiscriminator(nn.Module): def forward(self, input_tensor : Tensor) -> Tensor: return self.model(input_tensor) - - -class MultiscaleDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int, num_discriminators : int, kernel_size : int): - super(MultiscaleDiscriminator, self).__init__() - self.num_discriminators = num_discriminators - self.num_layers = num_layers - - for discriminator_index in range(num_discriminators): - single_discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers, kernel_size) - setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) - - self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] - - def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: - discriminator_outputs = [] - temp_tensor = input_tensor - - for discriminator_index in range(self.num_discriminators): - model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) - discriminator_outputs.append([ model_layers(temp_tensor) ]) - - if discriminator_index < (self.num_discriminators - 1): - temp_tensor = self.downsample(temp_tensor) - - return discriminator_outputs diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py new file mode 100644 index 0000000..12a4f76 --- /dev/null +++ b/face_swapper/src/models/generator.py @@ -0,0 +1,43 @@ +import configparser +from typing import Tuple + +import torch.nn as nn + +from face_swapper.src.networks.attribute_modulator import AADGenerator +from face_swapper.src.networks.encoder import UNet +from face_swapper.src.types import SourceEmbedding, TargetAttributes, VisionTensor + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +class AdaptiveEmbeddingIntegrationNetwork(nn.Module): + def __init__(self) -> None: + super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() + id_channels = CONFIG.getint('training.model.generator', 'id_channels') + num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') + + self.encoder = UNet() + self.generator = AADGenerator(id_channels, num_blocks) + self.encoder.apply(init_weight) + self.generator.apply(init_weight) + + def forward(self, target : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: + target_attributes = self.get_attributes(target) + swap_tensor = self.generator(target_attributes, source_embedding) + return swap_tensor, target_attributes + + def get_attributes(self, target : VisionTensor) -> TargetAttributes: + return self.encoder(target) + + +def init_weight(module : nn.Module) -> None: + if isinstance(module, nn.Linear): + module.weight.data.normal_(std = 0.001) + module.bias.data.zero_() + + if isinstance(module, nn.Conv2d): + nn.init.xavier_normal_(module.weight.data) + + if isinstance(module, nn.ConvTranspose2d): + nn.init.xavier_normal_(module.weight.data) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py new file mode 100644 index 0000000..2cd4bcb --- /dev/null +++ b/face_swapper/src/models/loss.py @@ -0,0 +1,137 @@ +import configparser +from typing import Tuple + +import torch +from pytorch_msssim import ssim +from torch import Tensor + +from face_swapper.src.helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from face_swapper.src.types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor + +CONFIG = configparser.ConfigParser() +CONFIG.read('config.ini') + + +class FaceSwapperLoss: + def __init__(self) -> None: + id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') + landmarker_path = CONFIG.get('training.model', 'landmarker_path') + motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') + self.batch_size = CONFIG.getint('training.loader', 'batch_size') + self.mse_loss = torch.nn.MSELoss() + self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.id_embedder.eval() + self.landmarker.eval() + self.motion_extractor.eval() + + def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: + source_tensor, target_tensor, is_same_person = batch + weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') + weight_id = CONFIG.getfloat('training.losses', 'weight_id') + weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') + weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') + weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') + weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') + generator_loss_set = {} + + generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) + generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) + generator_loss_set['loss_attribute'] = self.calc_attribute_loss(target_attributes, swap_attributes) + generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + + if weight_pose > 0: + generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_pose'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + if weight_gaze > 0: + generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) + else: + generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_pose') * weight_pose + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze + return generator_loss_set + + def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: + discriminator_loss_set = {} + loss_fake = torch.Tensor(0) + + for fake_discriminator_output in fake_discriminator_outputs: + loss_fake += hinge_fake_loss(fake_discriminator_output[0]).mean() + + loss_true = torch.Tensor(0) + + for true_discriminator_output in real_discriminator_outputs: + loss_true += hinge_real_loss(true_discriminator_output[0]).mean() + + discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 + return discriminator_loss_set + + def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: + loss_adversarial = torch.Tensor(0) + + for discriminator_output in discriminator_outputs: + loss_adversarial += hinge_real_loss(discriminator_output[0]) + + loss_adversarial = torch.mean(loss_adversarial) + return loss_adversarial + + def calc_attribute_loss(self, target_attributes : TargetAttributes, swap_attributes : SwapAttributes) -> LossTensor: + loss_attribute = torch.Tensor(0) + + for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): + loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() + + loss_attribute *= 0.5 + return loss_attribute + + def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: + loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) + loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 + loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) + loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() + loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 + return loss_reconstruction + + def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: + swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) + loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() + return loss_id + + def calc_pose_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: + swap_motion_features = self.get_pose_features(swap_tensor) + target_motion_features = self.get_pose_features(target_tensor) + loss_pose = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + + for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): + loss_pose += self.mse_loss(swap_motion_feature, target_motion_feature) + + return loss_pose + + def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: + swap_landmark = self.get_face_landmarks(swap_tensor) + target_landmark = self.get_face_landmarks(target_tensor) + left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) + right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) + gaze_loss = left_gaze_loss + right_gaze_loss + return gaze_loss + + def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') + landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) + return landmarks + + def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: + vision_tensor_norm = (vision_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) + return translation, scale, rotation diff --git a/face_swapper/src/generator.py b/face_swapper/src/networks/attribute_modulator.py similarity index 54% rename from face_swapper/src/generator.py rename to face_swapper/src/networks/attribute_modulator.py index 12f82e6..cc34463 100644 --- a/face_swapper/src/generator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -1,25 +1,7 @@ -from typing import Tuple - import torch -import torch.nn as nn -from torch import Tensor +from torch import Tensor, nn as nn -from .types import SourceEmbedding, TargetAttributes, VisionTensor - - -class AdaptiveEmbeddingIntegrationNetwork(nn.Module): - def __init__(self, id_channels : int, num_blocks : int) -> None: - super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() - self.encoder = UNet() - self.generator = AADGenerator(id_channels, num_blocks) - - def forward(self, target : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: - target_attributes = self.get_attributes(target) - swap_tensor = self.generator(target_attributes, source_embedding) - return swap_tensor, target_attributes - - def get_attributes(self, target : VisionTensor) -> TargetAttributes: - return self.encoder(target) +from face_swapper.src.types import SourceEmbedding, TargetAttributes class AADGenerator(nn.Module): @@ -34,7 +16,6 @@ class AADGenerator(nn.Module): self.res_block_6 = AADResBlock(256, 128, 128, id_channels, num_blocks) self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) - self.apply(init_weight) def forward(self, target_attributes : TargetAttributes, source_embedding : SourceEmbedding) -> Tensor: feature_map = self.upsample(source_embedding) @@ -49,42 +30,6 @@ class AADGenerator(nn.Module): return torch.tanh(output) -class UNet(nn.Module): - def __init__(self) -> None: - super(UNet, self).__init__() - self.downsampler_1 = DownSample(3, 32) - self.downsampler_2 = DownSample(32, 64) - self.downsampler_3 = DownSample(64, 128) - self.downsampler_4 = DownSample(128, 256) - self.downsampler_5 = DownSample(256, 512) - self.downsampler_6 = DownSample(512, 1024) - self.bottleneck = DownSample(1024, 1024) - self.upsampler_1 = Upsample(1024, 1024) - self.upsampler_2 = Upsample(2048, 512) - self.upsampler_3 = Upsample(1024, 256) - self.upsampler_4 = Upsample(512, 128) - self.upsampler_5 = Upsample(256, 64) - self.upsampler_6 = Upsample(128, 32) - self.apply(init_weight) - - def forward(self, target : VisionTensor) -> TargetAttributes: - downsample_feature_1 = self.downsampler_1(target) - downsample_feature_2 = self.downsampler_2(downsample_feature_1) - downsample_feature_3 = self.downsampler_3(downsample_feature_2) - downsample_feature_4 = self.downsampler_4(downsample_feature_3) - downsample_feature_5 = self.downsampler_5(downsample_feature_4) - downsample_feature_6 = self.downsampler_6(downsample_feature_5) - bottleneck_output = self.bottleneck(downsample_feature_6) - upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) - upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) - upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) - upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) - upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) - upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) - output = torch.nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) - return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output - - class AADLayer(nn.Module): def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: super(AADLayer, self).__init__() @@ -109,22 +54,18 @@ class AADLayer(nn.Module): return feature_blend -class AddBlocksSequential(nn.Sequential): - #todo: what are inputs? improve the name - def forward(self, *inputs : Tuple[Tensor, Tensor, SourceEmbedding]) -> Tuple[Tuple[Tensor, Tensor, SourceEmbedding], ...]: - _, attribute_embedding, id_embedding = inputs - modules = self._modules.values() #todo: what kind of modules? +class AADSequential(nn.Module): + def __init__(self, *args : nn.Module) -> None: + super(AADSequential, self).__init__() + self.layers = nn.ModuleList(args) - for module_index, module in enumerate(modules): - if module_index % 3 == 0 and module_index > 0: - inputs = (inputs, attribute_embedding, id_embedding) # type:ignore[assignment] - - if isinstance(inputs, torch.Tensor): - inputs = module(inputs) + def forward(self, feature_map: Tensor, attribute_embedding: Tensor, id_embedding: SourceEmbedding) -> Tensor: + for layer in self.layers: + if isinstance(layer, AADLayer): + feature_map = layer(feature_map, attribute_embedding, id_embedding) else: - inputs = module(*inputs) - - return inputs #todo: would be easier to read when you just return xxx_inputs, attribute_embedding, id_embedding ? + feature_map = layer(feature_map) + return feature_map class AADResBlock(nn.Module): @@ -147,11 +88,11 @@ class AADResBlock(nn.Module): nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False) ] ) - self.primary_add_blocks = AddBlocksSequential(*primary_add_blocks) + self.primary_add_blocks = AADSequential(*primary_add_blocks) def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int) -> None: if input_channels > output_channels: - auxiliary_add_blocks = AddBlocksSequential( + auxiliary_add_blocks = AADSequential( AADLayer(input_channels, attribute_channels, id_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) @@ -168,34 +109,6 @@ class AADResBlock(nn.Module): return output_feature -class DownSample(nn.Module): - def __init__(self, input_channels : int, output_channels : int) -> None: - super(DownSample, self).__init__() - self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - - def forward(self, temp : Tensor) -> Tensor: - temp = self.conv(temp) - temp = self.batch_norm(temp) - temp = self.leaky_relu(temp) - return temp - - -class Upsample(nn.Module): - def __init__(self, input_channels : int, output_channels : int) -> None: - super(Upsample, self).__init__() - self.deconv = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - - def forward(self, temp : Tensor, skip_tensor : Tensor) -> Tensor: - temp = self.deconv(temp) - temp = self.batch_norm(temp) - temp = self.leaky_relu(temp) - return torch.cat((temp, skip_tensor), dim = 1) - - class PixelShuffleUpsample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super(PixelShuffleUpsample, self).__init__() @@ -206,15 +119,3 @@ class PixelShuffleUpsample(nn.Module): temp = self.conv(temp.view(temp.shape[0], -1, 1, 1)) temp = self.pixel_shuffle(temp) return temp - - -def init_weight(module : nn.Module) -> None: - if isinstance(module, nn.Linear): - module.weight.data.normal_(std = 0.001) - module.bias.data.zero_() - - if isinstance(module, nn.Conv2d): - nn.init.xavier_normal_(module.weight.data) - - if isinstance(module, nn.ConvTranspose2d): - nn.init.xavier_normal_(module.weight.data) diff --git a/face_swapper/src/networks/encoder.py b/face_swapper/src/networks/encoder.py new file mode 100644 index 0000000..e380ab5 --- /dev/null +++ b/face_swapper/src/networks/encoder.py @@ -0,0 +1,67 @@ +import torch +from torch import Tensor, nn as nn + +from face_swapper.src.types import TargetAttributes, VisionTensor + + +class Upsample(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super(Upsample, self).__init__() + self.deconv = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, temp : Tensor, skip_tensor : Tensor) -> Tensor: + temp = self.deconv(temp) + temp = self.batch_norm(temp) + temp = self.leaky_relu(temp) + return torch.cat((temp, skip_tensor), dim = 1) + + +class DownSample(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super(DownSample, self).__init__() + self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, temp : Tensor) -> Tensor: + temp = self.conv(temp) + temp = self.batch_norm(temp) + temp = self.leaky_relu(temp) + return temp + + +class UNet(nn.Module): + def __init__(self) -> None: + super(UNet, self).__init__() + self.downsampler_1 = DownSample(3, 32) + self.downsampler_2 = DownSample(32, 64) + self.downsampler_3 = DownSample(64, 128) + self.downsampler_4 = DownSample(128, 256) + self.downsampler_5 = DownSample(256, 512) + self.downsampler_6 = DownSample(512, 1024) + self.bottleneck = DownSample(1024, 1024) + self.upsampler_1 = Upsample(1024, 1024) + self.upsampler_2 = Upsample(2048, 512) + self.upsampler_3 = Upsample(1024, 256) + self.upsampler_4 = Upsample(512, 128) + self.upsampler_5 = Upsample(256, 64) + self.upsampler_6 = Upsample(128, 32) + + def forward(self, target : VisionTensor) -> TargetAttributes: + downsample_feature_1 = self.downsampler_1(target) + downsample_feature_2 = self.downsampler_2(downsample_feature_1) + downsample_feature_3 = self.downsampler_3(downsample_feature_2) + downsample_feature_4 = self.downsampler_4(downsample_feature_3) + downsample_feature_5 = self.downsampler_5(downsample_feature_4) + downsample_feature_6 = self.downsampler_6(downsample_feature_5) + bottleneck_output = self.bottleneck(downsample_feature_6) + upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) + upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) + upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) + upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) + upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) + upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) + output = torch.nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) + return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e6ad370..ef094f0 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -8,157 +8,25 @@ import torchvision from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.utilities.types import Optimizer -from pytorch_msssim import ssim from torch import Tensor from torch.utils.data import DataLoader from .data_loader import DataLoaderVGG -from .discriminator import MultiscaleDiscriminator -from .generator import AdaptiveEmbeddingIntegrationNetwork -from .helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss -from .types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SourceEmbedding, SwapAttributes, TargetAttributes, VisionTensor +from .helper import calc_id_embedding +from .models.discriminator import MultiscaleDiscriminator +from .models.generator import AdaptiveEmbeddingIntegrationNetwork +from .models.loss import FaceSwapperLoss +from .types import Batch, SourceEmbedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapperLoss: - def __init__(self) -> None: - id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') - landmarker_path = CONFIG.get('training.model', 'landmarker_path') - motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') - self.batch_size = CONFIG.getint('training.loader', 'batch_size') - self.mse_loss = torch.nn.MSELoss() - self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.id_embedder.eval() - self.landmarker.eval() - self.motion_extractor.eval() - - def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - source_tensor, target_tensor, is_same_person = batch - weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') - weight_id = CONFIG.getfloat('training.losses', 'weight_id') - weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') - weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') - weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') - weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') - generator_loss_set = {} - - generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) - generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) - generator_loss_set['loss_attribute'] = self.calc_attribute_loss(target_attributes, swap_attributes) - generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) - - if weight_pose > 0: - generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_pose'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - if weight_gaze > 0: - generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_pose') * weight_pose - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze - return generator_loss_set - - def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: - discriminator_loss_set = {} - loss_fake = torch.Tensor(0) - - for fake_discriminator_output in fake_discriminator_outputs: - loss_fake += hinge_fake_loss(fake_discriminator_output[0]).mean() - - loss_true = torch.Tensor(0) - - for true_discriminator_output in real_discriminator_outputs: - loss_true += hinge_real_loss(true_discriminator_output[0]).mean() - - discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 - return discriminator_loss_set - - def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: - loss_adversarial = torch.Tensor(0) - - for discriminator_output in discriminator_outputs: - loss_adversarial += hinge_real_loss(discriminator_output[0]) - - loss_adversarial = torch.mean(loss_adversarial) - return loss_adversarial - - def calc_attribute_loss(self, target_attributes : TargetAttributes, swap_attributes : SwapAttributes) -> LossTensor: - loss_attribute = torch.Tensor(0) - - for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): - loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() - - loss_attribute *= 0.5 - return loss_attribute - - def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: - loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) - loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 - loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) - loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 - return loss_reconstruction - - def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) - loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() - return loss_id - - def calc_pose_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_motion_features = self.get_pose_features(swap_tensor) - target_motion_features = self.get_pose_features(target_tensor) - loss_pose = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): - loss_pose += self.mse_loss(swap_motion_feature, target_motion_feature) - - return loss_pose - - def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_landmark = self.get_face_landmarks(swap_tensor) - target_landmark = self.get_face_landmarks(target_tensor) - left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) - right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) - gaze_loss = left_gaze_loss + right_gaze_loss - return gaze_loss - - def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') - landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) - return landmarks - - def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - return translation, scale, rotation - - class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() - id_channels = CONFIG.getint('training.model.generator', 'id_channels') - num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') - num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') - num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') - num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') - kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') - self.generator = AdaptiveEmbeddingIntegrationNetwork(id_channels, num_blocks) - self.discriminator = MultiscaleDiscriminator(input_channels, num_filters, num_layers, num_discriminators, kernel_size) + self.generator = AdaptiveEmbeddingIntegrationNetwork() + self.discriminator = MultiscaleDiscriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') def forward(self, target_tensor : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: @@ -244,8 +112,8 @@ def train() -> None: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') file_path = CONFIG.get('training.output', 'file_path') - dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) + dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapperTrain() trainer = create_trainer() From 6381e755d71e2370ddce88e6afe5f1382ad4b6f4 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Feb 2025 16:43:08 +0530 Subject: [PATCH 031/394] list join --- face_swapper/src/models/discriminator.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 475d66f..aa2df6b 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -81,10 +81,7 @@ class NLayerDiscriminator(nn.Module): nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding_size), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ] - ] - model_layers +=\ - [ + ], [ nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding_size) ] From 611618e41345653f5c99f6c5d06a7269c217a0ad Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 10:31:53 +0100 Subject: [PATCH 032/394] Make Embedding great again --- arcface_converter/src/preparing.py | 12 ++++++------ arcface_converter/src/types.py | 2 +- face_swapper/src/helper.py | 4 ++-- face_swapper/src/inferencing.py | 4 ++-- face_swapper/src/models/generator.py | 4 ++-- face_swapper/src/networks/attribute_modulator.py | 10 +++++----- face_swapper/src/training.py | 4 ++-- face_swapper/src/types.py | 5 ++--- 8 files changed, 22 insertions(+), 23 deletions(-) diff --git a/arcface_converter/src/preparing.py b/arcface_converter/src/preparing.py index 92676d7..6ba75fe 100644 --- a/arcface_converter/src/preparing.py +++ b/arcface_converter/src/preparing.py @@ -9,7 +9,7 @@ from mxnet.io import ImageRecordIter from onnxruntime import InferenceSession from tqdm import tqdm -from .types import Embedding, EmbeddingPairs, VisionFrame +from .types import Embedding, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -35,9 +35,9 @@ def forward(inference_session : InferenceSession, crop_vision_frame : VisionFram return embedding -def process_embeddings(dataset_reader : ImageRecordIter, source_inference_session : InferenceSession, target_inference_session : InferenceSession) -> EmbeddingPairs: +def process_embeddings(dataset_reader : ImageRecordIter, source_inference_session : InferenceSession, target_inference_session : InferenceSession) -> Embedding: dataset_process_limit = CONFIG.getint('preparing.dataset', 'process_limit') - embedding_pairs = [] + embeddings = [] with tqdm(total = dataset_process_limit) as progress: for batch in dataset_reader: @@ -45,13 +45,13 @@ def process_embeddings(dataset_reader : ImageRecordIter, source_inference_sessio crop_vision_frame = prepare_crop_vision_frame(crop_vision_frame) source_embedding = forward(source_inference_session, crop_vision_frame) target_embedding = forward(target_inference_session, crop_vision_frame) - embedding_pairs.append([ source_embedding, target_embedding ]) + embeddings.append([ source_embedding, target_embedding ]) progress.update() if progress.n == dataset_process_limit: - return numpy.concatenate(embedding_pairs, axis = 1).T + return numpy.concatenate(embeddings, axis = 1).T - return numpy.concatenate(embedding_pairs, axis = 1).T + return numpy.concatenate(embeddings, axis = 1).T def prepare() -> None: diff --git a/arcface_converter/src/types.py b/arcface_converter/src/types.py index faeeec2..16e699a 100644 --- a/arcface_converter/src/types.py +++ b/arcface_converter/src/types.py @@ -8,6 +8,6 @@ Batch = Tuple[Tensor, Tensor] Loader = DataLoader[Tuple[Tensor, ...]] Embedding = NDArray[Any] -EmbeddingPairs = NDArray[Any] FaceLandmark5 = NDArray[Any] + VisionFrame = NDArray[Any] diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index c9fce5f..05ee770 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -4,7 +4,7 @@ import cv2 import numpy import torch -from .types import IdEmbedder, IdEmbedding, Padding, Tensor, VisionFrame, VisionTensor +from .types import Embedder, Embedding, Padding, Tensor, VisionFrame, VisionTensor def is_windows() -> bool: @@ -47,7 +47,7 @@ def hinge_fake_loss(tensor : Tensor) -> Tensor: return fake_loss -def calc_id_embedding(id_embedder : IdEmbedder, vision_tensor : VisionTensor, padding : Padding) -> IdEmbedding: +def calc_id_embedding(id_embedder : Embedder, vision_tensor : VisionTensor, padding : Padding) -> Embedding: crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') crop_vision_tensor[:, :, :padding[0], :] = 0 diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 3da1573..18451bc 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -5,13 +5,13 @@ import torch from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image from .models.generator import AdaptiveEmbeddingIntegrationNetwork -from .types import Generator, IdEmbedder, VisionFrame +from .types import Generator, Embedder, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def run_swap(generator : Generator, id_embedder : IdEmbedder, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: +def run_swap(generator : Generator, id_embedder : Embedder, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: source_vision_tensor = convert_to_vision_tensor(source_vision_frame) target_vision_tensor = convert_to_vision_tensor(target_vision_frame) source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 12a4f76..1b38dac 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -5,7 +5,7 @@ import torch.nn as nn from face_swapper.src.networks.attribute_modulator import AADGenerator from face_swapper.src.networks.encoder import UNet -from face_swapper.src.types import SourceEmbedding, TargetAttributes, VisionTensor +from face_swapper.src.types import Embedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -22,7 +22,7 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): self.encoder.apply(init_weight) self.generator.apply(init_weight) - def forward(self, target : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: + def forward(self, target : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: target_attributes = self.get_attributes(target) swap_tensor = self.generator(target_attributes, source_embedding) return swap_tensor, target_attributes diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index cc34463..69d71e1 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn as nn -from face_swapper.src.types import SourceEmbedding, TargetAttributes +from face_swapper.src.types import Embedding, TargetAttributes class AADGenerator(nn.Module): @@ -17,7 +17,7 @@ class AADGenerator(nn.Module): self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) - def forward(self, target_attributes : TargetAttributes, source_embedding : SourceEmbedding) -> Tensor: + def forward(self, target_attributes : TargetAttributes, source_embedding : Embedding) -> Tensor: feature_map = self.upsample(source_embedding) feature_map_1 = torch.nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) feature_map_2 = torch.nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) @@ -41,7 +41,7 @@ class AADLayer(nn.Module): self.instance_norm = nn.InstanceNorm2d(input_channels) self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) - def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : Embedding) -> Tensor: feature_map = self.instance_norm(feature_map) gamma_attribute = self.conv_gamma(attribute_embedding) beta_attribute = self.conv_beta(attribute_embedding) @@ -59,7 +59,7 @@ class AADSequential(nn.Module): super(AADSequential, self).__init__() self.layers = nn.ModuleList(args) - def forward(self, feature_map: Tensor, attribute_embedding: Tensor, id_embedding: SourceEmbedding) -> Tensor: + def forward(self, feature_map: Tensor, attribute_embedding: Tensor, id_embedding: Embedding) -> Tensor: for layer in self.layers: if isinstance(layer, AADLayer): feature_map = layer(feature_map, attribute_embedding, id_embedding) @@ -99,7 +99,7 @@ class AADResBlock(nn.Module): ) self.auxiliary_add_blocks = auxiliary_add_blocks - def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : SourceEmbedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : Embedding) -> Tensor: primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding) if self.input_channels > self.output_channels: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ef094f0..d023085 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .helper import calc_id_embedding from .models.discriminator import MultiscaleDiscriminator from .models.generator import AdaptiveEmbeddingIntegrationNetwork from .models.loss import FaceSwapperLoss -from .types import Batch, SourceEmbedding, TargetAttributes, VisionTensor +from .types import Batch, Embedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -29,7 +29,7 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): self.discriminator = MultiscaleDiscriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') - def forward(self, target_tensor : VisionTensor, source_embedding : SourceEmbedding) -> Tuple[VisionTensor, TargetAttributes]: + def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: output = self.generator(target_tensor, source_embedding) return output diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 96de434..e37001d 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -15,8 +15,7 @@ SwapAttributes = Tuple[Tensor, ...] TargetAttributes = Tuple[Tensor, ...] DiscriminatorOutputs = List[List[Tensor]] -IdEmbedding = Tensor -SourceEmbedding = IdEmbedding +Embedding = Tensor FaceLandmark203 = Tensor StateSet = OrderedDict[str, Any] @@ -30,4 +29,4 @@ GeneratorLossSet = Dict[str, Tensor] DiscriminatorLossSet = Dict[str, Tensor] Generator = torch.nn.Module -IdEmbedder = torch.nn.Module +Embedder = torch.nn.Module From 62a69cddd2df4d88f889acd1b017bde576fd9839 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 10:33:51 +0100 Subject: [PATCH 033/394] Fix import order --- face_swapper/src/inferencing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 18451bc..d915992 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -5,7 +5,7 @@ import torch from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image from .models.generator import AdaptiveEmbeddingIntegrationNetwork -from .types import Generator, Embedder, VisionFrame +from .types import Embedder, Generator, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From 1b6e7a6ca5aa7c99ef7daf75204a8246dad889d4 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:19:14 +0100 Subject: [PATCH 034/394] Rename ArcFace Converter to Embedding Converter, Add EmbeddingDataset, Add learning rate to config --- .../embedding_converter.png} | Bin .../face_swapper.png} | Bin arcface_converter/LICENSE.md | 3 -- embedding_converter/LICENSE.md | 3 ++ .../README.md | 15 ++++--- .../__init__.py | 0 .../config.ini | 1 + .../export.py | 0 .../prepare.py | 0 .../src/__init__.py | 0 .../src/exporting.py | 8 ++-- .../src/models/__init__.py | 0 .../src/models/embedding_converter.py | 4 +- .../src/preparing.py | 18 ++++---- .../src/training.py | 42 +++++++++--------- .../src/types.py | 1 + .../train.py | 0 face_swapper/LICENSE.md | 2 +- face_swapper/LivePortrait | 2 +- face_swapper/README.md | 4 +- face_swapper/config.ini | 2 +- face_swapper/src/models/loss.py | 13 +++--- face_swapper/src/training.py | 18 ++++---- 23 files changed, 70 insertions(+), 66 deletions(-) rename .github/{preview_arcface_converter.png => previews/embedding_converter.png} (100%) rename .github/{preview_face_swapper.png => previews/face_swapper.png} (100%) delete mode 100644 arcface_converter/LICENSE.md create mode 100644 embedding_converter/LICENSE.md rename {arcface_converter => embedding_converter}/README.md (78%) rename {arcface_converter => embedding_converter}/__init__.py (100%) rename {arcface_converter => embedding_converter}/config.ini (96%) rename {arcface_converter => embedding_converter}/export.py (100%) rename {arcface_converter => embedding_converter}/prepare.py (100%) rename {arcface_converter => embedding_converter}/src/__init__.py (100%) rename {arcface_converter => embedding_converter}/src/exporting.py (55%) rename {arcface_converter => embedding_converter}/src/models/__init__.py (100%) rename arcface_converter/src/models/arcface_converter.py => embedding_converter/src/models/embedding_converter.py (86%) rename {arcface_converter => embedding_converter}/src/preparing.py (78%) rename {arcface_converter => embedding_converter}/src/training.py (74%) rename {arcface_converter => embedding_converter}/src/types.py (88%) rename {arcface_converter => embedding_converter}/train.py (100%) diff --git a/.github/preview_arcface_converter.png b/.github/previews/embedding_converter.png similarity index 100% rename from .github/preview_arcface_converter.png rename to .github/previews/embedding_converter.png diff --git a/.github/preview_face_swapper.png b/.github/previews/face_swapper.png similarity index 100% rename from .github/preview_face_swapper.png rename to .github/previews/face_swapper.png diff --git a/arcface_converter/LICENSE.md b/arcface_converter/LICENSE.md deleted file mode 100644 index aae2360..0000000 --- a/arcface_converter/LICENSE.md +++ /dev/null @@ -1,3 +0,0 @@ -MIT license - -Copyright (c) 2024 Henry Ruhs diff --git a/embedding_converter/LICENSE.md b/embedding_converter/LICENSE.md new file mode 100644 index 0000000..9be381f --- /dev/null +++ b/embedding_converter/LICENSE.md @@ -0,0 +1,3 @@ +MIT license + +Copyright (c) 2025 Henry Ruhs diff --git a/arcface_converter/README.md b/embedding_converter/README.md similarity index 78% rename from arcface_converter/README.md rename to embedding_converter/README.md index 49b33d5..d802ede 100644 --- a/arcface_converter/README.md +++ b/embedding_converter/README.md @@ -1,7 +1,7 @@ -ArcFace Converter -================= +Embedding Converter +=================== -> Convert face embeddings between various ArcFace models. +> Convert face embeddings between various models. ![License](https://img.shields.io/badge/license-MIT-green) @@ -9,7 +9,7 @@ ArcFace Converter Preview ------- -![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/master/.github/preview_arcface_converter.png?sanitize=true) +![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/next/.github/previews/embedding_converter.png?sanitize=true) Installation @@ -23,7 +23,7 @@ pip install -r requirements.txt Setup ----- -This `config.ini` utilizes the MegaFace dataset to train an ArcFace Converter for SimSwap. +This `config.ini` utilizes the MegaFace dataset to train the Embedding Converter for SimSwap. ``` [preparing.dataset] @@ -54,6 +54,7 @@ num_workers = 8 ``` [training.trainer] +learning_rate = 0.001 max_epochs = 4096 ``` @@ -80,7 +81,7 @@ providers = CUDAExecutionProvider Preparing --------- -Prepare the face embedding pairs. +Prepare the embedding pairs. ``` python prepare.py @@ -90,7 +91,7 @@ python prepare.py Training -------- -Train the ArcFace converter model. +Train the Embedding Converter model. ``` python train.py diff --git a/arcface_converter/__init__.py b/embedding_converter/__init__.py similarity index 100% rename from arcface_converter/__init__.py rename to embedding_converter/__init__.py diff --git a/arcface_converter/config.ini b/embedding_converter/config.ini similarity index 96% rename from arcface_converter/config.ini rename to embedding_converter/config.ini index 70b8d14..4bda383 100644 --- a/arcface_converter/config.ini +++ b/embedding_converter/config.ini @@ -18,6 +18,7 @@ batch_size = num_workers = [training.trainer] +learning_rate = max_epochs = [training.output] diff --git a/arcface_converter/export.py b/embedding_converter/export.py similarity index 100% rename from arcface_converter/export.py rename to embedding_converter/export.py diff --git a/arcface_converter/prepare.py b/embedding_converter/prepare.py similarity index 100% rename from arcface_converter/prepare.py rename to embedding_converter/prepare.py diff --git a/arcface_converter/src/__init__.py b/embedding_converter/src/__init__.py similarity index 100% rename from arcface_converter/src/__init__.py rename to embedding_converter/src/__init__.py diff --git a/arcface_converter/src/exporting.py b/embedding_converter/src/exporting.py similarity index 55% rename from arcface_converter/src/exporting.py rename to embedding_converter/src/exporting.py index de551c8..bb01e17 100644 --- a/arcface_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -3,7 +3,7 @@ from os import makedirs import torch -from .training import ArcFaceConverterTrainer +from .training import EmbeddingConverterTrainer CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -16,7 +16,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - arcface_converter_trainer = ArcFaceConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') - arcface_converter_trainer.eval() + embedding_converter_trainer = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + embedding_converter_trainer.eval() input_tensor = torch.randn(1, 512) - torch.onnx.export(arcface_converter_trainer, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) + torch.onnx.export(embedding_converter_trainer, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/arcface_converter/src/models/__init__.py b/embedding_converter/src/models/__init__.py similarity index 100% rename from arcface_converter/src/models/__init__.py rename to embedding_converter/src/models/__init__.py diff --git a/arcface_converter/src/models/arcface_converter.py b/embedding_converter/src/models/embedding_converter.py similarity index 86% rename from arcface_converter/src/models/arcface_converter.py rename to embedding_converter/src/models/embedding_converter.py index cc6ecd9..0648b8e 100644 --- a/arcface_converter/src/models/arcface_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -3,9 +3,9 @@ import torch.nn as nn from torch import Tensor -class ArcFaceConverter(nn.Module): +class EmbeddingConverter(nn.Module): def __init__(self) -> None: - super(ArcFaceConverter, self).__init__() + super(EmbeddingConverter, self).__init__() self.fc1 = nn.Linear(512, 1024) self.fc2 = nn.Linear(1024, 2048) self.fc3 = nn.Linear(2048, 1024) diff --git a/arcface_converter/src/preparing.py b/embedding_converter/src/preparing.py similarity index 78% rename from arcface_converter/src/preparing.py rename to embedding_converter/src/preparing.py index 6ba75fe..141352d 100644 --- a/arcface_converter/src/preparing.py +++ b/embedding_converter/src/preparing.py @@ -9,7 +9,7 @@ from mxnet.io import ImageRecordIter from onnxruntime import InferenceSession from tqdm import tqdm -from .types import Embedding, VisionFrame +from .types import Embedding, EmbeddingDataset, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -35,9 +35,9 @@ def forward(inference_session : InferenceSession, crop_vision_frame : VisionFram return embedding -def process_embeddings(dataset_reader : ImageRecordIter, source_inference_session : InferenceSession, target_inference_session : InferenceSession) -> Embedding: +def create_embedding_dataset(dataset_reader : ImageRecordIter, source_inference_session : InferenceSession, target_inference_session : InferenceSession) -> EmbeddingDataset: dataset_process_limit = CONFIG.getint('preparing.dataset', 'process_limit') - embeddings = [] + embedding_pairs = [] with tqdm(total = dataset_process_limit) as progress: for batch in dataset_reader: @@ -45,13 +45,13 @@ def process_embeddings(dataset_reader : ImageRecordIter, source_inference_sessio crop_vision_frame = prepare_crop_vision_frame(crop_vision_frame) source_embedding = forward(source_inference_session, crop_vision_frame) target_embedding = forward(target_inference_session, crop_vision_frame) - embeddings.append([ source_embedding, target_embedding ]) + embedding_pairs.append([ source_embedding, target_embedding ]) progress.update() if progress.n == dataset_process_limit: - return numpy.concatenate(embeddings, axis = 1).T + return numpy.concatenate(embedding_pairs, axis = 1).T - return numpy.concatenate(embeddings, axis = 1).T + return numpy.concatenate(embedding_pairs, axis = 1).T def prepare() -> None: @@ -74,6 +74,6 @@ def prepare() -> None: ) source_inference_session = create_inference_session(model_source_path, execution_providers) target_inference_session = create_inference_session(model_target_path, execution_providers) - embedding_pairs = process_embeddings(dataset_reader, source_inference_session, target_inference_session) - numpy.save(input_source_path, embedding_pairs[..., 0].T) - numpy.save(input_target_path, embedding_pairs[..., 1].T) + embedding_dataset = create_embedding_dataset(dataset_reader, source_inference_session, target_inference_session) + numpy.save(input_source_path, embedding_dataset[..., 0].T) + numpy.save(input_target_path, embedding_dataset[..., 1].T) diff --git a/arcface_converter/src/training.py b/embedding_converter/src/training.py similarity index 74% rename from arcface_converter/src/training.py rename to embedding_converter/src/training.py index f843a52..8e8b086 100644 --- a/arcface_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -10,39 +10,39 @@ from pytorch_lightning.tuner.tuning import Tuner from torch import Tensor from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split -from .models.arcface_converter import ArcFaceConverter +from .models.embedding_converter import EmbeddingConverter from .types import Batch, Loader CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class ArcFaceConverterTrainer(pytorch_lightning.LightningModule): +class EmbeddingConverterTrainer(pytorch_lightning.LightningModule): def __init__(self) -> None: - super(ArcFaceConverterTrainer, self).__init__() - self.arcface_converter = ArcFaceConverter() - self.loss_fn = torch.nn.MSELoss() - self.lr = 0.001 + super(EmbeddingConverterTrainer, self).__init__() + self.embedding_converter = EmbeddingConverter() + self.mse_loss = torch.nn.MSELoss() def forward(self, source_embedding : Tensor) -> Tensor: - return self.arcface_converter(source_embedding) + return self.embedding_converter(source_embedding) def training_step(self, batch : Batch, batch_index : int) -> Tensor: - source_embedding, target_embedding = batch - output_embedding = self(source_embedding) - loss = self.loss_fn(output_embedding, target_embedding) - self.log('train_loss', loss, prog_bar = True, logger = True) - return loss + source, target = batch + output = self(source) + loss_training = self.mse_loss(output, target) + self.log('loss_training', loss_training, prog_bar = True) + return loss_training def validation_step(self, batch : Batch, batch_index : int) -> Tensor: - source_embedding, target_embedding = batch - output_embedding = self(source_embedding) - loss = self.loss_fn(output_embedding, target_embedding) - self.log('val_loss', loss, prog_bar = True, logger = True) - return loss + source, target = batch + output = self(source) + loss_validation = self.mse_loss(output, target) + self.log('loss_validation', loss_validation, prog_bar = True) + return loss_validation def configure_optimizers(self) -> Any: - optimizer = torch.optim.Adam(self.parameters(), lr = self.lr) + learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') + optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) return\ @@ -110,7 +110,7 @@ def create_trainer() -> Trainer: def train() -> None: trainer = create_trainer() training_loader, validation_loader = create_loaders() - arcface_converter = ArcFaceConverterTrainer() + embedding_converter = EmbeddingConverterTrainer() tuner = Tuner(trainer) - tuner.lr_find(arcface_converter, training_loader, validation_loader) - trainer.fit(arcface_converter, training_loader, validation_loader) + tuner.lr_find(embedding_converter, training_loader, validation_loader) + trainer.fit(embedding_converter, training_loader, validation_loader) diff --git a/arcface_converter/src/types.py b/embedding_converter/src/types.py similarity index 88% rename from arcface_converter/src/types.py rename to embedding_converter/src/types.py index 16e699a..533e8de 100644 --- a/arcface_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -8,6 +8,7 @@ Batch = Tuple[Tensor, Tensor] Loader = DataLoader[Tuple[Tensor, ...]] Embedding = NDArray[Any] +EmbeddingDataset = NDArray[Embedding] FaceLandmark5 = NDArray[Any] VisionFrame = NDArray[Any] diff --git a/arcface_converter/train.py b/embedding_converter/train.py similarity index 100% rename from arcface_converter/train.py rename to embedding_converter/train.py diff --git a/face_swapper/LICENSE.md b/face_swapper/LICENSE.md index 5da6fcd..cfdd95f 100644 --- a/face_swapper/LICENSE.md +++ b/face_swapper/LICENSE.md @@ -1,3 +1,3 @@ ResearchRAIL-M license -Copyright (c) 2024 Henry Ruhs +Copyright (c) 2025 Henry Ruhs diff --git a/face_swapper/LivePortrait b/face_swapper/LivePortrait index 1b22029..e09d1c9 160000 --- a/face_swapper/LivePortrait +++ b/face_swapper/LivePortrait @@ -1 +1 @@ -Subproject commit 1b22029ec07a827b48db2a82e2ced36393556c4a +Subproject commit e09d1c9f1c9519f9e3ccf4c185b3992993864f6d diff --git a/face_swapper/README.md b/face_swapper/README.md index c58332d..c261d02 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -9,7 +9,7 @@ Face Swapper Preview ------- -![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/next/.github/preview_face_swapper.png?sanitize=true) +![Preview](https://raw.githubusercontent.com/facefusion/facefusion-labs/next/.github/previews/face_swapper.png?sanitize=true) Installation @@ -72,8 +72,8 @@ weight_pose = 100 ``` [training.trainer] -max_epochs = 50 learning_rate = 0.0004 +max_epochs = 50 precision = 16-mixed automatic_optimization = false ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 6211385..effb748 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -32,8 +32,8 @@ weight_reconstruction = weight_pose = [training.trainer] -max_epochs = learning_rate = +max_epochs = precision = automatic_optimization = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2cd4bcb..517b88b 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -34,12 +34,13 @@ class FaceSwapperLoss: weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') - generator_loss_set = {} - - generator_loss_set['loss_adversarial'] = self.calc_adversarial_loss(discriminator_outputs) - generator_loss_set['loss_id'] = self.calc_id_loss(source_tensor, swap_tensor) - generator_loss_set['loss_attribute'] = self.calc_attribute_loss(target_attributes, swap_attributes) - generator_loss_set['loss_reconstruction'] = self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + generator_loss_set =\ + { + 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), + 'loss_id': self.calc_id_loss(source_tensor, swap_tensor), + 'loss_attribute': self.calc_attribute_loss(target_attributes, swap_attributes), + 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + } if weight_pose > 0: generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index d023085..b267d93 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -61,12 +61,12 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: self.generate_preview(source_tensor, target_tensor, swap_tensor) - self.log('l_G', generator_losses.get('loss_generator'), prog_bar = True) - self.log('l_D', discriminator_losses.get('loss_discriminator'), prog_bar = True) - self.log('l_ADV', generator_losses.get('loss_adversarial'), prog_bar = True) - self.log('l_ATTR', generator_losses.get('loss_attribute'), prog_bar = True) - self.log('l_ID', generator_losses.get('loss_id'), prog_bar = True) - self.log('l_REC', generator_losses.get('loss_reconstruction'), prog_bar = True) + self.log('loss_generator', generator_losses.get('loss_generator'), prog_bar = True) + self.log('loss_discriminator', discriminator_losses.get('loss_discriminator'), prog_bar = True) + self.log('loss_adversarial', generator_losses.get('loss_adversarial'), prog_bar = True) + self.log('loss_attribute', generator_losses.get('loss_attribute'), prog_bar = True) + self.log('loss_id', generator_losses.get('loss_id'), prog_bar = True) + self.log('loss_reconstruction', generator_losses.get('loss_reconstruction'), prog_bar = True) return generator_losses.get('loss_generator') def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: @@ -76,7 +76,7 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): swap_tensors = swap_tensor[:max_preview] rows = [ torch.cat([ source_tensor, target_tensor, swap_tensor ], dim = 2) for source_tensor, target_tensor, swap_tensor in zip(source_tensors, target_tensors, swap_tensors) ] grid = torchvision.utils.make_grid(torch.cat(rows, dim = 1).unsqueeze(0), nrow = 1, normalize = True, scale_each = True) - self.logger.experiment.add_image("Generator Preview", grid, self.global_step) + self.logger.experiment.add_image('preview', grid, self.global_step) def create_trainer() -> Trainer: @@ -111,10 +111,10 @@ def train() -> None: same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') - file_path = CONFIG.get('training.output', 'file_path') + output_file_path = CONFIG.get('training.output', 'file_path') dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapperTrain() trainer = create_trainer() - trainer.fit(face_swap_model, data_loader, ckpt_path = file_path) + trainer.fit(face_swap_model, data_loader, ckpt_path = output_file_path) From 1f4405be443b0329098b2881a85c40cfec3a252b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:20:34 +0100 Subject: [PATCH 035/394] Fix CI --- .github/workflows/ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 6b75de0..8bbeef5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -15,5 +15,5 @@ jobs: - run: pip install flake8 - run: pip install flake8-import-order - run: pip install mypy - - run: flake8 arcface_converter face_swapper - - run: mypy arcface_converter face_swapper + - run: flake8 embedding_converter face_swapper + - run: mypy embedding_converter face_swapper From 67ad9badac1c709b0389c35557052f0b89f3fb9a Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:22:54 +0100 Subject: [PATCH 036/394] Small typo --- embedding_converter/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index d802ede..e74162e 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -81,7 +81,7 @@ providers = CUDAExecutionProvider Preparing --------- -Prepare the embedding pairs. +Prepare the embedding dataset. ``` python prepare.py From 4d2038d4ce6b4b7f9026cd441c46b5fb2d853ead Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:38:07 +0100 Subject: [PATCH 037/394] Rename loss_id to loss_identity --- face_swapper/src/models/loss.py | 8 ++++---- face_swapper/src/training.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 517b88b..01a9d12 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -37,7 +37,7 @@ class FaceSwapperLoss: generator_loss_set =\ { 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), - 'loss_id': self.calc_id_loss(source_tensor, swap_tensor), + 'loss_identity': self.calc_identity_loss(source_tensor, swap_tensor), 'loss_attribute': self.calc_attribute_loss(target_attributes, swap_attributes), 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) } @@ -101,11 +101,11 @@ class FaceSwapperLoss: loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 return loss_reconstruction - def calc_id_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: + def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) - loss_id = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() - return loss_id + loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() + return loss_identity def calc_pose_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: swap_motion_features = self.get_pose_features(swap_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index b267d93..fc20ead 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -63,10 +63,10 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): self.log('loss_generator', generator_losses.get('loss_generator'), prog_bar = True) self.log('loss_discriminator', discriminator_losses.get('loss_discriminator'), prog_bar = True) - self.log('loss_adversarial', generator_losses.get('loss_adversarial'), prog_bar = True) - self.log('loss_attribute', generator_losses.get('loss_attribute'), prog_bar = True) - self.log('loss_id', generator_losses.get('loss_id'), prog_bar = True) - self.log('loss_reconstruction', generator_losses.get('loss_reconstruction'), prog_bar = True) + self.log('loss_adversarial', generator_losses.get('loss_adversarial')) + self.log('loss_attribute', generator_losses.get('loss_attribute')) + self.log('loss_identity', generator_losses.get('loss_identity')) + self.log('loss_reconstruction', generator_losses.get('loss_reconstruction')) return generator_losses.get('loss_generator') def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: From 58c81cd646fc25e3144312076e0c4ca1109d2784 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:48:50 +0100 Subject: [PATCH 038/394] Move magic methods up --- face_swapper/src/data_loader.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 49f1c21..5324509 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -19,6 +19,17 @@ class DataLoaderVGG(TensorDataset): self.dataset_total = len(self.image_paths) self.transforms = self.compose_transforms() + def __getitem__(self, index : int) -> Batch: + source_image_path = self.image_paths[index] + + if random.random() > self.same_person_probability: + return self.prepare_same_person(source_image_path) + + return self.prepare_different_person(source_image_path) + + def __len__(self) -> int: + return self.dataset_total + def prepare_image_paths(self, dataset_image_pattern : str) -> Tuple[ImagePathList, ImagePathSet]: image_paths = [] image_path_set = {} @@ -41,14 +52,6 @@ class DataLoaderVGG(TensorDataset): ]) return transform - def __getitem__(self, index : int) -> Batch: - source_image_path = self.image_paths[index] - - if random.random() > self.same_person_probability: - return self.prepare_same_person(source_image_path) - - return self.prepare_different_person(source_image_path) - def prepare_different_person(self, source_image_path : str) -> Batch: is_same_person = torch.tensor(0) target_image_path = random.choice(self.image_paths) @@ -66,6 +69,3 @@ class DataLoaderVGG(TensorDataset): source_tensor = self.transforms(source_vision_frame) target_tensor = self.transforms(target_vision_frame) return source_tensor, target_tensor, is_same_person - - def __len__(self) -> int: - return self.dataset_total From 746cc86d525d12c21d59c0936f901ea79233aff2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:51:45 +0100 Subject: [PATCH 039/394] Fix monitor --- embedding_converter/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 8e8b086..9e02685 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -94,7 +94,7 @@ def create_trainer() -> Trainer: callbacks = [ ModelCheckpoint( - monitor = 'train_loss', + monitor = 'loss_training', dirpath = output_directory_path, filename = output_file_pattern, every_n_epochs = 10, From 11c038cb8101c314f084022531797989d6bd8ebb Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 12:52:38 +0100 Subject: [PATCH 040/394] Fix monitor --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index fc20ead..381cfff 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -92,7 +92,7 @@ def create_trainer() -> Trainer: callbacks = [ ModelCheckpoint( - monitor = 'l_G', + monitor = 'loss_generator', dirpath = output_directory_path, filename = output_file_pattern, every_n_train_steps = 1000, From b42d2b06e7fb12d556db5578f797066d2a327a63 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 13:07:14 +0100 Subject: [PATCH 041/394] Improve generate_preview --- face_swapper/src/training.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 381cfff..c936772 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -69,14 +69,15 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): self.log('loss_reconstruction', generator_losses.get('loss_reconstruction')) return generator_losses.get('loss_generator') - def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> None: - max_preview = 8 - source_tensors = source_tensor[:max_preview] - target_tensors = target_tensor[:max_preview] - swap_tensors = swap_tensor[:max_preview] - rows = [ torch.cat([ source_tensor, target_tensor, swap_tensor ], dim = 2) for source_tensor, target_tensor, swap_tensor in zip(source_tensors, target_tensors, swap_tensors) ] - grid = torchvision.utils.make_grid(torch.cat(rows, dim = 1).unsqueeze(0), nrow = 1, normalize = True, scale_each = True) - self.logger.experiment.add_image('preview', grid, self.global_step) + def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, output_tensor : VisionTensor) -> None: + preview_limit = 8 + preview_items = [] + + for source_tensor, target_tensor, output_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit]): + preview_items.append(torch.cat([ source_tensor, target_tensor, output_tensor] , dim = 2)) + + preview_grid = torchvision.utils.make_grid(torch.cat(preview_items, dim = 1).unsqueeze(0), normalize = True, scale_each = True) + self.logger.experiment.add_image('preview', preview_grid, self.global_step) def create_trainer() -> Trainer: From 860771e482eff97bf1e592aced49c2eb6b9fdc3d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 13:30:16 +0100 Subject: [PATCH 042/394] Fix weight identity --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/models/loss.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index c261d02..0643c9e 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -64,7 +64,7 @@ kernel_size = 4 ``` [training.losses] weight_adversarial = 1 -weight_id = 20 +weight_identity = 20 weight_attribute = 10 weight_reconstruction = 10 weight_pose = 100 diff --git a/face_swapper/config.ini b/face_swapper/config.ini index effb748..faa6254 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -26,7 +26,7 @@ kernel_size = [training.losses] weight_adversarial = -weight_id = +weight_identity = weight_attribute = weight_reconstruction = weight_pose = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 01a9d12..a209d49 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -29,7 +29,7 @@ class FaceSwapperLoss: def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: source_tensor, target_tensor, is_same_person = batch weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') - weight_id = CONFIG.getfloat('training.losses', 'weight_id') + weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') @@ -53,7 +53,7 @@ class FaceSwapperLoss: generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_id') * weight_id + generator_loss_set['loss_generator'] += generator_loss_set.get('loss_identity') * weight_identity generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction generator_loss_set['loss_generator'] += generator_loss_set.get('loss_pose') * weight_pose From 494b84aecbdaa3f145a26ea746ac423f28740720 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 15:43:35 +0100 Subject: [PATCH 043/394] Refactor discriminator to use ModuleList, Reduce complexity of layer creation --- face_swapper/src/models/discriminator.py | 103 +++++++++++------------ face_swapper/src/training.py | 2 +- 2 files changed, 48 insertions(+), 57 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index aa2df6b..707985a 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -3,11 +3,9 @@ from itertools import chain from typing import List import numpy -import torch.nn import torch.nn as nn -from torch import Tensor -from face_swapper.src.types import DiscriminatorOutputs +from face_swapper.src.types import VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -16,77 +14,70 @@ CONFIG.read('config.ini') class MultiscaleDiscriminator(nn.Module): def __init__(self) -> None: super(MultiscaleDiscriminator, self).__init__() - self.input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') - self.num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') - self.kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') - self.num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') - self.num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') + self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) + self.discriminators = self.create_discriminators() - self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = [ 1, 1 ], count_include_pad = False) # type:ignore[arg-type] - self.prepare_discriminators() + def create_discriminators(self) -> nn.ModuleList: + num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') + input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') + num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') + kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') + num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') + discriminators = nn.ModuleList() - def prepare_discriminators(self) -> None: - for discriminator_index in range(self.num_discriminators): - single_discriminator = NLayerDiscriminator(self.input_channels, self.num_filters, self.num_layers, self.kernel_size) - setattr(self, 'discriminator_layer_{}'.format(discriminator_index), single_discriminator.model) + for _ in range(num_discriminators): + discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers, kernel_size).discriminator + self.discriminators.append(discriminator) - def forward(self, input_tensor : Tensor) -> DiscriminatorOutputs: - discriminator_outputs = [] + return discriminators + + def forward(self, input_tensor : VisionTensor) -> List[List[VisionTensor]]: temp_tensor = input_tensor + output_tensors = [] - for discriminator_index in range(self.num_discriminators): - model_layers = getattr(self, 'discriminator_layer_{}'.format(self.num_discriminators - 1 - discriminator_index)) - discriminator_outputs.append([ model_layers(temp_tensor) ]) + for discriminator in self.discriminators: + output_tensors.append([ discriminator(temp_tensor) ]) + temp_tensor = self.downsample(temp_tensor) - if discriminator_index < (self.num_discriminators - 1): - temp_tensor = self.downsample(temp_tensor) - - return discriminator_outputs + return output_tensors class NLayerDiscriminator(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super(NLayerDiscriminator, self).__init__() - self.num_layers = num_layers - model_layers = self.prepare_model_layers(input_channels, num_filters, num_layers, kernel_size) - self.model = nn.Sequential(*list(chain(*model_layers))) + layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) + self.discriminator = nn.Sequential(*layers) - def prepare_model_layers(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> List[List[torch.nn.Module]]: - padding_size = int(numpy.ceil((kernel_size - 1.0) / 2)) - - model_layers =\ - [ - [ - nn.Conv2d(input_channels, num_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), - nn.LeakyReLU(0.2, True) - ] - ] + @staticmethod + def create_layers(self, input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> List[nn.Module]: + padding = int(numpy.ceil((kernel_size - 1) / 2)) current_filters = num_filters + layers =\ + [ + nn.Conv2d(input_channels, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), + nn.LeakyReLU(0.2, True) + ] - for layer_index in range(1, num_layers): + for _ in range(1, num_layers): previous_filters = current_filters current_filters = min(current_filters * 2, 512) - model_layers +=\ + layers +=\ [ - [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding_size), - nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ] - ] - previous_filters = current_filters - current_filters = min(current_filters * 2, 512) - model_layers +=\ - [ - [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding_size), + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) - ], - [ - nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding_size) ] - ] - return model_layers - def forward(self, input_tensor : Tensor) -> Tensor: - return self.model(input_tensor) + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + layers +=\ + [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding), + nn.InstanceNorm2d(current_filters), + nn.LeakyReLU(0.2, True), + nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding) + ] + return layers + + def forward(self, input_tensor : VisionTensor) -> VisionTensor: + return self.discriminator(input_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index c936772..6233e9a 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -85,8 +85,8 @@ def create_trainer() -> Trainer: output_directory_path = CONFIG.get('training.output', 'directory_path') output_file_pattern = CONFIG.get('training.output', 'file_pattern') trainer_precision = CONFIG.get('training.trainer', 'precision') - os.makedirs(output_directory_path, exist_ok = True) + os.makedirs(output_directory_path, exist_ok = True) return Trainer( max_epochs = trainer_max_epochs, precision = trainer_precision, From 32dfdcf1b3e8cfc01c849512a3a947f53a40db32 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 15:44:19 +0100 Subject: [PATCH 044/394] Remove unsued --- face_swapper/src/models/discriminator.py | 1 - 1 file changed, 1 deletion(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 707985a..63da839 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -1,5 +1,4 @@ import configparser -from itertools import chain from typing import List import numpy From 71e0ae34c03cbab17fa5e24b615ebd5830da27fc Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 15:46:33 +0100 Subject: [PATCH 045/394] Fix self --- face_swapper/src/models/discriminator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 63da839..5a2865c 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -44,7 +44,7 @@ class MultiscaleDiscriminator(nn.Module): class NLayerDiscriminator(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super(NLayerDiscriminator, self).__init__() - layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) + layers = self.create_layers(self, input_channels, num_filters, num_layers, kernel_size) self.discriminator = nn.Sequential(*layers) @staticmethod From dd320ea5bed3f67a1df31241f40446e8dc4a64d1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 15:47:04 +0100 Subject: [PATCH 046/394] Fix self --- face_swapper/src/models/discriminator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 5a2865c..786b2a9 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -44,11 +44,11 @@ class MultiscaleDiscriminator(nn.Module): class NLayerDiscriminator(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super(NLayerDiscriminator, self).__init__() - layers = self.create_layers(self, input_channels, num_filters, num_layers, kernel_size) + layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) self.discriminator = nn.Sequential(*layers) @staticmethod - def create_layers(self, input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> List[nn.Module]: + def create_layers(input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> List[nn.Module]: padding = int(numpy.ceil((kernel_size - 1) / 2)) current_filters = num_filters layers =\ From b785525f3bc51b5177c09c6f737c2645ce379f21 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 11:37:12 +0100 Subject: [PATCH 047/394] Fix append --- face_swapper/src/models/discriminator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 786b2a9..0cc51d4 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -26,7 +26,7 @@ class MultiscaleDiscriminator(nn.Module): for _ in range(num_discriminators): discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers, kernel_size).discriminator - self.discriminators.append(discriminator) + discriminators.append(discriminator) return discriminators From 34d0bc10ed71e54f12f057272d95e65eb04fa886 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 11:37:32 +0100 Subject: [PATCH 048/394] Fix append --- face_swapper/src/models/discriminator.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 0cc51d4..3755c30 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -16,7 +16,8 @@ class MultiscaleDiscriminator(nn.Module): self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() - def create_discriminators(self) -> nn.ModuleList: + @staticmethod + def create_discriminators() -> nn.ModuleList: num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') From 3c6dfa4efe85c790bd4fa6d3ca1ab3c0ee3b36fd Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 17:04:20 +0100 Subject: [PATCH 049/394] Modernize to use ModuleList, Fix some types --- .../src/models/embedding_converter.py | 34 ++++++++++++------- embedding_converter/src/training.py | 24 ++++++------- embedding_converter/src/types.py | 6 ++-- 3 files changed, 37 insertions(+), 27 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index 0648b8e..d66ba9a 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -1,21 +1,31 @@ import torch import torch.nn as nn -from torch import Tensor + +from embedding_converter.src.types import VisionTensor class EmbeddingConverter(nn.Module): def __init__(self) -> None: super(EmbeddingConverter, self).__init__() - self.fc1 = nn.Linear(512, 1024) - self.fc2 = nn.Linear(1024, 2048) - self.fc3 = nn.Linear(2048, 1024) - self.fc4 = nn.Linear(1024, 512) + self.layers = self.create_layers() self.activation = nn.LeakyReLU() - def forward(self, inputs : Tensor) -> Tensor: - norm_inputs = inputs / torch.norm(inputs) - outputs = self.activation(self.fc1(norm_inputs)) - outputs = self.activation(self.fc2(outputs)) - outputs = self.activation(self.fc3(outputs)) - outputs = self.fc4(outputs) - return outputs + @staticmethod + def create_layers() -> nn.ModuleList: + layers = nn.ModuleList( + [ + nn.Linear(512, 1024), + nn.Linear(1024, 2048), + nn.Linear(2048, 1024), + nn.Linear(1024, 512) + ]) + return layers + + def forward(self, input_tensor: VisionTensor) -> VisionTensor: + output_tensor = input_tensor / torch.norm(input_tensor) + + for layer in self.layers[:-1]: + output_tensor = self.activation(layer(output_tensor)) + + output_tensor = self.layers[-1](output_tensor) + return output_tensor diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 9e02685..25e2217 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -11,7 +11,7 @@ from torch import Tensor from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Loader +from .types import Batch, Embedding CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -23,20 +23,20 @@ class EmbeddingConverterTrainer(pytorch_lightning.LightningModule): self.embedding_converter = EmbeddingConverter() self.mse_loss = torch.nn.MSELoss() - def forward(self, source_embedding : Tensor) -> Tensor: + def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) def training_step(self, batch : Batch, batch_index : int) -> Tensor: - source, target = batch - output = self(source) - loss_training = self.mse_loss(output, target) + source_tensor, target = batch + output_tensor = self(source_tensor) + loss_training = self.mse_loss(output_tensor, target) self.log('loss_training', loss_training, prog_bar = True) return loss_training def validation_step(self, batch : Batch, batch_index : int) -> Tensor: - source, target = batch - output = self(source) - loss_validation = self.mse_loss(output, target) + source_tensor, target_tensor = batch + output_tensor = self(source_tensor) + loss_validation = self.mse_loss(output_tensor, target_tensor) self.log('loss_validation', loss_validation, prog_bar = True) return loss_validation @@ -58,7 +58,7 @@ class EmbeddingConverterTrainer(pytorch_lightning.LightningModule): } -def create_loaders() -> Tuple[Loader, Loader]: +def create_loaders() -> Tuple[DataLoader, DataLoader]: loader_batch_size = CONFIG.getint('training.loader', 'batch_size') loader_num_workers = CONFIG.getint('training.loader', 'num_workers') @@ -73,9 +73,9 @@ def split_dataset() -> Tuple[Dataset[Any], Dataset[Any]]: input_target_path = CONFIG.get('preparing.input', 'target_path') loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') - source_input = torch.from_numpy(numpy.load(input_source_path)).float() - target_input = torch.from_numpy(numpy.load(input_target_path)).float() - dataset = TensorDataset(source_input, target_input) + source_tensor = torch.from_numpy(numpy.load(input_source_path)).float() + target_tensor = torch.from_numpy(numpy.load(input_target_path)).float() + dataset = TensorDataset(source_tensor, target_tensor) dataset_size = len(dataset) training_size = int(loader_split_ratio * len(dataset)) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 533e8de..324222e 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -4,11 +4,11 @@ from numpy.typing import NDArray from torch import Tensor from torch.utils.data import DataLoader -Batch = Tuple[Tensor, Tensor] -Loader = DataLoader[Tuple[Tensor, ...]] - Embedding = NDArray[Any] EmbeddingDataset = NDArray[Embedding] FaceLandmark5 = NDArray[Any] VisionFrame = NDArray[Any] +VisionTensor = Tensor + +Batch = Tuple[VisionTensor, VisionTensor] From dfe7ab3b6fa12bf246172d42613fda6e3b6ed5cd Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 17:05:37 +0100 Subject: [PATCH 050/394] Modernize to use ModuleList, Fix some types --- .flake8 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.flake8 b/.flake8 index 8a4f145..8363652 100644 --- a/.flake8 +++ b/.flake8 @@ -1,6 +1,6 @@ [flake8] select = E22, E23, E24, E27, E3, E4, E7, F, I1, I2 plugins = flake8-import-order -application_import_names = arcface_converter, face_swapper +application_import_names = embedding_converter, face_swapper import-order-style = pycharm per-file-ignores = preparing.py:E402 From c6d16c0cf65fef39faf78c99a8a2fc41d5b9f906 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 17:06:20 +0100 Subject: [PATCH 051/394] Modernize to use ModuleList, Fix some types --- embedding_converter/src/types.py | 1 - 1 file changed, 1 deletion(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 324222e..8b18e85 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -2,7 +2,6 @@ from typing import Any, Tuple from numpy.typing import NDArray from torch import Tensor -from torch.utils.data import DataLoader Embedding = NDArray[Any] EmbeddingDataset = NDArray[Embedding] From f3409c5aded789f12da9a3f7dabee9a00e712a1b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 17:08:12 +0100 Subject: [PATCH 052/394] Modernize to use ModuleList, Fix some types --- embedding_converter/src/models/embedding_converter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index d66ba9a..5490aa3 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -1,7 +1,7 @@ import torch import torch.nn as nn -from embedding_converter.src.types import VisionTensor +from ..types import VisionTensor class EmbeddingConverter(nn.Module): @@ -21,7 +21,7 @@ class EmbeddingConverter(nn.Module): ]) return layers - def forward(self, input_tensor: VisionTensor) -> VisionTensor: + def forward(self, input_tensor : VisionTensor) -> VisionTensor: output_tensor = input_tensor / torch.norm(input_tensor) for layer in self.layers[:-1]: From e33bc0d52a272bee19ae3fb6b29bde23da6f7081 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 17:15:15 +0100 Subject: [PATCH 053/394] Modernize to use ModuleList, Fix some types --- embedding_converter/src/types.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 8b18e85..9221412 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,13 +1,13 @@ -from typing import Any, Tuple +from typing import Any, Tuple, TypeAlias from numpy.typing import NDArray from torch import Tensor -Embedding = NDArray[Any] -EmbeddingDataset = NDArray[Embedding] -FaceLandmark5 = NDArray[Any] +Embedding : TypeAlias = NDArray[Any] +EmbeddingDataset : TypeAlias = NDArray[Embedding] +FaceLandmark5 : TypeAlias = NDArray[Any] -VisionFrame = NDArray[Any] -VisionTensor = Tensor +VisionFrame : TypeAlias = NDArray[Any] +VisionTensor : TypeAlias = Tensor -Batch = Tuple[VisionTensor, VisionTensor] +Batch : TypeAlias = Tuple[VisionTensor, VisionTensor] From b6b4f9f65b29962d75fcd6d631fbba363aa5f664 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 21:25:43 +0100 Subject: [PATCH 054/394] Improve lot of types, imports and names --- .../src/models/embedding_converter.py | 2 +- embedding_converter/src/training.py | 4 +- face_swapper/src/data_loader.py | 4 +- face_swapper/src/helper.py | 15 +++---- face_swapper/src/models/discriminator.py | 2 +- face_swapper/src/models/generator.py | 2 +- face_swapper/src/models/loss.py | 4 +- .../src/networks/attribute_modulator.py | 30 +++++++------- face_swapper/src/networks/encoder.py | 24 +++++------ face_swapper/src/types.py | 41 +++++++++---------- 10 files changed, 64 insertions(+), 64 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index 5490aa3..95a3581 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -1,5 +1,5 @@ import torch -import torch.nn as nn +from torch import nn from ..types import VisionTensor diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 25e2217..13b554b 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -7,7 +7,7 @@ import torch from pytorch_lightning import Trainer from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.tuner.tuning import Tuner -from torch import Tensor +from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split from .models.embedding_converter import EmbeddingConverter @@ -21,7 +21,7 @@ class EmbeddingConverterTrainer(pytorch_lightning.LightningModule): def __init__(self) -> None: super(EmbeddingConverterTrainer, self).__init__() self.embedding_converter = EmbeddingConverter() - self.mse_loss = torch.nn.MSELoss() + self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 5324509..6d7919b 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -12,9 +12,9 @@ from .types import Batch, ImagePathList, ImagePathSet class DataLoaderVGG(TensorDataset): - def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_folder_pattern : str, same_person_probability : float) -> None: + def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_directory_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability - self.directory_paths = glob.glob(dataset_folder_pattern.format(dataset_path)) + self.directory_paths = glob.glob(dataset_directory_pattern.format(dataset_path)) self.image_paths, self.image_path_set = self.prepare_image_paths(dataset_image_pattern) self.dataset_total = len(self.image_paths) self.transforms = self.compose_transforms() diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 05ee770..00faa66 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -3,8 +3,9 @@ import platform import cv2 import numpy import torch +from torch import Tensor, nn -from .types import Embedder, Embedding, Padding, Tensor, VisionFrame, VisionTensor +from .types import Embedder, Embedding, Padding, VisionFrame, VisionTensor def is_windows() -> bool: @@ -35,25 +36,25 @@ def convert_to_vision_frame(vision_tensor : VisionTensor) -> VisionFrame: return vision_frame -def hinge_real_loss(tensor : Tensor) -> Tensor: - real_loss = torch.relu(1 - tensor) +def hinge_real_loss(input_tensor : Tensor) -> Tensor: + real_loss = torch.relu(1 - input_tensor) real_loss = real_loss.mean(dim = [ 1, 2, 3 ]) return real_loss -def hinge_fake_loss(tensor : Tensor) -> Tensor: - fake_loss = torch.relu(tensor + 1) +def hinge_fake_loss(input_tensor : Tensor) -> Tensor: + fake_loss = torch.relu(input_tensor + 1) fake_loss = fake_loss.mean(dim = [ 1, 2, 3 ]) return fake_loss def calc_id_embedding(id_embedder : Embedder, vision_tensor : VisionTensor, padding : Padding) -> Embedding: crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] - crop_vision_tensor = torch.nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') + crop_vision_tensor = nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') crop_vision_tensor[:, :, :padding[0], :] = 0 crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 crop_vision_tensor[:, :, :, :padding[2]] = 0 crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 source_embedding = id_embedder(crop_vision_tensor) - source_embedding = torch.nn.functional.normalize(source_embedding, p = 2) + source_embedding = nn.functional.normalize(source_embedding, p = 2) return source_embedding diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 3755c30..60eb8e1 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -2,7 +2,7 @@ import configparser from typing import List import numpy -import torch.nn as nn +from torch import nn from face_swapper.src.types import VisionTensor diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 1b38dac..3fc26a4 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,7 +1,7 @@ import configparser from typing import Tuple -import torch.nn as nn +from torch import nn from face_swapper.src.networks.attribute_modulator import AADGenerator from face_swapper.src.networks.encoder import UNet diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index a209d49..55d074c 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -18,7 +18,7 @@ class FaceSwapperLoss: landmarker_path = CONFIG.get('training.model', 'landmarker_path') motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.batch_size = CONFIG.getint('training.loader', 'batch_size') - self.mse_loss = torch.nn.MSELoss() + self.mse_loss = nn.MSELoss() self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] @@ -127,7 +127,7 @@ class FaceSwapperLoss: def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: vision_tensor_norm = (vision_tensor + 1) * 0.5 - vision_tensor_norm = torch.nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') + vision_tensor_norm = nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) return landmarks diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index 69d71e1..1dae96c 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -1,5 +1,5 @@ import torch -from torch import Tensor, nn as nn +from torch import Tensor, nn from face_swapper.src.types import Embedding, TargetAttributes @@ -19,13 +19,13 @@ class AADGenerator(nn.Module): def forward(self, target_attributes : TargetAttributes, source_embedding : Embedding) -> Tensor: feature_map = self.upsample(source_embedding) - feature_map_1 = torch.nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_2 = torch.nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_3 = torch.nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_4 = torch.nn.functional.interpolate(self.res_block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_5 = torch.nn.functional.interpolate(self.res_block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_6 = torch.nn.functional.interpolate(self.res_block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_7 = torch.nn.functional.interpolate(self.res_block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_3 = nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_4 = nn.functional.interpolate(self.res_block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_5 = nn.functional.interpolate(self.res_block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_6 = nn.functional.interpolate(self.res_block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) + feature_map_7 = nn.functional.interpolate(self.res_block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) output = self.res_block_8(feature_map_7, target_attributes[7], source_embedding) return torch.tanh(output) @@ -41,7 +41,7 @@ class AADLayer(nn.Module): self.instance_norm = nn.InstanceNorm2d(input_channels) self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) - def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : Embedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: feature_map = self.instance_norm(feature_map) gamma_attribute = self.conv_gamma(attribute_embedding) beta_attribute = self.conv_beta(attribute_embedding) @@ -59,7 +59,7 @@ class AADSequential(nn.Module): super(AADSequential, self).__init__() self.layers = nn.ModuleList(args) - def forward(self, feature_map: Tensor, attribute_embedding: Tensor, id_embedding: Embedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: for layer in self.layers: if isinstance(layer, AADLayer): feature_map = layer(feature_map, attribute_embedding, id_embedding) @@ -99,7 +99,7 @@ class AADResBlock(nn.Module): ) self.auxiliary_add_blocks = auxiliary_add_blocks - def forward(self, feature_map : Tensor, attribute_embedding : Tensor, id_embedding : Embedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding) if self.input_channels > self.output_channels: @@ -115,7 +115,7 @@ class PixelShuffleUpsample(nn.Module): self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 3, padding = 1) self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) - def forward(self, temp : Tensor) -> Tensor: - temp = self.conv(temp.view(temp.shape[0], -1, 1, 1)) - temp = self.pixel_shuffle(temp) - return temp + def forward(self, input_tensor : Tensor) -> Tensor: + temp_tensor = self.conv(input_tensor.view(input_tensor.shape[0], -1, 1, 1)) + temp_tensor = self.pixel_shuffle(temp_tensor) + return temp_tensor diff --git a/face_swapper/src/networks/encoder.py b/face_swapper/src/networks/encoder.py index e380ab5..d4e270e 100644 --- a/face_swapper/src/networks/encoder.py +++ b/face_swapper/src/networks/encoder.py @@ -1,5 +1,5 @@ import torch -from torch import Tensor, nn as nn +from torch import Tensor, nn from face_swapper.src.types import TargetAttributes, VisionTensor @@ -11,11 +11,11 @@ class Upsample(nn.Module): self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - def forward(self, temp : Tensor, skip_tensor : Tensor) -> Tensor: - temp = self.deconv(temp) - temp = self.batch_norm(temp) - temp = self.leaky_relu(temp) - return torch.cat((temp, skip_tensor), dim = 1) + def forward(self, input_tensor : Tensor, skip_tensor : Tensor) -> Tensor: + temp_tensor = self.deconv(input_tensor) + temp_tensor = self.batch_norm(temp_tensor) + temp_tensor = self.leaky_relu(temp_tensor) + return torch.cat((temp_tensor, skip_tensor), dim = 1) class DownSample(nn.Module): @@ -25,11 +25,11 @@ class DownSample(nn.Module): self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - def forward(self, temp : Tensor) -> Tensor: - temp = self.conv(temp) - temp = self.batch_norm(temp) - temp = self.leaky_relu(temp) - return temp + def forward(self, input_tensor : Tensor) -> Tensor: + temp_tensor = self.conv(input_tensor) + temp_tensor = self.batch_norm(temp_tensor) + temp_tensor = self.leaky_relu(temp_tensor) + return temp_tensor class UNet(nn.Module): @@ -63,5 +63,5 @@ class UNet(nn.Module): upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) - output = torch.nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) + output = nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index e37001d..b5551d4 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,32 +1,31 @@ from collections import OrderedDict -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, TypeAlias -import torch.nn from numpy.typing import NDArray from torch import Tensor -from torch.utils.data import DataLoader +from torch.nn import Module -Batch = Tuple[Any, Any, Any] -Loader = DataLoader[Tuple[Tensor, ...]] -ImagePathList = List[str] -ImagePathSet = Dict[str, ImagePathList] +ImagePathList : TypeAlias = List[str] +ImagePathSet : TypeAlias = Dict[str, ImagePathList] -SwapAttributes = Tuple[Tensor, ...] -TargetAttributes = Tuple[Tensor, ...] -DiscriminatorOutputs = List[List[Tensor]] +SwapAttributes : TypeAlias = Tuple[Tensor, ...] +TargetAttributes : TypeAlias = Tuple[Tensor, ...] +DiscriminatorOutputs : TypeAlias = List[List[Tensor]] -Embedding = Tensor -FaceLandmark203 = Tensor +Embedding : TypeAlias = Tensor +FaceLandmark203 : TypeAlias = Tensor -StateSet = OrderedDict[str, Any] -Padding = Tuple[int, int, int, int] +StateSet : TypeAlias = OrderedDict[str, Any] +Padding : TypeAlias = Tuple[int, int, int, int] -LossTensor = Tensor -VisionTensor = Tensor -VisionFrame = NDArray[Any] +VisionFrame : TypeAlias = NDArray[Any] +LossTensor : TypeAlias = Tensor +VisionTensor : TypeAlias = Tensor -GeneratorLossSet = Dict[str, Tensor] -DiscriminatorLossSet = Dict[str, Tensor] +Batch : TypeAlias = Tuple[VisionTensor, VisionTensor, Tensor] -Generator = torch.nn.Module -Embedder = torch.nn.Module +GeneratorLossSet : TypeAlias = Dict[str, Tensor] +DiscriminatorLossSet : TypeAlias = Dict[str, Tensor] + +Generator : TypeAlias = Module +Embedder : TypeAlias = Module From 3be8368eaab9dca8639045244b27767e6c5da325 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Feb 2025 21:26:57 +0100 Subject: [PATCH 055/394] Improve lot of types, imports and names --- face_swapper/src/models/loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 55d074c..35d5152 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -3,7 +3,7 @@ from typing import Tuple import torch from pytorch_msssim import ssim -from torch import Tensor +from torch import Tensor, nn from face_swapper.src.helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss from face_swapper.src.types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor From 3fe32d78327cb33dda7586c6f928e727c3958402 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 12:37:57 +0100 Subject: [PATCH 056/394] Fix example config --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 0643c9e..332b175 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -28,7 +28,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [preparing.dataset] dataset_path = .datasets/train -folder_pattern = {}/* +directory_pattern = {}/* image_pattern = {}/*.*g same_person_probability = 0.2 ``` From f05ff6cdb12804ed8cd5281ee965b1baa6c812d3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 12:52:59 +0100 Subject: [PATCH 057/394] Enforce IR version for older onnxruntime --- embedding_converter/README.md | 1 + embedding_converter/config.ini | 1 + embedding_converter/src/exporting.py | 8 +++++--- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/exporting.py | 4 +++- 6 files changed, 12 insertions(+), 4 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index e74162e..873b2e3 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -69,6 +69,7 @@ file_pattern = arcface_converter_simswap_{epoch:02d}_{val_loss:.4f} directory_path = .exports source_path = .outputs/last.ckpt target_path = .exports/arcface_converter_simswap.onnx +ir_version = 10 opset_version = 15 ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 4bda383..71166fd 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -29,6 +29,7 @@ file_pattern = directory_path = source_path = target_path = +ir_version = opset_version = [execution] diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index bb01e17..8cb0532 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -13,10 +13,12 @@ def export() -> None: directory_path = CONFIG.get('exporting', 'directory_path') source_path = CONFIG.get('exporting', 'source_path') target_path = CONFIG.get('exporting', 'target_path') + ir_version = CONFIG.getint('exporting', 'ir_version') opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - embedding_converter_trainer = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') - embedding_converter_trainer.eval() + model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + model.eval() + model.ir_version = ir_version input_tensor = torch.randn(1, 512) - torch.onnx.export(embedding_converter_trainer, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) + torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/face_swapper/README.md b/face_swapper/README.md index 332b175..da0618d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -92,6 +92,7 @@ validation_frequency = 1000 directory_path = .exports source_path = .outputs/last.ckpt target_path = .exports/face_swapper.onnx +ir_version = 10 opset_version = 15 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index faa6254..01e343f 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -48,6 +48,7 @@ validation_frequency = directory_path = source_path = target_path = +ir_version = opset_version = [inferencing] diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 311fdc8..d4c12ec 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -13,6 +13,7 @@ def export() -> None: directory_path = CONFIG.get('exporting', 'directory_path') source_path = CONFIG.get('exporting', 'source_path') target_path = CONFIG.get('exporting', 'target_path') + ir_version = CONFIG.getint('exporting', 'ir_version') opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) @@ -20,6 +21,7 @@ def export() -> None: model = AdaptiveEmbeddingIntegrationNetwork() model.load_state_dict(state_dict) model.eval() + model.ir_version = ir_version source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, 256, 256) - torch.onnx.export(model, (target_tensor, source_tensor), target_path, input_names = [ 'target', 'source' ], output_names = [ 'output' ], opset_version = opset_version) + torch.onnx.export(model, (source_tensor, target_tensor), target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = opset_version) From 62d897f9d8719f2c53c94d7bea55c4cf75743e7b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 18:59:30 +0100 Subject: [PATCH 058/394] Direct return layers --- embedding_converter/src/models/embedding_converter.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index 95a3581..9485779 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -12,14 +12,13 @@ class EmbeddingConverter(nn.Module): @staticmethod def create_layers() -> nn.ModuleList: - layers = nn.ModuleList( + return nn.ModuleList( [ nn.Linear(512, 1024), nn.Linear(1024, 2048), nn.Linear(2048, 1024), nn.Linear(1024, 512) ]) - return layers def forward(self, input_tensor : VisionTensor) -> VisionTensor: output_tensor = input_tensor / torch.norm(input_tensor) From 257ab668eec2ad8144d668141952dfdf1716a15e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 19:02:00 +0100 Subject: [PATCH 059/394] Name what it is --- embedding_converter/src/models/embedding_converter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index 9485779..d9c61c4 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -8,7 +8,7 @@ class EmbeddingConverter(nn.Module): def __init__(self) -> None: super(EmbeddingConverter, self).__init__() self.layers = self.create_layers() - self.activation = nn.LeakyReLU() + self.leaky_relu = nn.LeakyReLU() @staticmethod def create_layers() -> nn.ModuleList: @@ -24,7 +24,7 @@ class EmbeddingConverter(nn.Module): output_tensor = input_tensor / torch.norm(input_tensor) for layer in self.layers[:-1]: - output_tensor = self.activation(layer(output_tensor)) + output_tensor = self.leaky_relu(layer(output_tensor)) output_tensor = self.layers[-1](output_tensor) return output_tensor From 29e82f909a635525b07370a1bb71ebf0550345ad Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 21:29:56 +0100 Subject: [PATCH 060/394] Move nld to networks --- face_swapper/src/models/discriminator.py | 53 +++--------------------- face_swapper/src/networks/nld.py | 43 +++++++++++++++++++ face_swapper/src/training.py | 4 +- 3 files changed, 51 insertions(+), 49 deletions(-) create mode 100644 face_swapper/src/networks/nld.py diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 60eb8e1..ea7b814 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -1,19 +1,19 @@ import configparser from typing import List -import numpy from torch import nn +from face_swapper.src.networks.nld import NLD from face_swapper.src.types import VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class MultiscaleDiscriminator(nn.Module): +class Discriminator(nn.Module): def __init__(self) -> None: - super(MultiscaleDiscriminator, self).__init__() - self.downsample = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) + super(Discriminator, self).__init__() + self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() @staticmethod @@ -26,7 +26,7 @@ class MultiscaleDiscriminator(nn.Module): discriminators = nn.ModuleList() for _ in range(num_discriminators): - discriminator = NLayerDiscriminator(input_channels, num_filters, num_layers, kernel_size).discriminator + discriminator = NLD(input_channels, num_filters, num_layers, kernel_size).nld discriminators.append(discriminator) return discriminators @@ -37,47 +37,6 @@ class MultiscaleDiscriminator(nn.Module): for discriminator in self.discriminators: output_tensors.append([ discriminator(temp_tensor) ]) - temp_tensor = self.downsample(temp_tensor) + temp_tensor = self.avg_pool(temp_tensor) return output_tensors - - -class NLayerDiscriminator(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: - super(NLayerDiscriminator, self).__init__() - layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) - self.discriminator = nn.Sequential(*layers) - - @staticmethod - def create_layers(input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> List[nn.Module]: - padding = int(numpy.ceil((kernel_size - 1) / 2)) - current_filters = num_filters - layers =\ - [ - nn.Conv2d(input_channels, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), - nn.LeakyReLU(0.2, True) - ] - - for _ in range(1, num_layers): - previous_filters = current_filters - current_filters = min(current_filters * 2, 512) - layers +=\ - [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), - nn.InstanceNorm2d(current_filters), - nn.LeakyReLU(0.2, True) - ] - - previous_filters = current_filters - current_filters = min(current_filters * 2, 512) - layers +=\ - [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding), - nn.InstanceNorm2d(current_filters), - nn.LeakyReLU(0.2, True), - nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding) - ] - return layers - - def forward(self, input_tensor : VisionTensor) -> VisionTensor: - return self.discriminator(input_tensor) diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py new file mode 100644 index 0000000..73fc5a3 --- /dev/null +++ b/face_swapper/src/networks/nld.py @@ -0,0 +1,43 @@ +import math + +from torch import Tensor, nn + + +class NLD(nn.Module): + def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: + super(NLD, self).__init__() + self.nld = self.create_nld(input_channels, num_filters, num_layers, kernel_size) + + @staticmethod + def create_nld(input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> nn.Sequential: + padding = math.ceil((kernel_size - 1) / 2) + current_filters = num_filters + layers =\ + [ + nn.Conv2d(input_channels, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), + nn.LeakyReLU(0.2, True) + ] + + for _ in range(1, num_layers): + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + layers +=\ + [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), + nn.InstanceNorm2d(current_filters), + nn.LeakyReLU(0.2, True) + ] + + previous_filters = current_filters + current_filters = min(current_filters * 2, 512) + layers +=\ + [ + nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding), + nn.InstanceNorm2d(current_filters), + nn.LeakyReLU(0.2, True), + nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding) + ] + return nn.Sequential(*layers) + + def forward(self, input_tensor : Tensor) -> Tensor: + return self.nld(input_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 6233e9a..a68dc7b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -13,7 +13,7 @@ from torch.utils.data import DataLoader from .data_loader import DataLoaderVGG from .helper import calc_id_embedding -from .models.discriminator import MultiscaleDiscriminator +from .models.discriminator import Discriminator from .models.generator import AdaptiveEmbeddingIntegrationNetwork from .models.loss import FaceSwapperLoss from .types import Batch, Embedding, TargetAttributes, VisionTensor @@ -26,7 +26,7 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() self.generator = AdaptiveEmbeddingIntegrationNetwork() - self.discriminator = MultiscaleDiscriminator() + self.discriminator = Discriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: From 1872f9958449810567962dbe317c56330a3d89fb Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 19:11:20 +0100 Subject: [PATCH 061/394] Refacto UNet --- face_swapper/src/models/generator.py | 8 +-- face_swapper/src/networks/encoder.py | 67 ---------------------- face_swapper/src/networks/unet.py | 85 ++++++++++++++++++++++++++++ 3 files changed, 89 insertions(+), 71 deletions(-) delete mode 100644 face_swapper/src/networks/encoder.py create mode 100644 face_swapper/src/networks/unet.py diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 3fc26a4..5a378d0 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -4,7 +4,7 @@ from typing import Tuple from torch import nn from face_swapper.src.networks.attribute_modulator import AADGenerator -from face_swapper.src.networks.encoder import UNet +from face_swapper.src.networks.unet import UNet from face_swapper.src.types import Embedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() @@ -17,9 +17,9 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - self.encoder = UNet() + self.unet = UNet() self.generator = AADGenerator(id_channels, num_blocks) - self.encoder.apply(init_weight) + self.unet.apply(init_weight) self.generator.apply(init_weight) def forward(self, target : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: @@ -28,7 +28,7 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): return swap_tensor, target_attributes def get_attributes(self, target : VisionTensor) -> TargetAttributes: - return self.encoder(target) + return self.unet(target) def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/networks/encoder.py b/face_swapper/src/networks/encoder.py deleted file mode 100644 index d4e270e..0000000 --- a/face_swapper/src/networks/encoder.py +++ /dev/null @@ -1,67 +0,0 @@ -import torch -from torch import Tensor, nn - -from face_swapper.src.types import TargetAttributes, VisionTensor - - -class Upsample(nn.Module): - def __init__(self, input_channels : int, output_channels : int) -> None: - super(Upsample, self).__init__() - self.deconv = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - - def forward(self, input_tensor : Tensor, skip_tensor : Tensor) -> Tensor: - temp_tensor = self.deconv(input_tensor) - temp_tensor = self.batch_norm(temp_tensor) - temp_tensor = self.leaky_relu(temp_tensor) - return torch.cat((temp_tensor, skip_tensor), dim = 1) - - -class DownSample(nn.Module): - def __init__(self, input_channels : int, output_channels : int) -> None: - super(DownSample, self).__init__() - self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) - - def forward(self, input_tensor : Tensor) -> Tensor: - temp_tensor = self.conv(input_tensor) - temp_tensor = self.batch_norm(temp_tensor) - temp_tensor = self.leaky_relu(temp_tensor) - return temp_tensor - - -class UNet(nn.Module): - def __init__(self) -> None: - super(UNet, self).__init__() - self.downsampler_1 = DownSample(3, 32) - self.downsampler_2 = DownSample(32, 64) - self.downsampler_3 = DownSample(64, 128) - self.downsampler_4 = DownSample(128, 256) - self.downsampler_5 = DownSample(256, 512) - self.downsampler_6 = DownSample(512, 1024) - self.bottleneck = DownSample(1024, 1024) - self.upsampler_1 = Upsample(1024, 1024) - self.upsampler_2 = Upsample(2048, 512) - self.upsampler_3 = Upsample(1024, 256) - self.upsampler_4 = Upsample(512, 128) - self.upsampler_5 = Upsample(256, 64) - self.upsampler_6 = Upsample(128, 32) - - def forward(self, target : VisionTensor) -> TargetAttributes: - downsample_feature_1 = self.downsampler_1(target) - downsample_feature_2 = self.downsampler_2(downsample_feature_1) - downsample_feature_3 = self.downsampler_3(downsample_feature_2) - downsample_feature_4 = self.downsampler_4(downsample_feature_3) - downsample_feature_5 = self.downsampler_5(downsample_feature_4) - downsample_feature_6 = self.downsampler_6(downsample_feature_5) - bottleneck_output = self.bottleneck(downsample_feature_6) - upsample_feature_1 = self.upsampler_1(bottleneck_output, downsample_feature_6) - upsample_feature_2 = self.upsampler_2(upsample_feature_1, downsample_feature_5) - upsample_feature_3 = self.upsampler_3(upsample_feature_2, downsample_feature_4) - upsample_feature_4 = self.upsampler_4(upsample_feature_3, downsample_feature_3) - upsample_feature_5 = self.upsampler_5(upsample_feature_4, downsample_feature_2) - upsample_feature_6 = self.upsampler_6(upsample_feature_5, downsample_feature_1) - output = nn.functional.interpolate(upsample_feature_6, scale_factor = 2, mode = 'bilinear', align_corners = False) - return bottleneck_output, upsample_feature_1, upsample_feature_2, upsample_feature_3, upsample_feature_4, upsample_feature_5, upsample_feature_6, output diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py new file mode 100644 index 0000000..6454455 --- /dev/null +++ b/face_swapper/src/networks/unet.py @@ -0,0 +1,85 @@ +import torch +from torch import Tensor, nn + +from face_swapper.src.types import TargetAttributes + + +class UNet(nn.Module): + def __init__(self) -> None: + super(UNet, self).__init__() + self.down = self.create_down() + self.up = self.create_up() + + @staticmethod + def create_down(): + return nn.ModuleList( + [ + Down(3, 32), + Down(32, 64), + Down(64, 128), + Down(128, 256), + Down(256, 512), + Down(512, 1024), + Down(1024, 1024) + ]) + + @staticmethod + def create_up(): + return nn.ModuleList( + [ + Up(1024, 1024), + Up(2048, 512), + Up(1024, 256), + Up(512, 128), + Up(256, 64), + Up(128, 32) + ]) + + def forward(self, target_tensor : Tensor) -> TargetAttributes: + down_features = [] + up_features = [] + temp_tensor = target_tensor + + for down in self.down: + temp_tensor = down(temp_tensor) + down_features.append(temp_tensor) + + bottleneck_tensor = down_features[-1] + temp_tensor = bottleneck_tensor + + for index, up in enumerate(self.up): + down_index = -(index + 2) + up_feature = up(temp_tensor, down_features[down_index]) + up_features.append(up_feature) + + output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) + return bottleneck_tensor, *up_features, output_tensor + + +class Up(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super(Up, self).__init__() + self.conv_transpose = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, input_tensor : Tensor, skip_tensor : Tensor) -> Tensor: + temp_tensor = self.conv_transpose(input_tensor) + temp_tensor = self.batch_norm(temp_tensor) + temp_tensor = self.leaky_relu(temp_tensor) + temp_tensor = torch.cat((temp_tensor, skip_tensor), dim = 1) + return temp_tensor + + +class Down(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super(Down, self).__init__() + self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) + self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + + def forward(self, input_tensor : Tensor) -> Tensor: + temp_tensor = self.conv(input_tensor) + temp_tensor = self.batch_norm(temp_tensor) + temp_tensor = self.leaky_relu(temp_tensor) + return temp_tensor From 7264884ff94041887c00033b94cb2f2114c0cf46 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 13 Feb 2025 19:13:49 +0100 Subject: [PATCH 062/394] Fix CI --- face_swapper/src/networks/unet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 6454455..ebf88ba 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -11,7 +11,7 @@ class UNet(nn.Module): self.up = self.create_up() @staticmethod - def create_down(): + def create_down() -> nn.ModuleList: return nn.ModuleList( [ Down(3, 32), @@ -24,7 +24,7 @@ class UNet(nn.Module): ]) @staticmethod - def create_up(): + def create_up() -> nn.ModuleList: return nn.ModuleList( [ Up(1024, 1024), From 58ad6af61984198b861a85c1a93f47a64e5c8bd9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 09:52:51 +0100 Subject: [PATCH 063/394] Add Sample suffix again --- face_swapper/src/networks/unet.py | 50 +++++++++++++++---------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index ebf88ba..b68001c 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -7,32 +7,32 @@ from face_swapper.src.types import TargetAttributes class UNet(nn.Module): def __init__(self) -> None: super(UNet, self).__init__() - self.down = self.create_down() - self.up = self.create_up() + self.down_samples = self.create_down_samples() + self.up_samples = self.create_up_sample() @staticmethod - def create_down() -> nn.ModuleList: + def create_down_samples() -> nn.ModuleList: return nn.ModuleList( [ - Down(3, 32), - Down(32, 64), - Down(64, 128), - Down(128, 256), - Down(256, 512), - Down(512, 1024), - Down(1024, 1024) + DownSample(3, 32), + DownSample(32, 64), + DownSample(64, 128), + DownSample(128, 256), + DownSample(256, 512), + DownSample(512, 1024), + DownSample(1024, 1024) ]) @staticmethod - def create_up() -> nn.ModuleList: + def create_up_sample() -> nn.ModuleList: return nn.ModuleList( [ - Up(1024, 1024), - Up(2048, 512), - Up(1024, 256), - Up(512, 128), - Up(256, 64), - Up(128, 32) + UpSample(1024, 1024), + UpSample(2048, 512), + UpSample(1024, 256), + UpSample(512, 128), + UpSample(256, 64), + UpSample(128, 32) ]) def forward(self, target_tensor : Tensor) -> TargetAttributes: @@ -40,25 +40,25 @@ class UNet(nn.Module): up_features = [] temp_tensor = target_tensor - for down in self.down: - temp_tensor = down(temp_tensor) + for down_sample in self.down_samples: + temp_tensor = down_sample(temp_tensor) down_features.append(temp_tensor) bottleneck_tensor = down_features[-1] temp_tensor = bottleneck_tensor - for index, up in enumerate(self.up): + for index, up_sample in enumerate(self.up_samples): down_index = -(index + 2) - up_feature = up(temp_tensor, down_features[down_index]) + up_feature = up_sample(temp_tensor, down_features[down_index]) up_features.append(up_feature) output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) return bottleneck_tensor, *up_features, output_tensor -class Up(nn.Module): +class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: - super(Up, self).__init__() + super(UpSample, self).__init__() self.conv_transpose = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) @@ -71,9 +71,9 @@ class Up(nn.Module): return temp_tensor -class Down(nn.Module): +class DownSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: - super(Down, self).__init__() + super(DownSample, self).__init__() self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) From 953525e6b0865604fc5f0daa4f4d12c28339fdb8 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 10:21:33 +0100 Subject: [PATCH 064/394] Fix typo --- face_swapper/src/networks/unet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index b68001c..5d6060c 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -8,7 +8,7 @@ class UNet(nn.Module): def __init__(self) -> None: super(UNet, self).__init__() self.down_samples = self.create_down_samples() - self.up_samples = self.create_up_sample() + self.up_samples = self.create_up_samples() @staticmethod def create_down_samples() -> nn.ModuleList: @@ -24,7 +24,7 @@ class UNet(nn.Module): ]) @staticmethod - def create_up_sample() -> nn.ModuleList: + def create_up_samples() -> nn.ModuleList: return nn.ModuleList( [ UpSample(1024, 1024), From b69f69d015dac07c6adcc2950d165e1e5cfdc0be Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 16:10:39 +0100 Subject: [PATCH 065/394] Fix UNet --- face_swapper/src/networks/unet.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 5d6060c..e5d0976 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -1,8 +1,8 @@ +from typing import Tuple + import torch from torch import Tensor, nn -from face_swapper.src.types import TargetAttributes - class UNet(nn.Module): def __init__(self) -> None: @@ -35,7 +35,7 @@ class UNet(nn.Module): UpSample(128, 32) ]) - def forward(self, target_tensor : Tensor) -> TargetAttributes: + def forward(self, target_tensor : Tensor) -> Tuple[Tensor, ...]: down_features = [] up_features = [] temp_tensor = target_tensor @@ -49,8 +49,8 @@ class UNet(nn.Module): for index, up_sample in enumerate(self.up_samples): down_index = -(index + 2) - up_feature = up_sample(temp_tensor, down_features[down_index]) - up_features.append(up_feature) + temp_tensor = up_sample(temp_tensor, down_features[down_index]) + up_features.append(temp_tensor) output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) return bottleneck_tensor, *up_features, output_tensor From a971506271d4f8aea271a87fa04e9a5f77f8a584 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 16:12:15 +0100 Subject: [PATCH 066/394] Revert changes in generator --- face_swapper/src/models/generator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 5a378d0..e9b25e0 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -17,9 +17,9 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - self.unet = UNet() + self.encoder = UNet() self.generator = AADGenerator(id_channels, num_blocks) - self.unet.apply(init_weight) + self.encoder.apply(init_weight) self.generator.apply(init_weight) def forward(self, target : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: @@ -28,7 +28,7 @@ class AdaptiveEmbeddingIntegrationNetwork(nn.Module): return swap_tensor, target_attributes def get_attributes(self, target : VisionTensor) -> TargetAttributes: - return self.unet(target) + return self.encoder(target) def init_weight(module : nn.Module) -> None: From 650551c19bf554468cf6ab1efa424778f2f8a906 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 16:19:14 +0100 Subject: [PATCH 067/394] Simplify Batch type --- embedding_converter/src/types.py | 5 ++--- face_swapper/src/types.py | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 9221412..3a71965 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -3,11 +3,10 @@ from typing import Any, Tuple, TypeAlias from numpy.typing import NDArray from torch import Tensor +Batch : TypeAlias = Tuple[Tensor, Tensor] + Embedding : TypeAlias = NDArray[Any] EmbeddingDataset : TypeAlias = NDArray[Embedding] FaceLandmark5 : TypeAlias = NDArray[Any] VisionFrame : TypeAlias = NDArray[Any] -VisionTensor : TypeAlias = Tensor - -Batch : TypeAlias = Tuple[VisionTensor, VisionTensor] diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index b5551d4..3a7bbed 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -5,6 +5,8 @@ from numpy.typing import NDArray from torch import Tensor from torch.nn import Module +Batch : TypeAlias = Tuple[Tensor, Tensor, Tensor] + ImagePathList : TypeAlias = List[str] ImagePathSet : TypeAlias = Dict[str, ImagePathList] @@ -22,8 +24,6 @@ VisionFrame : TypeAlias = NDArray[Any] LossTensor : TypeAlias = Tensor VisionTensor : TypeAlias = Tensor -Batch : TypeAlias = Tuple[VisionTensor, VisionTensor, Tensor] - GeneratorLossSet : TypeAlias = Dict[str, Tensor] DiscriminatorLossSet : TypeAlias = Dict[str, Tensor] From 88c4e53192ad3995ed504def4c9d199b6f30611b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 16:38:25 +0100 Subject: [PATCH 068/394] Use skip_tensor variable --- face_swapper/src/networks/unet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index e5d0976..8a97df3 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -48,8 +48,8 @@ class UNet(nn.Module): temp_tensor = bottleneck_tensor for index, up_sample in enumerate(self.up_samples): - down_index = -(index + 2) - temp_tensor = up_sample(temp_tensor, down_features[down_index]) + skip_tensor = down_features[-(index + 2)] + temp_tensor = up_sample(temp_tensor, skip_tensor) up_features.append(temp_tensor) output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) From e1e0c11bb53abe43ecdfbfb9f6bbdfb81dde9968 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 16:40:39 +0100 Subject: [PATCH 069/394] Fix CI --- embedding_converter/src/models/embedding_converter.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index d9c61c4..de483f3 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -1,7 +1,5 @@ import torch -from torch import nn - -from ..types import VisionTensor +from torch import Tensor, nn class EmbeddingConverter(nn.Module): @@ -20,7 +18,7 @@ class EmbeddingConverter(nn.Module): nn.Linear(1024, 512) ]) - def forward(self, input_tensor : VisionTensor) -> VisionTensor: + def forward(self, input_tensor : Tensor) -> Tensor: output_tensor = input_tensor / torch.norm(input_tensor) for layer in self.layers[:-1]: From 0e148845af59ea64022d66075362d4b09ce2cb92 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Feb 2025 19:52:19 +0100 Subject: [PATCH 070/394] Extend gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 52d5fa1..0fb04d8 100644 --- a/.gitignore +++ b/.gitignore @@ -4,5 +4,6 @@ __pycache__ .idea .inputs .exports +.models .outputs .vscode From 030d912c1b5fd8ec52b8f2cdff0f816e4b71124d Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sat, 15 Feb 2025 19:04:43 +0530 Subject: [PATCH 071/394] some fixes --- face_swapper/config.ini | 1 + face_swapper/src/models/discriminator.py | 4 +-- face_swapper/src/models/generator.py | 6 ++-- face_swapper/src/models/loss.py | 28 ++++++++++--------- .../src/networks/attribute_modulator.py | 2 +- face_swapper/src/training.py | 8 ++++-- 6 files changed, 28 insertions(+), 21 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 01e343f..92ee043 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -30,6 +30,7 @@ weight_identity = weight_attribute = weight_reconstruction = weight_pose = +weight_gaze = [training.trainer] learning_rate = diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index ea7b814..c8fc1c4 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -3,8 +3,8 @@ from typing import List from torch import nn -from face_swapper.src.networks.nld import NLD -from face_swapper.src.types import VisionTensor +from ..networks.nld import NLD +from ..types import VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index e9b25e0..33d9c80 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -3,9 +3,9 @@ from typing import Tuple from torch import nn -from face_swapper.src.networks.attribute_modulator import AADGenerator -from face_swapper.src.networks.unet import UNet -from face_swapper.src.types import Embedding, TargetAttributes, VisionTensor +from ..networks.attribute_modulator import AADGenerator +from ..networks.unet import UNet +from ..types import Embedding, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 35d5152..02196c7 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -5,8 +5,8 @@ import torch from pytorch_msssim import ssim from torch import Tensor, nn -from face_swapper.src.helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss -from face_swapper.src.types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor +from ..helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from ..types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -62,35 +62,37 @@ class FaceSwapperLoss: def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: discriminator_loss_set = {} - loss_fake = torch.Tensor(0) + loss_fakes = [] for fake_discriminator_output in fake_discriminator_outputs: - loss_fake += hinge_fake_loss(fake_discriminator_output[0]).mean() + loss_fakes.append(hinge_fake_loss(fake_discriminator_output[0])) - loss_true = torch.Tensor(0) + loss_trues = [] for true_discriminator_output in real_discriminator_outputs: - loss_true += hinge_real_loss(true_discriminator_output[0]).mean() + loss_trues.append(hinge_real_loss(true_discriminator_output[0])) - discriminator_loss_set['loss_discriminator'] = (loss_true.mean() + loss_fake.mean()) * 0.5 + loss_fake = torch.stack(loss_fakes).mean() + loss_true = torch.stack(loss_trues).mean() + discriminator_loss_set['loss_discriminator'] = (loss_true + loss_fake) * 0.5 return discriminator_loss_set def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: - loss_adversarial = torch.Tensor(0) + loss_adversarials = [] for discriminator_output in discriminator_outputs: - loss_adversarial += hinge_real_loss(discriminator_output[0]) + loss_adversarials.append(hinge_real_loss(discriminator_output[0]).mean()) - loss_adversarial = torch.mean(loss_adversarial) + loss_adversarial = torch.stack(loss_adversarials).mean() return loss_adversarial def calc_attribute_loss(self, target_attributes : TargetAttributes, swap_attributes : SwapAttributes) -> LossTensor: - loss_attribute = torch.Tensor(0) + loss_attributes = [] for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): - loss_attribute += torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean() + loss_attributes.append(torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean()) - loss_attribute *= 0.5 + loss_attribute = torch.stack(loss_attributes).mean() * 0.5 return loss_attribute def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index 1dae96c..4d3e973 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from face_swapper.src.types import Embedding, TargetAttributes +from ..types import Embedding, TargetAttributes class AADGenerator(nn.Module): diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a68dc7b..629048b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -25,6 +25,7 @@ CONFIG.read('config.ini') class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() + FaceSwapperLoss.__init__(self) self.generator = AdaptiveEmbeddingIntegrationNetwork() self.discriminator = Discriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') @@ -45,12 +46,12 @@ class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) swap_attributes = self.generator.get_attributes(swap_tensor) - real_discriminator_outputs = self.discriminator(source_tensor.detach()) + real_discriminator_outputs = self.discriminator(source_tensor) fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, swap_attributes, fake_discriminator_outputs, batch) generator_optimizer.zero_grad() - self.manual_backward(generator_losses.get('loss_generator')) + self.manual_backward(generator_losses.get('loss_generator'), retain_graph = True) generator_optimizer.step() discriminator_losses = self.calc_discriminator_loss(real_discriminator_outputs, fake_discriminator_outputs) @@ -114,6 +115,9 @@ def train() -> None: num_workers = CONFIG.getint('training.loader', 'num_workers') output_file_path = CONFIG.get('training.output', 'file_path') + if not os.path.isfile(output_file_path): + output_file_path = None + dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swap_model = FaceSwapperTrain() From 8f8dfecdbc51457348de6bceb8bbf0ab3f0d6b98 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sat, 15 Feb 2025 19:05:38 +0530 Subject: [PATCH 072/394] some fixes --- face_swapper/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/README.md b/face_swapper/README.md index da0618d..7d48f29 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -68,6 +68,7 @@ weight_identity = 20 weight_attribute = 10 weight_reconstruction = 10 weight_pose = 100 +weight_gaze = 10 ``` ``` From aa5094e5761a37b42d1fb98fde458540cc03167e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 15 Feb 2025 12:23:43 +0100 Subject: [PATCH 073/394] We need MS license --- face_swapper/LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/LICENSE.md b/face_swapper/LICENSE.md index cfdd95f..011a811 100644 --- a/face_swapper/LICENSE.md +++ b/face_swapper/LICENSE.md @@ -1,3 +1,3 @@ -ResearchRAIL-M license +ResearchRAIL-MS license Copyright (c) 2025 Henry Ruhs From 026bcf0c97c7f60272d706e96b33a33526d265da Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 15 Feb 2025 12:51:40 +0100 Subject: [PATCH 074/394] Adjust licenses --- embedding_converter/LICENSE.md | 2 +- embedding_converter/README.md | 2 +- face_swapper/README.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/embedding_converter/LICENSE.md b/embedding_converter/LICENSE.md index 9be381f..d683ca8 100644 --- a/embedding_converter/LICENSE.md +++ b/embedding_converter/LICENSE.md @@ -1,3 +1,3 @@ -MIT license +OpenhRAIL-MS license Copyright (c) 2025 Henry Ruhs diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 873b2e3..a840263 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -3,7 +3,7 @@ Embedding Converter > Convert face embeddings between various models. -![License](https://img.shields.io/badge/license-MIT-green) +![License](https://img.shields.io/badge/license-OpenhRAIL--MS-green) Preview diff --git a/face_swapper/README.md b/face_swapper/README.md index 7d48f29..23f6c30 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -3,7 +3,7 @@ Face Swapper > Face shape and feature aware identity transfer. -![License](https://img.shields.io/badge/license-ResearchRAIL--M-red) +![License](https://img.shields.io/badge/license-ResearchRAIL--MS-red) Preview From 0b7d25a36ed1c666136e5adf6b155347a47cdb65 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 15 Feb 2025 12:53:00 +0100 Subject: [PATCH 075/394] Adjust licenses --- embedding_converter/LICENSE.md | 2 +- embedding_converter/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/LICENSE.md b/embedding_converter/LICENSE.md index d683ca8..b31052b 100644 --- a/embedding_converter/LICENSE.md +++ b/embedding_converter/LICENSE.md @@ -1,3 +1,3 @@ -OpenhRAIL-MS license +OpenRAIL-MS license Copyright (c) 2025 Henry Ruhs diff --git a/embedding_converter/README.md b/embedding_converter/README.md index a840263..7194a03 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -3,7 +3,7 @@ Embedding Converter > Convert face embeddings between various models. -![License](https://img.shields.io/badge/license-OpenhRAIL--MS-green) +![License](https://img.shields.io/badge/license-OpenRAIL--MS-green) Preview From cd4b10c8329516a2bb442d94a550ebe4604e90b8 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 10:54:01 +0100 Subject: [PATCH 076/394] Fix imports and update dependencies --- requirements.txt | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index b04959e..722121a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,10 @@ -lightning==2.4.0 +--extra-index-url https://download.pytorch.org/whl/cu124 +lightning==2.5.0 numpy==1.26.4 onnx==1.17.0 -onnxruntime==1.20.0 -opencv-python==4.10.0.84 +onnxruntime==1.20.1 +opencv-python==4.11.0.86 mxnet==1.9.1 pytorch-msssim==1.0.0 +torch==2.6.0 +torchvision==0.21.0 From d9e10a9f7c0a0b7e56814565bf8f5ef6b762c443 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 11:56:01 +0100 Subject: [PATCH 077/394] Use lightning over pytorch_lightning import, Configure tensorboard --- .gitignore | 1 + embedding_converter/src/training.py | 12 +++++++----- face_swapper/README.md | 4 ++-- face_swapper/src/training.py | 14 +++++++++----- requirements.txt | 1 + 5 files changed, 20 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index 0fb04d8..454001b 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ __pycache__ .idea .inputs .exports +.logs .models .outputs .vscode diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 13b554b..5a198ba 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -1,12 +1,12 @@ import configparser from typing import Any, Tuple +import lightning import numpy -import pytorch_lightning import torch -from pytorch_lightning import Trainer -from pytorch_lightning.callbacks import ModelCheckpoint -from pytorch_lightning.tuner.tuning import Tuner +from lightning import Trainer +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split @@ -17,7 +17,7 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class EmbeddingConverterTrainer(pytorch_lightning.LightningModule): +class EmbeddingConverterTrainer(lightning.LightningModule): def __init__(self) -> None: super(EmbeddingConverterTrainer, self).__init__() self.embedding_converter = EmbeddingConverter() @@ -88,8 +88,10 @@ def create_trainer() -> Trainer: trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') output_directory_path = CONFIG.get('training.output', 'directory_path') output_file_pattern = CONFIG.get('training.output', 'file_pattern') + logger = TensorBoardLogger('.logs', name = 'embedding_converter') return Trainer( + logger = logger, max_epochs = trainer_max_epochs, callbacks = [ diff --git a/face_swapper/README.md b/face_swapper/README.md index 23f6c30..fc11333 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -35,8 +35,8 @@ same_person_probability = 0.2 ``` [training.loader] -batch_size = 24 -num_workers = 12 +batch_size = 8 +num_workers = 8 ``` ``` diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 629048b..2911f72 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -2,13 +2,15 @@ import configparser import os from typing import Tuple -import pytorch_lightning +import lightning import torch import torchvision -from pytorch_lightning import Trainer -from pytorch_lightning.callbacks import ModelCheckpoint -from pytorch_lightning.utilities.types import Optimizer +from lightning import Trainer +from lightning.pytorch.callbacks import ModelCheckpoint +from lightning.pytorch.loggers import TensorBoardLogger + from torch import Tensor +from torch.optim import Optimizer from torch.utils.data import DataLoader from .data_loader import DataLoaderVGG @@ -22,7 +24,7 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapperTrain(pytorch_lightning.LightningModule, FaceSwapperLoss): +class FaceSwapperTrain(lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() FaceSwapperLoss.__init__(self) @@ -86,9 +88,11 @@ def create_trainer() -> Trainer: output_directory_path = CONFIG.get('training.output', 'directory_path') output_file_pattern = CONFIG.get('training.output', 'file_pattern') trainer_precision = CONFIG.get('training.trainer', 'precision') + logger = TensorBoardLogger('.logs', name = 'face_swapper') os.makedirs(output_directory_path, exist_ok = True) return Trainer( + logger = logger, max_epochs = trainer_max_epochs, precision = trainer_precision, callbacks = diff --git a/requirements.txt b/requirements.txt index 722121a..3f6b720 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,3 +8,4 @@ mxnet==1.9.1 pytorch-msssim==1.0.0 torch==2.6.0 torchvision==0.21.0 +tensorboard==2.19.0 From 0949c1358b09c28b0194cb22fe3c21cfac16b2e5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 11:58:51 +0100 Subject: [PATCH 078/394] Fix CI --- embedding_converter/src/training.py | 1 + face_swapper/src/training.py | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 5a198ba..9c72530 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -7,6 +7,7 @@ import torch from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger +from lightning.pytorch.tuner import Tuner from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 2911f72..f62cd34 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -8,7 +8,6 @@ import torchvision from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger - from torch import Tensor from torch.optim import Optimizer from torch.utils.data import DataLoader From 777a8384c2d72b8ae830d6dbc809163f9356b899 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 12:45:37 +0100 Subject: [PATCH 079/394] Extend README with TensorBoard guide --- embedding_converter/README.md | 6 ++++++ face_swapper/README.md | 6 ++++++ face_swapper/infer.py | 3 +-- 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 7194a03..1666bc1 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -98,6 +98,12 @@ Train the Embedding Converter model. python train.py ``` +Launch the TensorBoard to monitor the training. + +``` +tensorboard --logdir=.logs +``` + Exporting --------- diff --git a/face_swapper/README.md b/face_swapper/README.md index fc11333..ae71b12 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -116,6 +116,12 @@ Train the Face Swapper model. python train.py ``` +Launch the TensorBoard to monitor the training. + +``` +tensorboard --logdir=.logs +``` + Exporting --------- diff --git a/face_swapper/infer.py b/face_swapper/infer.py index fc3eb29..dde2e9f 100644 --- a/face_swapper/infer.py +++ b/face_swapper/infer.py @@ -1,7 +1,6 @@ #!/usr/bin/env python3 -from face_swapper.src.inferencing import infer - +from src.inferencing import infer if __name__ == '__main__': infer() From 5bb41ecbb217468803544a4cf733c78eb525c46e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 17 Feb 2025 09:15:00 +0100 Subject: [PATCH 080/394] Adjust README --- face_swapper/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index ae71b12..5a64cce 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -27,7 +27,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [preparing.dataset] -dataset_path = .datasets/train +dataset_path = .datasets/vggface2 directory_pattern = {}/* image_pattern = {}/*.*g same_person_probability = 0.2 @@ -67,8 +67,8 @@ weight_adversarial = 1 weight_identity = 20 weight_attribute = 10 weight_reconstruction = 10 -weight_pose = 100 -weight_gaze = 10 +weight_pose = 0 +weight_gaze = 0 ``` ``` From 254f3efe68578e9ef68083df6f4548da9544b2c4 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 17 Feb 2025 00:08:40 +0100 Subject: [PATCH 081/394] Remove live portrait submodule --- .gitmodules | 0 face_swapper/LivePortrait | 1 - 2 files changed, 1 deletion(-) delete mode 100644 .gitmodules delete mode 160000 face_swapper/LivePortrait diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index e69de29..0000000 diff --git a/face_swapper/LivePortrait b/face_swapper/LivePortrait deleted file mode 160000 index e09d1c9..0000000 --- a/face_swapper/LivePortrait +++ /dev/null @@ -1 +0,0 @@ -Subproject commit e09d1c9f1c9519f9e3ccf4c185b3992993864f6d From 999f2c9cbec4537f36353f97efe7e7226202a938 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 17 Feb 2025 23:25:19 +0100 Subject: [PATCH 082/394] Rename variable of EmbeddingConverterTrainer --- embedding_converter/src/training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 9c72530..d95035f 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -113,7 +113,7 @@ def create_trainer() -> Trainer: def train() -> None: trainer = create_trainer() training_loader, validation_loader = create_loaders() - embedding_converter = EmbeddingConverterTrainer() + embedding_converter_trainer = EmbeddingConverterTrainer() tuner = Tuner(trainer) - tuner.lr_find(embedding_converter, training_loader, validation_loader) - trainer.fit(embedding_converter, training_loader, validation_loader) + tuner.lr_find(embedding_converter_trainer, training_loader, validation_loader) + trainer.fit(embedding_converter_trainer, training_loader, validation_loader) From 7b2b8f0f85c9faecd09f2942288ab6bb18132aaf Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 18 Feb 2025 10:59:43 +0530 Subject: [PATCH 083/394] fix discriminator training --- face_swapper/src/training.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f62cd34..6a62ef1 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -47,14 +47,16 @@ class FaceSwapperTrain(lightning.LightningModule, FaceSwapperLoss): source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) swap_attributes = self.generator.get_attributes(swap_tensor) - real_discriminator_outputs = self.discriminator(source_tensor) - fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) + fake_discriminator_outputs = self.discriminator(swap_tensor) generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, swap_attributes, fake_discriminator_outputs, batch) generator_optimizer.zero_grad() - self.manual_backward(generator_losses.get('loss_generator'), retain_graph = True) + self.manual_backward(generator_losses.get('loss_generator')) generator_optimizer.step() + real_discriminator_outputs = self.discriminator(source_tensor) + fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) + discriminator_losses = self.calc_discriminator_loss(real_discriminator_outputs, fake_discriminator_outputs) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_losses.get('loss_discriminator')) From 11bb9065ba6201bc198a9074f8fed6a10dde4675 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 17 Feb 2025 23:58:33 +0100 Subject: [PATCH 084/394] Uniform resume checkpoint approach --- embedding_converter/README.md | 3 ++- embedding_converter/config.ini | 1 + embedding_converter/src/training.py | 11 +++++++++-- face_swapper/README.md | 7 +++---- face_swapper/config.ini | 5 ++--- face_swapper/src/training.py | 19 ++++++++++--------- 6 files changed, 27 insertions(+), 19 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 1666bc1..e323b4a 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -61,7 +61,8 @@ max_epochs = 4096 ``` [training.output] directory_path = .outputs -file_pattern = arcface_converter_simswap_{epoch:02d}_{val_loss:.4f} +file_pattern = arcface_converter_simswap_{epoch}_{step} +resume_file_path = .outputs/last.ckpt ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 71166fd..bbcf4ca 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -24,6 +24,7 @@ max_epochs = [training.output] directory_path = file_pattern = +resume_file_path = [exporting] directory_path = diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index d95035f..a87848c 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -1,4 +1,5 @@ import configparser +import os from typing import Any, Tuple import lightning @@ -111,9 +112,15 @@ def create_trainer() -> Trainer: def train() -> None: - trainer = create_trainer() + resume_file_path = CONFIG.get('training.output', 'resume_file_path') + training_loader, validation_loader = create_loaders() embedding_converter_trainer = EmbeddingConverterTrainer() + trainer = create_trainer() tuner = Tuner(trainer) tuner.lr_find(embedding_converter_trainer, training_loader, validation_loader) - trainer.fit(embedding_converter_trainer, training_loader, validation_loader) + + if os.path.exists(resume_file_path): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = resume_file_path) + else: + trainer.fit(embedding_converter_trainer, training_loader, validation_loader) diff --git a/face_swapper/README.md b/face_swapper/README.md index 5a64cce..b331e2a 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -77,15 +77,14 @@ learning_rate = 0.0004 max_epochs = 50 precision = 16-mixed automatic_optimization = false +preview_frequency = 250 ``` ``` [training.output] directory_path = .outputs -file_path = .outputs/last.ckpt -file_pattern = 'checkpoint-{epoch}-{step}-{l_G:.4f}-{l_D:.4f}' -preview_frequency = 250 -validation_frequency = 1000 +file_pattern = 'face-swapper_{epoch}_{step}' +resume_file_path = .outputs/last.ckpt ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 92ee043..10f875f 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -37,13 +37,12 @@ learning_rate = max_epochs = precision = automatic_optimization = +preview_frequency = [training.output] directory_path = -file_path = file_pattern = -preview_frequency = -validation_frequency = +resume_file_path = [exporting] directory_path = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 6a62ef1..1b2852f 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -23,7 +23,7 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapperTrain(lightning.LightningModule, FaceSwapperLoss): +class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() FaceSwapperLoss.__init__(self) @@ -62,7 +62,7 @@ class FaceSwapperTrain(lightning.LightningModule, FaceSwapperLoss): self.manual_backward(discriminator_losses.get('loss_discriminator')) discriminator_optimizer.step() - if self.global_step % CONFIG.getint('training.output', 'preview_frequency') == 0: + if self.global_step % CONFIG.getint('training.trainer', 'preview_frequency') == 0: self.generate_preview(source_tensor, target_tensor, swap_tensor) self.log('loss_generator', generator_losses.get('loss_generator'), prog_bar = True) @@ -103,7 +103,7 @@ def create_trainer() -> Trainer: dirpath = output_directory_path, filename = output_file_pattern, every_n_train_steps = 1000, - save_top_k = 5, + save_top_k = 3, save_last = True ) ], @@ -118,13 +118,14 @@ def train() -> None: same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') - output_file_path = CONFIG.get('training.output', 'file_path') - - if not os.path.isfile(output_file_path): - output_file_path = None + resume_file_path = CONFIG.get('training.output', 'resume_file_path') dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - face_swap_model = FaceSwapperTrain() + face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() - trainer.fit(face_swap_model, data_loader, ckpt_path = output_file_path) + + if os.path.isfile(resume_file_path): + trainer.fit(face_swapper_trainer, data_loader, ckpt_path = resume_file_path) + else: + trainer.fit(face_swapper_trainer, data_loader) From f63bc788ac45f48e874e9179e24da9d92f92e8e1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 09:43:24 +0100 Subject: [PATCH 085/394] Fix typo --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index b331e2a..486ab6f 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -83,7 +83,7 @@ preview_frequency = 250 ``` [training.output] directory_path = .outputs -file_pattern = 'face-swapper_{epoch}_{step}' +file_pattern = face-swapper_{epoch}_{step} resume_file_path = .outputs/last.ckpt ``` From bf696be097d13ef3f63507b282bd201215f497bf Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 11:00:30 +0100 Subject: [PATCH 086/394] Improve generator namings, Flip args to source then target --- face_swapper/src/exporting.py | 4 ++-- face_swapper/src/inferencing.py | 4 ++-- face_swapper/src/models/generator.py | 28 ++++++++++++++-------------- face_swapper/src/training.py | 9 +++++---- face_swapper/src/types.py | 1 + 5 files changed, 24 insertions(+), 22 deletions(-) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index d4c12ec..3b0892d 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -3,7 +3,7 @@ from os import makedirs import torch -from .models.generator import AdaptiveEmbeddingIntegrationNetwork +from .models.generator import Generator CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -18,7 +18,7 @@ def export() -> None: makedirs(directory_path, exist_ok = True) state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') - model = AdaptiveEmbeddingIntegrationNetwork() + model = Generator() model.load_state_dict(state_dict) model.eval() model.ir_version = ir_version diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index d915992..939cc37 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -4,7 +4,7 @@ import cv2 import torch from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image -from .models.generator import AdaptiveEmbeddingIntegrationNetwork +from .models.generator import Generator from .types import Embedder, Generator, VisionFrame CONFIG = configparser.ConfigParser() @@ -28,7 +28,7 @@ def infer() -> None: output_path = CONFIG.get('inferencing', 'output_path') state_dict = torch.load(generator_path, map_location = 'cpu').get('state_dict').get('generator') - generator = AdaptiveEmbeddingIntegrationNetwork() + generator = Generator() generator.load_state_dict(state_dict) generator.eval() id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 33d9c80..6308f44 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,34 +1,34 @@ import configparser from typing import Tuple -from torch import nn +from torch import Tensor, nn from ..networks.attribute_modulator import AADGenerator from ..networks.unet import UNet -from ..types import Embedding, TargetAttributes, VisionTensor +from ..types import Embedding, Attributes CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class AdaptiveEmbeddingIntegrationNetwork(nn.Module): +class Generator(nn.Module): def __init__(self) -> None: - super(AdaptiveEmbeddingIntegrationNetwork, self).__init__() + super(Generator, self).__init__() id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - self.encoder = UNet() - self.generator = AADGenerator(id_channels, num_blocks) - self.encoder.apply(init_weight) - self.generator.apply(init_weight) + self.attribute_encoder = UNet() + self.attribute_generator = AADGenerator(id_channels, num_blocks) + self.attribute_encoder.apply(init_weight) + self.attribute_generator.apply(init_weight) - def forward(self, target : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: - target_attributes = self.get_attributes(target) - swap_tensor = self.generator(target_attributes, source_embedding) - return swap_tensor, target_attributes + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: + target_attributes = self.get_attributes(target_tensor) + output_tensor = self.attribute_generator(target_attributes, source_embedding) + return output_tensor - def get_attributes(self, target : VisionTensor) -> TargetAttributes: - return self.encoder(target) + def get_attributes(self, input_tensor : Tensor) -> Attributes: + return self.attribute_encoder(input_tensor) def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1b2852f..d7c14fd 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -15,7 +15,7 @@ from torch.utils.data import DataLoader from .data_loader import DataLoaderVGG from .helper import calc_id_embedding from .models.discriminator import Discriminator -from .models.generator import AdaptiveEmbeddingIntegrationNetwork +from .models.generator import Generator from .models.loss import FaceSwapperLoss from .types import Batch, Embedding, TargetAttributes, VisionTensor @@ -27,12 +27,12 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() FaceSwapperLoss.__init__(self) - self.generator = AdaptiveEmbeddingIntegrationNetwork() + self.generator = Generator() self.discriminator = Discriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: - output = self.generator(target_tensor, source_embedding) + output = self.generator(source_embedding, target_tensor) return output def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: @@ -45,7 +45,8 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): source_tensor, target_tensor, is_same_person = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - swap_tensor, target_attributes = self.generator(target_tensor, source_embedding) + swap_tensor = self.generator(source_embedding, target_tensor) + target_attributes = self.generator.get_attributes(target_tensor) swap_attributes = self.generator.get_attributes(swap_tensor) fake_discriminator_outputs = self.discriminator(swap_tensor) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 3a7bbed..3e78bdd 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -14,6 +14,7 @@ SwapAttributes : TypeAlias = Tuple[Tensor, ...] TargetAttributes : TypeAlias = Tuple[Tensor, ...] DiscriminatorOutputs : TypeAlias = List[List[Tensor]] +Attributes : TypeAlias = Tuple[Tensor, ...] Embedding : TypeAlias = Tensor FaceLandmark203 : TypeAlias = Tensor From e1ba81f220d2f7c8648ad19392230b2d2fe44901 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 11:01:33 +0100 Subject: [PATCH 087/394] Improve generator namings, Flip args to source then target --- face_swapper/src/models/generator.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 6308f44..60c3f6b 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,11 +1,10 @@ import configparser -from typing import Tuple from torch import Tensor, nn from ..networks.attribute_modulator import AADGenerator from ..networks.unet import UNet -from ..types import Embedding, Attributes +from ..types import Attributes, Embedding CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From 39818a16df492eb21c2d2ddec0e4039a4dfc056f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 11:05:58 +0100 Subject: [PATCH 088/394] Fix CI --- face_swapper/src/helper.py | 4 ++-- face_swapper/src/inferencing.py | 4 ++-- face_swapper/src/types.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 00faa66..6f14dae 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -5,7 +5,7 @@ import numpy import torch from torch import Tensor, nn -from .types import Embedder, Embedding, Padding, VisionFrame, VisionTensor +from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor def is_windows() -> bool: @@ -48,7 +48,7 @@ def hinge_fake_loss(input_tensor : Tensor) -> Tensor: return fake_loss -def calc_id_embedding(id_embedder : Embedder, vision_tensor : VisionTensor, padding : Padding) -> Embedding: +def calc_id_embedding(id_embedder : EmbedderModule, vision_tensor : VisionTensor, padding : Padding) -> Embedding: crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] crop_vision_tensor = nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') crop_vision_tensor[:, :, :padding[0], :] = 0 diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 939cc37..c6e7f5f 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -5,13 +5,13 @@ import torch from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image from .models.generator import Generator -from .types import Embedder, Generator, VisionFrame +from .types import EmbedderModule, GeneratorModule, VisionFrame CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def run_swap(generator : Generator, id_embedder : Embedder, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: +def run_swap(generator : GeneratorModule, id_embedder : EmbedderModule, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: source_vision_tensor = convert_to_vision_tensor(source_vision_frame) target_vision_tensor = convert_to_vision_tensor(target_vision_frame) source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 3e78bdd..bf2fbff 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -28,5 +28,5 @@ VisionTensor : TypeAlias = Tensor GeneratorLossSet : TypeAlias = Dict[str, Tensor] DiscriminatorLossSet : TypeAlias = Dict[str, Tensor] -Generator : TypeAlias = Module -Embedder : TypeAlias = Module +GeneratorModule : TypeAlias = Module +EmbedderModule : TypeAlias = Module From c161da2f25bf2479674074c9bc424cd68fa5159b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 19 Feb 2025 08:14:18 +0100 Subject: [PATCH 089/394] Adjust naming and typing --- face_swapper/src/models/generator.py | 12 ++++++------ face_swapper/src/training.py | 8 ++++---- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 60c3f6b..d6fa9b6 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -16,18 +16,18 @@ class Generator(nn.Module): id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - self.attribute_encoder = UNet() - self.attribute_generator = AADGenerator(id_channels, num_blocks) - self.attribute_encoder.apply(init_weight) - self.attribute_generator.apply(init_weight) + self.unet = UNet() + self.aad_generator = AADGenerator(id_channels, num_blocks) + self.unet.apply(init_weight) + self.aad_generator.apply(init_weight) def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: target_attributes = self.get_attributes(target_tensor) - output_tensor = self.attribute_generator(target_attributes, source_embedding) + output_tensor = self.aad_generator(target_attributes, source_embedding) return output_tensor def get_attributes(self, input_tensor : Tensor) -> Attributes: - return self.attribute_encoder(input_tensor) + return self.unet(input_tensor) def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index d7c14fd..388a987 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,7 @@ from .helper import calc_id_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import FaceSwapperLoss -from .types import Batch, Embedding, TargetAttributes, VisionTensor +from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -31,9 +31,9 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.discriminator = Discriminator() self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') - def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tuple[VisionTensor, TargetAttributes]: - output = self.generator(source_embedding, target_tensor) - return output + def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tensor: + output_tensor = self.generator(source_embedding, target_tensor) + return output_tensor def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') From 28977d37d633de3fe8bcdfbca2232638660a3359 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 14:14:23 +0100 Subject: [PATCH 090/394] Uniform validation, Add cosine_similarity validation to face swapper --- embedding_converter/src/training.py | 10 +++++----- face_swapper/src/training.py | 23 +++++++++++++++++------ 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index a87848c..3c248a9 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -38,9 +38,9 @@ class EmbeddingConverterTrainer(lightning.LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch output_tensor = self(source_tensor) - loss_validation = self.mse_loss(output_tensor, target_tensor) - self.log('loss_validation', loss_validation, prog_bar = True) - return loss_validation + validation = self.mse_loss(output_tensor, target_tensor) + self.log('validation', validation, prog_bar = True) + return validation def configure_optimizers(self) -> Any: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') @@ -94,6 +94,7 @@ def create_trainer() -> Trainer: return Trainer( logger = logger, + log_every_n_steps = 10, max_epochs = trainer_max_epochs, callbacks = [ @@ -106,8 +107,7 @@ def create_trainer() -> Trainer: save_last = True ) ], - enable_progress_bar = True, - log_every_n_steps = 2 + val_check_interval = 1000 ) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 388a987..89fa662 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -8,9 +8,9 @@ import torchvision from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger -from torch import Tensor +from torch import Tensor, nn from torch.optim import Optimizer -from torch.utils.data import DataLoader +from torch.utils.data import DataLoader, Subset from .data_loader import DataLoaderVGG from .helper import calc_id_embedding @@ -74,6 +74,15 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('loss_reconstruction', generator_losses.get('loss_reconstruction')) return generator_losses.get('loss_generator') + def validation_step(self, batch : Batch, batch_index : int) -> Tensor: + source_tensor, target_tensor, _ = batch + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + output_tensor, target_attributes = self.generator(target_tensor, source_embedding) + output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) + validation = nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + self.log('validation', validation) + return validation + def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, output_tensor : VisionTensor) -> None: preview_limit = 8 preview_items = [] @@ -95,6 +104,7 @@ def create_trainer() -> Trainer: os.makedirs(output_directory_path, exist_ok = True) return Trainer( logger = logger, + log_every_n_steps = 10, max_epochs = trainer_max_epochs, precision = trainer_precision, callbacks = @@ -108,7 +118,7 @@ def create_trainer() -> Trainer: save_last = True ) ], - log_every_n_steps = 10 + val_check_interval = 1000, ) @@ -122,11 +132,12 @@ def train() -> None: resume_file_path = CONFIG.get('training.output', 'resume_file_path') dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) - data_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + training_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = DataLoader(Subset(dataset, range(1000)), batch_size = batch_size, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() if os.path.isfile(resume_file_path): - trainer.fit(face_swapper_trainer, data_loader, ckpt_path = resume_file_path) + trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = resume_file_path) else: - trainer.fit(face_swapper_trainer, data_loader) + trainer.fit(face_swapper_trainer, training_loader, validation_loader) From ccf6fa7f434ef03a4121922bd012ec5265389878 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 14:16:47 +0100 Subject: [PATCH 091/394] Uniform validation, Add cosine_similarity validation to face swapper --- embedding_converter/src/training.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 3c248a9..b77e3a5 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -106,8 +106,7 @@ def create_trainer() -> Trainer: save_top_k = 3, save_last = True ) - ], - val_check_interval = 1000 + ] ) From 0d45568bd169af2ae0ad1a22462d5720e7ab5f81 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 18 Feb 2025 14:17:52 +0100 Subject: [PATCH 092/394] Uniform validation, Add cosine_similarity validation to face swapper --- embedding_converter/src/training.py | 3 ++- face_swapper/src/training.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index b77e3a5..ad69e97 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -106,7 +106,8 @@ def create_trainer() -> Trainer: save_top_k = 3, save_last = True ) - ] + ], + val_check_interval = 10 ) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 89fa662..1ca38ba 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -118,7 +118,7 @@ def create_trainer() -> Trainer: save_last = True ) ], - val_check_interval = 1000, + val_check_interval = 1000 ) From d25f2865a9c793c096469e090db86eb8fcda0dc9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 19 Feb 2025 08:40:50 +0100 Subject: [PATCH 093/394] Normalize validation output --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1ca38ba..4559bf6 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -79,7 +79,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) output_tensor, target_attributes = self.generator(target_tensor, source_embedding) output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) - validation = nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation', validation) return validation From 857365770f052d4a6aaae08a0c5081cc8f94a331 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 19 Feb 2025 09:14:57 +0100 Subject: [PATCH 094/394] Make validation step more solid, failed on empty checksums --- face_swapper/src/training.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4559bf6..63e439c 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -76,10 +76,15 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor, _ = batch - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - output_tensor, target_attributes = self.generator(target_tensor, source_embedding) - output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) - validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 + + if torch.isnan(source_tensor).any() and torch.isnan(target_tensor).any(): + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + output_tensor, target_attributes = self.generator(target_tensor, source_embedding) + output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) + validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 + else: + validation = torch.tensor(0.0) + self.log('validation', validation) return validation From dcf19634d14287a30c7c46cd1fe80f0e52642b7b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 19 Feb 2025 09:39:06 +0100 Subject: [PATCH 095/394] Brain fart --- face_swapper/src/training.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 63e439c..16c9e86 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -76,15 +76,10 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor, _ = batch - - if torch.isnan(source_tensor).any() and torch.isnan(target_tensor).any(): - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - output_tensor, target_attributes = self.generator(target_tensor, source_embedding) - output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) - validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 - else: - validation = torch.tensor(0.0) - + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + output_tensor = self.generator(source_embedding, target_tensor) + output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) + validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation', validation) return validation From b47c6b72eefcc519eb628752cbdeb8455df68c26 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 20 Feb 2025 18:09:37 +0530 Subject: [PATCH 096/394] changes --- embedding_converter/README.md | 41 +++---------- embedding_converter/config.ini | 19 ++----- embedding_converter/prepare.py | 6 -- embedding_converter/src/data_loader.py | 41 +++++++++++++ embedding_converter/src/exporting.py | 4 +- embedding_converter/src/helper.py | 17 ++++++ embedding_converter/src/preparing.py | 79 -------------------------- embedding_converter/src/training.py | 62 +++++++++++--------- embedding_converter/src/types.py | 11 ++-- requirements.txt | 2 - 10 files changed, 112 insertions(+), 170 deletions(-) delete mode 100644 embedding_converter/prepare.py create mode 100644 embedding_converter/src/data_loader.py create mode 100644 embedding_converter/src/helper.py delete mode 100644 embedding_converter/src/preparing.py diff --git a/embedding_converter/README.md b/embedding_converter/README.md index e323b4a..c088c39 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -27,31 +27,23 @@ This `config.ini` utilizes the MegaFace dataset to train the Embedding Converter ``` [preparing.dataset] -dataset_path = .datasets/megaface/train.rec -crop_size = 112 -process_limit = 650000 -``` - -``` -[preparing.model] -source_path = .models/arcface_w600k_r50.onnx -target_path = .models/arcface_simswap.onnx -``` - -``` -[preparing.input] -directory_path = .inputs -source_path = .inputs/arcface_w600k_r50.npy -target_path = .inputs/arcface_simswap.npy +dataset_path = .datasets/images +image_pattern = {}/*.*g ``` ``` [training.loader] split_ratio = 0.8 -batch_size = 51200 +batch_size = 256 num_workers = 8 ``` +``` +[training.model] +source_path = .models/arcface_w600k_r50.pt +target_path = .models/arcface_simswap.pt +``` + ``` [training.trainer] learning_rate = 0.001 @@ -74,21 +66,6 @@ ir_version = 10 opset_version = 15 ``` -``` -[execution] -providers = CUDAExecutionProvider -``` - - -Preparing ---------- - -Prepare the embedding dataset. - -``` -python prepare.py -``` - Training -------- diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index bbcf4ca..181d835 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -1,22 +1,16 @@ [preparing.dataset] dataset_path = -crop_size = -process_limit = - -[preparing.model] -source_path = -target_path = - -[preparing.input] -directory_path = -source_path = -target_path = +image_pattern = [training.loader] split_ratio = batch_size = num_workers = +[training.model] +source_path = +target_path = + [training.trainer] learning_rate = max_epochs = @@ -32,6 +26,3 @@ source_path = target_path = ir_version = opset_version = - -[execution] -providers = diff --git a/embedding_converter/prepare.py b/embedding_converter/prepare.py deleted file mode 100644 index 4cf7306..0000000 --- a/embedding_converter/prepare.py +++ /dev/null @@ -1,6 +0,0 @@ -#!/usr/bin/env python3 - -from src.preparing import prepare - -if __name__ == '__main__': - prepare() diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py new file mode 100644 index 0000000..92723b5 --- /dev/null +++ b/embedding_converter/src/data_loader.py @@ -0,0 +1,41 @@ +import glob +import random + +import torch +import torchvision.transforms as transforms +from torch.utils.data import Dataset + +from .helper import read_image +from .types import Batch, ImagePathList + + +class DataLoaderRecognition(Dataset[torch.Tensor]): + def __init__(self, dataset_path : str, dataset_image_pattern : str) -> None: + self.image_paths = self.prepare_image_paths(dataset_path, dataset_image_pattern) + self.dataset_total = len(self.image_paths) + self.transforms = self.compose_transforms() + + def __getitem__(self, index : int) -> Batch: + target_image_path = random.choice(self.image_paths) + target_vision_frame = read_image(target_image_path) + target_tensor = self.transforms(target_vision_frame) + return target_tensor + + def __len__(self) -> int: + return self.dataset_total + + def prepare_image_paths(self, dataset_path : str, dataset_image_pattern : str) -> ImagePathList: + image_paths = glob.glob(dataset_image_pattern.format(dataset_path)) + return image_paths + + def compose_transforms(self) -> transforms: + transform = transforms.Compose( + [ + transforms.ToPILImage(), + transforms.Resize((112, 112), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), + transforms.ToTensor(), + transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + return transform diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 8cb0532..4ede026 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -19,6 +19,6 @@ def export() -> None: makedirs(directory_path, exist_ok = True) model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') model.eval() - model.ir_version = ir_version - input_tensor = torch.randn(1, 512) + model.ir_version = torch.tensor(ir_version) + input_tensor = (torch.randn(1, 512), ) torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/embedding_converter/src/helper.py b/embedding_converter/src/helper.py new file mode 100644 index 0000000..50cf44a --- /dev/null +++ b/embedding_converter/src/helper.py @@ -0,0 +1,17 @@ +import platform + +import cv2 +import numpy + +from .types import VisionFrame + + +def is_windows() -> bool: + return platform.system().lower() == 'windows' + + +def read_image(image_path : str) -> VisionFrame: + if is_windows(): + image_buffer = numpy.fromfile(image_path, dtype = numpy.uint8) + return cv2.imdecode(image_buffer, cv2.IMREAD_COLOR) + return cv2.imread(image_path) diff --git a/embedding_converter/src/preparing.py b/embedding_converter/src/preparing.py deleted file mode 100644 index 141352d..0000000 --- a/embedding_converter/src/preparing.py +++ /dev/null @@ -1,79 +0,0 @@ -import configparser -from os import makedirs -from os.path import isfile -from typing import List - -import numpy -numpy.bool = numpy.bool_ -from mxnet.io import ImageRecordIter -from onnxruntime import InferenceSession -from tqdm import tqdm - -from .types import Embedding, EmbeddingDataset, VisionFrame - -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - - -def prepare_crop_vision_frame(crop_vision_frame : VisionFrame) -> VisionFrame: - crop_vision_frame = crop_vision_frame.astype(numpy.float32) / 255.0 - crop_vision_frame = (crop_vision_frame - 0.5) * 2 - return crop_vision_frame - - -def create_inference_session(model_path : str, execution_providers : List[str]) -> InferenceSession: - inference_session = InferenceSession(model_path, providers = execution_providers) - return inference_session - - -def forward(inference_session : InferenceSession, crop_vision_frame : VisionFrame) -> Embedding: - embedding = inference_session.run(None, - { - 'input': crop_vision_frame - })[0] - - return embedding - - -def create_embedding_dataset(dataset_reader : ImageRecordIter, source_inference_session : InferenceSession, target_inference_session : InferenceSession) -> EmbeddingDataset: - dataset_process_limit = CONFIG.getint('preparing.dataset', 'process_limit') - embedding_pairs = [] - - with tqdm(total = dataset_process_limit) as progress: - for batch in dataset_reader: - crop_vision_frame = batch.data[0].asnumpy() - crop_vision_frame = prepare_crop_vision_frame(crop_vision_frame) - source_embedding = forward(source_inference_session, crop_vision_frame) - target_embedding = forward(target_inference_session, crop_vision_frame) - embedding_pairs.append([ source_embedding, target_embedding ]) - progress.update() - - if progress.n == dataset_process_limit: - return numpy.concatenate(embedding_pairs, axis = 1).T - - return numpy.concatenate(embedding_pairs, axis = 1).T - - -def prepare() -> None: - dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') - dataset_crop_size = CONFIG.getint('preparing.dataset', 'crop_size') - model_source_path = CONFIG.get('preparing.model', 'source_path') - model_target_path = CONFIG.get('preparing.model', 'target_path') - input_directory_path = CONFIG.get('preparing.input', 'directory_path') - input_source_path = CONFIG.get('preparing.input', 'source_path') - input_target_path = CONFIG.get('preparing.input', 'target_path') - execution_providers = CONFIG.get('execution', 'providers').split(' ') - - makedirs(input_directory_path, exist_ok = True) - if isfile(dataset_path) and isfile(model_source_path) and isfile(model_target_path): - dataset_reader = ImageRecordIter( - path_imgrec = dataset_path, - data_shape = (3, dataset_crop_size, dataset_crop_size), - batch_size = 1, - shuffle = False - ) - source_inference_session = create_inference_session(model_source_path, execution_providers) - target_inference_session = create_inference_session(model_target_path, execution_providers) - embedding_dataset = create_embedding_dataset(dataset_reader, source_inference_session, target_inference_session) - numpy.save(input_source_path, embedding_dataset[..., 0].T) - numpy.save(input_target_path, embedding_dataset[..., 1].T) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index ad69e97..5e8697a 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -3,15 +3,15 @@ import os from typing import Any, Tuple import lightning -import numpy import torch from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from lightning.pytorch.tuner import Tuner from torch import Tensor, nn -from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split +from torch.utils.data import DataLoader, Dataset, random_split +from .data_loader import DataLoaderRecognition from .models.embedding_converter import EmbeddingConverter from .types import Batch, Embedding @@ -22,23 +22,34 @@ CONFIG.read('config.ini') class EmbeddingConverterTrainer(lightning.LightningModule): def __init__(self) -> None: super(EmbeddingConverterTrainer, self).__init__() + source_path = CONFIG.get('training.model', 'source_path') + target_path = CONFIG.get('training.model', 'target_path') + self.lr = CONFIG.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() + self.source_embedder = torch.jit.load(source_path) # type:ignore[no-untyped-call] + self.target_embedder = torch.jit.load(target_path) # type:ignore[no-untyped-call] + self.source_embedder.eval() + self.target_embedder.eval() self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) def training_step(self, batch : Batch, batch_index : int) -> Tensor: - source_tensor, target = batch - output_tensor = self(source_tensor) - loss_training = self.mse_loss(output_tensor, target) + with torch.no_grad(): + source_embedding = self.source_embedder(batch) + target_embedding = self.target_embedder(batch) + output_embedding = self(source_embedding) + loss_training = self.mse_loss(output_embedding, target_embedding) self.log('loss_training', loss_training, prog_bar = True) return loss_training def validation_step(self, batch : Batch, batch_index : int) -> Tensor: - source_tensor, target_tensor = batch - output_tensor = self(source_tensor) - validation = self.mse_loss(output_tensor, target_tensor) + with torch.no_grad(): + source_embedding = self.source_embedder(batch) + target_embedding = self.target_embedder(batch) + output_embedding = self(source_embedding) + validation = self.mse_loss(output_embedding, target_embedding) self.log('validation', validation, prog_bar = True) return validation @@ -53,36 +64,28 @@ class EmbeddingConverterTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': scheduler, - 'monitor': 'train_loss', + 'monitor': 'loss_training', 'interval': 'epoch', 'frequency': 1 } } -def create_loaders() -> Tuple[DataLoader, DataLoader]: - loader_batch_size = CONFIG.getint('training.loader', 'batch_size') - loader_num_workers = CONFIG.getint('training.loader', 'num_workers') +def create_loaders(dataset : Dataset[Any]) -> Tuple[DataLoader[Any], DataLoader[Any]]: + batch_size = CONFIG.getint('training.loader', 'batch_size') + num_workers = CONFIG.getint('training.loader', 'num_workers') - training_dataset, validate_dataset = split_dataset() - training_loader = DataLoader(training_dataset, batch_size = loader_batch_size, num_workers = loader_num_workers, shuffle = True, pin_memory = True) - validation_loader = DataLoader(validate_dataset, batch_size = loader_batch_size, num_workers = loader_num_workers, shuffle = False, pin_memory = True) + training_dataset, validate_dataset = split_dataset(dataset) + training_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, drop_last = False, pin_memory = True, persistent_workers = True) return training_loader, validation_loader -def split_dataset() -> Tuple[Dataset[Any], Dataset[Any]]: - input_source_path = CONFIG.get('preparing.input', 'source_path') - input_target_path = CONFIG.get('preparing.input', 'target_path') +def split_dataset(dataset : Dataset[Any]) -> Tuple[Dataset[Any], Dataset[Any]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') - - source_tensor = torch.from_numpy(numpy.load(input_source_path)).float() - target_tensor = torch.from_numpy(numpy.load(input_target_path)).float() - dataset = TensorDataset(source_tensor, target_tensor) - - dataset_size = len(dataset) - training_size = int(loader_split_ratio * len(dataset)) - validation_size = int(dataset_size - training_size) - training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) + training_size = int(loader_split_ratio * len(dataset)) # type:ignore[operator, arg-type] + validation_size = len(dataset) - training_size # type:ignore[arg-type] + training_dataset, validate_dataset = random_split(dataset, [training_size, validation_size]) return training_dataset, validate_dataset @@ -112,9 +115,12 @@ def create_trainer() -> Trainer: def train() -> None: + dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') + dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') resume_file_path = CONFIG.get('training.output', 'resume_file_path') - training_loader, validation_loader = create_loaders() + dataset = DataLoaderRecognition(dataset_path, dataset_image_pattern) + training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() tuner = Tuner(trainer) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 3a71965..78c6ec9 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,12 +1,9 @@ -from typing import Any, Tuple, TypeAlias +from typing import Any, List, TypeAlias from numpy.typing import NDArray from torch import Tensor -Batch : TypeAlias = Tuple[Tensor, Tensor] - -Embedding : TypeAlias = NDArray[Any] -EmbeddingDataset : TypeAlias = NDArray[Embedding] -FaceLandmark5 : TypeAlias = NDArray[Any] - +ImagePathList : TypeAlias = List[str] +Batch : TypeAlias = Tensor +Embedding : TypeAlias = Tensor VisionFrame : TypeAlias = NDArray[Any] diff --git a/requirements.txt b/requirements.txt index 3f6b720..601ba35 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,9 +2,7 @@ lightning==2.5.0 numpy==1.26.4 onnx==1.17.0 -onnxruntime==1.20.1 opencv-python==4.11.0.86 -mxnet==1.9.1 pytorch-msssim==1.0.0 torch==2.6.0 torchvision==0.21.0 From d1bf54276d767aeb766e3f532de58beb7a16bc7f Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 20 Feb 2025 21:14:54 +0530 Subject: [PATCH 097/394] changes --- embedding_converter/README.md | 5 ++--- embedding_converter/config.ini | 5 ++--- embedding_converter/src/data_loader.py | 11 +++++------ embedding_converter/src/helper.py | 10 ---------- embedding_converter/src/training.py | 5 ++--- embedding_converter/src/types.py | 2 +- requirements.txt | 1 + 7 files changed, 13 insertions(+), 26 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index c088c39..9c1dbc6 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -26,9 +26,8 @@ Setup This `config.ini` utilizes the MegaFace dataset to train the Embedding Converter for SimSwap. ``` -[preparing.dataset] -dataset_path = .datasets/images -image_pattern = {}/*.*g +[training.dataset] +dataset_file_pattern = .datasets/images/{}/*.*g ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 181d835..62504ef 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -1,6 +1,5 @@ -[preparing.dataset] -dataset_path = -image_pattern = +[training.dataset] +dataset_file_pattern = [training.loader] split_ratio = diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py index 92723b5..21f335e 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/data_loader.py @@ -6,12 +6,12 @@ import torchvision.transforms as transforms from torch.utils.data import Dataset from .helper import read_image -from .types import Batch, ImagePathList +from .types import Batch, Paths class DataLoaderRecognition(Dataset[torch.Tensor]): - def __init__(self, dataset_path : str, dataset_image_pattern : str) -> None: - self.image_paths = self.prepare_image_paths(dataset_path, dataset_image_pattern) + def __init__(self, dataset_file_pattern : str) -> None: + self.image_paths = self.prepare_image_paths(dataset_file_pattern) self.dataset_total = len(self.image_paths) self.transforms = self.compose_transforms() @@ -24,9 +24,8 @@ class DataLoaderRecognition(Dataset[torch.Tensor]): def __len__(self) -> int: return self.dataset_total - def prepare_image_paths(self, dataset_path : str, dataset_image_pattern : str) -> ImagePathList: - image_paths = glob.glob(dataset_image_pattern.format(dataset_path)) - return image_paths + def prepare_image_paths(self, dataset_file_pattern : str) -> Paths: + return glob.glob(dataset_file_pattern) def compose_transforms(self) -> transforms: transform = transforms.Compose( diff --git a/embedding_converter/src/helper.py b/embedding_converter/src/helper.py index 50cf44a..c6775f6 100644 --- a/embedding_converter/src/helper.py +++ b/embedding_converter/src/helper.py @@ -1,17 +1,7 @@ -import platform - import cv2 -import numpy from .types import VisionFrame -def is_windows() -> bool: - return platform.system().lower() == 'windows' - - def read_image(image_path : str) -> VisionFrame: - if is_windows(): - image_buffer = numpy.fromfile(image_path, dtype = numpy.uint8) - return cv2.imdecode(image_buffer, cv2.IMREAD_COLOR) return cv2.imread(image_path) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 5e8697a..2decc64 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -115,11 +115,10 @@ def create_trainer() -> Trainer: def train() -> None: - dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') - dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') + dataset_file_pattern = CONFIG.get('training.dataset', 'image_pattern') resume_file_path = CONFIG.get('training.output', 'resume_file_path') - dataset = DataLoaderRecognition(dataset_path, dataset_image_pattern) + dataset = DataLoaderRecognition(dataset_file_pattern) training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 78c6ec9..a40d4bb 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -3,7 +3,7 @@ from typing import Any, List, TypeAlias from numpy.typing import NDArray from torch import Tensor -ImagePathList : TypeAlias = List[str] +Paths : TypeAlias = List[str] Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor VisionFrame : TypeAlias = NDArray[Any] diff --git a/requirements.txt b/requirements.txt index 601ba35..36c6ea4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,6 +2,7 @@ lightning==2.5.0 numpy==1.26.4 onnx==1.17.0 +onnxruntime==1.20.1 opencv-python==4.11.0.86 pytorch-msssim==1.0.0 torch==2.6.0 From 3cf9711df0dfe5730767733ebe9ca4179a12ba66 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:09:57 +0100 Subject: [PATCH 098/394] Modernize data loader, remove read image helper --- embedding_converter/src/data_loader.py | 22 ++++++++-------------- embedding_converter/src/helper.py | 7 ------- face_swapper/src/data_loader.py | 22 +++++++++++----------- face_swapper/src/helper.py | 11 ----------- face_swapper/src/inferencing.py | 6 +++--- 5 files changed, 22 insertions(+), 46 deletions(-) delete mode 100644 embedding_converter/src/helper.py diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py index 21f335e..5585c5e 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/data_loader.py @@ -1,34 +1,29 @@ import glob import random +import cv2 import torch -import torchvision.transforms as transforms +import transforms from torchvision from torch.utils.data import Dataset -from .helper import read_image from .types import Batch, Paths class DataLoaderRecognition(Dataset[torch.Tensor]): def __init__(self, dataset_file_pattern : str) -> None: - self.image_paths = self.prepare_image_paths(dataset_file_pattern) - self.dataset_total = len(self.image_paths) + self.image_paths = glob.glob(dataset_file_pattern) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - target_image_path = random.choice(self.image_paths) - target_vision_frame = read_image(target_image_path) - target_tensor = self.transforms(target_vision_frame) - return target_tensor + image_path = random.choice(self.image_paths) + vision_frame = cv2.imread(image_path) + return self.transforms(vision_frame) def __len__(self) -> int: - return self.dataset_total - - def prepare_image_paths(self, dataset_file_pattern : str) -> Paths: - return glob.glob(dataset_file_pattern) + return len(self.image_paths) def compose_transforms(self) -> transforms: - transform = transforms.Compose( + return transforms.Compose( [ transforms.ToPILImage(), transforms.Resize((112, 112), interpolation = transforms.InterpolationMode.BICUBIC), @@ -37,4 +32,3 @@ class DataLoaderRecognition(Dataset[torch.Tensor]): transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - return transform diff --git a/embedding_converter/src/helper.py b/embedding_converter/src/helper.py deleted file mode 100644 index c6775f6..0000000 --- a/embedding_converter/src/helper.py +++ /dev/null @@ -1,7 +0,0 @@ -import cv2 - -from .types import VisionFrame - - -def read_image(image_path : str) -> VisionFrame: - return cv2.imread(image_path) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 6d7919b..c562b8a 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -3,11 +3,11 @@ import os.path import random from typing import Tuple +import cv2 import torch -import torchvision.transforms as transforms +import transforms from torchvision from torch.utils.data import TensorDataset -from .helper import read_image from .types import Batch, ImagePathList, ImagePathSet @@ -16,11 +16,10 @@ class DataLoaderVGG(TensorDataset): self.same_person_probability = same_person_probability self.directory_paths = glob.glob(dataset_directory_pattern.format(dataset_path)) self.image_paths, self.image_path_set = self.prepare_image_paths(dataset_image_pattern) - self.dataset_total = len(self.image_paths) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - source_image_path = self.image_paths[index] + source_image_path = self.image_paths.get(index) if random.random() > self.same_person_probability: return self.prepare_same_person(source_image_path) @@ -28,8 +27,9 @@ class DataLoaderVGG(TensorDataset): return self.prepare_different_person(source_image_path) def __len__(self) -> int: - return self.dataset_total + return len(self.image_paths) + #todo: remove this method - only use glob.glob in init() def prepare_image_paths(self, dataset_image_pattern : str) -> Tuple[ImagePathList, ImagePathSet]: image_paths = [] image_path_set = {} @@ -40,7 +40,7 @@ class DataLoaderVGG(TensorDataset): return image_paths, image_path_set def compose_transforms(self) -> transforms: - transform = transforms.Compose( + return transforms.Compose( [ transforms.ToPILImage(), transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), @@ -50,22 +50,22 @@ class DataLoaderVGG(TensorDataset): transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - return transform def prepare_different_person(self, source_image_path : str) -> Batch: is_same_person = torch.tensor(0) target_image_path = random.choice(self.image_paths) - source_vision_frame = read_image(source_image_path) - target_vision_frame = read_image(target_image_path) + source_vision_frame = cv2.imread(source_image_path) + target_vision_frame = cv2.imread(target_image_path) source_tensor = self.transforms(source_vision_frame) target_tensor = self.transforms(target_vision_frame) return source_tensor, target_tensor, is_same_person def prepare_same_person(self, source_image_path : str) -> Batch: is_same_person = torch.tensor(1) + #todo: why not like in prepare_different_person target_image_path = random.choice(self.image_path_set.get(os.path.dirname(source_image_path))) - source_vision_frame = read_image(source_image_path) - target_vision_frame = read_image(target_image_path) + source_vision_frame = cv2.imread(source_image_path) + target_vision_frame = cv2.imread(target_image_path) source_tensor = self.transforms(source_vision_frame) target_tensor = self.transforms(target_vision_frame) return source_tensor, target_tensor, is_same_person diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 6f14dae..ddcd067 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -8,17 +8,6 @@ from torch import Tensor, nn from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor -def is_windows() -> bool: - return platform.system().lower() == 'windows' - - -def read_image(image_path : str) -> VisionFrame: - if is_windows(): - image_buffer = numpy.fromfile(image_path, dtype = numpy.uint8) - return cv2.imdecode(image_buffer, cv2.IMREAD_COLOR) - return cv2.imread(image_path) - - def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor: vision_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) vision_tensor = vision_tensor / 255.0 diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index c6e7f5f..593ddd9 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,7 +3,7 @@ import configparser import cv2 import torch -from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor, read_image +from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor from .models.generator import Generator from .types import EmbedderModule, GeneratorModule, VisionFrame @@ -34,7 +34,7 @@ def infer() -> None: id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] id_embedder.eval() - source_vision_frame = read_image(source_path) - target_vision_frame = read_image(target_path) + source_vision_frame = cv2.imread(source_path) + target_vision_frame = cv2.imread(target_path) output_vision_frame = run_swap(generator, id_embedder, source_vision_frame, target_vision_frame) cv2.imwrite(output_path, output_vision_frame) From 39c031320204e22a6160c63d7a7766d0215412ed Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:11:28 +0100 Subject: [PATCH 099/394] Modernize data loader, remove read image helper --- face_swapper/src/helper.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index ddcd067..cd9a41a 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,6 +1,3 @@ -import platform - -import cv2 import numpy import torch from torch import Tensor, nn From 254bc17c989031ef579319f2a061e63e92ccaf8d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:13:37 +0100 Subject: [PATCH 100/394] Modernize data loader, remove read image helper --- embedding_converter/src/data_loader.py | 2 +- face_swapper/src/data_loader.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py index 5585c5e..11f5a51 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/data_loader.py @@ -3,8 +3,8 @@ import random import cv2 import torch -import transforms from torchvision from torch.utils.data import Dataset +from torchvision import transforms from .types import Batch, Paths diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index c562b8a..81f73a3 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -5,8 +5,8 @@ from typing import Tuple import cv2 import torch -import transforms from torchvision from torch.utils.data import TensorDataset +from torchvision import transforms from .types import Batch, ImagePathList, ImagePathSet From 354315502bdd922587407b1e049ff599c38cbd11 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:14:44 +0100 Subject: [PATCH 101/394] Modernize data loader, remove read image helper --- embedding_converter/src/data_loader.py | 2 +- embedding_converter/src/types.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py index 11f5a51..738abbc 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/data_loader.py @@ -6,7 +6,7 @@ import torch from torch.utils.data import Dataset from torchvision import transforms -from .types import Batch, Paths +from .types import Batch class DataLoaderRecognition(Dataset[torch.Tensor]): diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index a40d4bb..1a01548 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -3,7 +3,6 @@ from typing import Any, List, TypeAlias from numpy.typing import NDArray from torch import Tensor -Paths : TypeAlias = List[str] Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor VisionFrame : TypeAlias = NDArray[Any] From c04107395397ef04b84c299c2e018db2ae570738 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:15:28 +0100 Subject: [PATCH 102/394] Modernize data loader, remove read image helper --- embedding_converter/src/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 1a01548..73fcd3a 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, List, TypeAlias +from typing import Any, TypeAlias from numpy.typing import NDArray from torch import Tensor From 40dcef7fc78713382a966066e7f5d9eedd5589f2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 01:16:46 +0100 Subject: [PATCH 103/394] Modernize data loader, remove read image helper --- face_swapper/src/data_loader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 81f73a3..50f3f27 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -19,7 +19,7 @@ class DataLoaderVGG(TensorDataset): self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - source_image_path = self.image_paths.get(index) + source_image_path = self.image_paths[index] if random.random() > self.same_person_probability: return self.prepare_same_person(source_image_path) From 0a50e2d7062d205af6733604ac451839b63ccfb4 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 08:26:22 +0100 Subject: [PATCH 104/394] Rename data loader --- face_swapper/src/data_loader.py | 2 +- face_swapper/src/training.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 50f3f27..bcc3b15 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -11,7 +11,7 @@ from torchvision import transforms from .types import Batch, ImagePathList, ImagePathSet -class DataLoaderVGG(TensorDataset): +class DataLoader(TensorDataset): def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_directory_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability self.directory_paths = glob.glob(dataset_directory_pattern.format(dataset_path)) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 16c9e86..e950860 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -12,7 +12,7 @@ from torch import Tensor, nn from torch.optim import Optimizer from torch.utils.data import DataLoader, Subset -from .data_loader import DataLoaderVGG +from .data_loader import DataLoader from .helper import calc_id_embedding from .models.discriminator import Discriminator from .models.generator import Generator @@ -131,7 +131,7 @@ def train() -> None: num_workers = CONFIG.getint('training.loader', 'num_workers') resume_file_path = CONFIG.get('training.output', 'resume_file_path') - dataset = DataLoaderVGG(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) + dataset = DataLoader(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) training_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) validation_loader = DataLoader(Subset(dataset, range(1000)), batch_size = batch_size, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swapper_trainer = FaceSwapperTrainer() From 9bd68c3d14311d0bac29f132c5cccd0db48fea6b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 08:29:03 +0100 Subject: [PATCH 105/394] Similarity validation for embedding converter --- face_swapper/src/training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e950860..35a24de 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -10,7 +10,7 @@ from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn from torch.optim import Optimizer -from torch.utils.data import DataLoader, Subset +from torch.utils.data import DataLoader as TorchDataLoader, Subset from .data_loader import DataLoader from .helper import calc_id_embedding @@ -132,8 +132,8 @@ def train() -> None: resume_file_path = CONFIG.get('training.output', 'resume_file_path') dataset = DataLoader(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) - training_loader = DataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = DataLoader(Subset(dataset, range(1000)), batch_size = batch_size, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + training_loader = TorchDataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = TorchDataLoader(Subset(dataset, range(1000)), batch_size = batch_size, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From da51c5336d5ff57ea7b953c171bdda0e3f07be32 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 08:39:08 +0100 Subject: [PATCH 106/394] This can be marked static --- embedding_converter/src/data_loader.py | 3 ++- face_swapper/src/data_loader.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/data_loader.py index 738abbc..55eeed8 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/data_loader.py @@ -22,7 +22,8 @@ class DataLoaderRecognition(Dataset[torch.Tensor]): def __len__(self) -> int: return len(self.image_paths) - def compose_transforms(self) -> transforms: + @staticmethod + def compose_transforms() -> transforms: return transforms.Compose( [ transforms.ToPILImage(), diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index bcc3b15..1eaadcc 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -39,7 +39,8 @@ class DataLoader(TensorDataset): image_path_set[directory_path] = image_paths return image_paths, image_path_set - def compose_transforms(self) -> transforms: + @staticmethod + def compose_transforms() -> transforms: return transforms.Compose( [ transforms.ToPILImage(), From 251e610f0e577db58734e02a2f5717430d01d779 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 09:07:58 +0100 Subject: [PATCH 107/394] Follow the lightning naming and call this dataset, Improve config and types --- embedding_converter/README.md | 2 +- embedding_converter/config.ini | 2 +- .../src/{data_loader.py => dataset.py} | 5 +++-- embedding_converter/src/training.py | 18 +++++++++--------- embedding_converter/src/types.py | 4 +++- 5 files changed, 17 insertions(+), 14 deletions(-) rename embedding_converter/src/{data_loader.py => dataset.py} (93%) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 9c1dbc6..addc7b3 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -27,7 +27,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Embedding Converter ``` [training.dataset] -dataset_file_pattern = .datasets/images/{}/*.*g +file_pattern = .datasets/images/{}/*.*g ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 62504ef..88e01a1 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -1,5 +1,5 @@ [training.dataset] -dataset_file_pattern = +file_pattern = [training.loader] split_ratio = diff --git a/embedding_converter/src/data_loader.py b/embedding_converter/src/dataset.py similarity index 93% rename from embedding_converter/src/data_loader.py rename to embedding_converter/src/dataset.py index 55eeed8..4aae867 100644 --- a/embedding_converter/src/data_loader.py +++ b/embedding_converter/src/dataset.py @@ -2,14 +2,15 @@ import glob import random import cv2 -import torch +from torch import Tensor + from torch.utils.data import Dataset from torchvision import transforms from .types import Batch -class DataLoaderRecognition(Dataset[torch.Tensor]): +class DynamicDataset(Dataset[Tensor]): def __init__(self, dataset_file_pattern : str) -> None: self.image_paths = glob.glob(dataset_file_pattern) self.transforms = self.compose_transforms() diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 2decc64..2f90890 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -1,6 +1,6 @@ import configparser import os -from typing import Any, Tuple +from typing import Tuple import lightning import torch @@ -11,9 +11,9 @@ from lightning.pytorch.tuner import Tuner from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, random_split -from .data_loader import DataLoaderRecognition +from .dataset import DynamicDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Embedding +from .types import Batch, Embedding, OptimizerConfig CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -53,7 +53,7 @@ class EmbeddingConverterTrainer(lightning.LightningModule): self.log('validation', validation, prog_bar = True) return validation - def configure_optimizers(self) -> Any: + def configure_optimizers(self) -> OptimizerConfig: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) @@ -71,17 +71,17 @@ class EmbeddingConverterTrainer(lightning.LightningModule): } -def create_loaders(dataset : Dataset[Any]) -> Tuple[DataLoader[Any], DataLoader[Any]]: +def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataLoader[Tensor]]: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) training_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, drop_last = False, pin_memory = True, persistent_workers = True) + validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader -def split_dataset(dataset : Dataset[Any]) -> Tuple[Dataset[Any], Dataset[Any]]: +def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') training_size = int(loader_split_ratio * len(dataset)) # type:ignore[operator, arg-type] validation_size = len(dataset) - training_size # type:ignore[arg-type] @@ -115,10 +115,10 @@ def create_trainer() -> Trainer: def train() -> None: - dataset_file_pattern = CONFIG.get('training.dataset', 'image_pattern') + dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') resume_file_path = CONFIG.get('training.output', 'resume_file_path') - dataset = DataLoaderRecognition(dataset_file_pattern) + dataset = DynamicDataset(dataset_file_pattern) training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 73fcd3a..62db931 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, TypeAlias +from typing import Any, Dict, TypeAlias from numpy.typing import NDArray from torch import Tensor @@ -6,3 +6,5 @@ from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor VisionFrame : TypeAlias = NDArray[Any] + +OptimizerConfig : TypeAlias = Dict[str, Any] From 15ee6fa763e1fb8f1bb9bead0d6934bf9cce3afc Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 09:11:43 +0100 Subject: [PATCH 108/394] Simplify sizes --- embedding_converter/src/training.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 2f90890..ab31c2b 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -83,9 +83,10 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataL def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') - training_size = int(loader_split_ratio * len(dataset)) # type:ignore[operator, arg-type] - validation_size = len(dataset) - training_size # type:ignore[arg-type] - training_dataset, validate_dataset = random_split(dataset, [training_size, validation_size]) + dataset_size = len(dataset) + training_size = dataset_size * loader_split_ratio + validation_size = dataset_size - training_size + training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset From 40786810310cb49cd04cd59f17c29618860fd7f7 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 09:17:59 +0100 Subject: [PATCH 109/394] Fix typing --- embedding_converter/src/training.py | 2 +- embedding_converter/src/types.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index ab31c2b..64be995 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -83,7 +83,7 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataL def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') - dataset_size = len(dataset) + dataset_size = len(dataset) # type:ignore[arg-type] training_size = dataset_size * loader_split_ratio validation_size = dataset_size - training_size training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 62db931..7513a43 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -7,4 +7,4 @@ Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor VisionFrame : TypeAlias = NDArray[Any] -OptimizerConfig : TypeAlias = Dict[str, Any] +OptimizerConfig : TypeAlias = Any From c1bed34c278eb4806ef1123e81166d949366793a Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 09:45:44 +0100 Subject: [PATCH 110/394] More tweaks --- embedding_converter/README.md | 2 +- embedding_converter/src/dataset.py | 8 ++++---- embedding_converter/src/training.py | 8 +++++--- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index addc7b3..4b71e5b 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -27,7 +27,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Embedding Converter ``` [training.dataset] -file_pattern = .datasets/images/{}/*.*g +file_pattern = .datasets/megaface/**/*.jpg ``` ``` diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 4aae867..f165fa0 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -11,17 +11,17 @@ from .types import Batch class DynamicDataset(Dataset[Tensor]): - def __init__(self, dataset_file_pattern : str) -> None: - self.image_paths = glob.glob(dataset_file_pattern) + def __init__(self, file_pattern : str) -> None: + self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - image_path = random.choice(self.image_paths) + image_path = random.choice(self.file_paths) vision_frame = cv2.imread(image_path) return self.transforms(vision_frame) def __len__(self) -> int: - return len(self.image_paths) + return len(self.file_paths) @staticmethod def compose_transforms() -> transforms: diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 64be995..2fa7e1a 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -24,13 +24,15 @@ class EmbeddingConverterTrainer(lightning.LightningModule): super(EmbeddingConverterTrainer, self).__init__() source_path = CONFIG.get('training.model', 'source_path') target_path = CONFIG.get('training.model', 'target_path') - self.lr = CONFIG.getfloat('training.trainer', 'learning_rate') + learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') + self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(source_path) # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(target_path) # type:ignore[no-untyped-call] self.source_embedder.eval() self.target_embedder.eval() self.mse_loss = nn.MSELoss() + self.lr = learning_rate def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) @@ -84,8 +86,8 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataL def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') dataset_size = len(dataset) # type:ignore[arg-type] - training_size = dataset_size * loader_split_ratio - validation_size = dataset_size - training_size + training_size = int(dataset_size * loader_split_ratio) + validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset From 04eaa831ea5845499676cf32a5d67537730d23a5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 10:06:07 +0100 Subject: [PATCH 111/394] Rename to resume_path only --- embedding_converter/README.md | 2 +- embedding_converter/config.ini | 2 +- embedding_converter/src/dataset.py | 1 - embedding_converter/src/training.py | 6 +++--- embedding_converter/src/types.py | 2 +- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/training.py | 6 +++--- 8 files changed, 11 insertions(+), 12 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 4b71e5b..36aa0a5 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -53,7 +53,7 @@ max_epochs = 4096 [training.output] directory_path = .outputs file_pattern = arcface_converter_simswap_{epoch}_{step} -resume_file_path = .outputs/last.ckpt +resume_path = .outputs/last.ckpt ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 88e01a1..5388075 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -17,7 +17,7 @@ max_epochs = [training.output] directory_path = file_pattern = -resume_file_path = +resume_path = [exporting] directory_path = diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index f165fa0..3d61aea 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -3,7 +3,6 @@ import random import cv2 from torch import Tensor - from torch.utils.data import Dataset from torchvision import transforms diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 2fa7e1a..6c1aba6 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -119,7 +119,7 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - resume_file_path = CONFIG.get('training.output', 'resume_file_path') + output_resume_path = CONFIG.get('training.output', 'resume_path') dataset = DynamicDataset(dataset_file_pattern) training_loader, validation_loader = create_loaders(dataset) @@ -128,7 +128,7 @@ def train() -> None: tuner = Tuner(trainer) tuner.lr_find(embedding_converter_trainer, training_loader, validation_loader) - if os.path.exists(resume_file_path): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = resume_file_path) + if os.path.exists(output_resume_path): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = output_resume_path) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 7513a43..d9d4ee9 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, TypeAlias +from typing import Any, TypeAlias from numpy.typing import NDArray from torch import Tensor diff --git a/face_swapper/README.md b/face_swapper/README.md index 486ab6f..2270858 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -84,7 +84,7 @@ preview_frequency = 250 [training.output] directory_path = .outputs file_pattern = face-swapper_{epoch}_{step} -resume_file_path = .outputs/last.ckpt +resume_path = .outputs/last.ckpt ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 10f875f..6b835fe 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -42,7 +42,7 @@ preview_frequency = [training.output] directory_path = file_pattern = -resume_file_path = +resume_path = [exporting] directory_path = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 35a24de..0a33709 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -129,7 +129,7 @@ def train() -> None: same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') - resume_file_path = CONFIG.get('training.output', 'resume_file_path') + output_resume_path = CONFIG.get('training.output', 'resume_path') dataset = DataLoader(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) training_loader = TorchDataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) @@ -137,7 +137,7 @@ def train() -> None: face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() - if os.path.isfile(resume_file_path): - trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = resume_file_path) + if os.path.isfile(output_resume_path): + trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = output_resume_path) else: trainer.fit(face_swapper_trainer, training_loader, validation_loader) From 5934b47961ad983a18902bea2b7f684fa780e149 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 10:15:56 +0100 Subject: [PATCH 112/394] Add cosine_similarity --- embedding_converter/src/training.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 6c1aba6..4b1f6b1 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -49,9 +49,8 @@ class EmbeddingConverterTrainer(lightning.LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: with torch.no_grad(): source_embedding = self.source_embedder(batch) - target_embedding = self.target_embedder(batch) output_embedding = self(source_embedding) - validation = self.mse_loss(output_embedding, target_embedding) + validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation', validation, prog_bar = True) return validation @@ -113,7 +112,7 @@ def create_trainer() -> Trainer: save_last = True ) ], - val_check_interval = 10 + val_check_interval = 100 ) From 3d9ff4add02d6625befc611533b3a3a8e870df8b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 10:26:07 +0100 Subject: [PATCH 113/394] Adjust validation to one time per epoch --- embedding_converter/README.md | 2 +- embedding_converter/src/training.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 36aa0a5..399f9db 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -32,7 +32,7 @@ file_pattern = .datasets/megaface/**/*.jpg ``` [training.loader] -split_ratio = 0.8 +split_ratio = 0.95 batch_size = 256 num_workers = 8 ``` diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 4b1f6b1..3d66360 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -111,8 +111,7 @@ def create_trainer() -> Trainer: save_top_k = 3, save_last = True ) - ], - val_check_interval = 100 + ] ) From b33281425a7adc1a971677f38c140d289978b69b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 11:03:31 +0100 Subject: [PATCH 114/394] This is not a TensorDataset --- face_swapper/src/data_loader.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index 1eaadcc..d1145e1 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -5,13 +5,14 @@ from typing import Tuple import cv2 import torch -from torch.utils.data import TensorDataset +from torch import Tensor +from torch.utils.data import Dataset from torchvision import transforms from .types import Batch, ImagePathList, ImagePathSet -class DataLoader(TensorDataset): +class DataLoader(Dataset[Tensor]): def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_directory_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability self.directory_paths = glob.glob(dataset_directory_pattern.format(dataset_path)) From db44c91dd897d7ae2053328c93e156e97e377c78 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 21 Feb 2025 19:40:44 +0530 Subject: [PATCH 115/394] changes --- face_swapper/README.md | 7 +++--- face_swapper/config.ini | 7 +++--- face_swapper/src/data_loader.py | 41 +++++++++---------------------- face_swapper/src/exporting.py | 2 +- face_swapper/src/helper.py | 10 +++++++- face_swapper/src/models/loss.py | 29 ++++++++++++++-------- face_swapper/src/training.py | 43 +++++++++++++++++++++------------ face_swapper/src/types.py | 5 +--- 8 files changed, 75 insertions(+), 69 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 2270858..26f454d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -26,10 +26,8 @@ Setup This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` -[preparing.dataset] -dataset_path = .datasets/vggface2 -directory_pattern = {}/* -image_pattern = {}/*.*g +[training.dataset] +file_pattern = .datasets/vggface2/**/*.jpg same_person_probability = 0.2 ``` @@ -37,6 +35,7 @@ same_person_probability = 0.2 [training.loader] batch_size = 8 num_workers = 8 +split_ratio = 0.95 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 6b835fe..fbc18eb 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,12 +1,11 @@ -[preparing.dataset] -dataset_path = -directory_pattern = -image_pattern = +[training.dataset] +file_pattern = same_person_probability = [training.loader] batch_size = num_workers = +split_ratio = [training.model] id_embedder_path = diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/data_loader.py index d1145e1..7410c09 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/data_loader.py @@ -1,44 +1,30 @@ import glob -import os.path import random -from typing import Tuple import cv2 -import torch from torch import Tensor from torch.utils.data import Dataset from torchvision import transforms -from .types import Batch, ImagePathList, ImagePathSet +from .types import Batch class DataLoader(Dataset[Tensor]): - def __init__(self, dataset_path : str, dataset_image_pattern : str, dataset_directory_pattern : str, same_person_probability : float) -> None: + def __init__(self, file_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability - self.directory_paths = glob.glob(dataset_directory_pattern.format(dataset_path)) - self.image_paths, self.image_path_set = self.prepare_image_paths(dataset_image_pattern) + self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() - def __getitem__(self, index : int) -> Batch: - source_image_path = self.image_paths[index] + def __getitem__(self, index : int) -> Batch: # type:ignore[override] + source_image_path = self.file_paths[index] - if random.random() > self.same_person_probability: + if random.random() < self.same_person_probability: return self.prepare_same_person(source_image_path) return self.prepare_different_person(source_image_path) def __len__(self) -> int: - return len(self.image_paths) - - #todo: remove this method - only use glob.glob in init() - def prepare_image_paths(self, dataset_image_pattern : str) -> Tuple[ImagePathList, ImagePathSet]: - image_paths = [] - image_path_set = {} - - for directory_path in self.directory_paths: - image_paths.extend(glob.glob(dataset_image_pattern.format(directory_path))) - image_path_set[directory_path] = image_paths - return image_paths, image_path_set + return len(self.file_paths) @staticmethod def compose_transforms() -> transforms: @@ -54,20 +40,15 @@ class DataLoader(Dataset[Tensor]): ]) def prepare_different_person(self, source_image_path : str) -> Batch: - is_same_person = torch.tensor(0) - target_image_path = random.choice(self.image_paths) + target_image_path = random.choice(self.file_paths) source_vision_frame = cv2.imread(source_image_path) target_vision_frame = cv2.imread(target_image_path) source_tensor = self.transforms(source_vision_frame) target_tensor = self.transforms(target_vision_frame) - return source_tensor, target_tensor, is_same_person + return source_tensor, target_tensor def prepare_same_person(self, source_image_path : str) -> Batch: - is_same_person = torch.tensor(1) - #todo: why not like in prepare_different_person - target_image_path = random.choice(self.image_path_set.get(os.path.dirname(source_image_path))) source_vision_frame = cv2.imread(source_image_path) - target_vision_frame = cv2.imread(target_image_path) source_tensor = self.transforms(source_vision_frame) - target_tensor = self.transforms(target_vision_frame) - return source_tensor, target_tensor, is_same_person + target_tensor = source_tensor.clone() + return source_tensor, target_tensor diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 3b0892d..6631fe1 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -21,7 +21,7 @@ def export() -> None: model = Generator() model.load_state_dict(state_dict) model.eval() - model.ir_version = ir_version + model.ir_version = torch.tensor(ir_version) source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, 256, 256) torch.onnx.export(model, (source_tensor, target_tensor), target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index cd9a41a..8770e97 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,6 +1,7 @@ import numpy import torch from torch import Tensor, nn +from pytorch_msssim import ssim from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor @@ -41,6 +42,13 @@ def calc_id_embedding(id_embedder : EmbedderModule, vision_tensor : VisionTensor crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 crop_vision_tensor[:, :, :, :padding[2]] = 0 crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 - source_embedding = id_embedder(crop_vision_tensor) + with torch.no_grad(): + source_embedding = id_embedder(crop_vision_tensor) source_embedding = nn.functional.normalize(source_embedding, p = 2) return source_embedding + + +def calc_structural_similarity(swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Tensor: + swap_data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor)) + structural_similarity = 1 - ssim(swap_tensor, target_tensor, data_range = swap_data_range).mean() + return structural_similarity diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 02196c7..0d5c49d 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -2,10 +2,9 @@ import configparser from typing import Tuple import torch -from pytorch_msssim import ssim from torch import Tensor, nn -from ..helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from ..helper import calc_id_embedding, calc_structural_similarity, hinge_fake_loss, hinge_real_loss from ..types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() @@ -27,7 +26,7 @@ class FaceSwapperLoss: self.motion_extractor.eval() def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - source_tensor, target_tensor, is_same_person = batch + source_tensor, target_tensor = batch weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') @@ -39,7 +38,7 @@ class FaceSwapperLoss: 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), 'loss_identity': self.calc_identity_loss(source_tensor, swap_tensor), 'loss_attribute': self.calc_attribute_loss(target_attributes, swap_attributes), - 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) + 'loss_reconstruction': self.calc_reconstruction_loss(source_tensor, target_tensor, swap_tensor) } if weight_pose > 0: @@ -95,12 +94,22 @@ class FaceSwapperLoss: loss_attribute = torch.stack(loss_attributes).mean() * 0.5 return loss_attribute - def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: - loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) - loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 - loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) - loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 + def calc_reconstruction_loss(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_id_embedding(self.id_embedder, target_tensor, (0, 0, 0, 0)) + face_similarities = (torch.cosine_similarity(source_embedding, target_embedding) + 1) * 0.5 + loss_reconstructions = [] + + for index, face_similarity in enumerate(face_similarities): + if face_similarity.item() > 0.9: + loss_mse = self.mse_loss(swap_tensor[index], target_tensor[index]) + loss_ssim = calc_structural_similarity(swap_tensor[index].unsqueeze(0), target_tensor[index].unsqueeze(0)) + loss_reconstruction = (loss_mse + loss_ssim) * 0.5 + loss_reconstructions.append(loss_reconstruction) + else: + loss_reconstructions.append(torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype)) + + loss_reconstruction = torch.stack(loss_reconstructions).mean() return loss_reconstruction def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 0a33709..f47093d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,6 +1,6 @@ import configparser import os -from typing import Tuple +from typing import Any, Tuple import lightning import torch @@ -10,7 +10,7 @@ from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn from torch.optim import Optimizer -from torch.utils.data import DataLoader as TorchDataLoader, Subset +from torch.utils.data import DataLoader as TorchDataLoader, Dataset, random_split from .data_loader import DataLoader from .helper import calc_id_embedding @@ -42,7 +42,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): return generator_optimizer, discriminator_optimizer def training_step(self, batch : Batch, batch_index : int) -> Tensor: - source_tensor, target_tensor, is_same_person = batch + source_tensor, target_tensor = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) swap_tensor = self.generator(source_embedding, target_tensor) @@ -75,7 +75,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): return generator_losses.get('loss_generator') def validation_step(self, batch : Batch, batch_index : int) -> Tensor: - source_tensor, target_tensor, _ = batch + source_tensor, target_tensor = batch source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) output_tensor = self.generator(source_embedding, target_tensor) output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) @@ -91,7 +91,25 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): preview_items.append(torch.cat([ source_tensor, target_tensor, output_tensor] , dim = 2)) preview_grid = torchvision.utils.make_grid(torch.cat(preview_items, dim = 1).unsqueeze(0), normalize = True, scale_each = True) - self.logger.experiment.add_image('preview', preview_grid, self.global_step) + self.logger.experiment.add_image('preview', preview_grid, self.global_step) # type:ignore[attr-defined] + + +def create_loaders(dataset : Dataset[Any]): # type:ignore[type-arg] + batch_size = CONFIG.getint('training.loader', 'batch_size') + num_workers = CONFIG.getint('training.loader', 'num_workers') + + training_dataset, validate_dataset = split_dataset(dataset) + training_loader = TorchDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = TorchDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, drop_last = False, pin_memory = True, persistent_workers = True) + return training_loader, validation_loader # type:ignore[return-value] + + +def split_dataset(dataset : Dataset[Any]) -> Tuple[Dataset[Any], Dataset[Any]]: + loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') + training_size = int(loader_split_ratio * len(dataset)) # type:ignore[operator, arg-type] + validation_size = len(dataset) - training_size # type:ignore[arg-type] + training_dataset, validate_dataset = random_split(dataset, [training_size, validation_size]) + return training_dataset, validate_dataset def create_trainer() -> Trainer: @@ -106,7 +124,7 @@ def create_trainer() -> Trainer: logger = logger, log_every_n_steps = 10, max_epochs = trainer_max_epochs, - precision = trainer_precision, + precision = trainer_precision, # type:ignore[arg-type] callbacks = [ ModelCheckpoint( @@ -123,17 +141,12 @@ def create_trainer() -> Trainer: def train() -> None: - dataset_path = CONFIG.get('preparing.dataset', 'dataset_path') - dataset_image_pattern = CONFIG.get('preparing.dataset', 'image_pattern') - dataset_directory_pattern = CONFIG.get('preparing.dataset', 'directory_pattern') - same_person_probability = CONFIG.getfloat('preparing.dataset', 'same_person_probability') - batch_size = CONFIG.getint('training.loader', 'batch_size') - num_workers = CONFIG.getint('training.loader', 'num_workers') + dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') + same_person_probability = CONFIG.getfloat('training.dataset', 'same_person_probability') output_resume_path = CONFIG.get('training.output', 'resume_path') - dataset = DataLoader(dataset_path, dataset_image_pattern, dataset_directory_pattern, same_person_probability) - training_loader = TorchDataLoader(dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = TorchDataLoader(Subset(dataset, range(1000)), batch_size = batch_size, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + dataset = DataLoader(dataset_file_pattern, same_person_probability) + training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index bf2fbff..83d6e6e 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -5,10 +5,7 @@ from numpy.typing import NDArray from torch import Tensor from torch.nn import Module -Batch : TypeAlias = Tuple[Tensor, Tensor, Tensor] - -ImagePathList : TypeAlias = List[str] -ImagePathSet : TypeAlias = Dict[str, ImagePathList] +Batch : TypeAlias = Tuple[Tensor, Tensor] SwapAttributes : TypeAlias = Tuple[Tensor, ...] TargetAttributes : TypeAlias = Tuple[Tensor, ...] From 4d8433f54a316129aefe8e83f04e6e2652314ca8 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 21 Feb 2025 20:12:48 +0530 Subject: [PATCH 116/394] changes --- face_swapper/src/helper.py | 5 ++--- face_swapper/src/models/loss.py | 7 ++++--- face_swapper/src/training.py | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 8770e97..3811118 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,7 +1,7 @@ import numpy import torch -from torch import Tensor, nn from pytorch_msssim import ssim +from torch import Tensor, nn from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor @@ -42,8 +42,7 @@ def calc_id_embedding(id_embedder : EmbedderModule, vision_tensor : VisionTensor crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 crop_vision_tensor[:, :, :, :padding[2]] = 0 crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 - with torch.no_grad(): - source_embedding = id_embedder(crop_vision_tensor) + source_embedding = id_embedder(crop_vision_tensor) source_embedding = nn.functional.normalize(source_embedding, p = 2) return source_embedding diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 0d5c49d..9ef34b9 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -95,14 +95,15 @@ class FaceSwapperLoss: return loss_attribute def calc_reconstruction_loss(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - target_embedding = calc_id_embedding(self.id_embedder, target_tensor, (0, 0, 0, 0)) + with torch.no_grad(): + source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_id_embedding(self.id_embedder, target_tensor, (0, 0, 0, 0)) face_similarities = (torch.cosine_similarity(source_embedding, target_embedding) + 1) * 0.5 loss_reconstructions = [] for index, face_similarity in enumerate(face_similarities): if face_similarity.item() > 0.9: - loss_mse = self.mse_loss(swap_tensor[index], target_tensor[index]) + loss_mse = self.mse_loss(swap_tensor[index].unsqueeze(0), target_tensor[index].unsqueeze(0)) loss_ssim = calc_structural_similarity(swap_tensor[index].unsqueeze(0), target_tensor[index].unsqueeze(0)) loss_reconstruction = (loss_mse + loss_ssim) * 0.5 loss_reconstructions.append(loss_reconstruction) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f47093d..ecc70b6 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -94,14 +94,14 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.logger.experiment.add_image('preview', preview_grid, self.global_step) # type:ignore[attr-defined] -def create_loaders(dataset : Dataset[Any]): # type:ignore[type-arg] +def create_loaders(dataset : Dataset[Any]) -> Tuple[TorchDataLoader[Any], TorchDataLoader[Any]]: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) training_loader = TorchDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) validation_loader = TorchDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, drop_last = False, pin_memory = True, persistent_workers = True) - return training_loader, validation_loader # type:ignore[return-value] + return training_loader, validation_loader def split_dataset(dataset : Dataset[Any]) -> Tuple[Dataset[Any], Dataset[Any]]: From b4bbd862e21f7c7f279a2cf91696373c86df43de Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:13:13 +0100 Subject: [PATCH 117/394] Follow convention of the other project --- face_swapper/src/{data_loader.py => dataset.py} | 2 +- face_swapper/src/training.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) rename face_swapper/src/{data_loader.py => dataset.py} (98%) diff --git a/face_swapper/src/data_loader.py b/face_swapper/src/dataset.py similarity index 98% rename from face_swapper/src/data_loader.py rename to face_swapper/src/dataset.py index 7410c09..2d42149 100644 --- a/face_swapper/src/data_loader.py +++ b/face_swapper/src/dataset.py @@ -9,7 +9,7 @@ from torchvision import transforms from .types import Batch -class DataLoader(Dataset[Tensor]): +class DynamicDataset(Dataset[Tensor]): def __init__(self, file_pattern : str, same_person_probability : float) -> None: self.same_person_probability = same_person_probability self.file_paths = glob.glob(file_pattern) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ecc70b6..4e2b970 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -12,7 +12,7 @@ from torch import Tensor, nn from torch.optim import Optimizer from torch.utils.data import DataLoader as TorchDataLoader, Dataset, random_split -from .data_loader import DataLoader +from .dataset import DynamicDataset from .helper import calc_id_embedding from .models.discriminator import Discriminator from .models.generator import Generator @@ -145,7 +145,7 @@ def train() -> None: same_person_probability = CONFIG.getfloat('training.dataset', 'same_person_probability') output_resume_path = CONFIG.get('training.output', 'resume_path') - dataset = DataLoader(dataset_file_pattern, same_person_probability) + dataset = DynamicDataset(dataset_file_pattern, same_person_probability) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From 09e913233b0d2b9b6b557fce4784f686c99f97ff Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:23:54 +0100 Subject: [PATCH 118/394] Follow convention of the other project --- face_swapper/src/training.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4e2b970..5412068 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,6 +1,6 @@ import configparser import os -from typing import Any, Tuple +from typing import Tuple import lightning import torch @@ -10,7 +10,7 @@ from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn from torch.optim import Optimizer -from torch.utils.data import DataLoader as TorchDataLoader, Dataset, random_split +from torch.utils.data import DataLoader, Dataset, random_split from .dataset import DynamicDataset from .helper import calc_id_embedding @@ -94,21 +94,22 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.logger.experiment.add_image('preview', preview_grid, self.global_step) # type:ignore[attr-defined] -def create_loaders(dataset : Dataset[Any]) -> Tuple[TorchDataLoader[Any], TorchDataLoader[Any]]: +def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataLoader[Tensor]]: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) - training_loader = TorchDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = TorchDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, drop_last = False, pin_memory = True, persistent_workers = True) + training_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader -def split_dataset(dataset : Dataset[Any]) -> Tuple[Dataset[Any], Dataset[Any]]: +def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') - training_size = int(loader_split_ratio * len(dataset)) # type:ignore[operator, arg-type] - validation_size = len(dataset) - training_size # type:ignore[arg-type] - training_dataset, validate_dataset = random_split(dataset, [training_size, validation_size]) + dataset_size = len(dataset) # type:ignore[arg-type] + training_size = int(dataset_size * loader_split_ratio) + validation_size = int(dataset_size - training_size) + training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset From 723e9fde781066cd32157979c62add26b794efd5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:29:26 +0100 Subject: [PATCH 119/394] follow the not invented here syndrome --- face_swapper/config.ini | 2 +- face_swapper/src/dataset.py | 19 +++++++++---------- face_swapper/src/training.py | 4 ++-- 3 files changed, 12 insertions(+), 13 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index fbc18eb..c8a9f50 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,6 +1,6 @@ [training.dataset] file_pattern = -same_person_probability = +equal_probability = [training.loader] batch_size = diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 2d42149..e947de3 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -10,18 +10,18 @@ from .types import Batch class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, same_person_probability : float) -> None: - self.same_person_probability = same_person_probability + def __init__(self, file_pattern : str, equal_probability : float) -> None: self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() + self.equal_probability = equal_probability - def __getitem__(self, index : int) -> Batch: # type:ignore[override] + def __getitem__(self, index : int) -> Batch: source_image_path = self.file_paths[index] - if random.random() < self.same_person_probability: - return self.prepare_same_person(source_image_path) + if random.random() < self.equal_probability: + return self.prepare_equal_batch(source_image_path) - return self.prepare_different_person(source_image_path) + return self.prepare_different_batch(source_image_path) def __len__(self) -> int: return len(self.file_paths) @@ -39,7 +39,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - def prepare_different_person(self, source_image_path : str) -> Batch: + def prepare_different_batch(self, source_image_path : str) -> Batch: target_image_path = random.choice(self.file_paths) source_vision_frame = cv2.imread(source_image_path) target_vision_frame = cv2.imread(target_image_path) @@ -47,8 +47,7 @@ class DynamicDataset(Dataset[Tensor]): target_tensor = self.transforms(target_vision_frame) return source_tensor, target_tensor - def prepare_same_person(self, source_image_path : str) -> Batch: + def prepare_equal_batch(self, source_image_path : str) -> Batch: source_vision_frame = cv2.imread(source_image_path) source_tensor = self.transforms(source_vision_frame) - target_tensor = source_tensor.clone() - return source_tensor, target_tensor + return source_tensor, source_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 5412068..20cfa77 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -143,10 +143,10 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - same_person_probability = CONFIG.getfloat('training.dataset', 'same_person_probability') + dataset_equal_probability = CONFIG.getfloat('training.dataset', 'equal_probability') output_resume_path = CONFIG.get('training.output', 'resume_path') - dataset = DynamicDataset(dataset_file_pattern, same_person_probability) + dataset = DynamicDataset(dataset_file_pattern, dataset_equal_probability) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From 84503761b98f60e3a1c842564f20a8b6ac53e8da Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:30:27 +0100 Subject: [PATCH 120/394] follow the not invented here syndrome --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 26f454d..94943cd 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -28,7 +28,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg -same_person_probability = 0.2 +equal_probability = 0.2 ``` ``` From c17378f3c7a2997eab89126f38c3127f73bef624 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:38:53 +0100 Subject: [PATCH 121/394] Let's call it batch ratio --- embedding_converter/README.md | 2 +- embedding_converter/config.ini | 2 +- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/dataset.py | 6 +++--- face_swapper/src/training.py | 4 ++-- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 399f9db..2752f67 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -32,9 +32,9 @@ file_pattern = .datasets/megaface/**/*.jpg ``` [training.loader] -split_ratio = 0.95 batch_size = 256 num_workers = 8 +split_ratio = 0.95 ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index 5388075..e8360e2 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -2,9 +2,9 @@ file_pattern = [training.loader] -split_ratio = batch_size = num_workers = +split_ratio = [training.model] source_path = diff --git a/face_swapper/README.md b/face_swapper/README.md index 94943cd..518d481 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -28,7 +28,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg -equal_probability = 0.2 +batch_ratio = 0.2 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index c8a9f50..f578f27 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,6 +1,6 @@ [training.dataset] file_pattern = -equal_probability = +batch_ratio = [training.loader] batch_size = diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index e947de3..de7f344 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -10,15 +10,15 @@ from .types import Batch class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, equal_probability : float) -> None: + def __init__(self, file_pattern : str, batch_ratio : float) -> None: self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() - self.equal_probability = equal_probability + self.batch_ratio = batch_ratio def __getitem__(self, index : int) -> Batch: source_image_path = self.file_paths[index] - if random.random() < self.equal_probability: + if random.random() < self.batch_ratio: return self.prepare_equal_batch(source_image_path) return self.prepare_different_batch(source_image_path) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 20cfa77..9687048 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -143,10 +143,10 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - dataset_equal_probability = CONFIG.getfloat('training.dataset', 'equal_probability') + dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') - dataset = DynamicDataset(dataset_file_pattern, dataset_equal_probability) + dataset = DynamicDataset(dataset_file_pattern, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From 5a6e3393e2112bf897d15e327034581b87bc3638 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 16:53:32 +0100 Subject: [PATCH 122/394] Revert loss for the moment --- face_swapper/src/helper.py | 7 ------- face_swapper/src/models/loss.py | 31 +++++++++++-------------------- 2 files changed, 11 insertions(+), 27 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 3811118..cd9a41a 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,6 +1,5 @@ import numpy import torch -from pytorch_msssim import ssim from torch import Tensor, nn from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor @@ -45,9 +44,3 @@ def calc_id_embedding(id_embedder : EmbedderModule, vision_tensor : VisionTensor source_embedding = id_embedder(crop_vision_tensor) source_embedding = nn.functional.normalize(source_embedding, p = 2) return source_embedding - - -def calc_structural_similarity(swap_tensor : VisionTensor, target_tensor : VisionTensor) -> Tensor: - swap_data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor)) - structural_similarity = 1 - ssim(swap_tensor, target_tensor, data_range = swap_data_range).mean() - return structural_similarity diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 9ef34b9..73db988 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -2,9 +2,10 @@ import configparser from typing import Tuple import torch +from pytorch_msssim import ssim from torch import Tensor, nn -from ..helper import calc_id_embedding, calc_structural_similarity, hinge_fake_loss, hinge_real_loss +from ..helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss from ..types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() @@ -26,19 +27,20 @@ class FaceSwapperLoss: self.motion_extractor.eval() def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - source_tensor, target_tensor = batch weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') + source_tensor, target_tensor = batch + is_same_person = torch.tensor(0) if source_tensor == target_tensor else torch.tensor(1) generator_loss_set =\ { 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), 'loss_identity': self.calc_identity_loss(source_tensor, swap_tensor), 'loss_attribute': self.calc_attribute_loss(target_attributes, swap_attributes), - 'loss_reconstruction': self.calc_reconstruction_loss(source_tensor, target_tensor, swap_tensor) + 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) } if weight_pose > 0: @@ -94,23 +96,12 @@ class FaceSwapperLoss: loss_attribute = torch.stack(loss_attributes).mean() * 0.5 return loss_attribute - def calc_reconstruction_loss(self, source_tensor : VisionTensor, target_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - with torch.no_grad(): - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - target_embedding = calc_id_embedding(self.id_embedder, target_tensor, (0, 0, 0, 0)) - face_similarities = (torch.cosine_similarity(source_embedding, target_embedding) + 1) * 0.5 - loss_reconstructions = [] - - for index, face_similarity in enumerate(face_similarities): - if face_similarity.item() > 0.9: - loss_mse = self.mse_loss(swap_tensor[index].unsqueeze(0), target_tensor[index].unsqueeze(0)) - loss_ssim = calc_structural_similarity(swap_tensor[index].unsqueeze(0), target_tensor[index].unsqueeze(0)) - loss_reconstruction = (loss_mse + loss_ssim) * 0.5 - loss_reconstructions.append(loss_reconstruction) - else: - loss_reconstructions.append(torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype)) - - loss_reconstruction = torch.stack(loss_reconstructions).mean() + def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: + loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) + loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 + loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) + loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() + loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 return loss_reconstruction def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: From 00dccf07b94343d63c9f524f0671ba9957da1021 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 17:01:22 +0100 Subject: [PATCH 123/394] Fix tensor foobar --- face_swapper/src/models/loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 73db988..bb0b9be 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -34,7 +34,7 @@ class FaceSwapperLoss: weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') source_tensor, target_tensor = batch - is_same_person = torch.tensor(0) if source_tensor == target_tensor else torch.tensor(1) + is_same_person = torch.tensor(0) if torch.equal(source_tensor, target_tensor) else torch.tensor(1) generator_loss_set =\ { 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), From d153c6881358219b5ccc883791dd7ae5e4d1a946 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 17:16:11 +0100 Subject: [PATCH 124/394] Better split_ratio --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 518d481..ace46ea 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -35,7 +35,7 @@ batch_ratio = 0.2 [training.loader] batch_size = 8 num_workers = 8 -split_ratio = 0.95 +split_ratio = 0.9995 ``` ``` From 575f2154085acb2f0e8e056d3eb1754a94ab942f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 21 Feb 2025 18:40:11 +0100 Subject: [PATCH 125/394] lr only in optimizer --- embedding_converter/src/training.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 3d66360..b323fe2 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -24,7 +24,6 @@ class EmbeddingConverterTrainer(lightning.LightningModule): super(EmbeddingConverterTrainer, self).__init__() source_path = CONFIG.get('training.model', 'source_path') target_path = CONFIG.get('training.model', 'target_path') - learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(source_path) # type:ignore[no-untyped-call] @@ -32,7 +31,6 @@ class EmbeddingConverterTrainer(lightning.LightningModule): self.source_embedder.eval() self.target_embedder.eval() self.mse_loss = nn.MSELoss() - self.lr = learning_rate def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) From 83ef075b1db0c00b08f5447cd4048fffaf5b1647 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 09:40:47 +0100 Subject: [PATCH 126/394] Remove map_location --- embedding_converter/src/exporting.py | 2 +- face_swapper/README.md | 6 +++--- face_swapper/src/exporting.py | 2 +- face_swapper/src/inferencing.py | 4 ++-- face_swapper/src/models/loss.py | 6 +++--- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 4ede026..e0fbae3 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -17,7 +17,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + model = EmbeddingConverterTrainer.load_from_checkpoint(source_path) model.eval() model.ir_version = torch.tensor(ir_version) input_tensor = (torch.randn(1, 512), ) diff --git a/face_swapper/README.md b/face_swapper/README.md index ace46ea..ccd0d6d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -62,10 +62,10 @@ kernel_size = 4 ``` [training.losses] -weight_adversarial = 1 -weight_identity = 20 +weight_adversarial = 1.5 +weight_identity = 15 weight_attribute = 10 -weight_reconstruction = 10 +weight_reconstruction = 15 weight_pose = 0 weight_gaze = 0 ``` diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 6631fe1..bae3287 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -17,7 +17,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') + state_dict = torch.load(source_path).get('state_dict').get('generator') model = Generator() model.load_state_dict(state_dict) model.eval() diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 593ddd9..c77af45 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -27,11 +27,11 @@ def infer() -> None: target_path = CONFIG.get('inferencing', 'target_path') output_path = CONFIG.get('inferencing', 'output_path') - state_dict = torch.load(generator_path, map_location = 'cpu').get('state_dict').get('generator') + state_dict = torch.load(generator_path).get('state_dict').get('generator') generator = Generator() generator.load_state_dict(state_dict) generator.eval() - id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + id_embedder = torch.jit.load(id_embedder_path) # type:ignore[no-untyped-call] id_embedder.eval() source_vision_frame = cv2.imread(source_path) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index bb0b9be..2d0e281 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -19,9 +19,9 @@ class FaceSwapperLoss: motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.batch_size = CONFIG.getint('training.loader', 'batch_size') self.mse_loss = nn.MSELoss() - self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.id_embedder = torch.jit.load(id_embedder_path) # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path) # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path) # type:ignore[no-untyped-call] self.id_embedder.eval() self.landmarker.eval() self.motion_extractor.eval() From f6c59257d9aca2f590500539ebb159c05758b642 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 23:12:23 +0100 Subject: [PATCH 127/394] Partial use Resnet34 as a DownSample replacement --- face_swapper/src/networks/unet.py | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 8a97df3..a5c3d9a 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -2,23 +2,32 @@ from typing import Tuple import torch from torch import Tensor, nn +from torchvision import models class UNet(nn.Module): def __init__(self) -> None: super(UNet, self).__init__() - self.down_samples = self.create_down_samples() + self.resnet = models.resnet34(pretrained = True) + self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() @staticmethod - def create_down_samples() -> nn.ModuleList: + def create_down_samples(self) -> nn.ModuleList: return nn.ModuleList( [ - DownSample(3, 32), + nn.Sequential( + self.resnet.conv1, + self.resnet.bn1, + self.resnet.relu, + nn.Conv2d(64, 32, kernel_size = 1, bias = False), + nn.BatchNorm2d(32), + nn.LeakyReLU(0.1, inplace = True) + ), DownSample(32, 64), - DownSample(64, 128), - DownSample(128, 256), - DownSample(256, 512), + self.resnet.layer2, + self.resnet.layer3, + self.resnet.layer4, DownSample(512, 1024), DownSample(1024, 1024) ]) From 094d5cea9e7a9564f6ea44ebdc3814e72eb4cafc Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Feb 2025 23:18:49 +0100 Subject: [PATCH 128/394] Fix CI --- face_swapper/src/networks/unet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index a5c3d9a..b70b44d 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -13,7 +13,7 @@ class UNet(nn.Module): self.up_samples = self.create_up_samples() @staticmethod - def create_down_samples(self) -> nn.ModuleList: + def create_down_samples(self : nn.Module) -> nn.ModuleList: return nn.ModuleList( [ nn.Sequential( From 10ce04ed58b87c492a7578fb190f2f66f6aa37af Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 10:08:23 +0100 Subject: [PATCH 129/394] Introduce feature flag for Unet --- face_swapper/README.md | 1 + face_swapper/src/models/generator.py | 18 +++++++----- face_swapper/src/networks/unet.py | 44 ++++++++++++++++++++-------- 3 files changed, 44 insertions(+), 19 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index ccd0d6d..5f6370b 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -47,6 +47,7 @@ motion_extractor_path = .models/motion_extractor.pt ``` [training.model.generator] +encoder_type = unet-pro num_blocks = 2 id_channels = 512 ``` diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index d6fa9b6..be9c236 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -3,7 +3,7 @@ import configparser from torch import Tensor, nn from ..networks.attribute_modulator import AADGenerator -from ..networks.unet import UNet +from ..networks.unet import UNet, UNetPro from ..types import Attributes, Embedding CONFIG = configparser.ConfigParser() @@ -13,21 +13,25 @@ CONFIG.read('config.ini') class Generator(nn.Module): def __init__(self) -> None: super(Generator, self).__init__() + encoder_type = CONFIG.getint('training.model.generator', 'encoder_type') id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - self.unet = UNet() - self.aad_generator = AADGenerator(id_channels, num_blocks) - self.unet.apply(init_weight) - self.aad_generator.apply(init_weight) + if encoder_type == 'unet': + self.encoder = UNet() + if encoder_type == 'unet-pro': + self.encoder = UNetPro() + self.generator = AADGenerator(id_channels, num_blocks) + self.encoder.apply(init_weight) + self.generator.apply(init_weight) def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: target_attributes = self.get_attributes(target_tensor) - output_tensor = self.aad_generator(target_attributes, source_embedding) + output_tensor = self.generator(target_attributes, source_embedding) return output_tensor def get_attributes(self, input_tensor : Tensor) -> Attributes: - return self.unet(input_tensor) + return self.encoder(input_tensor) def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index b70b44d..127fdef 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -8,7 +8,6 @@ from torchvision import models class UNet(nn.Module): def __init__(self) -> None: super(UNet, self).__init__() - self.resnet = models.resnet34(pretrained = True) self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() @@ -16,18 +15,11 @@ class UNet(nn.Module): def create_down_samples(self : nn.Module) -> nn.ModuleList: return nn.ModuleList( [ - nn.Sequential( - self.resnet.conv1, - self.resnet.bn1, - self.resnet.relu, - nn.Conv2d(64, 32, kernel_size = 1, bias = False), - nn.BatchNorm2d(32), - nn.LeakyReLU(0.1, inplace = True) - ), + DownSample(3, 32), DownSample(32, 64), - self.resnet.layer2, - self.resnet.layer3, - self.resnet.layer4, + DownSample(64, 128), + DownSample(128, 256), + DownSample(256, 512), DownSample(512, 1024), DownSample(1024, 1024) ]) @@ -65,6 +57,34 @@ class UNet(nn.Module): return bottleneck_tensor, *up_features, output_tensor +class UNetPro(UNet): + def __init__(self) -> None: + super(UNetPro, self).__init__() + self.resnet = models.resnet34(pretrained = True) + self.down_samples = self.create_down_samples(self) + self.up_samples = self.create_up_samples() + + @staticmethod + def create_down_samples(self : nn.Module) -> nn.ModuleList: + return nn.ModuleList( + [ + nn.Sequential( + self.resnet.conv1, + self.resnet.bn1, + self.resnet.relu, + nn.Conv2d(64, 32, kernel_size = 1, bias = False), + nn.BatchNorm2d(32), + nn.LeakyReLU(0.1, inplace = True) + ), + DownSample(32, 64), + self.resnet.layer2, + self.resnet.layer3, + self.resnet.layer4, + DownSample(512, 1024), + DownSample(1024, 1024) + ]) + + class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super(UpSample, self).__init__() From dc0abff0ce2076642f789df3e085e237ba332a68 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 10:32:54 +0100 Subject: [PATCH 130/394] Minor typo --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 5f6370b..fda306a 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -83,7 +83,7 @@ preview_frequency = 250 ``` [training.output] directory_path = .outputs -file_pattern = face-swapper_{epoch}_{step} +file_pattern = face_swapper_{epoch}_{step} resume_path = .outputs/last.ckpt ``` From 206a1411d12eacf58d21fd2c59f801ff26277122 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 10:43:08 +0100 Subject: [PATCH 131/394] Fix Generator --- face_swapper/src/models/generator.py | 2 +- face_swapper/src/networks/unet.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index be9c236..6f757ec 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -13,7 +13,7 @@ CONFIG.read('config.ini') class Generator(nn.Module): def __init__(self) -> None: super(Generator, self).__init__() - encoder_type = CONFIG.getint('training.model.generator', 'encoder_type') + encoder_type = CONFIG.get('training.model.generator', 'encoder_type') id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 127fdef..5f9cb05 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -59,7 +59,7 @@ class UNet(nn.Module): class UNetPro(UNet): def __init__(self) -> None: - super(UNetPro, self).__init__() + super(UNet, self).__init__() self.resnet = models.resnet34(pretrained = True) self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() From ac41bab3a2b1483ecbba497a4960f95cacaa18c8 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 11:21:34 +0100 Subject: [PATCH 132/394] Restore map_location = 'cpu' --- embedding_converter/src/exporting.py | 2 +- face_swapper/src/exporting.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index e0fbae3..4ede026 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -17,7 +17,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - model = EmbeddingConverterTrainer.load_from_checkpoint(source_path) + model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') model.eval() model.ir_version = torch.tensor(ir_version) input_tensor = (torch.randn(1, 512), ) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index bae3287..6631fe1 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -17,7 +17,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - state_dict = torch.load(source_path).get('state_dict').get('generator') + state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') model = Generator() model.load_state_dict(state_dict) model.eval() From a6e1405c70d4271e14f3fbeaa5b0b65f665ad6fa Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 11:26:35 +0100 Subject: [PATCH 133/394] Restore map_location = 'cpu' --- embedding_converter/src/training.py | 4 ++-- face_swapper/src/inferencing.py | 2 +- face_swapper/src/models/loss.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index b323fe2..632ba1c 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -26,8 +26,8 @@ class EmbeddingConverterTrainer(lightning.LightningModule): target_path = CONFIG.get('training.model', 'target_path') self.embedding_converter = EmbeddingConverter() - self.source_embedder = torch.jit.load(source_path) # type:ignore[no-untyped-call] - self.target_embedder = torch.jit.load(target_path) # type:ignore[no-untyped-call] + self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.source_embedder.eval() self.target_embedder.eval() self.mse_loss = nn.MSELoss() diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index c77af45..658ec69 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -31,7 +31,7 @@ def infer() -> None: generator = Generator() generator.load_state_dict(state_dict) generator.eval() - id_embedder = torch.jit.load(id_embedder_path) # type:ignore[no-untyped-call] + id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] id_embedder.eval() source_vision_frame = cv2.imread(source_path) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2d0e281..093cc43 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -19,9 +19,9 @@ class FaceSwapperLoss: motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.batch_size = CONFIG.getint('training.loader', 'batch_size') self.mse_loss = nn.MSELoss() - self.id_embedder = torch.jit.load(id_embedder_path) # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path) # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path) # type:ignore[no-untyped-call] + self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.id_embedder.eval() self.landmarker.eval() self.motion_extractor.eval() From f482d46798c1ac5cee50a85b0e75bb13acca2585 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 12:19:44 +0100 Subject: [PATCH 134/394] Remove VisionTensor from Discriminator --- face_swapper/src/models/discriminator.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index c8fc1c4..bd78ae0 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -1,10 +1,9 @@ import configparser from typing import List -from torch import nn +from torch import Tensor, nn from ..networks.nld import NLD -from ..types import VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -31,7 +30,7 @@ class Discriminator(nn.Module): return discriminators - def forward(self, input_tensor : VisionTensor) -> List[List[VisionTensor]]: + def forward(self, input_tensor : Tensor) -> List[List[Tensor]]: temp_tensor = input_tensor output_tensors = [] From 2220f5ef08359ad23c9d4318d420e9aa4e133c08 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 00:31:31 +0100 Subject: [PATCH 135/394] Add missing encoder_type to config --- face_swapper/config.ini | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index f578f27..44f453d 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -13,6 +13,7 @@ landmarker_path = motion_extractor_path = [training.model.generator] +encoder_type = num_blocks = id_channels = From 085c493e18deef8af38396e0603a03261bb6bda3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 12:37:47 +0100 Subject: [PATCH 136/394] Rename id_embedder to embedder, Tons of naming in training step, Introduce new IdentityLoss class --- face_swapper/README.md | 4 +- face_swapper/src/helper.py | 20 +++++----- face_swapper/src/inferencing.py | 14 +++---- face_swapper/src/models/loss.py | 26 ++++++++++--- face_swapper/src/training.py | 65 +++++++++++++++++++++------------ 5 files changed, 80 insertions(+), 49 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index fda306a..633b832 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -40,7 +40,7 @@ split_ratio = 0.9995 ``` [training.model] -id_embedder_path = .models/id_embedder.pt +embedder_path = .models/arcface.pt landmarker_path = .models/landmarker.pt motion_extractor_path = .models/motion_extractor.pt ``` @@ -99,7 +99,7 @@ opset_version = 15 ``` [inferencing] generator_path = .outputs/last.ckpt -id_embedder_path = .models/id_embedder.pt +embedder_path = .models/arcface.pt source_path = .assets/source.jpg target_path = .assets/target.jpg output_path = .outputs/output.jpg diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index cd9a41a..01acf39 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -34,13 +34,13 @@ def hinge_fake_loss(input_tensor : Tensor) -> Tensor: return fake_loss -def calc_id_embedding(id_embedder : EmbedderModule, vision_tensor : VisionTensor, padding : Padding) -> Embedding: - crop_vision_tensor = vision_tensor[:, :, 15 : 241, 15 : 241] - crop_vision_tensor = nn.functional.interpolate(crop_vision_tensor, size = (112, 112), mode = 'area') - crop_vision_tensor[:, :, :padding[0], :] = 0 - crop_vision_tensor[:, :, 112 - padding[1]:, :] = 0 - crop_vision_tensor[:, :, :, :padding[2]] = 0 - crop_vision_tensor[:, :, :, 112 - padding[3]:] = 0 - source_embedding = id_embedder(crop_vision_tensor) - source_embedding = nn.functional.normalize(source_embedding, p = 2) - return source_embedding +def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: + crop_tensor = input_tensor[:, :, 15: 241, 15: 241] + crop_tensor = nn.functional.interpolate(crop_tensor, size = (112, 112), mode = 'area') + crop_tensor[:, :, :padding[0], :] = 0 + crop_tensor[:, :, 112 - padding[1]:, :] = 0 + crop_tensor[:, :, :, :padding[2]] = 0 + crop_tensor[:, :, :, 112 - padding[3]:] = 0 + embedding = embedder(crop_tensor) + embedding = nn.functional.normalize(embedding, p = 2) + return embedding diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 658ec69..d01510d 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,7 +3,7 @@ import configparser import cv2 import torch -from .helper import calc_id_embedding, convert_to_vision_frame, convert_to_vision_tensor +from .helper import calc_embedding, convert_to_vision_frame, convert_to_vision_tensor from .models.generator import Generator from .types import EmbedderModule, GeneratorModule, VisionFrame @@ -11,10 +11,10 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def run_swap(generator : GeneratorModule, id_embedder : EmbedderModule, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: +def run_swap(generator : GeneratorModule, embedder : EmbedderModule, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: source_vision_tensor = convert_to_vision_tensor(source_vision_frame) target_vision_tensor = convert_to_vision_tensor(target_vision_frame) - source_embedding = calc_id_embedding(id_embedder, source_vision_tensor, (0, 0, 0, 0)) + source_embedding = calc_embedding(embedder, source_vision_tensor, (0, 0, 0, 0)) output_vision_tensor = generator(source_embedding, target_vision_tensor)[0] output_vision_frame = convert_to_vision_frame(output_vision_tensor) return output_vision_frame @@ -22,7 +22,7 @@ def run_swap(generator : GeneratorModule, id_embedder : EmbedderModule, source_v def infer() -> None: generator_path = CONFIG.get('inferencing', 'generator_path') - id_embedder_path = CONFIG.get('inferencing', 'id_embedder_path') + embedder_path = CONFIG.get('inferencing', 'embedder_path') source_path = CONFIG.get('inferencing', 'source_path') target_path = CONFIG.get('inferencing', 'target_path') output_path = CONFIG.get('inferencing', 'output_path') @@ -31,10 +31,10 @@ def infer() -> None: generator = Generator() generator.load_state_dict(state_dict) generator.eval() - id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - id_embedder.eval() + embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + embedder.eval() source_vision_frame = cv2.imread(source_path) target_vision_frame = cv2.imread(target_path) - output_vision_frame = run_swap(generator, id_embedder, source_vision_frame, target_vision_frame) + output_vision_frame = run_swap(generator, embedder, source_vision_frame, target_vision_frame) cv2.imwrite(output_path, output_vision_frame) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 093cc43..b66a2e6 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -5,7 +5,7 @@ import torch from pytorch_msssim import ssim from torch import Tensor, nn -from ..helper import calc_id_embedding, hinge_fake_loss, hinge_real_loss +from ..helper import calc_embedding, hinge_fake_loss, hinge_real_loss from ..types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() @@ -14,15 +14,15 @@ CONFIG.read('config.ini') class FaceSwapperLoss: def __init__(self) -> None: - id_embedder_path = CONFIG.get('training.model', 'id_embedder_path') + embedder_path = CONFIG.get('training.model', 'embedder_path') landmarker_path = CONFIG.get('training.model', 'landmarker_path') motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.batch_size = CONFIG.getint('training.loader', 'batch_size') self.mse_loss = nn.MSELoss() - self.id_embedder = torch.jit.load(id_embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.id_embedder.eval() + self.embedder.eval() self.landmarker.eval() self.motion_extractor.eval() @@ -105,8 +105,8 @@ class FaceSwapperLoss: return loss_reconstruction def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - swap_embedding = calc_id_embedding(self.id_embedder, swap_tensor, (30, 0, 10, 10)) - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (30, 0, 10, 10)) + swap_embedding = calc_embedding(self.embedder, swap_tensor, (30, 0, 10, 10)) + source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() return loss_identity @@ -139,3 +139,17 @@ class FaceSwapperLoss: pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) rotation = torch.cat([ pitch, yaw, roll ], dim = 1) return translation, scale, rotation + + +class IdentityLoss(torch.nn.Module): + def __init__(self) -> None: + super(IdentityLoss, self).__init__() + embedder_path = CONFIG.get('training.model', 'embedder_path') + self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.embedder.eval() + + def calc_loss(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor: + output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) + source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) + loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() + return loss diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 9687048..169e6d8 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -13,10 +13,10 @@ from torch.optim import Optimizer from torch.utils.data import DataLoader, Dataset, random_split from .dataset import DynamicDataset -from .helper import calc_id_embedding +from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import FaceSwapperLoss +from .models.loss import FaceSwapperLoss, IdentityLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -27,9 +27,12 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def __init__(self) -> None: super().__init__() FaceSwapperLoss.__init__(self) + automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') + self.generator = Generator() self.discriminator = Discriminator() - self.automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') + self.identity_loss = IdentityLoss() + self.automatic_optimization = automatic_optimization def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tensor: output_tensor = self.generator(source_embedding, target_tensor) @@ -42,43 +45,57 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): return generator_optimizer, discriminator_optimizer def training_step(self, batch : Batch, batch_index : int) -> Tensor: + preview_frequency = CONFIG.getfloat('training.trainer', 'preview_frequency') + source_tensor, target_tensor = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) - swap_tensor = self.generator(source_embedding, target_tensor) + source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) - swap_attributes = self.generator.get_attributes(swap_tensor) - fake_discriminator_outputs = self.discriminator(swap_tensor) + generator_output_tensor = self.generator(source_embedding, target_tensor) + generator_output_attributes = self.generator.get_attributes(generator_output_tensor) + discriminator_output_tensor = self.discriminator(generator_output_tensor) - generator_losses = self.calc_generator_loss(swap_tensor, target_attributes, swap_attributes, fake_discriminator_outputs, batch) + generator_loss_set = self.calc_generator_loss(generator_output_tensor, target_attributes, generator_output_attributes, discriminator_output_tensor, batch) generator_optimizer.zero_grad() - self.manual_backward(generator_losses.get('loss_generator')) + self.manual_backward(generator_loss_set.get('loss_generator')) generator_optimizer.step() - real_discriminator_outputs = self.discriminator(source_tensor) - fake_discriminator_outputs = self.discriminator(swap_tensor.detach()) + discriminator_source_tensor = self.discriminator(source_tensor) + discriminator_output_tensor = self.discriminator(generator_output_tensor.detach()) - discriminator_losses = self.calc_discriminator_loss(real_discriminator_outputs, fake_discriminator_outputs) + discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensor, discriminator_output_tensor) discriminator_optimizer.zero_grad() - self.manual_backward(discriminator_losses.get('loss_discriminator')) + self.manual_backward(discriminator_loss_set.get('loss_discriminator')) discriminator_optimizer.step() - if self.global_step % CONFIG.getint('training.trainer', 'preview_frequency') == 0: - self.generate_preview(source_tensor, target_tensor, swap_tensor) + if self.global_step % preview_frequency == 0: + self.generate_preview(source_tensor, target_tensor, generator_output_tensor) - self.log('loss_generator', generator_losses.get('loss_generator'), prog_bar = True) - self.log('loss_discriminator', discriminator_losses.get('loss_discriminator'), prog_bar = True) - self.log('loss_adversarial', generator_losses.get('loss_adversarial')) - self.log('loss_attribute', generator_losses.get('loss_attribute')) - self.log('loss_identity', generator_losses.get('loss_identity')) - self.log('loss_reconstruction', generator_losses.get('loss_reconstruction')) - return generator_losses.get('loss_generator') + self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) + self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator'), prog_bar = True) + self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) + self.log('loss_attribute', generator_loss_set.get('loss_attribute')) + self.log('loss_identity', generator_loss_set.get('loss_identity'), prog_bar = True) + self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) + + identity_loss = self.identity_loss.calc_loss(generator_output_tensor, source_tensor) + generator_loss = self.calc_generator_loss_new(identity_loss) + + self.log('loss_generator_new', generator_loss, prog_bar = True) + self.log('loss_identity_new', identity_loss, prog_bar = True) + return generator_loss_set.get('loss_generator') + + def calc_generator_loss_new(self, identity_loss : Tensor) -> Tensor: + weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') + generator_loss = identity_loss * weight_identity + + return generator_loss def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch - source_embedding = calc_id_embedding(self.id_embedder, source_tensor, (0, 0, 0, 0)) + source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) output_tensor = self.generator(source_embedding, target_tensor) - output_embedding = calc_id_embedding(self.id_embedder, output_tensor, (0, 0, 0, 0)) + output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation', validation) return validation From 086d9eed870f0ea127fd80b32decf1a0a23c77d6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 14:29:16 +0100 Subject: [PATCH 137/394] Introduce new ReconstructionLoss class --- face_swapper/README.md | 12 ++++++------ face_swapper/src/models/loss.py | 28 +++++++++++++++++++++++++--- face_swapper/src/training.py | 23 +++++++++++++++-------- 3 files changed, 46 insertions(+), 17 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 633b832..fc19b7a 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -63,12 +63,12 @@ kernel_size = 4 ``` [training.losses] -weight_adversarial = 1.5 -weight_identity = 15 -weight_attribute = 10 -weight_reconstruction = 15 -weight_pose = 0 -weight_gaze = 0 +adversarial_weight = 1.5 +attribute_weight = 10 +reconstruction_weight = 15 +identity_weight = 15 +pose_weight = 0 +gaze_weight = 0 ``` ``` diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index b66a2e6..1f85614 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -141,6 +141,28 @@ class FaceSwapperLoss: return translation, scale, rotation +class ReconstructionLoss(torch.nn.Module): + def __init__(self) -> None: + super(ReconstructionLoss, self).__init__() + + def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tensor: + batch_size = CONFIG.getint('training.loader', 'batch_size') + + loss_tensor = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) + loss_tensor = torch.mean(loss_tensor, dim = 1) * 0.5 + + if torch.equal(source_tensor, target_tensor): + loss_tensor = torch.sum(loss_tensor * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) + else: + loss_tensor = torch.sum(loss_tensor * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) + + data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) + similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() + + loss_tensor = (loss_tensor + similarity) * 0.5 + return loss_tensor + + class IdentityLoss(torch.nn.Module): def __init__(self) -> None: super(IdentityLoss, self).__init__() @@ -148,8 +170,8 @@ class IdentityLoss(torch.nn.Module): self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.embedder.eval() - def calc_loss(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor: + def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor: output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) - loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() - return loss + loss_tensor = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() + return loss_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 169e6d8..4e3e0c2 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import FaceSwapperLoss, IdentityLoss +from .models.loss import FaceSwapperLoss, IdentityLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -31,6 +31,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generator = Generator() self.discriminator = Discriminator() + self.reconstruction_loss = ReconstructionLoss() self.identity_loss = IdentityLoss() self.automatic_optimization = automatic_optimization @@ -76,18 +77,24 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) self.log('loss_attribute', generator_loss_set.get('loss_attribute')) self.log('loss_identity', generator_loss_set.get('loss_identity'), prog_bar = True) - self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) + self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction'), prog_bar = True) - identity_loss = self.identity_loss.calc_loss(generator_output_tensor, source_tensor) - generator_loss = self.calc_generator_loss_new(identity_loss) + reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) + identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) + generator_loss = self.calc_generator_loss_new(reconstruction_loss, identity_loss) - self.log('loss_generator_new', generator_loss, prog_bar = True) + self.log('generator_loss_new', generator_loss, prog_bar = True) + self.log('loss_reconstruction_new', reconstruction_loss, prog_bar = True) self.log('loss_identity_new', identity_loss, prog_bar = True) return generator_loss_set.get('loss_generator') - def calc_generator_loss_new(self, identity_loss : Tensor) -> Tensor: - weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') - generator_loss = identity_loss * weight_identity + @staticmethod + def calc_generator_loss_new(reconstruction_loss : Tensor, identity_loss : Tensor) -> Tensor: + reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') + identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') + + generator_loss = reconstruction_loss * reconstruction_weight + generator_loss += identity_loss * identity_weight return generator_loss From 3b7d3b6688d49a3324c8ec2926afd78e8a9be17f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 14:30:52 +0100 Subject: [PATCH 138/394] Introduce new ReconstructionLoss class --- face_swapper/src/models/loss.py | 1 - 1 file changed, 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 1f85614..356f80b 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -147,7 +147,6 @@ class ReconstructionLoss(torch.nn.Module): def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tensor: batch_size = CONFIG.getint('training.loader', 'batch_size') - loss_tensor = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) loss_tensor = torch.mean(loss_tensor, dim = 1) * 0.5 From f2833a32c3166cca18f62294a284c7c002719541 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 16:24:31 +0100 Subject: [PATCH 139/394] Introduce new AdversarialLoss class --- face_swapper/src/models/loss.py | 47 +++++++++++++++++++++++++-------- face_swapper/src/training.py | 41 +++++++++++++--------------- 2 files changed, 54 insertions(+), 34 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 356f80b..8d43a2f 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -1,5 +1,6 @@ import configparser -from typing import Tuple +from typing import List, Tuple +from warnings import deprecated import torch from pytorch_msssim import ssim @@ -78,6 +79,7 @@ class FaceSwapperLoss: discriminator_loss_set['loss_discriminator'] = (loss_true + loss_fake) * 0.5 return discriminator_loss_set + @deprecated def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: loss_adversarials = [] @@ -96,6 +98,7 @@ class FaceSwapperLoss: loss_attribute = torch.stack(loss_attributes).mean() * 0.5 return loss_attribute + @deprecated def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 @@ -104,6 +107,7 @@ class FaceSwapperLoss: loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 return loss_reconstruction + @deprecated def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: swap_embedding = calc_embedding(self.embedder, swap_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) @@ -141,25 +145,44 @@ class FaceSwapperLoss: return translation, scale, rotation +class AdversarialLoss(torch.nn.Module): + def __init__(self) -> None: + super(AdversarialLoss, self).__init__() + + def calc(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: + adversarial_weight = CONFIG.getfloat('training.losses', 'adversarial_weight') + temp_tensors = [] + + for discriminator_output_tensor in discriminator_output_tensors: + temp_tensor = torch.relu(1 - discriminator_output_tensor[0]).mean() + temp_tensors.append(temp_tensor) + + loss = torch.stack(temp_tensors).mean() + weighted_loss = loss * adversarial_weight + return loss, weighted_loss + + class ReconstructionLoss(torch.nn.Module): def __init__(self) -> None: super(ReconstructionLoss, self).__init__() - def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tensor: + def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: batch_size = CONFIG.getint('training.loader', 'batch_size') - loss_tensor = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) - loss_tensor = torch.mean(loss_tensor, dim = 1) * 0.5 + reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') + loss = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) + loss = torch.mean(loss, dim = 1) * 0.5 if torch.equal(source_tensor, target_tensor): - loss_tensor = torch.sum(loss_tensor * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) + loss = torch.sum(loss * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) else: - loss_tensor = torch.sum(loss_tensor * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) + loss = torch.sum(loss * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() - loss_tensor = (loss_tensor + similarity) * 0.5 - return loss_tensor + loss = (loss + similarity) * 0.5 + weighted_loss = loss * reconstruction_weight + return loss, weighted_loss class IdentityLoss(torch.nn.Module): @@ -169,8 +192,10 @@ class IdentityLoss(torch.nn.Module): self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.embedder.eval() - def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tensor: + def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) - loss_tensor = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() - return loss_tensor + loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() + weighted_loss = loss * identity_weight + return loss, weighted_loss diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4e3e0c2..a36b58d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import FaceSwapperLoss, IdentityLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, FaceSwapperLoss, IdentityLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -31,6 +31,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generator = Generator() self.discriminator = Discriminator() + self.adversarial_loss = AdversarialLoss() self.reconstruction_loss = ReconstructionLoss() self.identity_loss = IdentityLoss() self.automatic_optimization = automatic_optimization @@ -54,17 +55,17 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): target_attributes = self.generator.get_attributes(target_tensor) generator_output_tensor = self.generator(source_embedding, target_tensor) generator_output_attributes = self.generator.get_attributes(generator_output_tensor) - discriminator_output_tensor = self.discriminator(generator_output_tensor) + discriminator_output_tensors = self.discriminator(generator_output_tensor) - generator_loss_set = self.calc_generator_loss(generator_output_tensor, target_attributes, generator_output_attributes, discriminator_output_tensor, batch) + generator_loss_set = self.calc_generator_loss(generator_output_tensor, target_attributes, generator_output_attributes, discriminator_output_tensors, batch) generator_optimizer.zero_grad() self.manual_backward(generator_loss_set.get('loss_generator')) generator_optimizer.step() discriminator_source_tensor = self.discriminator(source_tensor) - discriminator_output_tensor = self.discriminator(generator_output_tensor.detach()) + discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) - discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensor, discriminator_output_tensor) + discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensor, discriminator_output_tensors) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_loss_set.get('loss_discriminator')) discriminator_optimizer.step() @@ -74,30 +75,24 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator'), prog_bar = True) - self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) + self.log('loss_adversarial', generator_loss_set.get('loss_adversarial'), prog_bar = True) self.log('loss_attribute', generator_loss_set.get('loss_attribute')) - self.log('loss_identity', generator_loss_set.get('loss_identity'), prog_bar = True) - self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction'), prog_bar = True) + self.log('loss_identity', generator_loss_set.get('loss_identity')) + self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) - reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) - identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) - generator_loss = self.calc_generator_loss_new(reconstruction_loss, identity_loss) + ############################################### + + adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) + reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) + identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) + generator_loss = weighted_adversarial_loss+ weighted_reconstruction_loss + weighted_identity_loss self.log('generator_loss_new', generator_loss, prog_bar = True) - self.log('loss_reconstruction_new', reconstruction_loss, prog_bar = True) - self.log('loss_identity_new', identity_loss, prog_bar = True) + self.log('adversarial_loss_new', adversarial_loss, prog_bar = True) + self.log('loss_reconstruction_new', reconstruction_loss) + self.log('loss_identity_new', identity_loss) return generator_loss_set.get('loss_generator') - @staticmethod - def calc_generator_loss_new(reconstruction_loss : Tensor, identity_loss : Tensor) -> Tensor: - reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') - identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') - - generator_loss = reconstruction_loss * reconstruction_weight - generator_loss += identity_loss * identity_weight - - return generator_loss - def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) From 38211f0340e20a72f0529f6ae85857950ff04b97 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 16:26:40 +0100 Subject: [PATCH 140/394] Introduce new AdversarialLoss class --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a36b58d..0eac0e2 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -85,7 +85,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) - generator_loss = weighted_adversarial_loss+ weighted_reconstruction_loss + weighted_identity_loss + generator_loss = weighted_adversarial_loss + weighted_reconstruction_loss + weighted_identity_loss self.log('generator_loss_new', generator_loss, prog_bar = True) self.log('adversarial_loss_new', adversarial_loss, prog_bar = True) From 30e787129a667d5a4029f5bdf3c12d69cd9a27ed Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 16:28:00 +0100 Subject: [PATCH 141/394] Introduce new AdversarialLoss class --- face_swapper/src/models/loss.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 8d43a2f..81b1fb3 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -1,6 +1,5 @@ import configparser from typing import List, Tuple -from warnings import deprecated import torch from pytorch_msssim import ssim @@ -79,7 +78,6 @@ class FaceSwapperLoss: discriminator_loss_set['loss_discriminator'] = (loss_true + loss_fake) * 0.5 return discriminator_loss_set - @deprecated def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: loss_adversarials = [] @@ -98,7 +96,6 @@ class FaceSwapperLoss: loss_attribute = torch.stack(loss_attributes).mean() * 0.5 return loss_attribute - @deprecated def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 @@ -107,7 +104,6 @@ class FaceSwapperLoss: loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 return loss_reconstruction - @deprecated def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: swap_embedding = calc_embedding(self.embedder, swap_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) From 7848d28b023dafb99778a1109c404c035b77c63b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 16:59:28 +0100 Subject: [PATCH 142/394] Introduce new AdversarialLoss class fix --- face_swapper/src/models/loss.py | 2 +- face_swapper/src/training.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 81b1fb3..5029efb 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -150,7 +150,7 @@ class AdversarialLoss(torch.nn.Module): temp_tensors = [] for discriminator_output_tensor in discriminator_output_tensors: - temp_tensor = torch.relu(1 - discriminator_output_tensor[0]).mean() + temp_tensor = torch.relu(1 - discriminator_output_tensor[0]).mean(dim = [ 1, 2, 3 ]).mean() temp_tensors.append(temp_tensor) loss = torch.stack(temp_tensors).mean() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 0eac0e2..48e1199 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -88,7 +88,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): generator_loss = weighted_adversarial_loss + weighted_reconstruction_loss + weighted_identity_loss self.log('generator_loss_new', generator_loss, prog_bar = True) - self.log('adversarial_loss_new', adversarial_loss, prog_bar = True) + self.log('loss_adversarial_new', adversarial_loss, prog_bar = True) self.log('loss_reconstruction_new', reconstruction_loss) self.log('loss_identity_new', identity_loss) return generator_loss_set.get('loss_generator') From 6eabcad1d0935f7030d78a155a75a27315eb5f7e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 22:31:44 +0100 Subject: [PATCH 143/394] Introduce new AttributeLoss class --- embedding_converter/src/training.py | 10 +++--- face_swapper/README.md | 4 +-- face_swapper/src/models/loss.py | 47 ++++++++++++++++++++--------- face_swapper/src/training.py | 17 ++++++----- 4 files changed, 50 insertions(+), 28 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 632ba1c..09ebe54 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -40,9 +40,9 @@ class EmbeddingConverterTrainer(lightning.LightningModule): source_embedding = self.source_embedder(batch) target_embedding = self.target_embedder(batch) output_embedding = self(source_embedding) - loss_training = self.mse_loss(output_embedding, target_embedding) - self.log('loss_training', loss_training, prog_bar = True) - return loss_training + training_loss = self.mse_loss(output_embedding, target_embedding) + self.log('training_loss', training_loss, prog_bar = True) + return training_loss def validation_step(self, batch : Batch, batch_index : int) -> Tensor: with torch.no_grad(): @@ -63,7 +63,7 @@ class EmbeddingConverterTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': scheduler, - 'monitor': 'loss_training', + 'monitor': 'training_loss', 'interval': 'epoch', 'frequency': 1 } @@ -102,7 +102,7 @@ def create_trainer() -> Trainer: callbacks = [ ModelCheckpoint( - monitor = 'loss_training', + monitor = 'training_loss', dirpath = output_directory_path, filename = output_file_pattern, every_n_epochs = 10, diff --git a/face_swapper/README.md b/face_swapper/README.md index fc19b7a..e3a5c37 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -65,8 +65,8 @@ kernel_size = 4 [training.losses] adversarial_weight = 1.5 attribute_weight = 10 -reconstruction_weight = 15 -identity_weight = 15 +reconstruction_weight = 20 +identity_weight = 20 pose_weight = 0 gaze_weight = 0 ``` diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 5029efb..4f5c621 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -3,10 +3,11 @@ from typing import List, Tuple import torch from pytorch_msssim import ssim +from sqlalchemy.dialects.mssql.information_schema import identity_columns from torch import Tensor, nn from ..helper import calc_embedding, hinge_fake_loss, hinge_real_loss -from ..types import Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor +from ..types import Attributes, Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -153,9 +154,27 @@ class AdversarialLoss(torch.nn.Module): temp_tensor = torch.relu(1 - discriminator_output_tensor[0]).mean(dim = [ 1, 2, 3 ]).mean() temp_tensors.append(temp_tensor) - loss = torch.stack(temp_tensors).mean() - weighted_loss = loss * adversarial_weight - return loss, weighted_loss + adversarial_loss = torch.stack(temp_tensors).mean() + weighted_adversarial_loss = adversarial_loss * adversarial_weight + return adversarial_loss, weighted_adversarial_loss + + +class AttributeLoss(torch.nn.Module): + def __init__(self) -> None: + super(AttributeLoss, self).__init__() + + def calc(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: + batch_size = CONFIG.getint('training.loader', 'batch_size') + attribute_weight = CONFIG.getfloat('training.losses', 'attribute_weight') + temp_tensors = [] + + for target_attribute, output_attribute in zip(target_attributes, output_attributes): + temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(batch_size, -1), dim = 1).mean() + temp_tensors.append(temp_tensor) + + attribute_loss = torch.stack(temp_tensors).mean() * 0.5 + weighted_attribute_loss = attribute_loss * attribute_weight + return attribute_loss, weighted_attribute_loss class ReconstructionLoss(torch.nn.Module): @@ -165,20 +184,20 @@ class ReconstructionLoss(torch.nn.Module): def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: batch_size = CONFIG.getint('training.loader', 'batch_size') reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') - loss = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) - loss = torch.mean(loss, dim = 1) * 0.5 + reconstruction_loss = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) + reconstruction_loss = torch.mean(reconstruction_loss, dim = 1) * 0.5 if torch.equal(source_tensor, target_tensor): - loss = torch.sum(loss * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) + reconstruction_loss = torch.sum(reconstruction_loss * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) else: - loss = torch.sum(loss * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) + reconstruction_loss = torch.sum(reconstruction_loss * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() - loss = (loss + similarity) * 0.5 - weighted_loss = loss * reconstruction_weight - return loss, weighted_loss + reconstruction_loss = (reconstruction_loss + similarity) * 0.5 + weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight + return reconstruction_loss, weighted_reconstruction_loss class IdentityLoss(torch.nn.Module): @@ -192,6 +211,6 @@ class IdentityLoss(torch.nn.Module): identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) - loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() - weighted_loss = loss * identity_weight - return loss, weighted_loss + identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() + weighted_identity_loss = identity_loss * identity_weight + return identity_loss, weighted_identity_loss diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 48e1199..e52c6cd 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, FaceSwapperLoss, IdentityLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, IdentityLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -32,6 +32,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generator = Generator() self.discriminator = Discriminator() self.adversarial_loss = AdversarialLoss() + self.attribute_loss = AttributeLoss() self.reconstruction_loss = ReconstructionLoss() self.identity_loss = IdentityLoss() self.automatic_optimization = automatic_optimization @@ -74,23 +75,25 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generate_preview(source_tensor, target_tensor, generator_output_tensor) self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) - self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator'), prog_bar = True) + self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator')) self.log('loss_adversarial', generator_loss_set.get('loss_adversarial'), prog_bar = True) - self.log('loss_attribute', generator_loss_set.get('loss_attribute')) + self.log('loss_attribute', generator_loss_set.get('loss_attribute'), prog_bar = True) self.log('loss_identity', generator_loss_set.get('loss_identity')) self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) ############################################### adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) + attribute_loss, weighted_attribute_loss = self.attribute_loss.calc(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) - generator_loss = weighted_adversarial_loss + weighted_reconstruction_loss + weighted_identity_loss + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss self.log('generator_loss_new', generator_loss, prog_bar = True) - self.log('loss_adversarial_new', adversarial_loss, prog_bar = True) - self.log('loss_reconstruction_new', reconstruction_loss) - self.log('loss_identity_new', identity_loss) + self.log('adversarial_loss_new', adversarial_loss) + self.log('attribute_loss_new', attribute_loss, prog_bar = True) + self.log('reconstruction_loss_new', reconstruction_loss) + self.log('identity_loss_new', identity_loss) return generator_loss_set.get('loss_generator') def validation_step(self, batch : Batch, batch_index : int) -> Tensor: From a797548329fe2bb1c0bb7783eba3dfe11e453ac9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 23:15:39 +0100 Subject: [PATCH 144/394] Introduce new PoseLoss class (switched to mean) --- face_swapper/src/models/loss.py | 40 ++++++++++++++++++++++++--------- face_swapper/src/training.py | 20 +++++++++++------ 2 files changed, 43 insertions(+), 17 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 4f5c621..c47068a 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -3,7 +3,6 @@ from typing import List, Tuple import torch from pytorch_msssim import ssim -from sqlalchemy.dialects.mssql.information_schema import identity_columns from torch import Tensor, nn from ..helper import calc_embedding, hinge_fake_loss, hinge_real_loss @@ -44,15 +43,8 @@ class FaceSwapperLoss: 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) } - if weight_pose > 0: - generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_pose'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - if weight_gaze > 0: - generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) - else: - generator_loss_set['loss_gaze'] = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) + generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) + generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial generator_loss_set['loss_generator'] += generator_loss_set.get('loss_identity') * weight_identity @@ -214,3 +206,31 @@ class IdentityLoss(torch.nn.Module): identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() weighted_identity_loss = identity_loss * identity_weight return identity_loss, weighted_identity_loss + + +class PoseLoss(torch.nn.Module): + def __init__(self) -> None: + super(PoseLoss, self).__init__() + motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.mse_loss = nn.MSELoss() + + def calc(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + pose_weight = CONFIG.getfloat('training.losses', 'pose_weight') + output_motion_features = self.get_motion_features(output_tensor) + target_motion_features = self.get_motion_features(target_tensor) + temp_tensors = [] + + for target_motion_feature, output_motion_feature in zip(target_motion_features, output_motion_features): + temp_tensor = self.mse_loss(target_motion_feature, output_motion_feature) + temp_tensors.append(temp_tensor) + + pose_loss = torch.stack(temp_tensors).mean() + weighted_pose_loss = pose_loss * pose_weight + return pose_loss, weighted_pose_loss + + def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: + vision_tensor_norm = (input_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) + return translation, scale, rotation diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e52c6cd..1879549 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, IdentityLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, IdentityLoss, PoseLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -35,6 +35,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.attribute_loss = AttributeLoss() self.reconstruction_loss = ReconstructionLoss() self.identity_loss = IdentityLoss() + self.pose_loss = PoseLoss() self.automatic_optimization = automatic_optimization def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tensor: @@ -77,9 +78,10 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator')) self.log('loss_adversarial', generator_loss_set.get('loss_adversarial'), prog_bar = True) - self.log('loss_attribute', generator_loss_set.get('loss_attribute'), prog_bar = True) + self.log('loss_attribute', generator_loss_set.get('loss_attribute')) self.log('loss_identity', generator_loss_set.get('loss_identity')) self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) + self.log('loss_pose', generator_loss_set.get('loss_pose'), prog_bar = True) ############################################### @@ -87,13 +89,15 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): attribute_loss, weighted_attribute_loss = self.attribute_loss.calc(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + pose_loss, weighted_pose_loss = self.pose_loss.calc(target_tensor, generator_output_tensor) + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss self.log('generator_loss_new', generator_loss, prog_bar = True) self.log('adversarial_loss_new', adversarial_loss) - self.log('attribute_loss_new', attribute_loss, prog_bar = True) + self.log('attribute_loss_new', attribute_loss) self.log('reconstruction_loss_new', reconstruction_loss) self.log('identity_loss_new', identity_loss) + self.log('pose_loss_new', pose_loss, prog_bar = True) return generator_loss_set.get('loss_generator') def validation_step(self, batch : Batch, batch_index : int) -> Tensor: @@ -107,12 +111,14 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, output_tensor : VisionTensor) -> None: preview_limit = 8 - preview_items = [] + preview_cells = [] for source_tensor, target_tensor, output_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit]): - preview_items.append(torch.cat([ source_tensor, target_tensor, output_tensor] , dim = 2)) + preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor] , dim = 2) + preview_cells.append(preview_cell) - preview_grid = torchvision.utils.make_grid(torch.cat(preview_items, dim = 1).unsqueeze(0), normalize = True, scale_each = True) + preview_cells = torch.cat(preview_cells, dim = 1).unsqueeze(0) + preview_grid = torchvision.utils.make_grid(preview_cells, normalize = True, scale_each = True) self.logger.experiment.add_image('preview', preview_grid, self.global_step) # type:ignore[attr-defined] From 579d3ef51cc33c2ac780b3288215cbfe43d070e6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 22 Feb 2025 23:48:50 +0100 Subject: [PATCH 145/394] Introduce new GazeLoss class (switched to mean) --- embedding_converter/src/training.py | 2 -- face_swapper/src/models/loss.py | 30 +++++++++++++++++++++++++---- face_swapper/src/training.py | 12 ++++++++---- 3 files changed, 34 insertions(+), 10 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 09ebe54..93e3ac2 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -28,8 +28,6 @@ class EmbeddingConverterTrainer(lightning.LightningModule): self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.source_embedder.eval() - self.target_embedder.eval() self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index c47068a..801257e 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -22,9 +22,6 @@ class FaceSwapperLoss: self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.embedder.eval() - self.landmarker.eval() - self.motion_extractor.eval() def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') @@ -197,7 +194,6 @@ class IdentityLoss(torch.nn.Module): super(IdentityLoss, self).__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.embedder.eval() def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') @@ -234,3 +230,29 @@ class PoseLoss(torch.nn.Module): pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) rotation = torch.cat([ pitch, yaw, roll ], dim = 1) return translation, scale, rotation + + +class GazeLoss(torch.nn.Module): + def __init__(self) -> None: + super(GazeLoss, self).__init__() + landmarker_path = CONFIG.get('training.model', 'landmarker_path') + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.mse_loss = nn.MSELoss() + + def calc(self, target_tensor : VisionTensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') + output_face_landmark = self.detect_face_landmark(output_tensor) + target_face_landmark = self.detect_face_landmark(target_tensor) + + left_gaze_loss = self.mse_loss(output_face_landmark[:, 198], target_face_landmark[:, 198]) + right_gaze_loss = self.mse_loss(output_face_landmark[:, 197], target_face_landmark[:, 197]) + + gaze_loss = left_gaze_loss + right_gaze_loss + weighted_gaze_loss = gaze_loss * gaze_weight + return gaze_loss, weighted_gaze_loss + + def detect_face_landmark(self, input_tensor : Tensor) -> FaceLandmark203: + input_tensor = (input_tensor + 1) * 0.5 + input_tensor = nn.functional.interpolate(input_tensor, size = (224, 224), mode = 'bilinear') + face_landmarks_203 = self.landmarker(input_tensor)[2].view(-1, 203, 2) + return face_landmarks_203 diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1879549..0c2ab5e 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, IdentityLoss, PoseLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -36,6 +36,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.reconstruction_loss = ReconstructionLoss() self.identity_loss = IdentityLoss() self.pose_loss = PoseLoss() + self.gaze_loss = GazeLoss() self.automatic_optimization = automatic_optimization def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tensor: @@ -77,11 +78,12 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator')) - self.log('loss_adversarial', generator_loss_set.get('loss_adversarial'), prog_bar = True) + self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) self.log('loss_attribute', generator_loss_set.get('loss_attribute')) self.log('loss_identity', generator_loss_set.get('loss_identity')) self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) - self.log('loss_pose', generator_loss_set.get('loss_pose'), prog_bar = True) + self.log('loss_pose', generator_loss_set.get('loss_pose')) + self.log('loss_gaze', generator_loss_set.get('loss_gaze'), prog_bar = True) ############################################### @@ -90,6 +92,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss = self.pose_loss.calc(target_tensor, generator_output_tensor) + gaze_loss, weighted_gaze_loss = self.gaze_loss.calc(target_tensor, generator_output_tensor) generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss self.log('generator_loss_new', generator_loss, prog_bar = True) @@ -97,7 +100,8 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('attribute_loss_new', attribute_loss) self.log('reconstruction_loss_new', reconstruction_loss) self.log('identity_loss_new', identity_loss) - self.log('pose_loss_new', pose_loss, prog_bar = True) + self.log('pose_loss_new', pose_loss) + self.log('gaze_loss_new', gaze_loss, prog_bar = True) return generator_loss_set.get('loss_generator') def validation_step(self, batch : Batch, batch_index : int) -> Tensor: From 14b9bccafe5a43359bf58278a1f061f32aaaf870 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 00:29:41 +0100 Subject: [PATCH 146/394] Introduce new DiscriminatorLoss class, Remove useless super call params --- .../src/models/embedding_converter.py | 2 +- face_swapper/config.ini | 12 ++-- face_swapper/src/helper.py | 12 ---- face_swapper/src/models/discriminator.py | 2 +- face_swapper/src/models/generator.py | 2 +- face_swapper/src/models/loss.py | 70 +++++++++++++++---- .../src/networks/attribute_modulator.py | 8 +-- face_swapper/src/networks/nld.py | 2 +- face_swapper/src/networks/unet.py | 6 +- face_swapper/src/training.py | 17 +++-- 10 files changed, 84 insertions(+), 49 deletions(-) diff --git a/embedding_converter/src/models/embedding_converter.py b/embedding_converter/src/models/embedding_converter.py index de483f3..bc599c3 100644 --- a/embedding_converter/src/models/embedding_converter.py +++ b/embedding_converter/src/models/embedding_converter.py @@ -4,7 +4,7 @@ from torch import Tensor, nn class EmbeddingConverter(nn.Module): def __init__(self) -> None: - super(EmbeddingConverter, self).__init__() + super().__init__() self.layers = self.create_layers() self.leaky_relu = nn.LeakyReLU() diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 44f453d..d627d88 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -25,12 +25,12 @@ num_discriminators = kernel_size = [training.losses] -weight_adversarial = -weight_identity = -weight_attribute = -weight_reconstruction = -weight_pose = -weight_gaze = +adversarial_weight = +attribute_weight = +reconstruction_weight = +identity_weight = +pose_weight = +gaze_weight = [training.trainer] learning_rate = diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 01acf39..855a317 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -22,18 +22,6 @@ def convert_to_vision_frame(vision_tensor : VisionTensor) -> VisionFrame: return vision_frame -def hinge_real_loss(input_tensor : Tensor) -> Tensor: - real_loss = torch.relu(1 - input_tensor) - real_loss = real_loss.mean(dim = [ 1, 2, 3 ]) - return real_loss - - -def hinge_fake_loss(input_tensor : Tensor) -> Tensor: - fake_loss = torch.relu(input_tensor + 1) - fake_loss = fake_loss.mean(dim = [ 1, 2, 3 ]) - return fake_loss - - def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: crop_tensor = input_tensor[:, :, 15: 241, 15: 241] crop_tensor = nn.functional.interpolate(crop_tensor, size = (112, 112), mode = 'area') diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index bd78ae0..4f49cd2 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -11,7 +11,7 @@ CONFIG.read('config.ini') class Discriminator(nn.Module): def __init__(self) -> None: - super(Discriminator, self).__init__() + super().__init__() self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 6f757ec..6e530a3 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -12,7 +12,7 @@ CONFIG.read('config.ini') class Generator(nn.Module): def __init__(self) -> None: - super(Generator, self).__init__() + super().__init__() encoder_type = CONFIG.get('training.model.generator', 'encoder_type') id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 801257e..8397652 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -5,13 +5,25 @@ import torch from pytorch_msssim import ssim from torch import Tensor, nn -from ..helper import calc_embedding, hinge_fake_loss, hinge_real_loss +from ..helper import calc_embedding from ..types import Attributes, Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') +def hinge_real_loss(input_tensor : Tensor) -> Tensor: + real_loss = torch.relu(1 - input_tensor) + real_loss = real_loss.mean(dim = [ 1, 2, 3 ]) + return real_loss + + +def hinge_fake_loss(input_tensor : Tensor) -> Tensor: + fake_loss = torch.relu(input_tensor + 1) + fake_loss = fake_loss.mean(dim = [ 1, 2, 3 ]) + return fake_loss + + class FaceSwapperLoss: def __init__(self) -> None: embedder_path = CONFIG.get('training.model', 'embedder_path') @@ -51,6 +63,16 @@ class FaceSwapperLoss: generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze return generator_loss_set + def hinge_real_loss(input_tensor: Tensor) -> Tensor: + real_loss = torch.relu(1 - input_tensor) + real_loss = real_loss.mean(dim = [1, 2, 3]) + return real_loss + + def hinge_fake_loss(input_tensor: Tensor) -> Tensor: + fake_loss = torch.relu(input_tensor + 1) + fake_loss = fake_loss.mean(dim = [1, 2, 3]) + return fake_loss + def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: discriminator_loss_set = {} loss_fakes = [] @@ -131,9 +153,31 @@ class FaceSwapperLoss: return translation, scale, rotation -class AdversarialLoss(torch.nn.Module): +class DiscriminatorLoss(nn.Module): def __init__(self) -> None: - super(AdversarialLoss, self).__init__() + super().__init__() + + def calc(self, discriminator_source_tensors : List[Tensor], discriminator_output_tensors : List[Tensor]) -> Tensor: + temp1_tensors = [] + temp2_tensors = [] + + for discriminator_output_tensor in discriminator_output_tensors: + temp1_tensor = torch.relu(discriminator_output_tensor[0] + 1).mean(dim = [ 1, 2, 3 ]) + temp1_tensors.append(temp1_tensor) + + for discriminator_source_tensor in discriminator_source_tensors: + temp2_tensor = torch.relu(1 - discriminator_source_tensor[0]).mean(dim = [ 1, 2, 3 ]) + temp2_tensors.append(temp2_tensor) + + discriminator1_loss = torch.stack(temp1_tensors).mean() + discriminator2_loss = torch.stack(temp2_tensors).mean() + discriminator_loss = (discriminator1_loss + discriminator2_loss) * 0.5 + return discriminator_loss + + +class AdversarialLoss(nn.Module): + def __init__(self) -> None: + super().__init__() def calc(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: adversarial_weight = CONFIG.getfloat('training.losses', 'adversarial_weight') @@ -148,9 +192,9 @@ class AdversarialLoss(torch.nn.Module): return adversarial_loss, weighted_adversarial_loss -class AttributeLoss(torch.nn.Module): +class AttributeLoss(nn.Module): def __init__(self) -> None: - super(AttributeLoss, self).__init__() + super().__init__() def calc(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: batch_size = CONFIG.getint('training.loader', 'batch_size') @@ -166,9 +210,9 @@ class AttributeLoss(torch.nn.Module): return attribute_loss, weighted_attribute_loss -class ReconstructionLoss(torch.nn.Module): +class ReconstructionLoss(nn.Module): def __init__(self) -> None: - super(ReconstructionLoss, self).__init__() + super().__init__() def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: batch_size = CONFIG.getint('training.loader', 'batch_size') @@ -189,9 +233,9 @@ class ReconstructionLoss(torch.nn.Module): return reconstruction_loss, weighted_reconstruction_loss -class IdentityLoss(torch.nn.Module): +class IdentityLoss(nn.Module): def __init__(self) -> None: - super(IdentityLoss, self).__init__() + super().__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] @@ -204,9 +248,9 @@ class IdentityLoss(torch.nn.Module): return identity_loss, weighted_identity_loss -class PoseLoss(torch.nn.Module): +class PoseLoss(nn.Module): def __init__(self) -> None: - super(PoseLoss, self).__init__() + super().__init__() motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() @@ -232,9 +276,9 @@ class PoseLoss(torch.nn.Module): return translation, scale, rotation -class GazeLoss(torch.nn.Module): +class GazeLoss(nn.Module): def __init__(self) -> None: - super(GazeLoss, self).__init__() + super().__init__() landmarker_path = CONFIG.get('training.model', 'landmarker_path') self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index 4d3e973..cdf44e1 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -6,7 +6,7 @@ from ..types import Embedding, TargetAttributes class AADGenerator(nn.Module): def __init__(self, id_channels : int, num_blocks : int) -> None: - super(AADGenerator, self).__init__() + super().__init__() self.upsample = PixelShuffleUpsample(id_channels, 1024 * 4) self.res_block_1 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks) self.res_block_2 = AADResBlock(1024, 1024, 2048, id_channels, num_blocks) @@ -56,7 +56,7 @@ class AADLayer(nn.Module): class AADSequential(nn.Module): def __init__(self, *args : nn.Module) -> None: - super(AADSequential, self).__init__() + super().__init__() self.layers = nn.ModuleList(args) def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: @@ -70,7 +70,7 @@ class AADSequential(nn.Module): class AADResBlock(nn.Module): def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, id_channels : int, num_blocks : int) -> None: - super(AADResBlock, self).__init__() + super().__init__() self.input_channels = input_channels self.output_channels = output_channels self.prepare_primary_add_blocks(input_channels, attribute_channels, id_channels, output_channels, num_blocks) @@ -111,7 +111,7 @@ class AADResBlock(nn.Module): class PixelShuffleUpsample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: - super(PixelShuffleUpsample, self).__init__() + super().__init__() self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 3, padding = 1) self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index 73fc5a3..5fee9e1 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -5,7 +5,7 @@ from torch import Tensor, nn class NLD(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: - super(NLD, self).__init__() + super().__init__() self.nld = self.create_nld(input_channels, num_filters, num_layers, kernel_size) @staticmethod diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 5f9cb05..7ea9b2a 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -7,7 +7,7 @@ from torchvision import models class UNet(nn.Module): def __init__(self) -> None: - super(UNet, self).__init__() + super().__init__() self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() @@ -87,7 +87,7 @@ class UNetPro(UNet): class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: - super(UpSample, self).__init__() + super().__init__() self.conv_transpose = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) @@ -102,7 +102,7 @@ class UpSample(nn.Module): class DownSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: - super(DownSample, self).__init__() + super().__init__() self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 0c2ab5e..e5760cb 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, FaceSwapperLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, FaceSwapperLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss from .types import Batch, Embedding, VisionTensor CONFIG = configparser.ConfigParser() @@ -31,6 +31,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generator = Generator() self.discriminator = Discriminator() + self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss() self.attribute_loss = AttributeLoss() self.reconstruction_loss = ReconstructionLoss() @@ -65,10 +66,10 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.manual_backward(generator_loss_set.get('loss_generator')) generator_optimizer.step() - discriminator_source_tensor = self.discriminator(source_tensor) + discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) - discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensor, discriminator_output_tensors) + discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_loss_set.get('loss_discriminator')) discriminator_optimizer.step() @@ -77,16 +78,17 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.generate_preview(source_tensor, target_tensor, generator_output_tensor) self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) - self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator')) + self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator'), prog_bar = True) self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) self.log('loss_attribute', generator_loss_set.get('loss_attribute')) self.log('loss_identity', generator_loss_set.get('loss_identity')) self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) self.log('loss_pose', generator_loss_set.get('loss_pose')) - self.log('loss_gaze', generator_loss_set.get('loss_gaze'), prog_bar = True) + self.log('loss_gaze', generator_loss_set.get('loss_gaze')) ############################################### + discriminator_loss = self.discriminator_loss.calc(discriminator_source_tensors, discriminator_output_tensors) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) attribute_loss, weighted_attribute_loss = self.attribute_loss.calc(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) @@ -96,12 +98,13 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss self.log('generator_loss_new', generator_loss, prog_bar = True) + self.log('discriminator_loss_new', discriminator_loss, prog_bar = True) self.log('adversarial_loss_new', adversarial_loss) self.log('attribute_loss_new', attribute_loss) self.log('reconstruction_loss_new', reconstruction_loss) self.log('identity_loss_new', identity_loss) self.log('pose_loss_new', pose_loss) - self.log('gaze_loss_new', gaze_loss, prog_bar = True) + self.log('gaze_loss_new', gaze_loss) return generator_loss_set.get('loss_generator') def validation_step(self, batch : Batch, batch_index : int) -> Tensor: @@ -161,7 +164,7 @@ def create_trainer() -> Trainer: callbacks = [ ModelCheckpoint( - monitor = 'loss_generator', + monitor = 'generator_loss', dirpath = output_directory_path, filename = output_file_pattern, every_n_train_steps = 1000, From 63e4bea3cd16e6c95b9be97f332b196286b47d3e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 00:39:48 +0100 Subject: [PATCH 147/394] Use new config for old code --- face_swapper/src/models/loss.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 8397652..7e810e5 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -36,12 +36,12 @@ class FaceSwapperLoss: self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - weight_adversarial = CONFIG.getfloat('training.losses', 'weight_adversarial') - weight_identity = CONFIG.getfloat('training.losses', 'weight_identity') - weight_attribute = CONFIG.getfloat('training.losses', 'weight_attribute') - weight_reconstruction = CONFIG.getfloat('training.losses', 'weight_reconstruction') - weight_pose = CONFIG.getfloat('training.losses', 'weight_pose') - weight_gaze = CONFIG.getfloat('training.losses', 'weight_gaze') + weight_adversarial = CONFIG.getfloat('training.losses', 'adversarial_weight') + weight_identity = CONFIG.getfloat('training.losses', 'identity_weight') + weight_attribute = CONFIG.getfloat('training.losses', 'attribute_weight') + weight_reconstruction = CONFIG.getfloat('training.losses', 'reconstruction_weight') + weight_pose = CONFIG.getfloat('training.losses', 'pose_weight') + weight_gaze = CONFIG.getfloat('training.losses', 'gaze_weight') source_tensor, target_tensor = batch is_same_person = torch.tensor(0) if torch.equal(source_tensor, target_tensor) else torch.tensor(1) generator_loss_set =\ From ed0f6ae897b8a3ac790a17c9c9556bbca466b6b3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 01:05:01 +0100 Subject: [PATCH 148/394] Use new loss code, Remove unused code, Remove old types, Ban VisionTensor naming --- face_swapper/src/helper.py | 18 +-- face_swapper/src/inferencing.py | 12 +- face_swapper/src/models/loss.py | 149 +----------------- .../src/networks/attribute_modulator.py | 8 +- face_swapper/src/training.py | 74 ++++----- face_swapper/src/types.py | 10 -- 6 files changed, 54 insertions(+), 217 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 855a317..2e3cf60 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -2,19 +2,19 @@ import numpy import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding, VisionFrame, VisionTensor +from .types import EmbedderModule, Embedding, Padding, VisionFrame -def convert_to_vision_tensor(vision_frame : VisionFrame) -> VisionTensor: - vision_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) - vision_tensor = vision_tensor / 255.0 - vision_tensor = (vision_tensor - 0.5) * 2 - vision_tensor = vision_tensor.unsqueeze(0) - return vision_tensor +def convert_to_tensor(vision_frame : VisionFrame) -> Tensor: + output_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) + output_tensor = output_tensor / 255.0 + output_tensor = (output_tensor - 0.5) * 2 + output_tensor = output_tensor.unsqueeze(0) + return output_tensor -def convert_to_vision_frame(vision_tensor : VisionTensor) -> VisionFrame: - vision_frame = vision_tensor.detach().cpu().numpy()[0] +def convert_to_vision_frame(input_tensor : Tensor) -> VisionFrame: + vision_frame = input_tensor.detach().cpu().numpy()[0] vision_frame = vision_frame.transpose(1, 2, 0) vision_frame = (vision_frame + 1) * 127.5 vision_frame = vision_frame.clip(0, 255).astype(numpy.uint8) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index d01510d..30d3da5 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,7 +3,7 @@ import configparser import cv2 import torch -from .helper import calc_embedding, convert_to_vision_frame, convert_to_vision_tensor +from .helper import calc_embedding, convert_to_vision_frame, convert_to_tensor from .models.generator import Generator from .types import EmbedderModule, GeneratorModule, VisionFrame @@ -12,11 +12,11 @@ CONFIG.read('config.ini') def run_swap(generator : GeneratorModule, embedder : EmbedderModule, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: - source_vision_tensor = convert_to_vision_tensor(source_vision_frame) - target_vision_tensor = convert_to_vision_tensor(target_vision_frame) - source_embedding = calc_embedding(embedder, source_vision_tensor, (0, 0, 0, 0)) - output_vision_tensor = generator(source_embedding, target_vision_tensor)[0] - output_vision_frame = convert_to_vision_frame(output_vision_tensor) + source_tensor = convert_to_tensor(source_vision_frame) + target_tensor = convert_to_tensor(target_vision_frame) + source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) + output_tensor = generator(source_embedding, target_tensor)[0] + output_vision_frame = convert_to_vision_frame(output_tensor) return output_vision_frame diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 7e810e5..6df2c7b 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -6,153 +6,12 @@ from pytorch_msssim import ssim from torch import Tensor, nn from ..helper import calc_embedding -from ..types import Attributes, Batch, DiscriminatorLossSet, DiscriminatorOutputs, FaceLandmark203, GeneratorLossSet, LossTensor, SwapAttributes, TargetAttributes, VisionTensor +from ..types import Attributes, FaceLandmark203 CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def hinge_real_loss(input_tensor : Tensor) -> Tensor: - real_loss = torch.relu(1 - input_tensor) - real_loss = real_loss.mean(dim = [ 1, 2, 3 ]) - return real_loss - - -def hinge_fake_loss(input_tensor : Tensor) -> Tensor: - fake_loss = torch.relu(input_tensor + 1) - fake_loss = fake_loss.mean(dim = [ 1, 2, 3 ]) - return fake_loss - - -class FaceSwapperLoss: - def __init__(self) -> None: - embedder_path = CONFIG.get('training.model', 'embedder_path') - landmarker_path = CONFIG.get('training.model', 'landmarker_path') - motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') - self.batch_size = CONFIG.getint('training.loader', 'batch_size') - self.mse_loss = nn.MSELoss() - self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] - - def calc_generator_loss(self, swap_tensor : VisionTensor, target_attributes : TargetAttributes, swap_attributes : SwapAttributes, discriminator_outputs : DiscriminatorOutputs, batch : Batch) -> GeneratorLossSet: - weight_adversarial = CONFIG.getfloat('training.losses', 'adversarial_weight') - weight_identity = CONFIG.getfloat('training.losses', 'identity_weight') - weight_attribute = CONFIG.getfloat('training.losses', 'attribute_weight') - weight_reconstruction = CONFIG.getfloat('training.losses', 'reconstruction_weight') - weight_pose = CONFIG.getfloat('training.losses', 'pose_weight') - weight_gaze = CONFIG.getfloat('training.losses', 'gaze_weight') - source_tensor, target_tensor = batch - is_same_person = torch.tensor(0) if torch.equal(source_tensor, target_tensor) else torch.tensor(1) - generator_loss_set =\ - { - 'loss_adversarial': self.calc_adversarial_loss(discriminator_outputs), - 'loss_identity': self.calc_identity_loss(source_tensor, swap_tensor), - 'loss_attribute': self.calc_attribute_loss(target_attributes, swap_attributes), - 'loss_reconstruction': self.calc_reconstruction_loss(swap_tensor, target_tensor, is_same_person) - } - - generator_loss_set['loss_pose'] = self.calc_pose_loss(swap_tensor, target_tensor) - generator_loss_set['loss_gaze'] = self.calc_gaze_loss(swap_tensor, target_tensor) - - generator_loss_set['loss_generator'] = generator_loss_set.get('loss_adversarial') * weight_adversarial - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_identity') * weight_identity - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_attribute') * weight_attribute - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_reconstruction') * weight_reconstruction - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_pose') * weight_pose - generator_loss_set['loss_generator'] += generator_loss_set.get('loss_gaze') * weight_gaze - return generator_loss_set - - def hinge_real_loss(input_tensor: Tensor) -> Tensor: - real_loss = torch.relu(1 - input_tensor) - real_loss = real_loss.mean(dim = [1, 2, 3]) - return real_loss - - def hinge_fake_loss(input_tensor: Tensor) -> Tensor: - fake_loss = torch.relu(input_tensor + 1) - fake_loss = fake_loss.mean(dim = [1, 2, 3]) - return fake_loss - - def calc_discriminator_loss(self, real_discriminator_outputs : DiscriminatorOutputs, fake_discriminator_outputs : DiscriminatorOutputs) -> DiscriminatorLossSet: - discriminator_loss_set = {} - loss_fakes = [] - - for fake_discriminator_output in fake_discriminator_outputs: - loss_fakes.append(hinge_fake_loss(fake_discriminator_output[0])) - - loss_trues = [] - - for true_discriminator_output in real_discriminator_outputs: - loss_trues.append(hinge_real_loss(true_discriminator_output[0])) - - loss_fake = torch.stack(loss_fakes).mean() - loss_true = torch.stack(loss_trues).mean() - discriminator_loss_set['loss_discriminator'] = (loss_true + loss_fake) * 0.5 - return discriminator_loss_set - - def calc_adversarial_loss(self, discriminator_outputs : DiscriminatorOutputs) -> LossTensor: - loss_adversarials = [] - - for discriminator_output in discriminator_outputs: - loss_adversarials.append(hinge_real_loss(discriminator_output[0]).mean()) - - loss_adversarial = torch.stack(loss_adversarials).mean() - return loss_adversarial - - def calc_attribute_loss(self, target_attributes : TargetAttributes, swap_attributes : SwapAttributes) -> LossTensor: - loss_attributes = [] - - for swap_attribute, target_attribute in zip(swap_attributes, target_attributes): - loss_attributes.append(torch.mean(torch.pow(swap_attribute - target_attribute, 2).reshape(self.batch_size, -1), dim = 1).mean()) - - loss_attribute = torch.stack(loss_attributes).mean() * 0.5 - return loss_attribute - - def calc_reconstruction_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor, is_same_person : Tensor) -> LossTensor: - loss_reconstruction = torch.pow(swap_tensor - target_tensor, 2).reshape(self.batch_size, -1) - loss_reconstruction = torch.mean(loss_reconstruction, dim = 1) * 0.5 - loss_reconstruction = torch.sum(loss_reconstruction * is_same_person) / (is_same_person.sum() + 1e-4) - loss_ssim = 1 - ssim(swap_tensor, target_tensor, data_range = float(torch.max(swap_tensor) - torch.min(swap_tensor))).mean() - loss_reconstruction = (loss_reconstruction + loss_ssim) * 0.5 - return loss_reconstruction - - def calc_identity_loss(self, source_tensor : VisionTensor, swap_tensor : VisionTensor) -> LossTensor: - swap_embedding = calc_embedding(self.embedder, swap_tensor, (30, 0, 10, 10)) - source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) - loss_identity = (1 - torch.cosine_similarity(source_embedding, swap_embedding)).mean() - return loss_identity - - def calc_pose_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_motion_features = self.get_pose_features(swap_tensor) - target_motion_features = self.get_pose_features(target_tensor) - loss_pose = torch.tensor(0).to(swap_tensor.device).to(swap_tensor.dtype) - - for swap_motion_feature, target_motion_feature in zip(swap_motion_features, target_motion_features): - loss_pose += self.mse_loss(swap_motion_feature, target_motion_feature) - - return loss_pose - - def calc_gaze_loss(self, swap_tensor : VisionTensor, target_tensor : VisionTensor) -> LossTensor: - swap_landmark = self.get_face_landmarks(swap_tensor) - target_landmark = self.get_face_landmarks(target_tensor) - left_gaze_loss = self.mse_loss(swap_landmark[:, 198], target_landmark[:, 198]) - right_gaze_loss = self.mse_loss(swap_landmark[:, 197], target_landmark[:, 197]) - gaze_loss = left_gaze_loss + right_gaze_loss - return gaze_loss - - def get_face_landmarks(self, vision_tensor : VisionTensor) -> FaceLandmark203: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - vision_tensor_norm = nn.functional.interpolate(vision_tensor_norm, size = (224, 224), mode = 'bilinear') - landmarks = self.landmarker(vision_tensor_norm)[2].view(-1, 203, 2) - return landmarks - - def get_pose_features(self, vision_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - vision_tensor_norm = (vision_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - return translation, scale, rotation - - class DiscriminatorLoss(nn.Module): def __init__(self) -> None: super().__init__() @@ -270,8 +129,8 @@ class PoseLoss(nn.Module): return pose_loss, weighted_pose_loss def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - vision_tensor_norm = (input_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(vision_tensor_norm) + input_tensor = (input_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(input_tensor) rotation = torch.cat([ pitch, yaw, roll ], dim = 1) return translation, scale, rotation @@ -283,7 +142,7 @@ class GazeLoss(nn.Module): self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() - def calc(self, target_tensor : VisionTensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + def calc(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') output_face_landmark = self.detect_face_landmark(output_tensor) target_face_landmark = self.detect_face_landmark(target_tensor) diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index cdf44e1..f342d6c 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from ..types import Embedding, TargetAttributes +from ..types import Attributes, Embedding class AADGenerator(nn.Module): @@ -17,7 +17,7 @@ class AADGenerator(nn.Module): self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) - def forward(self, target_attributes : TargetAttributes, source_embedding : Embedding) -> Tensor: + def forward(self, target_attributes : Attributes, source_embedding : Embedding) -> Tensor: feature_map = self.upsample(source_embedding) feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) @@ -59,10 +59,10 @@ class AADSequential(nn.Module): super().__init__() self.layers = nn.ModuleList(args) - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: for layer in self.layers: if isinstance(layer, AADLayer): - feature_map = layer(feature_map, attribute_embedding, id_embedding) + feature_map = layer(feature_map, attribute_embedding, identity_embedding) else: feature_map = layer(feature_map) return feature_map diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e5760cb..fb52b12 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,18 +16,18 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, FaceSwapperLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss -from .types import Batch, Embedding, VisionTensor +from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss +from .types import Batch, Embedding CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): +class FaceSwapperTrainer(lightning.LightningModule): def __init__(self) -> None: super().__init__() - FaceSwapperLoss.__init__(self) automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') + embedder_path = CONFIG.get('training.model', 'embedder_path') self.generator = Generator() self.discriminator = Discriminator() @@ -38,9 +38,10 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.identity_loss = IdentityLoss() self.pose_loss = PoseLoss() self.gaze_loss = GazeLoss() + self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.automatic_optimization = automatic_optimization - def forward(self, target_tensor : VisionTensor, source_embedding : Embedding) -> Tensor: + def forward(self, target_tensor : Tensor, source_embedding : Embedding) -> Tensor: output_tensor = self.generator(source_embedding, target_tensor) return output_tensor @@ -61,34 +62,6 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) - generator_loss_set = self.calc_generator_loss(generator_output_tensor, target_attributes, generator_output_attributes, discriminator_output_tensors, batch) - generator_optimizer.zero_grad() - self.manual_backward(generator_loss_set.get('loss_generator')) - generator_optimizer.step() - - discriminator_source_tensors = self.discriminator(source_tensor) - discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) - - discriminator_loss_set = self.calc_discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) - discriminator_optimizer.zero_grad() - self.manual_backward(discriminator_loss_set.get('loss_discriminator')) - discriminator_optimizer.step() - - if self.global_step % preview_frequency == 0: - self.generate_preview(source_tensor, target_tensor, generator_output_tensor) - - self.log('loss_generator', generator_loss_set.get('loss_generator'), prog_bar = True) - self.log('loss_discriminator', discriminator_loss_set.get('loss_discriminator'), prog_bar = True) - self.log('loss_adversarial', generator_loss_set.get('loss_adversarial')) - self.log('loss_attribute', generator_loss_set.get('loss_attribute')) - self.log('loss_identity', generator_loss_set.get('loss_identity')) - self.log('loss_reconstruction', generator_loss_set.get('loss_reconstruction')) - self.log('loss_pose', generator_loss_set.get('loss_pose')) - self.log('loss_gaze', generator_loss_set.get('loss_gaze')) - - ############################################### - - discriminator_loss = self.discriminator_loss.calc(discriminator_source_tensors, discriminator_output_tensors) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) attribute_loss, weighted_attribute_loss = self.attribute_loss.calc(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) @@ -97,15 +70,30 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): gaze_loss, weighted_gaze_loss = self.gaze_loss.calc(target_tensor, generator_output_tensor) generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss - self.log('generator_loss_new', generator_loss, prog_bar = True) - self.log('discriminator_loss_new', discriminator_loss, prog_bar = True) - self.log('adversarial_loss_new', adversarial_loss) - self.log('attribute_loss_new', attribute_loss) - self.log('reconstruction_loss_new', reconstruction_loss) - self.log('identity_loss_new', identity_loss) - self.log('pose_loss_new', pose_loss) - self.log('gaze_loss_new', gaze_loss) - return generator_loss_set.get('loss_generator') + generator_optimizer.zero_grad() + self.manual_backward(generator_loss) + generator_optimizer.step() + + discriminator_source_tensors = self.discriminator(source_tensor) + discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) + discriminator_loss = self.discriminator_loss.calc(discriminator_source_tensors, discriminator_output_tensors) + + discriminator_optimizer.zero_grad() + self.manual_backward(discriminator_loss) + discriminator_optimizer.step() + + if self.global_step % preview_frequency == 0: + self.generate_preview(source_tensor, target_tensor, generator_output_tensor) + + self.log('generator_loss', generator_loss, prog_bar = True) + self.log('discriminator_loss', discriminator_loss, prog_bar = True) + self.log('adversarial_loss', adversarial_loss) + self.log('attribute_loss', attribute_loss) + self.log('reconstruction_loss', reconstruction_loss) + self.log('identity_loss', identity_loss) + self.log('pose_loss', pose_loss) + self.log('gaze_loss', gaze_loss) + return generator_loss def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch @@ -116,7 +104,7 @@ class FaceSwapperTrainer(lightning.LightningModule, FaceSwapperLoss): self.log('validation', validation) return validation - def generate_preview(self, source_tensor : VisionTensor, target_tensor : VisionTensor, output_tensor : VisionTensor) -> None: + def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> None: preview_limit = 8 preview_cells = [] diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 83d6e6e..5dd0628 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -7,23 +7,13 @@ from torch.nn import Module Batch : TypeAlias = Tuple[Tensor, Tensor] -SwapAttributes : TypeAlias = Tuple[Tensor, ...] -TargetAttributes : TypeAlias = Tuple[Tensor, ...] -DiscriminatorOutputs : TypeAlias = List[List[Tensor]] - Attributes : TypeAlias = Tuple[Tensor, ...] Embedding : TypeAlias = Tensor FaceLandmark203 : TypeAlias = Tensor -StateSet : TypeAlias = OrderedDict[str, Any] Padding : TypeAlias = Tuple[int, int, int, int] VisionFrame : TypeAlias = NDArray[Any] -LossTensor : TypeAlias = Tensor -VisionTensor : TypeAlias = Tensor - -GeneratorLossSet : TypeAlias = Dict[str, Tensor] -DiscriminatorLossSet : TypeAlias = Dict[str, Tensor] GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module From de6cfbc35b75c530e7fa24215962fad376299e51 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 01:06:18 +0100 Subject: [PATCH 149/394] Fix CI --- face_swapper/src/inferencing.py | 2 +- face_swapper/src/types.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 30d3da5..64047cc 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -3,7 +3,7 @@ import configparser import cv2 import torch -from .helper import calc_embedding, convert_to_vision_frame, convert_to_tensor +from .helper import calc_embedding, convert_to_tensor, convert_to_vision_frame from .models.generator import Generator from .types import EmbedderModule, GeneratorModule, VisionFrame diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 5dd0628..5560b46 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,5 +1,4 @@ -from collections import OrderedDict -from typing import Any, Dict, List, Tuple, TypeAlias +from typing import Any, Tuple, TypeAlias from numpy.typing import NDArray from torch import Tensor From 6fed877d338fb31aefdb6b970b7d785adcc2db27 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Feb 2025 13:30:08 +0530 Subject: [PATCH 150/394] reconstruction-loss fix --- face_swapper/src/models/loss.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 6df2c7b..e4592f6 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -72,21 +72,22 @@ class AttributeLoss(nn.Module): class ReconstructionLoss(nn.Module): def __init__(self) -> None: super().__init__() + self.mse_loss = nn.MSELoss() def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - batch_size = CONFIG.getint('training.loader', 'batch_size') reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') - reconstruction_loss = torch.pow(output_tensor - target_tensor, 2).reshape(batch_size, -1) - reconstruction_loss = torch.mean(reconstruction_loss, dim = 1) * 0.5 + temp_tensors = [] - if torch.equal(source_tensor, target_tensor): - reconstruction_loss = torch.sum(reconstruction_loss * torch.tensor(0)) / (torch.tensor(0).sum() + 1e-4) - else: - reconstruction_loss = torch.sum(reconstruction_loss * torch.tensor(1)) / (torch.tensor(1).sum() + 1e-4) + for _source_tensor, _target_tensor in zip(source_tensor, target_tensor): + temp_tensor = self.mse_loss(_source_tensor, _target_tensor) + if torch.equal(_source_tensor, _target_tensor): + temp_tensors.append(temp_tensor) + else: + temp_tensors.append(temp_tensor * 0) + reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5 data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() - reconstruction_loss = (reconstruction_loss + similarity) * 0.5 weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight return reconstruction_loss, weighted_reconstruction_loss From e75a3c58f9acbb1c3d63e96eaf48e0298f86684a Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 09:22:10 +0100 Subject: [PATCH 151/394] Replace calc() with forward(), Rename temp1 with positive and temp2 with negative --- face_swapper/src/models/loss.py | 34 +++++++++++++++++---------------- face_swapper/src/training.py | 14 +++++++------- 2 files changed, 25 insertions(+), 23 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index e4592f6..68b702c 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -16,21 +16,21 @@ class DiscriminatorLoss(nn.Module): def __init__(self) -> None: super().__init__() - def calc(self, discriminator_source_tensors : List[Tensor], discriminator_output_tensors : List[Tensor]) -> Tensor: - temp1_tensors = [] - temp2_tensors = [] + def forward(self, discriminator_source_tensors : List[Tensor], discriminator_output_tensors : List[Tensor]) -> Tensor: + positive_tensors = [] + negative_tensors = [] for discriminator_output_tensor in discriminator_output_tensors: - temp1_tensor = torch.relu(discriminator_output_tensor[0] + 1).mean(dim = [ 1, 2, 3 ]) - temp1_tensors.append(temp1_tensor) + positive_tensor = torch.relu(discriminator_output_tensor[0] + 1).mean(dim = [ 1, 2, 3 ]) + positive_tensors.append(positive_tensor) for discriminator_source_tensor in discriminator_source_tensors: - temp2_tensor = torch.relu(1 - discriminator_source_tensor[0]).mean(dim = [ 1, 2, 3 ]) - temp2_tensors.append(temp2_tensor) + negative_tensor = torch.relu(1 - discriminator_source_tensor[0]).mean(dim = [ 1, 2, 3 ]) + negative_tensors.append(negative_tensor) - discriminator1_loss = torch.stack(temp1_tensors).mean() - discriminator2_loss = torch.stack(temp2_tensors).mean() - discriminator_loss = (discriminator1_loss + discriminator2_loss) * 0.5 + discriminator_positive_loss = torch.stack(positive_tensors).mean() + discriminator_negative_loss = torch.stack(negative_tensors).mean() + discriminator_loss = (discriminator_positive_loss + discriminator_negative_loss) * 0.5 return discriminator_loss @@ -38,7 +38,7 @@ class AdversarialLoss(nn.Module): def __init__(self) -> None: super().__init__() - def calc(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: + def forward(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: adversarial_weight = CONFIG.getfloat('training.losses', 'adversarial_weight') temp_tensors = [] @@ -55,7 +55,7 @@ class AttributeLoss(nn.Module): def __init__(self) -> None: super().__init__() - def calc(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: + def forward(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: batch_size = CONFIG.getint('training.loader', 'batch_size') attribute_weight = CONFIG.getfloat('training.losses', 'attribute_weight') temp_tensors = [] @@ -74,7 +74,7 @@ class ReconstructionLoss(nn.Module): super().__init__() self.mse_loss = nn.MSELoss() - def calc(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') temp_tensors = [] @@ -85,9 +85,11 @@ class ReconstructionLoss(nn.Module): temp_tensors.append(temp_tensor) else: temp_tensors.append(temp_tensor * 0) + reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5 data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() + reconstruction_loss = (reconstruction_loss + similarity) * 0.5 weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight return reconstruction_loss, weighted_reconstruction_loss @@ -99,7 +101,7 @@ class IdentityLoss(nn.Module): embedder_path = CONFIG.get('training.model', 'embedder_path') self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - def calc(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) @@ -115,7 +117,7 @@ class PoseLoss(nn.Module): self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() - def calc(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: pose_weight = CONFIG.getfloat('training.losses', 'pose_weight') output_motion_features = self.get_motion_features(output_tensor) target_motion_features = self.get_motion_features(target_tensor) @@ -143,7 +145,7 @@ class GazeLoss(nn.Module): self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() - def calc(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') output_face_landmark = self.detect_face_landmark(output_tensor) target_face_landmark = self.detect_face_landmark(target_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index fb52b12..f3b132a 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -62,12 +62,12 @@ class FaceSwapperTrainer(lightning.LightningModule): generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) - adversarial_loss, weighted_adversarial_loss = self.adversarial_loss.calc(discriminator_output_tensors) - attribute_loss, weighted_attribute_loss = self.attribute_loss.calc(target_attributes, generator_output_attributes) - reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss.calc(source_tensor, target_tensor, generator_output_tensor) - identity_loss, weighted_identity_loss = self.identity_loss.calc(generator_output_tensor, source_tensor) - pose_loss, weighted_pose_loss = self.pose_loss.calc(target_tensor, generator_output_tensor) - gaze_loss, weighted_gaze_loss = self.gaze_loss.calc(target_tensor, generator_output_tensor) + adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) + attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes) + reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) + identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) + pose_loss, weighted_pose_loss = self.pose_loss(target_tensor, generator_output_tensor) + gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss generator_optimizer.zero_grad() @@ -76,7 +76,7 @@ class FaceSwapperTrainer(lightning.LightningModule): discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) - discriminator_loss = self.discriminator_loss.calc(discriminator_source_tensors, discriminator_output_tensors) + discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) discriminator_optimizer.zero_grad() self.manual_backward(discriminator_loss) From fad38da8648dcce7aec4af9d9be0bde8cde3afca Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 09:35:18 +0100 Subject: [PATCH 152/394] Rename temp1 with positive and temp2 with negative --- face_swapper/src/models/loss.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 68b702c..e7476a3 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -28,9 +28,9 @@ class DiscriminatorLoss(nn.Module): negative_tensor = torch.relu(1 - discriminator_source_tensor[0]).mean(dim = [ 1, 2, 3 ]) negative_tensors.append(negative_tensor) - discriminator_positive_loss = torch.stack(positive_tensors).mean() - discriminator_negative_loss = torch.stack(negative_tensors).mean() - discriminator_loss = (discriminator_positive_loss + discriminator_negative_loss) * 0.5 + positive_loss = torch.stack(positive_tensors).mean() + negative_loss = torch.stack(negative_tensors).mean() + discriminator_loss = (positive_loss + negative_loss) * 0.5 return discriminator_loss From 14bbece850272ff16ea74298b5098a8287b0dbf6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 14:11:04 +0100 Subject: [PATCH 153/394] Rename id_ to identity_ --- face_swapper/README.md | 2 +- face_swapper/config.ini | 6 +- face_swapper/src/models/generator.py | 4 +- .../src/networks/attribute_modulator.py | 60 +++++++++---------- 4 files changed, 36 insertions(+), 36 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index e3a5c37..9c7a73b 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -49,7 +49,7 @@ motion_extractor_path = .models/motion_extractor.pt [training.model.generator] encoder_type = unet-pro num_blocks = 2 -id_channels = 512 +identity_channels = 512 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index d627d88..4b3ba86 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -8,14 +8,14 @@ num_workers = split_ratio = [training.model] -id_embedder_path = +embedder_path = landmarker_path = motion_extractor_path = [training.model.generator] encoder_type = num_blocks = -id_channels = +identity_channels = [training.model.discriminator] input_channels = @@ -53,7 +53,7 @@ opset_version = [inferencing] generator_path = -id_embedder_path = +embedder_path = source_path = target_path = output_path = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 6e530a3..a4e47f2 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -14,14 +14,14 @@ class Generator(nn.Module): def __init__(self) -> None: super().__init__() encoder_type = CONFIG.get('training.model.generator', 'encoder_type') - id_channels = CONFIG.getint('training.model.generator', 'id_channels') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') + identity_channels = CONFIG.getint('training.model.generator', 'identity_channels') if encoder_type == 'unet': self.encoder = UNet() if encoder_type == 'unet-pro': self.encoder = UNetPro() - self.generator = AADGenerator(id_channels, num_blocks) + self.generator = AADGenerator(identity_channels, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/attribute_modulator.py index f342d6c..b1d6a0f 100644 --- a/face_swapper/src/networks/attribute_modulator.py +++ b/face_swapper/src/networks/attribute_modulator.py @@ -5,17 +5,17 @@ from ..types import Attributes, Embedding class AADGenerator(nn.Module): - def __init__(self, id_channels : int, num_blocks : int) -> None: + def __init__(self, identity_channels : int, num_blocks : int) -> None: super().__init__() - self.upsample = PixelShuffleUpsample(id_channels, 1024 * 4) - self.res_block_1 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks) - self.res_block_2 = AADResBlock(1024, 1024, 2048, id_channels, num_blocks) - self.res_block_3 = AADResBlock(1024, 1024, 1024, id_channels, num_blocks) - self.res_block_4 = AADResBlock(1024, 512, 512, id_channels, num_blocks) - self.res_block_5 = AADResBlock(512, 256, 256, id_channels, num_blocks) - self.res_block_6 = AADResBlock(256, 128, 128, id_channels, num_blocks) - self.res_block_7 = AADResBlock(128, 64, 64, id_channels, num_blocks) - self.res_block_8 = AADResBlock(64, 3, 64, id_channels, num_blocks) + self.upsample = PixelShuffleUpsample(identity_channels, 1024 * 4) + self.res_block_1 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) + self.res_block_2 = AADResBlock(1024, 1024, 2048, identity_channels, num_blocks) + self.res_block_3 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) + self.res_block_4 = AADResBlock(1024, 512, 512, identity_channels, num_blocks) + self.res_block_5 = AADResBlock(512, 256, 256, identity_channels, num_blocks) + self.res_block_6 = AADResBlock(256, 128, 128, identity_channels, num_blocks) + self.res_block_7 = AADResBlock(128, 64, 64, identity_channels, num_blocks) + self.res_block_8 = AADResBlock(64, 3, 64, identity_channels, num_blocks) def forward(self, target_attributes : Attributes, source_embedding : Embedding) -> Tensor: feature_map = self.upsample(source_embedding) @@ -31,26 +31,26 @@ class AADGenerator(nn.Module): class AADLayer(nn.Module): - def __init__(self, input_channels : int, attr_channels : int, id_channels : int) -> None: + def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: super(AADLayer, self).__init__() self.input_channels = input_channels - self.conv_beta = nn.Conv2d(attr_channels, input_channels, kernel_size = 1) - self.conv_gamma = nn.Conv2d(attr_channels, input_channels, kernel_size = 1) - self.fc_beta = nn.Linear(id_channels, input_channels) - self.fc_gamma = nn.Linear(id_channels, input_channels) + self.conv_beta = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.conv_gamma = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.fc_beta = nn.Linear(identity_channels, input_channels) + self.fc_gamma = nn.Linear(identity_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels) self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: feature_map = self.instance_norm(feature_map) gamma_attribute = self.conv_gamma(attribute_embedding) beta_attribute = self.conv_beta(attribute_embedding) attribute_modulation = gamma_attribute * feature_map + beta_attribute - id_gamma = self.fc_gamma(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - id_beta = self.fc_beta(id_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - id_modulation = id_gamma * feature_map + id_beta + identity_gamma = self.fc_gamma(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + identity_beta = self.fc_beta(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + identity_modulation = identity_gamma * feature_map + identity_beta feature_mask = torch.sigmoid(self.conv_mask(feature_map)) - feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * id_modulation + feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * identity_modulation return feature_blend @@ -69,41 +69,41 @@ class AADSequential(nn.Module): class AADResBlock(nn.Module): - def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, id_channels : int, num_blocks : int) -> None: + def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> None: super().__init__() self.input_channels = input_channels self.output_channels = output_channels - self.prepare_primary_add_blocks(input_channels, attribute_channels, id_channels, output_channels, num_blocks) - self.prepare_auxiliary_add_blocks(input_channels, attribute_channels, id_channels, output_channels) + self.prepare_primary_add_blocks(input_channels, attribute_channels, identity_channels, output_channels, num_blocks) + self.prepare_auxiliary_add_blocks(input_channels, attribute_channels, identity_channels, output_channels) - def prepare_primary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int, num_blocks : int) -> None: + def prepare_primary_add_blocks(self, input_channels : int, attribute_channels : int, identity_channels : int, output_channels : int, num_blocks : int) -> None: primary_add_blocks = [] for index in range(num_blocks): intermediate_channels = input_channels if index < (num_blocks - 1) else output_channels primary_add_blocks.extend( [ - AADLayer(input_channels, attribute_channels, id_channels), + AADLayer(input_channels, attribute_channels, identity_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False) ] ) self.primary_add_blocks = AADSequential(*primary_add_blocks) - def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, id_channels : int, output_channels : int) -> None: + def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, identity_channels : int, output_channels : int) -> None: if input_channels > output_channels: auxiliary_add_blocks = AADSequential( - AADLayer(input_channels, attribute_channels, id_channels), + AADLayer(input_channels, attribute_channels, identity_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) ) self.auxiliary_add_blocks = auxiliary_add_blocks - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, id_embedding : Embedding) -> Tensor: - primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, id_embedding) + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, identity_embedding) if self.input_channels > self.output_channels: - feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, id_embedding) + feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, identity_embedding) output_feature = primary_feature + feature_map return output_feature From 335d597e53fa2b1f6d8d212cd6fe9fb73573d561 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 14:13:37 +0100 Subject: [PATCH 154/394] Rename to aienet.py --- face_swapper/src/networks/{attribute_modulator.py => aienet.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename face_swapper/src/networks/{attribute_modulator.py => aienet.py} (100%) diff --git a/face_swapper/src/networks/attribute_modulator.py b/face_swapper/src/networks/aienet.py similarity index 100% rename from face_swapper/src/networks/attribute_modulator.py rename to face_swapper/src/networks/aienet.py From ee3fc40e8393bff3551023f47312af05fe86edfa Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 14:26:01 +0100 Subject: [PATCH 155/394] Move output channels to config --- face_swapper/README.md | 3 ++- face_swapper/config.ini | 3 ++- face_swapper/src/models/generator.py | 7 ++++--- face_swapper/src/networks/aienet.py | 12 ++++++------ 4 files changed, 14 insertions(+), 11 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 9c7a73b..d520c1c 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -48,8 +48,9 @@ motion_extractor_path = .models/motion_extractor.pt ``` [training.model.generator] encoder_type = unet-pro -num_blocks = 2 identity_channels = 512 +output_channels = 4096 +num_blocks = 2 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 4b3ba86..6472703 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -14,8 +14,9 @@ motion_extractor_path = [training.model.generator] encoder_type = -num_blocks = identity_channels = +output_channels = +num_blocks = [training.model.discriminator] input_channels = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index a4e47f2..7fb479e 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -2,7 +2,7 @@ import configparser from torch import Tensor, nn -from ..networks.attribute_modulator import AADGenerator +from ..networks.aienet import AADGenerator from ..networks.unet import UNet, UNetPro from ..types import Attributes, Embedding @@ -14,14 +14,15 @@ class Generator(nn.Module): def __init__(self) -> None: super().__init__() encoder_type = CONFIG.get('training.model.generator', 'encoder_type') - num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') identity_channels = CONFIG.getint('training.model.generator', 'identity_channels') + output_channels = CONFIG.getint('training.model.generator', 'output_channels') + num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') if encoder_type == 'unet': self.encoder = UNet() if encoder_type == 'unet-pro': self.encoder = UNetPro() - self.generator = AADGenerator(identity_channels, num_blocks) + self.generator = AADGenerator(identity_channels, output_channels, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/networks/aienet.py b/face_swapper/src/networks/aienet.py index b1d6a0f..52930be 100644 --- a/face_swapper/src/networks/aienet.py +++ b/face_swapper/src/networks/aienet.py @@ -5,9 +5,9 @@ from ..types import Attributes, Embedding class AADGenerator(nn.Module): - def __init__(self, identity_channels : int, num_blocks : int) -> None: + def __init__(self, identity_channels : int, output_channels : int, num_blocks : int) -> None: super().__init__() - self.upsample = PixelShuffleUpsample(identity_channels, 1024 * 4) + self.pixel_shuffle_up_sample = PixelShuffleUpSample(identity_channels, output_channels) self.res_block_1 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) self.res_block_2 = AADResBlock(1024, 1024, 2048, identity_channels, num_blocks) self.res_block_3 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) @@ -18,7 +18,7 @@ class AADGenerator(nn.Module): self.res_block_8 = AADResBlock(64, 3, 64, identity_channels, num_blocks) def forward(self, target_attributes : Attributes, source_embedding : Embedding) -> Tensor: - feature_map = self.upsample(source_embedding) + feature_map = self.pixel_shuffle_up_sample(source_embedding) feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) feature_map_3 = nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) @@ -32,7 +32,7 @@ class AADGenerator(nn.Module): class AADLayer(nn.Module): def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: - super(AADLayer, self).__init__() + super().__init__() self.input_channels = input_channels self.conv_beta = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) self.conv_gamma = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) @@ -109,10 +109,10 @@ class AADResBlock(nn.Module): return output_feature -class PixelShuffleUpsample(nn.Module): +class PixelShuffleUpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 3, padding = 1) + self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1) self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) def forward(self, input_tensor : Tensor) -> Tensor: From bbcb1c35f0547452108617dc8268409614562993 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 17:21:12 +0100 Subject: [PATCH 156/394] Modernize AIENet --- face_swapper/src/models/generator.py | 6 +- face_swapper/src/networks/aienet.py | 116 ++++++++++++++------------- 2 files changed, 64 insertions(+), 58 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 7fb479e..ec3eadf 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -2,7 +2,7 @@ import configparser from torch import Tensor, nn -from ..networks.aienet import AADGenerator +from ..networks.aienet import AIENet from ..networks.unet import UNet, UNetPro from ..types import Attributes, Embedding @@ -22,13 +22,13 @@ class Generator(nn.Module): self.encoder = UNet() if encoder_type == 'unet-pro': self.encoder = UNetPro() - self.generator = AADGenerator(identity_channels, output_channels, num_blocks) + self.generator = AIENet(identity_channels, output_channels, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: target_attributes = self.get_attributes(target_tensor) - output_tensor = self.generator(target_attributes, source_embedding) + output_tensor = self.generator(source_embedding, target_attributes) return output_tensor def get_attributes(self, input_tensor : Tensor) -> Attributes: diff --git a/face_swapper/src/networks/aienet.py b/face_swapper/src/networks/aienet.py index 52930be..1b2bee3 100644 --- a/face_swapper/src/networks/aienet.py +++ b/face_swapper/src/networks/aienet.py @@ -4,68 +4,36 @@ from torch import Tensor, nn from ..types import Attributes, Embedding -class AADGenerator(nn.Module): +class AIENet(nn.Module): def __init__(self, identity_channels : int, output_channels : int, num_blocks : int) -> None: super().__init__() self.pixel_shuffle_up_sample = PixelShuffleUpSample(identity_channels, output_channels) - self.res_block_1 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) - self.res_block_2 = AADResBlock(1024, 1024, 2048, identity_channels, num_blocks) - self.res_block_3 = AADResBlock(1024, 1024, 1024, identity_channels, num_blocks) - self.res_block_4 = AADResBlock(1024, 512, 512, identity_channels, num_blocks) - self.res_block_5 = AADResBlock(512, 256, 256, identity_channels, num_blocks) - self.res_block_6 = AADResBlock(256, 128, 128, identity_channels, num_blocks) - self.res_block_7 = AADResBlock(128, 64, 64, identity_channels, num_blocks) - self.res_block_8 = AADResBlock(64, 3, 64, identity_channels, num_blocks) + self.layers = self.create_layers(identity_channels, num_blocks) - def forward(self, target_attributes : Attributes, source_embedding : Embedding) -> Tensor: - feature_map = self.pixel_shuffle_up_sample(source_embedding) - feature_map_1 = nn.functional.interpolate(self.res_block_1(feature_map, target_attributes[0], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_2 = nn.functional.interpolate(self.res_block_2(feature_map_1, target_attributes[1], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_3 = nn.functional.interpolate(self.res_block_3(feature_map_2, target_attributes[2], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_4 = nn.functional.interpolate(self.res_block_4(feature_map_3, target_attributes[3], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_5 = nn.functional.interpolate(self.res_block_5(feature_map_4, target_attributes[4], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_6 = nn.functional.interpolate(self.res_block_6(feature_map_5, target_attributes[5], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - feature_map_7 = nn.functional.interpolate(self.res_block_7(feature_map_6, target_attributes[6], source_embedding), scale_factor = 2, mode = 'bilinear', align_corners = False) - output = self.res_block_8(feature_map_7, target_attributes[7], source_embedding) - return torch.tanh(output) + @staticmethod + def create_layers(identity_channels : int, num_blocks : int) -> nn.ModuleList: + return nn.ModuleList( + [ + AADResBlock(1024, 1024, 1024, identity_channels, num_blocks), + AADResBlock(1024, 1024, 2048, identity_channels, num_blocks), + AADResBlock(1024, 1024, 1024, identity_channels, num_blocks), + AADResBlock(1024, 512, 512, identity_channels, num_blocks), + AADResBlock(512, 256, 256, identity_channels, num_blocks), + AADResBlock(256, 128, 128, identity_channels, num_blocks), + AADResBlock(128, 64, 64, identity_channels, num_blocks), + AADResBlock(64, 3, 64, identity_channels, num_blocks) + ]) + def forward(self, source_embedding : Embedding, target_attributes : Attributes) -> Tensor: + temp_tensors = self.pixel_shuffle_up_sample(source_embedding) -class AADLayer(nn.Module): - def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: - super().__init__() - self.input_channels = input_channels - self.conv_beta = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) - self.conv_gamma = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) - self.fc_beta = nn.Linear(identity_channels, input_channels) - self.fc_gamma = nn.Linear(identity_channels, input_channels) - self.instance_norm = nn.InstanceNorm2d(input_channels) - self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) + for index, layer in enumerate(self.layers[:-1]): + temp_tensor = layer(temp_tensors, target_attributes[index], source_embedding) + temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: - feature_map = self.instance_norm(feature_map) - gamma_attribute = self.conv_gamma(attribute_embedding) - beta_attribute = self.conv_beta(attribute_embedding) - attribute_modulation = gamma_attribute * feature_map + beta_attribute - identity_gamma = self.fc_gamma(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - identity_beta = self.fc_beta(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - identity_modulation = identity_gamma * feature_map + identity_beta - feature_mask = torch.sigmoid(self.conv_mask(feature_map)) - feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * identity_modulation - return feature_blend - - -class AADSequential(nn.Module): - def __init__(self, *args : nn.Module) -> None: - super().__init__() - self.layers = nn.ModuleList(args) - - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: - for layer in self.layers: - if isinstance(layer, AADLayer): - feature_map = layer(feature_map, attribute_embedding, identity_embedding) - else: - feature_map = layer(feature_map) - return feature_map + temp_tensors = self.layers[-1](temp_tensors, target_attributes[-1], source_embedding) + output_tensor = torch.tanh(temp_tensors) + return output_tensor class AADResBlock(nn.Module): @@ -109,6 +77,44 @@ class AADResBlock(nn.Module): return output_feature +class AADSequential(nn.Module): + def __init__(self, *args : nn.Module) -> None: + super().__init__() + self.layers = nn.ModuleList(args) + + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + for layer in self.layers: + if isinstance(layer, AADLayer): + feature_map = layer(feature_map, attribute_embedding, identity_embedding) + else: + feature_map = layer(feature_map) + return feature_map + + +class AADLayer(nn.Module): + def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: + super().__init__() + self.input_channels = input_channels + self.conv_beta = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.conv_gamma = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.fc_beta = nn.Linear(identity_channels, input_channels) + self.fc_gamma = nn.Linear(identity_channels, input_channels) + self.instance_norm = nn.InstanceNorm2d(input_channels) + self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) + + def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + feature_map = self.instance_norm(feature_map) + gamma_attribute = self.conv_gamma(attribute_embedding) + beta_attribute = self.conv_beta(attribute_embedding) + attribute_modulation = gamma_attribute * feature_map + beta_attribute + identity_gamma = self.fc_gamma(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + identity_beta = self.fc_beta(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) + identity_modulation = identity_gamma * feature_map + identity_beta + feature_mask = torch.sigmoid(self.conv_mask(feature_map)) + feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * identity_modulation + return feature_blend + + class PixelShuffleUpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() From 94480e16eba610db2399b9dd68930e2f5aea9e7f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 19:02:45 +0100 Subject: [PATCH 157/394] Rename to AAD as this is not a full AIENet --- face_swapper/src/models/generator.py | 4 ++-- face_swapper/src/networks/{aienet.py => aad.py} | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) rename face_swapper/src/networks/{aienet.py => aad.py} (99%) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index ec3eadf..a8ae9ff 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -2,7 +2,7 @@ import configparser from torch import Tensor, nn -from ..networks.aienet import AIENet +from ..networks.aad import AAD from ..networks.unet import UNet, UNetPro from ..types import Attributes, Embedding @@ -22,7 +22,7 @@ class Generator(nn.Module): self.encoder = UNet() if encoder_type == 'unet-pro': self.encoder = UNetPro() - self.generator = AIENet(identity_channels, output_channels, num_blocks) + self.generator = AAD(identity_channels, output_channels, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/networks/aienet.py b/face_swapper/src/networks/aad.py similarity index 99% rename from face_swapper/src/networks/aienet.py rename to face_swapper/src/networks/aad.py index 1b2bee3..de8852c 100644 --- a/face_swapper/src/networks/aienet.py +++ b/face_swapper/src/networks/aad.py @@ -4,7 +4,7 @@ from torch import Tensor, nn from ..types import Attributes, Embedding -class AIENet(nn.Module): +class AAD(nn.Module): def __init__(self, identity_channels : int, output_channels : int, num_blocks : int) -> None: super().__init__() self.pixel_shuffle_up_sample = PixelShuffleUpSample(identity_channels, output_channels) From 5bba2a1c69acefe304fb4f9734962bd6f1e90e4b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 19:10:38 +0100 Subject: [PATCH 158/394] Remove the condition from reconstruction loss --- face_swapper/src/models/loss.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index e7476a3..df3343a 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -79,12 +79,8 @@ class ReconstructionLoss(nn.Module): temp_tensors = [] for _source_tensor, _target_tensor in zip(source_tensor, target_tensor): - temp_tensor = self.mse_loss(_source_tensor, _target_tensor) - - if torch.equal(_source_tensor, _target_tensor): - temp_tensors.append(temp_tensor) - else: - temp_tensors.append(temp_tensor * 0) + temp_tensor = self.mse_loss(_source_tensor, _target_tensor) * torch.equal(_source_tensor, _target_tensor) + temp_tensors.append(temp_tensor) reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5 data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) From 8b2b6892aae4221114cd33dd2e480984fd20c19c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 21:39:01 +0100 Subject: [PATCH 159/394] Improve naming --- face_swapper/src/networks/aad.py | 41 +++++++++++++++++-------------- face_swapper/src/networks/unet.py | 18 +++++++------- 2 files changed, 31 insertions(+), 28 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index de8852c..b766979 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -95,24 +95,27 @@ class AADLayer(nn.Module): def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: super().__init__() self.input_channels = input_channels - self.conv_beta = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) - self.conv_gamma = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) - self.fc_beta = nn.Linear(identity_channels, input_channels) - self.fc_gamma = nn.Linear(identity_channels, input_channels) + self.conv1 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.conv2 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.conv3 = nn.Conv2d(input_channels, 1, kernel_size = 1) + self.linear1 = nn.Linear(identity_channels, input_channels) + self.linear2 = nn.Linear(identity_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels) - self.conv_mask = nn.Conv2d(input_channels, 1, kernel_size = 1) - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: - feature_map = self.instance_norm(feature_map) - gamma_attribute = self.conv_gamma(attribute_embedding) - beta_attribute = self.conv_beta(attribute_embedding) - attribute_modulation = gamma_attribute * feature_map + beta_attribute - identity_gamma = self.fc_gamma(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - identity_beta = self.fc_beta(identity_embedding).reshape(feature_map.shape[0], self.input_channels, 1, 1).expand_as(feature_map) - identity_modulation = identity_gamma * feature_map + identity_beta - feature_mask = torch.sigmoid(self.conv_mask(feature_map)) - feature_blend = (1 - feature_mask) * attribute_modulation + feature_mask * identity_modulation - return feature_blend + def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + temp_tensor = self.instance_norm(input_tensor) + + attribute_scale = self.conv1(attribute_embedding) + attribute_shift = self.conv2(attribute_embedding) + attribute_modulation = attribute_scale * temp_tensor + attribute_shift + + identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.input_channels, 1, 1).expand_as(temp_tensor) + identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.input_channels, 1, 1).expand_as(temp_tensor) + identity_modulation = identity_scale * temp_tensor + identity_shift + + temp_mask = torch.sigmoid(self.conv3(temp_tensor)) + output_tensor = (1 - temp_mask) * attribute_modulation + temp_mask * identity_modulation + return output_tensor class PixelShuffleUpSample(nn.Module): @@ -122,6 +125,6 @@ class PixelShuffleUpSample(nn.Module): self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) def forward(self, input_tensor : Tensor) -> Tensor: - temp_tensor = self.conv(input_tensor.view(input_tensor.shape[0], -1, 1, 1)) - temp_tensor = self.pixel_shuffle(temp_tensor) - return temp_tensor + output_tensor = self.conv(input_tensor.view(input_tensor.shape[0], -1, 1, 1)) + output_tensor = self.pixel_shuffle(output_tensor) + return output_tensor diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 7ea9b2a..d1a4f2a 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -93,11 +93,11 @@ class UpSample(nn.Module): self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) def forward(self, input_tensor : Tensor, skip_tensor : Tensor) -> Tensor: - temp_tensor = self.conv_transpose(input_tensor) - temp_tensor = self.batch_norm(temp_tensor) - temp_tensor = self.leaky_relu(temp_tensor) - temp_tensor = torch.cat((temp_tensor, skip_tensor), dim = 1) - return temp_tensor + output_tensor = self.conv_transpose(input_tensor) + output_tensor = self.batch_norm(output_tensor) + output_tensor = self.leaky_relu(output_tensor) + output_tensor = torch.cat((output_tensor, skip_tensor), dim = 1) + return output_tensor class DownSample(nn.Module): @@ -108,7 +108,7 @@ class DownSample(nn.Module): self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) def forward(self, input_tensor : Tensor) -> Tensor: - temp_tensor = self.conv(input_tensor) - temp_tensor = self.batch_norm(temp_tensor) - temp_tensor = self.leaky_relu(temp_tensor) - return temp_tensor + output_tensor = self.conv(input_tensor) + output_tensor = self.batch_norm(output_tensor) + output_tensor = self.leaky_relu(output_tensor) + return output_tensor From d7158749c2b1f319e5f15e85200972ea108f791b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Feb 2025 22:07:30 +0100 Subject: [PATCH 160/394] Improve naming --- face_swapper/src/networks/aad.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index b766979..c018e96 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -51,7 +51,7 @@ class AADResBlock(nn.Module): intermediate_channels = input_channels if index < (num_blocks - 1) else output_channels primary_add_blocks.extend( [ - AADLayer(input_channels, attribute_channels, identity_channels), + FeatureModulation(input_channels, attribute_channels, identity_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False) ] @@ -61,7 +61,7 @@ class AADResBlock(nn.Module): def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, identity_channels : int, output_channels : int) -> None: if input_channels > output_channels: auxiliary_add_blocks = AADSequential( - AADLayer(input_channels, attribute_channels, identity_channels), + FeatureModulation(input_channels, attribute_channels, identity_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) ) @@ -84,14 +84,14 @@ class AADSequential(nn.Module): def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: for layer in self.layers: - if isinstance(layer, AADLayer): + if isinstance(layer, FeatureModulation): feature_map = layer(feature_map, attribute_embedding, identity_embedding) else: feature_map = layer(feature_map) return feature_map -class AADLayer(nn.Module): +class FeatureModulation(nn.Module): def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: super().__init__() self.input_channels = input_channels From bc174186ebd78c2e8fcd9fef513a63bb88aa0b66 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 00:15:13 +0100 Subject: [PATCH 161/394] Final refactoring for AAD done --- face_swapper/src/networks/aad.py | 112 +++++++++++++++++++------------ face_swapper/src/networks/nld.py | 2 +- 2 files changed, 70 insertions(+), 44 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index c018e96..6d2e17d 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -14,14 +14,14 @@ class AAD(nn.Module): def create_layers(identity_channels : int, num_blocks : int) -> nn.ModuleList: return nn.ModuleList( [ - AADResBlock(1024, 1024, 1024, identity_channels, num_blocks), - AADResBlock(1024, 1024, 2048, identity_channels, num_blocks), - AADResBlock(1024, 1024, 1024, identity_channels, num_blocks), - AADResBlock(1024, 512, 512, identity_channels, num_blocks), - AADResBlock(512, 256, 256, identity_channels, num_blocks), - AADResBlock(256, 128, 128, identity_channels, num_blocks), - AADResBlock(128, 64, 64, identity_channels, num_blocks), - AADResBlock(64, 3, 64, identity_channels, num_blocks) + AdaptiveFeatureModulation(1024, 1024, 1024, identity_channels, num_blocks), + AdaptiveFeatureModulation(1024, 1024, 2048, identity_channels, num_blocks), + AdaptiveFeatureModulation(1024, 1024, 1024, identity_channels, num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, identity_channels, num_blocks), + AdaptiveFeatureModulation(512, 256, 256, identity_channels, num_blocks), + AdaptiveFeatureModulation(256, 128, 128, identity_channels, num_blocks), + AdaptiveFeatureModulation(128, 64, 64, identity_channels, num_blocks), + AdaptiveFeatureModulation(64, 3, 64, identity_channels, num_blocks) ]) def forward(self, source_embedding : Embedding, target_attributes : Attributes) -> Tensor: @@ -36,59 +36,85 @@ class AAD(nn.Module): return output_tensor -class AADResBlock(nn.Module): +class AdaptiveFeatureModulation(nn.Module): def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> None: super().__init__() self.input_channels = input_channels self.output_channels = output_channels - self.prepare_primary_add_blocks(input_channels, attribute_channels, identity_channels, output_channels, num_blocks) - self.prepare_auxiliary_add_blocks(input_channels, attribute_channels, identity_channels, output_channels) + self.primary_layers = self.create_primary_layers(input_channels, output_channels, attribute_channels, identity_channels, num_blocks) + self.shortcut_layers = self.create_shortcut_layers(input_channels, output_channels, attribute_channels, identity_channels) - def prepare_primary_add_blocks(self, input_channels : int, attribute_channels : int, identity_channels : int, output_channels : int, num_blocks : int) -> None: - primary_add_blocks = [] + @staticmethod + def create_primary_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> nn.ModuleList: + primary_layers = nn.ModuleList() for index in range(num_blocks): - intermediate_channels = input_channels if index < (num_blocks - 1) else output_channels - primary_add_blocks.extend( - [ - FeatureModulation(input_channels, attribute_channels, identity_channels), - nn.ReLU(inplace = True), - nn.Conv2d(input_channels, intermediate_channels, kernel_size = 3, padding = 1, bias = False) - ] - ) - self.primary_add_blocks = AADSequential(*primary_add_blocks) + primary_layers.extend( + [ + FeatureModulation(input_channels, attribute_channels, identity_channels), + nn.ReLU(inplace = True) + ]) + + if index < num_blocks - 1: + primary_layers.append(nn.Conv2d(input_channels, input_channels, kernel_size = 3, padding = 1, bias = False)) + else: + primary_layers.append(nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False)) + + return primary_layers + + @staticmethod + def _create_primary_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> nn.ModuleList: + primary_layers = nn.ModuleList() + + for index in range(num_blocks): + primary_layers.extend( + [ + FeatureModulation(input_channels, attribute_channels, identity_channels), + nn.ReLU(inplace = True) + ]) + + if index < num_blocks - 1: + primary_layers.append(nn.Conv2d(input_channels, input_channels, kernel_size = 3, padding = 1, bias = False)) + else: + primary_layers.append(nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False)) + + return primary_layers + + @staticmethod + def create_shortcut_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int) -> nn.ModuleList: + shortcut_layers = nn.ModuleList() - def prepare_auxiliary_add_blocks(self, input_channels : int, attribute_channels : int, identity_channels : int, output_channels : int) -> None: if input_channels > output_channels: - auxiliary_add_blocks = AADSequential( + shortcut_layers.extend( + [ FeatureModulation(input_channels, attribute_channels, identity_channels), nn.ReLU(inplace = True), nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) - ) - self.auxiliary_add_blocks = auxiliary_add_blocks + ]) - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: - primary_feature = self.primary_add_blocks(feature_map, attribute_embedding, identity_embedding) + return shortcut_layers + + def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + primary_tensor = input_tensor + + for primary_layer in self.primary_layers: + if isinstance(primary_layer, FeatureModulation): + primary_tensor = primary_layer(primary_tensor, attribute_embedding, identity_embedding) + else: + primary_tensor = primary_layer(primary_tensor) if self.input_channels > self.output_channels: - feature_map = self.auxiliary_add_blocks(feature_map, attribute_embedding, identity_embedding) + shortcut_tensor = input_tensor - output_feature = primary_feature + feature_map - return output_feature + for shortcut_layer in self.shortcut_layers: + if isinstance(shortcut_layer, FeatureModulation): + shortcut_tensor = shortcut_layer(shortcut_tensor, attribute_embedding, identity_embedding) + else: + shortcut_tensor = shortcut_layer(shortcut_tensor) + input_tensor = shortcut_tensor -class AADSequential(nn.Module): - def __init__(self, *args : nn.Module) -> None: - super().__init__() - self.layers = nn.ModuleList(args) - - def forward(self, feature_map : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: - for layer in self.layers: - if isinstance(layer, FeatureModulation): - feature_map = layer(feature_map, attribute_embedding, identity_embedding) - else: - feature_map = layer(feature_map) - return feature_map + return primary_tensor + input_tensor class FeatureModulation(nn.Module): diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index 5fee9e1..97e3ba4 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -9,7 +9,7 @@ class NLD(nn.Module): self.nld = self.create_nld(input_channels, num_filters, num_layers, kernel_size) @staticmethod - def create_nld(input_channels : int, num_filters : int, num_layers: int, kernel_size : int) -> nn.Sequential: + def create_nld(input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> nn.Sequential: padding = math.ceil((kernel_size - 1) / 2) current_filters = num_filters layers =\ From 303cbfa024b6ed8ff0415c9d5d4b6b6ff2ec6aa1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 00:18:35 +0100 Subject: [PATCH 162/394] Final refactoring for AAD done --- face_swapper/src/networks/aad.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 6d2e17d..ac5b252 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -62,24 +62,6 @@ class AdaptiveFeatureModulation(nn.Module): return primary_layers - @staticmethod - def _create_primary_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> nn.ModuleList: - primary_layers = nn.ModuleList() - - for index in range(num_blocks): - primary_layers.extend( - [ - FeatureModulation(input_channels, attribute_channels, identity_channels), - nn.ReLU(inplace = True) - ]) - - if index < num_blocks - 1: - primary_layers.append(nn.Conv2d(input_channels, input_channels, kernel_size = 3, padding = 1, bias = False)) - else: - primary_layers.append(nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False)) - - return primary_layers - @staticmethod def create_shortcut_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int) -> nn.ModuleList: shortcut_layers = nn.ModuleList() From 7d8cb146a4e99940cb3dbb0e4030efbbdfc7b965 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 00:30:12 +0100 Subject: [PATCH 163/394] Use latest numpy --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 36c6ea4..a231824 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ --extra-index-url https://download.pytorch.org/whl/cu124 lightning==2.5.0 -numpy==1.26.4 +numpy==2.2.3 onnx==1.17.0 onnxruntime==1.20.1 opencv-python==4.11.0.86 From 8b53c76a0a8dea432a9271fb0380687335e1bc63 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 00:47:12 +0100 Subject: [PATCH 164/394] Remove deprecated argument --- face_swapper/src/networks/unet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index d1a4f2a..97a361c 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -60,7 +60,7 @@ class UNet(nn.Module): class UNetPro(UNet): def __init__(self) -> None: super(UNet, self).__init__() - self.resnet = models.resnet34(pretrained = True) + self.resnet = models.resnet34() self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() From 84be7d1ffba2fc0c78f6daa81ca1fc88510f73f9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 00:49:38 +0100 Subject: [PATCH 165/394] Remove deprecated argument --- face_swapper/src/networks/unet.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 97a361c..2d6147f 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -3,6 +3,7 @@ from typing import Tuple import torch from torch import Tensor, nn from torchvision import models +from torchvision.models import ResNet34_Weights class UNet(nn.Module): @@ -60,7 +61,7 @@ class UNet(nn.Module): class UNetPro(UNet): def __init__(self) -> None: super(UNet, self).__init__() - self.resnet = models.resnet34() + self.resnet = models.resnet34(weights = ResNet34_Weights.DEFAULT) self.down_samples = self.create_down_samples(self) self.up_samples = self.create_up_samples() From 18a2531b54a4dde6866e698522ac8d8b037e47ae Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 09:25:28 +0100 Subject: [PATCH 166/394] Fix naming in dataset.py --- embedding_converter/src/dataset.py | 4 ++-- face_swapper/src/dataset.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 3d61aea..f9e074c 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -15,8 +15,8 @@ class DynamicDataset(Dataset[Tensor]): self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - image_path = random.choice(self.file_paths) - vision_frame = cv2.imread(image_path) + file_path = random.choice(self.file_paths) + vision_frame = cv2.imread(file_path) return self.transforms(vision_frame) def __len__(self) -> int: diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index de7f344..61bc4a3 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -16,12 +16,12 @@ class DynamicDataset(Dataset[Tensor]): self.batch_ratio = batch_ratio def __getitem__(self, index : int) -> Batch: - source_image_path = self.file_paths[index] + file_path = self.file_paths[index] if random.random() < self.batch_ratio: - return self.prepare_equal_batch(source_image_path) + return self.prepare_equal_batch(file_path) - return self.prepare_different_batch(source_image_path) + return self.prepare_different_batch(file_path) def __len__(self) -> int: return len(self.file_paths) From f19908ccd66d2cfe1a56abc54e1dcdf0a1f9fd11 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 09:26:46 +0100 Subject: [PATCH 167/394] Fix spacing for lambda --- embedding_converter/src/dataset.py | 2 +- face_swapper/src/dataset.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index f9e074c..62a8fae 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -30,6 +30,6 @@ class DynamicDataset(Dataset[Tensor]): transforms.Resize((112, 112), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.ToTensor(), - transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), + transforms.Lambda(lambda temp_tensor: temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 61bc4a3..017827f 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -35,7 +35,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - transforms.Lambda(lambda temp_tensor : temp_tensor[[2, 1, 0], :, :]), + transforms.Lambda(lambda temp_tensor: temp_tensor[[2, 1, 0], :, :]), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) From 257e5e56a4d0f45a58fd24e6b5ab984257494fc9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 09:39:54 +0100 Subject: [PATCH 168/394] Rename split ratio --- embedding_converter/src/training.py | 4 ++-- face_swapper/src/training.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 93e3ac2..14358fa 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -79,9 +79,9 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataL def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') + split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * loader_split_ratio) + training_size = int(dataset_size * split_ratio) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f3b132a..e71d909 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -128,9 +128,9 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataL def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - loader_split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') + split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * loader_split_ratio) + training_size = int(dataset_size * split_ratio) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset From f5cd6b63360820d57fe87198a1ce0ad70815ff27 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 10:25:19 +0100 Subject: [PATCH 169/394] This should be a StaticDataset, Fix learning rate finder --- embedding_converter/src/dataset.py | 4 ++-- embedding_converter/src/training.py | 6 ++++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 62a8fae..6ed1a4f 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -9,13 +9,13 @@ from torchvision import transforms from .types import Batch -class DynamicDataset(Dataset[Tensor]): +class StaticDataset(Dataset[Tensor]): def __init__(self, file_pattern : str) -> None: self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: - file_path = random.choice(self.file_paths) + file_path = self.file_paths[index] vision_frame = cv2.imread(file_path) return self.transforms(vision_frame) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 14358fa..b43429b 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -11,7 +11,7 @@ from lightning.pytorch.tuner import Tuner from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, random_split -from .dataset import DynamicDataset +from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter from .types import Batch, Embedding, OptimizerConfig @@ -24,11 +24,13 @@ class EmbeddingConverterTrainer(lightning.LightningModule): super(EmbeddingConverterTrainer, self).__init__() source_path = CONFIG.get('training.model', 'source_path') target_path = CONFIG.get('training.model', 'target_path') + learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() + self.lr = learning_rate def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) @@ -115,7 +117,7 @@ def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') output_resume_path = CONFIG.get('training.output', 'resume_path') - dataset = DynamicDataset(dataset_file_pattern) + dataset = StaticDataset(dataset_file_pattern) training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() From 93cbbf52d0a17fce904a765fbd77ff5cc6794466 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 10:26:39 +0100 Subject: [PATCH 170/394] Fix CI --- embedding_converter/src/dataset.py | 1 - 1 file changed, 1 deletion(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 6ed1a4f..060dee5 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -1,5 +1,4 @@ import glob -import random import cv2 from torch import Tensor From 607c55ff1f40f52e7fb26b42b0f5ca4af5a73601 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 12:23:57 +0100 Subject: [PATCH 171/394] Remove Numpy and CV2 to fully use Tensors --- embedding_converter/src/dataset.py | 10 ++++------ embedding_converter/src/types.py | 2 -- face_swapper/src/dataset.py | 18 ++++++++---------- face_swapper/src/helper.py | 21 +-------------------- face_swapper/src/inferencing.py | 22 ++++++++++------------ face_swapper/src/types.py | 5 +---- requirements.txt | 2 -- 7 files changed, 24 insertions(+), 56 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 060dee5..cdc867f 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -1,9 +1,8 @@ import glob -import cv2 from torch import Tensor from torch.utils.data import Dataset -from torchvision import transforms +from torchvision import transforms, io from .types import Batch @@ -15,8 +14,8 @@ class StaticDataset(Dataset[Tensor]): def __getitem__(self, index : int) -> Batch: file_path = self.file_paths[index] - vision_frame = cv2.imread(file_path) - return self.transforms(vision_frame) + temp_tensor = io.read_image(file_path) + return self.transforms(temp_tensor) def __len__(self) -> int: return len(self.file_paths) @@ -29,6 +28,5 @@ class StaticDataset(Dataset[Tensor]): transforms.Resize((112, 112), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.ToTensor(), - transforms.Lambda(lambda temp_tensor: temp_tensor[[2, 1, 0], :, :]), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + transforms.Normalize(0.5, 0.5) ]) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index d9d4ee9..7f41a40 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,10 +1,8 @@ from typing import Any, TypeAlias -from numpy.typing import NDArray from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor -VisionFrame : TypeAlias = NDArray[Any] OptimizerConfig : TypeAlias = Any diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 017827f..09b30b2 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -1,10 +1,9 @@ import glob import random -import cv2 from torch import Tensor from torch.utils.data import Dataset -from torchvision import transforms +from torchvision import transforms, io from .types import Batch @@ -35,19 +34,18 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - transforms.Lambda(lambda temp_tensor: temp_tensor[[2, 1, 0], :, :]), - transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + transforms.Normalize(0.5, 0.5) ]) def prepare_different_batch(self, source_image_path : str) -> Batch: target_image_path = random.choice(self.file_paths) - source_vision_frame = cv2.imread(source_image_path) - target_vision_frame = cv2.imread(target_image_path) - source_tensor = self.transforms(source_vision_frame) - target_tensor = self.transforms(target_vision_frame) + source_tensor = io.read_image(source_image_path) + target_tensor = io.read_image(target_image_path) + source_tensor = self.transforms(source_tensor) + target_tensor = self.transforms(target_tensor) return source_tensor, target_tensor def prepare_equal_batch(self, source_image_path : str) -> Batch: - source_vision_frame = cv2.imread(source_image_path) - source_tensor = self.transforms(source_vision_frame) + source_tensor = io.read_image(source_image_path) + source_tensor = self.transforms(source_tensor) return source_tensor, source_tensor diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 2e3cf60..ebc11f8 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,25 +1,6 @@ -import numpy -import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding, VisionFrame - - -def convert_to_tensor(vision_frame : VisionFrame) -> Tensor: - output_tensor = torch.from_numpy(vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)) - output_tensor = output_tensor / 255.0 - output_tensor = (output_tensor - 0.5) * 2 - output_tensor = output_tensor.unsqueeze(0) - return output_tensor - - -def convert_to_vision_frame(input_tensor : Tensor) -> VisionFrame: - vision_frame = input_tensor.detach().cpu().numpy()[0] - vision_frame = vision_frame.transpose(1, 2, 0) - vision_frame = (vision_frame + 1) * 127.5 - vision_frame = vision_frame.clip(0, 255).astype(numpy.uint8) - vision_frame = vision_frame[:, :, ::-1] - return vision_frame +from .types import EmbedderModule, Embedding, Padding def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 64047cc..d1112db 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -1,23 +1,21 @@ import configparser -import cv2 import torch +from torch import Tensor +from torchvision import io -from .helper import calc_embedding, convert_to_tensor, convert_to_vision_frame +from .helper import calc_embedding from .models.generator import Generator -from .types import EmbedderModule, GeneratorModule, VisionFrame +from .types import EmbedderModule, GeneratorModule CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def run_swap(generator : GeneratorModule, embedder : EmbedderModule, source_vision_frame : VisionFrame, target_vision_frame : VisionFrame) -> VisionFrame: - source_tensor = convert_to_tensor(source_vision_frame) - target_tensor = convert_to_tensor(target_vision_frame) +def run_swap(generator : GeneratorModule, embedder : EmbedderModule, source_tensor : Tensor, target_tensor : Tensor) -> Tensor: source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) output_tensor = generator(source_embedding, target_tensor)[0] - output_vision_frame = convert_to_vision_frame(output_tensor) - return output_vision_frame + return output_tensor def infer() -> None: @@ -34,7 +32,7 @@ def infer() -> None: embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] embedder.eval() - source_vision_frame = cv2.imread(source_path) - target_vision_frame = cv2.imread(target_path) - output_vision_frame = run_swap(generator, embedder, source_vision_frame, target_vision_frame) - cv2.imwrite(output_path, output_vision_frame) + source_tensor = io.read_image(source_path) + target_tensor = io.read_image(target_path) + output_tensor = run_swap(generator, embedder, source_tensor, target_tensor) + io.write_jpeg(output_tensor, output_path) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 5560b46..27058ed 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,6 +1,5 @@ -from typing import Any, Tuple, TypeAlias +from typing import Tuple, TypeAlias -from numpy.typing import NDArray from torch import Tensor from torch.nn import Module @@ -12,7 +11,5 @@ FaceLandmark203 : TypeAlias = Tensor Padding : TypeAlias = Tuple[int, int, int, int] -VisionFrame : TypeAlias = NDArray[Any] - GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module diff --git a/requirements.txt b/requirements.txt index a231824..1620651 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,7 @@ --extra-index-url https://download.pytorch.org/whl/cu124 lightning==2.5.0 -numpy==2.2.3 onnx==1.17.0 onnxruntime==1.20.1 -opencv-python==4.11.0.86 pytorch-msssim==1.0.0 torch==2.6.0 torchvision==0.21.0 From bfcbd6bf951c00f32a28a7e3306240358ecf4e87 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 12:24:56 +0100 Subject: [PATCH 172/394] Fix CI --- embedding_converter/src/dataset.py | 2 +- face_swapper/src/dataset.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index cdc867f..05595eb 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -2,7 +2,7 @@ import glob from torch import Tensor from torch.utils.data import Dataset -from torchvision import transforms, io +from torchvision import io, transforms from .types import Batch diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 09b30b2..b0cd6db 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -3,7 +3,7 @@ import random from torch import Tensor from torch.utils.data import Dataset -from torchvision import transforms, io +from torchvision import io, transforms from .types import Batch From 5b3b2abdd7f943a5a05d75dcab61f8eed83c1877 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 15:40:50 +0100 Subject: [PATCH 173/394] Remove annoying Tuner, Match both trainer configs a bit more --- embedding_converter/README.md | 1 + embedding_converter/config.ini | 1 + embedding_converter/src/training.py | 10 ++++------ face_swapper/README.md | 1 - face_swapper/config.ini | 1 - face_swapper/src/training.py | 3 +-- 6 files changed, 7 insertions(+), 10 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 2752f67..502670f 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -47,6 +47,7 @@ target_path = .models/arcface_simswap.pt [training.trainer] learning_rate = 0.001 max_epochs = 4096 +precision = 16-mixed ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index e8360e2..cb24d3f 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -13,6 +13,7 @@ target_path = [training.trainer] learning_rate = max_epochs = +precision = [training.output] directory_path = diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index b43429b..93b42b3 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -7,7 +7,6 @@ import torch from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger -from lightning.pytorch.tuner import Tuner from torch import Tensor, nn from torch.utils.data import DataLoader, Dataset, random_split @@ -24,13 +23,11 @@ class EmbeddingConverterTrainer(lightning.LightningModule): super(EmbeddingConverterTrainer, self).__init__() source_path = CONFIG.get('training.model', 'source_path') target_path = CONFIG.get('training.model', 'target_path') - learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() - self.lr = learning_rate def forward(self, source_embedding : Embedding) -> Embedding: return self.embedding_converter(source_embedding) @@ -93,19 +90,22 @@ def create_trainer() -> Trainer: trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') output_directory_path = CONFIG.get('training.output', 'directory_path') output_file_pattern = CONFIG.get('training.output', 'file_pattern') + trainer_precision = CONFIG.get('training.trainer', 'precision') logger = TensorBoardLogger('.logs', name = 'embedding_converter') + os.makedirs(output_directory_path, exist_ok = True) return Trainer( logger = logger, log_every_n_steps = 10, max_epochs = trainer_max_epochs, + precision = trainer_precision, # type:ignore[arg-type] callbacks = [ ModelCheckpoint( monitor = 'training_loss', dirpath = output_directory_path, filename = output_file_pattern, - every_n_epochs = 10, + every_n_epochs = 1, save_top_k = 3, save_last = True ) @@ -121,8 +121,6 @@ def train() -> None: training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() - tuner = Tuner(trainer) - tuner.lr_find(embedding_converter_trainer, training_loader, validation_loader) if os.path.exists(output_resume_path): trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = output_resume_path) diff --git a/face_swapper/README.md b/face_swapper/README.md index d520c1c..7daa0a3 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -77,7 +77,6 @@ gaze_weight = 0 learning_rate = 0.0004 max_epochs = 50 precision = 16-mixed -automatic_optimization = false preview_frequency = 250 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 6472703..c3ce49b 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -37,7 +37,6 @@ gaze_weight = learning_rate = max_epochs = precision = -automatic_optimization = preview_frequency = [training.output] diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e71d909..1d90d13 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -26,7 +26,6 @@ CONFIG.read('config.ini') class FaceSwapperTrainer(lightning.LightningModule): def __init__(self) -> None: super().__init__() - automatic_optimization = CONFIG.getboolean('training.trainer', 'automatic_optimization') embedder_path = CONFIG.get('training.model', 'embedder_path') self.generator = Generator() @@ -39,7 +38,7 @@ class FaceSwapperTrainer(lightning.LightningModule): self.pose_loss = PoseLoss() self.gaze_loss = GazeLoss() self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.automatic_optimization = automatic_optimization + self.automatic_optimization = False def forward(self, target_tensor : Tensor, source_embedding : Embedding) -> Tensor: output_tensor = self.generator(source_embedding, target_tensor) From 6eff69a41ab0d3e7cd00c31337b348e32e62710c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 15:47:56 +0100 Subject: [PATCH 174/394] There is no need to make directories --- embedding_converter/src/training.py | 1 - face_swapper/src/training.py | 1 - 2 files changed, 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 93b42b3..fedeffa 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -93,7 +93,6 @@ def create_trainer() -> Trainer: trainer_precision = CONFIG.get('training.trainer', 'precision') logger = TensorBoardLogger('.logs', name = 'embedding_converter') - os.makedirs(output_directory_path, exist_ok = True) return Trainer( logger = logger, log_every_n_steps = 10, diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1d90d13..5e0f844 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -142,7 +142,6 @@ def create_trainer() -> Trainer: trainer_precision = CONFIG.get('training.trainer', 'precision') logger = TensorBoardLogger('.logs', name = 'face_swapper') - os.makedirs(output_directory_path, exist_ok = True) return Trainer( logger = logger, log_every_n_steps = 10, From a951d700fcc94059742e4600b16fa1281a62bc6d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Feb 2025 16:21:37 +0100 Subject: [PATCH 175/394] Restore RGB normalization --- embedding_converter/src/dataset.py | 2 +- face_swapper/src/dataset.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 05595eb..5680d59 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -28,5 +28,5 @@ class StaticDataset(Dataset[Tensor]): transforms.Resize((112, 112), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.ToTensor(), - transforms.Normalize(0.5, 0.5) + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index b0cd6db..87bb5f0 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -34,7 +34,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - transforms.Normalize(0.5, 0.5) + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) def prepare_different_batch(self, source_image_path : str) -> Batch: From e8cc2bfff167d603a9dd67fbd5ddd3a5fbca719e Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 25 Feb 2025 13:59:02 +0530 Subject: [PATCH 176/394] add gaze loss --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 5e0f844..56cacb4 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -67,7 +67,7 @@ class FaceSwapperTrainer(lightning.LightningModule): identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss = self.pose_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss generator_optimizer.zero_grad() self.manual_backward(generator_loss) From 484a49c27d2429e7ab291fe454c1f40970efdc7b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 25 Feb 2025 12:54:54 +0100 Subject: [PATCH 177/394] Restore dataset behaviour for same person --- face_swapper/src/dataset.py | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 87bb5f0..5dc73b0 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -1,4 +1,5 @@ import glob +import os import random from torch import Tensor @@ -37,15 +38,20 @@ class DynamicDataset(Dataset[Tensor]): transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - def prepare_different_batch(self, source_image_path : str) -> Batch: - target_image_path = random.choice(self.file_paths) - source_tensor = io.read_image(source_image_path) - target_tensor = io.read_image(target_image_path) + def prepare_different_batch(self, source_path : str) -> Batch: + target_path = random.choice(self.file_paths) + source_tensor = io.read_image(source_path) source_tensor = self.transforms(source_tensor) + target_tensor = io.read_image(target_path) target_tensor = self.transforms(target_tensor) return source_tensor, target_tensor - def prepare_equal_batch(self, source_image_path : str) -> Batch: - source_tensor = io.read_image(source_image_path) + def prepare_equal_batch(self, source_path : str) -> Batch: + target_directory_path = os.path.dirname(source_path) + target_file_name_and_extension = random.choice(os.listdir(target_directory_path)) + target_path = os.path.join(target_directory_path, target_file_name_and_extension) + source_tensor = io.read_image(source_path) source_tensor = self.transforms(source_tensor) - return source_tensor, source_tensor + target_tensor = io.read_image(target_path) + target_tensor = self.transforms(target_tensor) + return source_tensor, target_tensor From 0ad2556c4cd723c8f609913c16e46c1413935b40 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 00:01:18 +0100 Subject: [PATCH 178/394] Improve optimizer configs --- embedding_converter/src/training.py | 5 +++-- face_swapper/src/training.py | 31 ++++++++++++++++++++++++++--- face_swapper/src/types.py | 4 +++- 3 files changed, 34 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index fedeffa..331ce39 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -53,8 +53,7 @@ class EmbeddingConverterTrainer(lightning.LightningModule): learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) - - return\ + config =\ { 'optimizer': optimizer, 'lr_scheduler': @@ -66,6 +65,8 @@ class EmbeddingConverterTrainer(lightning.LightningModule): } } + return config + def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataLoader[Tensor]]: batch_size = CONFIG.getint('training.loader', 'batch_size') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 56cacb4..dfbf9c6 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss -from .types import Batch, Embedding +from .types import Batch, Embedding, OptimizerConfig CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -44,11 +44,36 @@ class FaceSwapperTrainer(lightning.LightningModule): output_tensor = self.generator(source_embedding, target_tensor) return output_tensor - def configure_optimizers(self) -> Tuple[Optimizer, Optimizer]: + def configure_optimizers(self) -> Tuple[OptimizerConfig, OptimizerConfig]: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - return generator_optimizer, discriminator_optimizer + generator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(generator_optimizer) + discriminator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(discriminator_optimizer) + + generator_config =\ + { + 'optimizer': generator_optimizer, + 'lr_scheduler': + { + 'scheduler': generator_scheduler, + 'monitor': 'generator_loss', + 'interval': 'step', + 'frequency': 1000 + } + } + discriminator_config =\ + { + 'optimizer': discriminator_optimizer, + 'lr_scheduler': + { + 'scheduler': discriminator_scheduler, + 'monitor': 'discriminator_loss', + 'interval': 'step', + 'frequency': 1000 + } + } + return generator_config, discriminator_config def training_step(self, batch : Batch, batch_index : int) -> Tensor: preview_frequency = CONFIG.getfloat('training.trainer', 'preview_frequency') diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 27058ed..cc2f46e 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,4 +1,4 @@ -from typing import Tuple, TypeAlias +from typing import Any, Tuple, TypeAlias from torch import Tensor from torch.nn import Module @@ -13,3 +13,5 @@ Padding : TypeAlias = Tuple[int, int, int, int] GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module + +OptimizerConfig : TypeAlias = Any From 8c24c9ec2757c66854ef5fbaed89ef712c2992fe Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 00:02:54 +0100 Subject: [PATCH 179/394] Fix CI --- face_swapper/src/training.py | 1 - 1 file changed, 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index dfbf9c6..a8cc6f6 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -9,7 +9,6 @@ from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn -from torch.optim import Optimizer from torch.utils.data import DataLoader, Dataset, random_split from .dataset import DynamicDataset From 7ce9d270978989e85f58854a8d38eb02e0f82794 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 00:03:57 +0100 Subject: [PATCH 180/394] Different naming --- face_swapper/src/models/loss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index df3343a..a41d76e 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -78,8 +78,8 @@ class ReconstructionLoss(nn.Module): reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') temp_tensors = [] - for _source_tensor, _target_tensor in zip(source_tensor, target_tensor): - temp_tensor = self.mse_loss(_source_tensor, _target_tensor) * torch.equal(_source_tensor, _target_tensor) + for __source_tensor__, __target_tensor__ in zip(source_tensor, target_tensor): + temp_tensor = self.mse_loss(__source_tensor__, __target_tensor__) * torch.equal(__source_tensor__, __target_tensor__) temp_tensors.append(temp_tensor) reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5 From 578b07a7f46acb51454d06d0e98e3afd4ec3e038 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 00:43:42 +0100 Subject: [PATCH 181/394] Add StatefulDataloader, Manual trigger scheduler --- embedding_converter/src/training.py | 9 +++++---- face_swapper/src/training.py | 17 +++++++++++------ requirements.txt | 1 + 3 files changed, 17 insertions(+), 10 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 331ce39..76a534f 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -8,7 +8,8 @@ from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn -from torch.utils.data import DataLoader, Dataset, random_split +from torch.utils.data import Dataset, random_split +from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter @@ -68,13 +69,13 @@ class EmbeddingConverterTrainer(lightning.LightningModule): return config -def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataLoader[Tensor]]: +def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) - training_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a8cc6f6..d732d37 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -9,7 +9,8 @@ from lightning import Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn -from torch.utils.data import DataLoader, Dataset, random_split +from torch.utils.data import Dataset, random_split +from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import DynamicDataset from .helper import calc_embedding @@ -56,7 +57,6 @@ class FaceSwapperTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': generator_scheduler, - 'monitor': 'generator_loss', 'interval': 'step', 'frequency': 1000 } @@ -67,7 +67,6 @@ class FaceSwapperTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': discriminator_scheduler, - 'monitor': 'discriminator_loss', 'interval': 'step', 'frequency': 1000 } @@ -97,6 +96,9 @@ class FaceSwapperTrainer(lightning.LightningModule): self.manual_backward(generator_loss) generator_optimizer.step() + generator_scheduler = self.lr_schedulers()[0] + generator_scheduler.step(generator_loss) + discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) @@ -105,6 +107,9 @@ class FaceSwapperTrainer(lightning.LightningModule): self.manual_backward(discriminator_loss) discriminator_optimizer.step() + discriminator_scheduler = self.lr_schedulers()[1] + discriminator_scheduler.step(discriminator_loss) + if self.global_step % preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor) @@ -140,13 +145,13 @@ class FaceSwapperTrainer(lightning.LightningModule): self.logger.experiment.add_image('preview', preview_grid, self.global_step) # type:ignore[attr-defined] -def create_loaders(dataset : Dataset[Tensor]) -> Tuple[DataLoader[Tensor], DataLoader[Tensor]]: +def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: batch_size = CONFIG.getint('training.loader', 'batch_size') num_workers = CONFIG.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) - training_loader = DataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = DataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader diff --git a/requirements.txt b/requirements.txt index 1620651..d635148 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,5 +4,6 @@ onnx==1.17.0 onnxruntime==1.20.1 pytorch-msssim==1.0.0 torch==2.6.0 +torchdata==0.11.0 torchvision==0.21.0 tensorboard==2.19.0 From ab0a59fb74f30bebf94fd0912dc69fe5a8054cbf Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 26 Feb 2025 14:01:41 +0530 Subject: [PATCH 182/394] changes --- face_swapper/src/training.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index d732d37..046073b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -48,8 +48,8 @@ class FaceSwapperTrainer(lightning.LightningModule): learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - generator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(generator_optimizer) - discriminator_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(discriminator_optimizer) + generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(generator_optimizer, T_max = 10, eta_min = 1e-6) + discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(discriminator_optimizer, T_max = 10, eta_min = 1e-6) generator_config =\ { @@ -58,7 +58,6 @@ class FaceSwapperTrainer(lightning.LightningModule): { 'scheduler': generator_scheduler, 'interval': 'step', - 'frequency': 1000 } } discriminator_config =\ @@ -68,7 +67,6 @@ class FaceSwapperTrainer(lightning.LightningModule): { 'scheduler': discriminator_scheduler, 'interval': 'step', - 'frequency': 1000 } } return generator_config, discriminator_config @@ -97,7 +95,7 @@ class FaceSwapperTrainer(lightning.LightningModule): generator_optimizer.step() generator_scheduler = self.lr_schedulers()[0] - generator_scheduler.step(generator_loss) + generator_scheduler.step() discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) @@ -108,7 +106,7 @@ class FaceSwapperTrainer(lightning.LightningModule): discriminator_optimizer.step() discriminator_scheduler = self.lr_schedulers()[1] - discriminator_scheduler.step(discriminator_loss) + discriminator_scheduler.step() if self.global_step % preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor) From 58a85a80bb5cc5c274d37691a65bd1330d06f554 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 10:54:38 +0100 Subject: [PATCH 183/394] Use high float32 matmul precision --- embedding_converter/src/training.py | 3 +++ face_swapper/src/training.py | 3 +++ 2 files changed, 6 insertions(+) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 76a534f..0be3ce7 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -118,6 +118,9 @@ def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') output_resume_path = CONFIG.get('training.output', 'resume_path') + if torch.cuda.is_available(): + torch.set_float32_matmul_precision('high') + dataset = StaticDataset(dataset_file_pattern) training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 046073b..29fa196 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -194,6 +194,9 @@ def train() -> None: dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') + if torch.cuda.is_available(): + torch.set_float32_matmul_precision('high') + dataset = DynamicDataset(dataset_file_pattern, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() From cadbe9cf76bb8ad34701af2da0332d33ae2758e9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 14:19:26 +0100 Subject: [PATCH 184/394] Follow the concept of layers and sequences --- face_swapper/src/models/discriminator.py | 2 +- face_swapper/src/networks/nld.py | 13 +++++++------ 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 4f49cd2..d9f0d33 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -25,7 +25,7 @@ class Discriminator(nn.Module): discriminators = nn.ModuleList() for _ in range(num_discriminators): - discriminator = NLD(input_channels, num_filters, num_layers, kernel_size).nld + discriminator = NLD(input_channels, num_filters, num_layers, kernel_size).sequences discriminators.append(discriminator) return discriminators diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index 97e3ba4..015d612 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -6,17 +6,18 @@ from torch import Tensor, nn class NLD(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super().__init__() - self.nld = self.create_nld(input_channels, num_filters, num_layers, kernel_size) + self.layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) + self.sequences = nn.Sequential(*self.layers) @staticmethod - def create_nld(input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> nn.Sequential: + def create_layers(input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> nn.ModuleList: padding = math.ceil((kernel_size - 1) / 2) current_filters = num_filters - layers =\ + layers = nn.ModuleList( [ nn.Conv2d(input_channels, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), nn.LeakyReLU(0.2, True) - ] + ]) for _ in range(1, num_layers): previous_filters = current_filters @@ -37,7 +38,7 @@ class NLD(nn.Module): nn.LeakyReLU(0.2, True), nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding) ] - return nn.Sequential(*layers) + return layers def forward(self, input_tensor : Tensor) -> Tensor: - return self.nld(input_tensor) + return self.sequences(input_tensor) From 84b445136672abe634594b35d3e6411184d3fceb Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 16:50:40 +0100 Subject: [PATCH 185/394] Fix export using Trainer --- embedding_converter/src/exporting.py | 2 +- face_swapper/src/exporting.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 4ede026..9102c71 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -20,5 +20,5 @@ def export() -> None: model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') model.eval() model.ir_version = torch.tensor(ir_version) - input_tensor = (torch.randn(1, 512), ) + input_tensor = torch.randn(1, 512) torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 6631fe1..c0c74e1 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -3,7 +3,7 @@ from os import makedirs import torch -from .models.generator import Generator +from .training import FaceSwapperTrainer CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -17,9 +17,7 @@ def export() -> None: opset_version = CONFIG.getint('exporting', 'opset_version') makedirs(directory_path, exist_ok = True) - state_dict = torch.load(source_path, map_location = 'cpu').get('state_dict').get('generator') - model = Generator() - model.load_state_dict(state_dict) + model = FaceSwapperTrainer.load_from_checkpoint(source_path, map_location = 'cpu') model.eval() model.ir_version = torch.tensor(ir_version) source_tensor = torch.randn(1, 512) From c8801ececd1ad5b30c8b16efea2a8b5d94572724 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 26 Feb 2025 16:56:02 +0100 Subject: [PATCH 186/394] Fix export using Trainer --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 29fa196..e016b80 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -40,7 +40,7 @@ class FaceSwapperTrainer(lightning.LightningModule): self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.automatic_optimization = False - def forward(self, target_tensor : Tensor, source_embedding : Embedding) -> Tensor: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: output_tensor = self.generator(source_embedding, target_tensor) return output_tensor From 2ddcf52b6677c49a960b778128b69c95fce41757 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 12:18:09 +0530 Subject: [PATCH 187/394] changes --- face_swapper/src/models/loss.py | 21 +++++++++------------ face_swapper/src/training.py | 6 +++--- 2 files changed, 12 insertions(+), 15 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index a41d76e..803286a 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -70,32 +70,29 @@ class AttributeLoss(nn.Module): class ReconstructionLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, embedder : nn.Module) -> None: super().__init__() + self.embedder = embedder self.mse_loss = nn.MSELoss() def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') - temp_tensors = [] - - for __source_tensor__, __target_tensor__ in zip(source_tensor, target_tensor): - temp_tensor = self.mse_loss(__source_tensor__, __target_tensor__) * torch.equal(__source_tensor__, __target_tensor__) - temp_tensors.append(temp_tensor) - - reconstruction_loss = torch.stack(temp_tensors).mean() * 0.5 + source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) + same_person = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 + reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) + reconstruction_loss = (reconstruction_loss * same_person).mean() * 0.5 data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() - reconstruction_loss = (reconstruction_loss + similarity) * 0.5 weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight return reconstruction_loss, weighted_reconstruction_loss class IdentityLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, embedder : nn.Module) -> None: super().__init__() - embedder_path = CONFIG.get('training.model', 'embedder_path') - self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.embedder = embedder def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e016b80..44d635d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -28,16 +28,16 @@ class FaceSwapperTrainer(lightning.LightningModule): super().__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') + self.embedder = torch.jit.load(embedder_path, map_location='cpu') # type:ignore[no-untyped-call] self.generator = Generator() self.discriminator = Discriminator() self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss() self.attribute_loss = AttributeLoss() - self.reconstruction_loss = ReconstructionLoss() - self.identity_loss = IdentityLoss() + self.reconstruction_loss = ReconstructionLoss(self.embedder) + self.identity_loss = IdentityLoss(self.embedder) self.pose_loss = PoseLoss() self.gaze_loss = GazeLoss() - self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: From 5d1b90ff19fdf12a18156fc327c093fa51104767 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 13:13:20 +0530 Subject: [PATCH 188/394] changes --- face_swapper/src/models/loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 803286a..f2936df 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -78,7 +78,7 @@ class ReconstructionLoss(nn.Module): def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - target_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) + target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) same_person = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) reconstruction_loss = (reconstruction_loss * same_person).mean() * 0.5 From d87f6c0b156715a00183ca233aff13095ffe3e16 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 15:46:50 +0530 Subject: [PATCH 189/394] changes --- face_swapper/src/models/loss.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index f2936df..2614f99 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -6,7 +6,7 @@ from pytorch_msssim import ssim from torch import Tensor, nn from ..helper import calc_embedding -from ..types import Attributes, FaceLandmark203 +from ..types import Attributes, EmbedderModule, FaceLandmark203 CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -70,7 +70,7 @@ class AttributeLoss(nn.Module): class ReconstructionLoss(nn.Module): - def __init__(self, embedder : nn.Module) -> None: + def __init__(self, embedder : EmbedderModule) -> None: super().__init__() self.embedder = embedder self.mse_loss = nn.MSELoss() @@ -90,7 +90,7 @@ class ReconstructionLoss(nn.Module): class IdentityLoss(nn.Module): - def __init__(self, embedder : nn.Module) -> None: + def __init__(self, embedder : EmbedderModule) -> None: super().__init__() self.embedder = embedder From b27b8663e5908835b75219f01e4ace4eeca73a11 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 16:18:53 +0530 Subject: [PATCH 190/394] changes --- face_swapper/src/training.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 44d635d..01cb2c8 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -48,8 +48,8 @@ class FaceSwapperTrainer(lightning.LightningModule): learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(generator_optimizer, T_max = 10, eta_min = 1e-6) - discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(discriminator_optimizer, T_max = 10, eta_min = 1e-6) + generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) + discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) generator_config =\ { @@ -82,6 +82,7 @@ class FaceSwapperTrainer(lightning.LightningModule): generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) + self.toggle_optimizer(generator_optimizer) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) @@ -93,10 +94,9 @@ class FaceSwapperTrainer(lightning.LightningModule): generator_optimizer.zero_grad() self.manual_backward(generator_loss) generator_optimizer.step() + self.untoggle_optimizer(generator_optimizer) - generator_scheduler = self.lr_schedulers()[0] - generator_scheduler.step() - + self.toggle_optimizer(discriminator_optimizer) discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) @@ -104,9 +104,7 @@ class FaceSwapperTrainer(lightning.LightningModule): discriminator_optimizer.zero_grad() self.manual_backward(discriminator_loss) discriminator_optimizer.step() - - discriminator_scheduler = self.lr_schedulers()[1] - discriminator_scheduler.step() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor) From a5eb7d6aa1794e680913814f5866137273d5eb2f Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 19:05:41 +0530 Subject: [PATCH 191/394] change Adam to AdamW --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 01cb2c8..704be44 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -46,8 +46,8 @@ class FaceSwapperTrainer(lightning.LightningModule): def configure_optimizers(self) -> Tuple[OptimizerConfig, OptimizerConfig]: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') - generator_optimizer = torch.optim.Adam(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) From ea1b0205f00e8f53c8ab5cfc45344b7b4e12da74 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 19:17:11 +0530 Subject: [PATCH 192/394] constructor injection --- face_swapper/src/models/loss.py | 12 +++++------- face_swapper/src/training.py | 11 ++++++++--- face_swapper/src/types.py | 2 ++ 3 files changed, 15 insertions(+), 10 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2614f99..c0699f6 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -6,7 +6,7 @@ from pytorch_msssim import ssim from torch import Tensor, nn from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, FaceLandmark203 +from ..types import Attributes, EmbedderModule, FaceLandmark203, LandmarkerModule, MotionExtractorModule CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -104,10 +104,9 @@ class IdentityLoss(nn.Module): class PoseLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, motion_extractor : MotionExtractorModule) -> None: super().__init__() - motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.motion_extractor = motion_extractor self.mse_loss = nn.MSELoss() def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: @@ -132,10 +131,9 @@ class PoseLoss(nn.Module): class GazeLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, landmarker : LandmarkerModule) -> None: super().__init__() - landmarker_path = CONFIG.get('training.model', 'landmarker_path') - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.landmarker = landmarker self.mse_loss = nn.MSELoss() def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 704be44..2d38f53 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -27,8 +27,13 @@ class FaceSwapperTrainer(lightning.LightningModule): def __init__(self) -> None: super().__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') + landmarker_path = CONFIG.get('training.model', 'landmarker_path') + motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') + + self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.embedder = torch.jit.load(embedder_path, map_location='cpu') # type:ignore[no-untyped-call] self.generator = Generator() self.discriminator = Discriminator() self.discriminator_loss = DiscriminatorLoss() @@ -36,8 +41,8 @@ class FaceSwapperTrainer(lightning.LightningModule): self.attribute_loss = AttributeLoss() self.reconstruction_loss = ReconstructionLoss(self.embedder) self.identity_loss = IdentityLoss(self.embedder) - self.pose_loss = PoseLoss() - self.gaze_loss = GazeLoss() + self.pose_loss = PoseLoss(self.motion_extractor) + self.gaze_loss = GazeLoss(self.landmarker) self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index cc2f46e..dd3ed87 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -13,5 +13,7 @@ Padding : TypeAlias = Tuple[int, int, int, int] GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module +LandmarkerModule : TypeAlias = Module +MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any From 35b779b1edf409ca875b46a9fb03acc9efca19e3 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 19:24:12 +0530 Subject: [PATCH 193/394] change pretrained models mode to eval --- face_swapper/src/training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 2d38f53..e8dd59f 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -30,9 +30,9 @@ class FaceSwapperTrainer(lightning.LightningModule): landmarker_path = CONFIG.get('training.model', 'landmarker_path') motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') - self.embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.embedder = torch.jit.load(embedder_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] self.generator = Generator() self.discriminator = Discriminator() From 0055c0c97f07e4f4e93715b1d9453fc326413e5d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 1 Mar 2025 16:02:35 +0100 Subject: [PATCH 194/394] Adjust some namings --- face_swapper/src/models/loss.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index c0699f6..2679725 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -79,9 +79,11 @@ class ReconstructionLoss(nn.Module): reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) - same_person = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 + is_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 + reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) - reconstruction_loss = (reconstruction_loss * same_person).mean() * 0.5 + reconstruction_loss = (reconstruction_loss * is_similar_identity).mean() * 0.5 + data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() reconstruction_loss = (reconstruction_loss + similarity) * 0.5 From a22adaf51fe130d24643d82397dd07ca3f71a8c3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 1 Mar 2025 16:06:38 +0100 Subject: [PATCH 195/394] Adjust some namings --- face_swapper/src/models/loss.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2679725..c2be15e 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -79,14 +79,14 @@ class ReconstructionLoss(nn.Module): reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) - is_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 + has_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) - reconstruction_loss = (reconstruction_loss * is_similar_identity).mean() * 0.5 + reconstruction_loss = (reconstruction_loss * has_similar_identity).mean() * 0.5 data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) - similarity = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() - reconstruction_loss = (reconstruction_loss + similarity) * 0.5 + visual_loss = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() + reconstruction_loss = (reconstruction_loss + visual_loss) * 0.5 weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight return reconstruction_loss, weighted_reconstruction_loss From 56be3f0b9b76048707c9ca4639464675884db58e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 1 Mar 2025 16:14:07 +0100 Subject: [PATCH 196/394] Rename to validation_score --- embedding_converter/src/training.py | 6 +++--- face_swapper/src/training.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 0be3ce7..1fa0009 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -46,9 +46,9 @@ class EmbeddingConverterTrainer(lightning.LightningModule): with torch.no_grad(): source_embedding = self.source_embedder(batch) output_embedding = self(source_embedding) - validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 - self.log('validation', validation, prog_bar = True) - return validation + validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 + self.log('validation_score', validation_score, prog_bar = True) + return validation_score def configure_optimizers(self) -> OptimizerConfig: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index e8dd59f..abaf6d1 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -129,9 +129,9 @@ class FaceSwapperTrainer(lightning.LightningModule): source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) output_tensor = self.generator(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) - validation = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 - self.log('validation', validation) - return validation + validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 + self.log('validation_score', validation_score, prog_bar = True) + return validation_score def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> None: preview_limit = 8 From 65ab796835ef074eeb578ae3ef0bc98287bfc2f6 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 27 Feb 2025 23:17:06 +0530 Subject: [PATCH 197/394] changes --- face_swapper/src/dataset.py | 2 ++ face_swapper/src/helper.py | 26 ++++++++++++++++++++++++-- face_swapper/src/types.py | 4 +++- 3 files changed, 29 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 5dc73b0..8fa31d0 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -6,6 +6,7 @@ from torch import Tensor from torch.utils.data import Dataset from torchvision import io, transforms +from .helper import warp_tensor from .types import Batch @@ -35,6 +36,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), + lambda temp: warp_tensor(temp.unsqueeze(0), '__vgg_face_hq__to__arcface_128_v2__').squeeze(0), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index ebc11f8..4b5d692 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,10 +1,32 @@ +import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding +from .types import AlignmentMatrices, EmbedderModule, Embedding, Padding + +ALIGNMENT_MATRICES: AlignmentMatrices =\ +{ + '__vgg_face_hq__to__arcface_128_v2__': torch.tensor( + [ + [ 1.01305414, -0.00140513, -0.00585911 ], + [ 0.00140513, 1.01305414, 0.11169602 ] + ], dtype = torch.float32), + '__arcface_128_v2__to__arcface_112_v2__': torch.tensor( + [ + [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], + [ 1.07193451e-08, 8.75000016e-01, -1.25000007e-01 ] + ], dtype = torch.float32) +} + + +def warp_tensor(input_tensor : Tensor, alignment_matrix : str) -> Tensor: + matrix = ALIGNMENT_MATRICES.get(alignment_matrix).repeat(input_tensor.shape[0], 1, 1) + grid = nn.functional.affine_grid(matrix.to(input_tensor.device), list(input_tensor.shape)) + output_tensor = nn.functional.grid_sample(input_tensor, grid, align_corners = False, padding_mode = 'reflection') + return output_tensor def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: - crop_tensor = input_tensor[:, :, 15: 241, 15: 241] + crop_tensor = warp_tensor(input_tensor, '__arcface_128_v2__to__arcface_112_v2__') crop_tensor = nn.functional.interpolate(crop_tensor, size = (112, 112), mode = 'area') crop_tensor[:, :, :padding[0], :] = 0 crop_tensor[:, :, 112 - padding[1]:, :] = 0 diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index dd3ed87..7040abe 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, Tuple, TypeAlias +from typing import Any, Dict, Tuple, TypeAlias from torch import Tensor from torch.nn import Module @@ -17,3 +17,5 @@ LandmarkerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any + +AlignmentMatrices : TypeAlias = Dict[str, Tensor] From 16c8b322698096ab2554f2f1a473759a40d0b37d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 08:33:52 +0100 Subject: [PATCH 198/394] Fix some naming and types --- face_swapper/src/helper.py | 40 ++++++++++++++++++++++++++------------ face_swapper/src/types.py | 2 +- 2 files changed, 29 insertions(+), 13 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 4b5d692..6638aea 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,32 +1,48 @@ import torch from torch import Tensor, nn -from .types import AlignmentMatrices, EmbedderModule, Embedding, Padding +from .types import WarpMatrixSet, EmbedderModule, Embedding, Padding -ALIGNMENT_MATRICES: AlignmentMatrices =\ +WARP_MATRIX_SET : WarpMatrixSet =\ { - '__vgg_face_hq__to__arcface_128_v2__': torch.tensor( + 'vgg_face_hq_to_arcface_128_v2': torch.tensor( + [ [ - [ 1.01305414, -0.00140513, -0.00585911 ], - [ 0.00140513, 1.01305414, 0.11169602 ] - ], dtype = torch.float32), - '__arcface_128_v2__to__arcface_112_v2__': torch.tensor( + 1.01305414, + -0.00140513, + -0.00585911 + ], [ - [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], - [ 1.07193451e-08, 8.75000016e-01, -1.25000007e-01 ] - ], dtype = torch.float32) + 0.00140513, + 1.01305414, + 0.11169602 + ] + ], dtype = torch.float32), + 'arcface_128_v2_to_arcface_112_v2': torch.tensor( + [ + [ + 8.75000016e-01, + -1.07193451e-08, + 3.80446920e-10 + ], + [ + 1.07193451e-08, + 8.75000016e-01, + -1.25000007e-01 + ] + ], dtype = torch.float32) } def warp_tensor(input_tensor : Tensor, alignment_matrix : str) -> Tensor: - matrix = ALIGNMENT_MATRICES.get(alignment_matrix).repeat(input_tensor.shape[0], 1, 1) + matrix = WARP_MATRIX_SET.get(alignment_matrix).repeat(input_tensor.shape[0], 1, 1) grid = nn.functional.affine_grid(matrix.to(input_tensor.device), list(input_tensor.shape)) output_tensor = nn.functional.grid_sample(input_tensor, grid, align_corners = False, padding_mode = 'reflection') return output_tensor def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: - crop_tensor = warp_tensor(input_tensor, '__arcface_128_v2__to__arcface_112_v2__') + crop_tensor = warp_tensor(input_tensor, 'arcface_128_v2_to_arcface_112_v2') crop_tensor = nn.functional.interpolate(crop_tensor, size = (112, 112), mode = 'area') crop_tensor[:, :, :padding[0], :] = 0 crop_tensor[:, :, 112 - padding[1]:, :] = 0 diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 7040abe..440ddc2 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -18,4 +18,4 @@ MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any -AlignmentMatrices : TypeAlias = Dict[str, Tensor] +WarpMatrixSet : TypeAlias = Dict[str, Tensor] From d3a2035d7a8f3928cd3faf0418170bd89238b621 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 08:41:56 +0100 Subject: [PATCH 199/394] Fix order --- face_swapper/src/helper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 6638aea..1916588 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from .types import WarpMatrixSet, EmbedderModule, Embedding, Padding +from .types import EmbedderModule, Embedding, Padding, WarpMatrixSet WARP_MATRIX_SET : WarpMatrixSet =\ { From 8443f3512ade95ea1858efb4ea1e29400f7a1f28 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 08:46:20 +0100 Subject: [PATCH 200/394] torch.float32 is the default --- face_swapper/src/helper.py | 28 ++++++---------------------- 1 file changed, 6 insertions(+), 22 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 1916588..765fe6a 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -7,30 +7,14 @@ WARP_MATRIX_SET : WarpMatrixSet =\ { 'vgg_face_hq_to_arcface_128_v2': torch.tensor( [ - [ - 1.01305414, - -0.00140513, - -0.00585911 - ], - [ - 0.00140513, - 1.01305414, - 0.11169602 - ] - ], dtype = torch.float32), + [ 1.01305414, -0.00140513, -0.00585911 ], + [ 0.00140513, 1.01305414, 0.11169602 ] + ]), 'arcface_128_v2_to_arcface_112_v2': torch.tensor( [ - [ - 8.75000016e-01, - -1.07193451e-08, - 3.80446920e-10 - ], - [ - 1.07193451e-08, - 8.75000016e-01, - -1.25000007e-01 - ] - ], dtype = torch.float32) + [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], + [ 1.07193451e-08, 8.75000016e-01, -1.25000007e-01 ] + ]) } From 23ab7dc89ded8060c8c44dcfbbf54eb2a60265e5 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 13:20:37 +0530 Subject: [PATCH 201/394] fix --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 8fa31d0..c4e7d57 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -36,7 +36,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - lambda temp: warp_tensor(temp.unsqueeze(0), '__vgg_face_hq__to__arcface_128_v2__').squeeze(0), + lambda temp: warp_tensor(temp.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) From c35b0a6f4cbd91ae207df32ee474095ea4bbd3bf Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 13:28:55 +0530 Subject: [PATCH 202/394] changes --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index c4e7d57..2494e1c 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -36,7 +36,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - lambda temp: warp_tensor(temp.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0), + transforms.Lambda(lambda temp: warp_tensor(temp.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0)), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) From 34a7f3ef55b990f320daa9973a406915ff8c0294 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 13:30:00 +0530 Subject: [PATCH 203/394] changes --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 2494e1c..b2d752b 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -36,7 +36,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - transforms.Lambda(lambda temp: warp_tensor(temp.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0)), + transforms.Lambda(lambda temp_tensor: warp_tensor(temp_tensor.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0)), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) From 589568bfb58f14382ae56ee6b290a41e3eed2453 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:23:40 +0100 Subject: [PATCH 204/394] Add config to dataloader alignment --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/dataset.py | 13 ++++++++----- face_swapper/src/helper.py | 8 ++++---- face_swapper/src/training.py | 7 ++++--- face_swapper/src/types.py | 4 +++- 6 files changed, 21 insertions(+), 13 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 7daa0a3..0962162 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -28,6 +28,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg +warp_matrix = vgg_face_hq_to_arcface_128_v2 batch_ratio = 0.2 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index c3ce49b..63aa355 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,5 +1,6 @@ [training.dataset] file_pattern = +warp_matrix = batch_ratio = [training.loader] diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index b2d752b..7bcde99 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -7,13 +7,14 @@ from torch.utils.data import Dataset from torchvision import io, transforms from .helper import warp_tensor -from .types import Batch +from .types import Batch, WarpMatrix class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, batch_ratio : float) -> None: + def __init__(self, file_pattern : str, warp_matrix : WarpMatrix, batch_ratio : float) -> None: self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() + self.warp_matrix = warp_matrix self.batch_ratio = batch_ratio def __getitem__(self, index : int) -> Batch: @@ -27,8 +28,7 @@ class DynamicDataset(Dataset[Tensor]): def __len__(self) -> int: return len(self.file_paths) - @staticmethod - def compose_transforms() -> transforms: + def compose_transforms(self) -> transforms: return transforms.Compose( [ transforms.ToPILImage(), @@ -36,10 +36,13 @@ class DynamicDataset(Dataset[Tensor]): transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), transforms.ToTensor(), - transforms.Lambda(lambda temp_tensor: warp_tensor(temp_tensor.unsqueeze(0), 'vgg_face_hq_to_arcface_128_v2').squeeze(0)), + transforms.Lambda(self.warp_tensor), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) + def warp_tensor(self, temp_tensor : Tensor): + return warp_tensor(temp_tensor.unsqueeze(0), self.warp_matrix).squeeze(0) + def prepare_different_batch(self, source_path : str) -> Batch: target_path = random.choice(self.file_paths) source_tensor = io.read_image(source_path) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 765fe6a..eb8f059 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding, WarpMatrixSet +from .types import EmbedderModule, Embedding, Padding, WarpMatrix, WarpMatrixSet WARP_MATRIX_SET : WarpMatrixSet =\ { @@ -18,9 +18,9 @@ WARP_MATRIX_SET : WarpMatrixSet =\ } -def warp_tensor(input_tensor : Tensor, alignment_matrix : str) -> Tensor: - matrix = WARP_MATRIX_SET.get(alignment_matrix).repeat(input_tensor.shape[0], 1, 1) - grid = nn.functional.affine_grid(matrix.to(input_tensor.device), list(input_tensor.shape)) +def warp_tensor(input_tensor : Tensor, warp_matrix : WarpMatrix) -> Tensor: + warp_matrix = WARP_MATRIX_SET.get(warp_matrix).repeat(input_tensor.shape[0], 1, 1) + grid = nn.functional.affine_grid(warp_matrix.to(input_tensor.device), list(input_tensor.shape)) output_tensor = nn.functional.grid_sample(input_tensor, grid, align_corners = False, padding_mode = 'reflection') return output_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index abaf6d1..3e88f72 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -62,7 +62,7 @@ class FaceSwapperTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': generator_scheduler, - 'interval': 'step', + 'interval': 'step' } } discriminator_config =\ @@ -71,7 +71,7 @@ class FaceSwapperTrainer(lightning.LightningModule): 'lr_scheduler': { 'scheduler': discriminator_scheduler, - 'interval': 'step', + 'interval': 'step' } } return generator_config, discriminator_config @@ -194,13 +194,14 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') + dataset_warp_matrix = CONFIG.get('training.dataset', 'warp_matrix') dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_batch_ratio) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_matrix, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 440ddc2..3190341 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,5 +1,6 @@ from typing import Any, Dict, Tuple, TypeAlias +from jinja2.nodes import Literal from torch import Tensor from torch.nn import Module @@ -18,4 +19,5 @@ MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any -WarpMatrixSet : TypeAlias = Dict[str, Tensor] +WarpMatrix = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] +WarpMatrixSet : TypeAlias = Dict[WarpMatrix, Tensor] From cc9a0ba83e3b3f434953385d349f75c76b1f3363 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:32:44 +0100 Subject: [PATCH 205/394] Add config to dataloader alignment --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 7bcde99..f1ad8b6 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -40,7 +40,7 @@ class DynamicDataset(Dataset[Tensor]): transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - def warp_tensor(self, temp_tensor : Tensor): + def warp_tensor(self, temp_tensor : Tensor) -> Tensor: return warp_tensor(temp_tensor.unsqueeze(0), self.warp_matrix).squeeze(0) def prepare_different_batch(self, source_path : str) -> Batch: From 1ab57b1d3f8acc5adda7959334ed9c60b4aefadb Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:39:56 +0100 Subject: [PATCH 206/394] Add config to dataloader alignment --- face_swapper/src/types.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 3190341..74ba33e 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,6 +1,5 @@ -from typing import Any, Dict, Tuple, TypeAlias +from typing import Any, Dict, Tuple, TypeAlias, Type, Literal -from jinja2.nodes import Literal from torch import Tensor from torch.nn import Module From c9e70ebc18faae965306e13f5474df34813d0736 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:41:04 +0100 Subject: [PATCH 207/394] Add config to dataloader alignment --- face_swapper/src/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 74ba33e..b1213c1 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Tuple, TypeAlias, Type, Literal +from typing import Any, Dict, Tuple, TypeAlias, Literal from torch import Tensor from torch.nn import Module From 43f99db5d75103aff47231c6a0f122b5f406a83a Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:41:40 +0100 Subject: [PATCH 208/394] Add config to dataloader alignment --- face_swapper/src/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index b1213c1..ec28757 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Tuple, TypeAlias, Literal +from typing import Any, Dict, Literal, Tuple, TypeAlias from torch import Tensor from torch.nn import Module From 6ca68f14085bac663d158ad0ca035f5b6e7804c2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:45:02 +0100 Subject: [PATCH 209/394] Add config to dataloader alignment --- face_swapper/src/training.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 3e88f72..4324b68 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,6 +1,6 @@ import configparser import os -from typing import Tuple +from typing import cast, Tuple import lightning import torch @@ -17,7 +17,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss -from .types import Batch, Embedding, OptimizerConfig +from .types import Batch, Embedding, OptimizerConfig, WarpMatrix CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -194,7 +194,7 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - dataset_warp_matrix = CONFIG.get('training.dataset', 'warp_matrix') + dataset_warp_matrix = cast(WarpMatrix, CONFIG.get('training.dataset', 'warp_matrix')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') From 5e68de91708abab0453298bc405ce74a8b45b1aa Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:45:41 +0100 Subject: [PATCH 210/394] Add config to dataloader alignment --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4324b68..d4cef33 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,6 +1,6 @@ import configparser import os -from typing import cast, Tuple +from typing import Tuple, cast import lightning import torch From 80d1694e233d989c4c87125923ff649a9969290c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 09:57:27 +0100 Subject: [PATCH 211/394] Replace naming warp_matrix with warp_template --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/dataset.py | 8 ++++---- face_swapper/src/helper.py | 12 ++++++------ face_swapper/src/training.py | 6 +++--- face_swapper/src/types.py | 4 ++-- 6 files changed, 17 insertions(+), 17 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 0962162..48d0c51 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -28,7 +28,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. ``` [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg -warp_matrix = vgg_face_hq_to_arcface_128_v2 +warp_template = vgg_face_hq_to_arcface_128_v2 batch_ratio = 0.2 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 63aa355..72233e8 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,6 +1,6 @@ [training.dataset] file_pattern = -warp_matrix = +warp_template = batch_ratio = [training.loader] diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index f1ad8b6..e289ac9 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -7,14 +7,14 @@ from torch.utils.data import Dataset from torchvision import io, transforms from .helper import warp_tensor -from .types import Batch, WarpMatrix +from .types import Batch, WarpTemplate class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, warp_matrix : WarpMatrix, batch_ratio : float) -> None: + def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_ratio : float) -> None: self.file_paths = glob.glob(file_pattern) self.transforms = self.compose_transforms() - self.warp_matrix = warp_matrix + self.warp_template = warp_template self.batch_ratio = batch_ratio def __getitem__(self, index : int) -> Batch: @@ -41,7 +41,7 @@ class DynamicDataset(Dataset[Tensor]): ]) def warp_tensor(self, temp_tensor : Tensor) -> Tensor: - return warp_tensor(temp_tensor.unsqueeze(0), self.warp_matrix).squeeze(0) + return warp_tensor(temp_tensor.unsqueeze(0), self.warp_template).squeeze(0) def prepare_different_batch(self, source_path : str) -> Batch: target_path = random.choice(self.file_paths) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index eb8f059..557fcbe 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,9 +1,9 @@ import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding, WarpMatrix, WarpMatrixSet +from .types import EmbedderModule, Embedding, Padding, WarpTemplate, WarpTemplateSet -WARP_MATRIX_SET : WarpMatrixSet =\ +WARP_TEMPLATE_SET : WarpTemplateSet =\ { 'vgg_face_hq_to_arcface_128_v2': torch.tensor( [ @@ -18,10 +18,10 @@ WARP_MATRIX_SET : WarpMatrixSet =\ } -def warp_tensor(input_tensor : Tensor, warp_matrix : WarpMatrix) -> Tensor: - warp_matrix = WARP_MATRIX_SET.get(warp_matrix).repeat(input_tensor.shape[0], 1, 1) - grid = nn.functional.affine_grid(warp_matrix.to(input_tensor.device), list(input_tensor.shape)) - output_tensor = nn.functional.grid_sample(input_tensor, grid, align_corners = False, padding_mode = 'reflection') +def warp_tensor(input_tensor : Tensor, warp_template : WarpTemplate) -> Tensor: + normed_warp_template = WARP_TEMPLATE_SET.get(warp_template).repeat(input_tensor.shape[0], 1, 1) + affine_grid = nn.functional.affine_grid(normed_warp_template.to(input_tensor.device), list(input_tensor.shape)) + output_tensor = nn.functional.grid_sample(input_tensor, affine_grid, align_corners = False, padding_mode = 'reflection') return output_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index d4cef33..590606c 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss -from .types import Batch, Embedding, OptimizerConfig, WarpMatrix +from .types import Batch, Embedding, OptimizerConfig, WarpTemplate CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -194,14 +194,14 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - dataset_warp_matrix = cast(WarpMatrix, CONFIG.get('training.dataset', 'warp_matrix')) + dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_matrix, dataset_batch_ratio) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index ec28757..7742764 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -18,5 +18,5 @@ MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any -WarpMatrix = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] -WarpMatrixSet : TypeAlias = Dict[WarpMatrix, Tensor] +WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] +WarpTemplateSet : TypeAlias = Dict[WarpTemplate, Tensor] From e6ea4543608d01d3216100212e0d64b302a42e5b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 15:12:35 +0530 Subject: [PATCH 212/394] supress warning --- face_swapper/src/training.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 590606c..637f094 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,5 +1,6 @@ import configparser import os +import warnings from typing import Tuple, cast import lightning @@ -19,6 +20,8 @@ from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss from .types import Batch, Embedding, OptimizerConfig, WarpTemplate +warnings.filterwarnings("ignore", category=UserWarning, module="torch") + CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From 2fb0b4289d97500fc62dd7ce734033f115432e0e Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 15:13:34 +0530 Subject: [PATCH 213/394] changes --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 637f094..df97894 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -20,7 +20,7 @@ from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss from .types import Batch, Embedding, OptimizerConfig, WarpTemplate -warnings.filterwarnings("ignore", category=UserWarning, module="torch") +warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From dfd9e99aed722f6dd7fdb1c090403670e6f54cfd Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 16:41:26 +0530 Subject: [PATCH 214/394] changes --- face_swapper/config.ini | 2 +- face_swapper/src/models/loss.py | 36 ++++++++++++++++++++------------- face_swapper/src/training.py | 6 +++--- face_swapper/src/types.py | 4 ++-- 4 files changed, 28 insertions(+), 20 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 72233e8..4cf9f80 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -10,7 +10,7 @@ split_ratio = [training.model] embedder_path = -landmarker_path = +gazer_path = motion_extractor_path = [training.model.generator] diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index c2be15e..8ab6533 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -4,9 +4,10 @@ from typing import List, Tuple import torch from pytorch_msssim import ssim from torch import Tensor, nn +from torchvision import transforms from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, FaceLandmark203, LandmarkerModule, MotionExtractorModule +from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -133,25 +134,32 @@ class PoseLoss(nn.Module): class GazeLoss(nn.Module): - def __init__(self, landmarker : LandmarkerModule) -> None: + def __init__(self, gazer : GazerModule) -> None: super().__init__() - self.landmarker = landmarker - self.mse_loss = nn.MSELoss() + self.gazer = gazer + self.mae_loss = nn.L1Loss() + self.transform = transforms.Compose( + [ + transforms.Resize(448), + transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ]) + ]) def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') - output_face_landmark = self.detect_face_landmark(output_tensor) - target_face_landmark = self.detect_face_landmark(target_tensor) + output_pitch_tensor, output_yaw_tensor = self.detect_gaze(output_tensor) + target_pitch_tensor, target_yaw_tensor = self.detect_gaze(target_tensor) - left_gaze_loss = self.mse_loss(output_face_landmark[:, 198], target_face_landmark[:, 198]) - right_gaze_loss = self.mse_loss(output_face_landmark[:, 197], target_face_landmark[:, 197]) + pitch_gaze_loss = self.mae_loss(output_pitch_tensor, target_pitch_tensor) + yaw_gaze_loss = self.mae_loss(output_yaw_tensor, target_yaw_tensor) - gaze_loss = left_gaze_loss + right_gaze_loss + gaze_loss = (pitch_gaze_loss + yaw_gaze_loss) * 0.5 weighted_gaze_loss = gaze_loss * gaze_weight return gaze_loss, weighted_gaze_loss - def detect_face_landmark(self, input_tensor : Tensor) -> FaceLandmark203: - input_tensor = (input_tensor + 1) * 0.5 - input_tensor = nn.functional.interpolate(input_tensor, size = (224, 224), mode = 'bilinear') - face_landmarks_203 = self.landmarker(input_tensor)[2].view(-1, 203, 2) - return face_landmarks_203 + def detect_gaze(self, input_tensor : Tensor) -> Gaze: + crop_tensor = input_tensor[:, :, 60: 224, 16: 205] + crop_tensor = (crop_tensor + 1) * 0.5 + crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) + crop_tensor = nn.functional.interpolate(crop_tensor, size = (448, 448), mode = 'bicubic') + pitch_tensor, yaw_tensor = self.gazer(crop_tensor) + return pitch_tensor, yaw_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index df97894..91f20bf 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -30,11 +30,11 @@ class FaceSwapperTrainer(lightning.LightningModule): def __init__(self) -> None: super().__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') - landmarker_path = CONFIG.get('training.model', 'landmarker_path') + gazer_path = CONFIG.get('training.model', 'gazer_path') motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') self.embedder = torch.jit.load(embedder_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.landmarker = torch.jit.load(landmarker_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.gazer = torch.jit.load(gazer_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] self.generator = Generator() @@ -45,7 +45,7 @@ class FaceSwapperTrainer(lightning.LightningModule): self.reconstruction_loss = ReconstructionLoss(self.embedder) self.identity_loss = IdentityLoss(self.embedder) self.pose_loss = PoseLoss(self.motion_extractor) - self.gaze_loss = GazeLoss(self.landmarker) + self.gaze_loss = GazeLoss(self.gazer) self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 7742764..44a1e8a 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -7,13 +7,13 @@ Batch : TypeAlias = Tuple[Tensor, Tensor] Attributes : TypeAlias = Tuple[Tensor, ...] Embedding : TypeAlias = Tensor -FaceLandmark203 : TypeAlias = Tensor +Gaze : TypeAlias = Tuple[Tensor, Tensor] Padding : TypeAlias = Tuple[int, int, int, int] GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module -LandmarkerModule : TypeAlias = Module +GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module OptimizerConfig : TypeAlias = Any From 83c20f833162d0f18c715991233308c06ed5fcce Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 3 Mar 2025 16:42:21 +0530 Subject: [PATCH 215/394] changes --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 48d0c51..5215a5d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -42,7 +42,7 @@ split_ratio = 0.9995 ``` [training.model] embedder_path = .models/arcface.pt -landmarker_path = .models/landmarker.pt +gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt ``` From 3a61da8bab331ab34d358e1e0bb98b9002ed7bab Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 12:13:54 +0100 Subject: [PATCH 216/394] Introduce batch mode via config for equal and same batches --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/dataset.py | 17 +++++++++++++---- face_swapper/src/training.py | 5 +++-- face_swapper/src/types.py | 1 + 5 files changed, 19 insertions(+), 6 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 5215a5d..86731ef 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -29,6 +29,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg warp_template = vgg_face_hq_to_arcface_128_v2 +batch_mode = same batch_ratio = 0.2 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 4cf9f80..0b585f4 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,6 +1,7 @@ [training.dataset] file_pattern = warp_template = +batch_mode = batch_ratio = [training.loader] diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index e289ac9..ee9a224 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -7,21 +7,25 @@ from torch.utils.data import Dataset from torchvision import io, transforms from .helper import warp_tensor -from .types import Batch, WarpTemplate +from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_ratio : float) -> None: + def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_mode : BatchMode, batch_ratio : float) -> None: self.file_paths = glob.glob(file_pattern) - self.transforms = self.compose_transforms() self.warp_template = warp_template + self.batch_mode = batch_mode self.batch_ratio = batch_ratio + self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: file_path = self.file_paths[index] if random.random() < self.batch_ratio: - return self.prepare_equal_batch(file_path) + if self.batch_mode == 'equal': + return self.prepare_equal_batch(file_path) + if self.batch_mode == 'same': + return self.prepare_same_batch(file_path) return self.prepare_different_batch(file_path) @@ -51,6 +55,11 @@ class DynamicDataset(Dataset[Tensor]): target_tensor = self.transforms(target_tensor) return source_tensor, target_tensor + def prepare_same_batch(self, source_path : str) -> Batch: + source_tensor = io.read_image(source_path) + source_tensor = self.transforms(source_tensor) + return source_tensor, source_tensor + def prepare_equal_batch(self, source_path : str) -> Batch: target_directory_path = os.path.dirname(source_path) target_file_name_and_extension = random.choice(os.listdir(target_directory_path)) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 91f20bf..90dab65 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -18,7 +18,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss -from .types import Batch, Embedding, OptimizerConfig, WarpTemplate +from .types import Batch, BatchMode, Embedding, OptimizerConfig, WarpTemplate warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -198,13 +198,14 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) + dataset_batch_mode = cast(BatchMode, CONFIG.get('training.dataset', 'batch_mode')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_ratio) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_mode, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 44a1e8a..5aed9bd 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -4,6 +4,7 @@ from torch import Tensor from torch.nn import Module Batch : TypeAlias = Tuple[Tensor, Tensor] +BatchMode = Literal['equal', 'same'] Attributes : TypeAlias = Tuple[Tensor, ...] Embedding : TypeAlias = Tensor From d93daf9e5f64a1179418e7136814c1e9dd3504d7 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 12:15:01 +0100 Subject: [PATCH 217/394] Introduce batch mode via config for equal and same batches --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 86731ef..c52cf60 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -29,7 +29,7 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg warp_template = vgg_face_hq_to_arcface_128_v2 -batch_mode = same +batch_mode = equal batch_ratio = 0.2 ``` From 9dc1031fa5d3c88873aa2f78318f29a3aa84a078 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 12:21:47 +0100 Subject: [PATCH 218/394] Fix same and equal --- face_swapper/src/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index ee9a224..aa1c46d 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -55,12 +55,12 @@ class DynamicDataset(Dataset[Tensor]): target_tensor = self.transforms(target_tensor) return source_tensor, target_tensor - def prepare_same_batch(self, source_path : str) -> Batch: + def prepare_equal_batch(self, source_path : str) -> Batch: source_tensor = io.read_image(source_path) source_tensor = self.transforms(source_tensor) return source_tensor, source_tensor - def prepare_equal_batch(self, source_path : str) -> Batch: + def prepare_same_batch(self, source_path : str) -> Batch: target_directory_path = os.path.dirname(source_path) target_file_name_and_extension = random.choice(os.listdir(target_directory_path)) target_path = os.path.join(target_directory_path, target_file_name_and_extension) From e5a4a54e61df36e9632bc61f09699ff9af367291 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 18:23:18 +0100 Subject: [PATCH 219/394] Introduce Transform classes, Add albumentations --- face_swapper/src/dataset.py | 41 +++++++++++++++++++++++++++++++------ 1 file changed, 35 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index aa1c46d..95e6304 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -2,6 +2,7 @@ import glob import os import random +import albumentations from torch import Tensor from torch.utils.data import Dataset from torchvision import io, transforms @@ -37,16 +38,12 @@ class DynamicDataset(Dataset[Tensor]): [ transforms.ToPILImage(), transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), - transforms.ColorJitter(brightness = 0.2, contrast = 0.2, saturation = 0.2, hue = 0.1), - transforms.RandomAffine(4, translate = (0.01, 0.01), scale = (0.98, 1.02), shear = (1, 1)), + AlterTransform(), transforms.ToTensor(), - transforms.Lambda(self.warp_tensor), + WarpTransform(self.warp_template), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) - def warp_tensor(self, temp_tensor : Tensor) -> Tensor: - return warp_tensor(temp_tensor.unsqueeze(0), self.warp_template).squeeze(0) - def prepare_different_batch(self, source_path : str) -> Batch: target_path = random.choice(self.file_paths) source_tensor = io.read_image(source_path) @@ -69,3 +66,35 @@ class DynamicDataset(Dataset[Tensor]): target_tensor = io.read_image(target_path) target_tensor = self.transforms(target_tensor) return source_tensor, target_tensor + + +class AlterTransform: + def __init__(self) -> None: + self.transforms = self.compose_transforms() + + def __call__(self, input_tensor : Tensor) -> Tensor: + temp_tensor = input_tensor.numpy().transpose(1, 2, 0) + return self.transforms(temp_tensor).get('image') + + @staticmethod + def compose_transforms() -> albumentations.Compose: + return albumentations.Compose( + [ + albumentations.RandomBrightnessContrast(p = 0.3), + albumentations.OneOf( + [ + albumentations.MotionBlur(p = 0.1), + albumentations.MedianBlur(p = 0.1) + ], p = 0.3), + albumentations.ColorJitter(p = 0.1), + ]) + + +class WarpTransform: + def __init__(self, warp_template : WarpTemplate) -> None: + self.warp_template = warp_template + + def __call__(self, input_tensor : Tensor) -> Tensor: + temp_tensor = input_tensor.unsqueeze(0) + return warp_tensor(temp_tensor, self.warp_template).squeeze(0) + From dd0a2fe649ab99b79aba44469de0d9fea55c6b98 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 18:24:09 +0100 Subject: [PATCH 220/394] Add albumentations --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index d635148..103dce8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ --extra-index-url https://download.pytorch.org/whl/cu124 +albumentations==2.0.5 lightning==2.5.0 onnx==1.17.0 onnxruntime==1.20.1 From ceb3c0cfdfcc38e4bea1102bd5badb443c197c33 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 3 Mar 2025 18:29:51 +0100 Subject: [PATCH 221/394] Rename to AugmentTransform --- face_swapper/src/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 95e6304..a0f00e7 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -38,7 +38,7 @@ class DynamicDataset(Dataset[Tensor]): [ transforms.ToPILImage(), transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), - AlterTransform(), + AugmentTransform(), transforms.ToTensor(), WarpTransform(self.warp_template), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) @@ -68,7 +68,7 @@ class DynamicDataset(Dataset[Tensor]): return source_tensor, target_tensor -class AlterTransform: +class AugmentTransform: def __init__(self) -> None: self.transforms = self.compose_transforms() From f2d3f8a19fe50ee03adeff4893be37e54043edfd Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 4 Mar 2025 14:23:20 +0530 Subject: [PATCH 222/394] changes --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/dataset.py | 4 +-- face_swapper/src/models/loss.py | 50 ++++++++++++++++++++++++--------- face_swapper/src/training.py | 8 +++--- 5 files changed, 45 insertions(+), 19 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index c52cf60..147245d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -72,6 +72,7 @@ reconstruction_weight = 20 identity_weight = 20 pose_weight = 0 gaze_weight = 0 +expression_weight = 0 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 0b585f4..db2437a 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -34,6 +34,7 @@ reconstruction_weight = identity_weight = pose_weight = gaze_weight = +expression_weight = [training.trainer] learning_rate = diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index a0f00e7..852648b 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -36,9 +36,9 @@ class DynamicDataset(Dataset[Tensor]): def compose_transforms(self) -> transforms: return transforms.Compose( [ + AugmentTransform(), transforms.ToPILImage(), transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), - AugmentTransform(), transforms.ToTensor(), WarpTransform(self.warp_template), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) @@ -74,7 +74,7 @@ class AugmentTransform: def __call__(self, input_tensor : Tensor) -> Tensor: temp_tensor = input_tensor.numpy().transpose(1, 2, 0) - return self.transforms(temp_tensor).get('image') + return self.transforms(image = temp_tensor).get('image') @staticmethod def compose_transforms() -> albumentations.Compose: diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 8ab6533..093e260 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -106,32 +106,56 @@ class IdentityLoss(nn.Module): return identity_loss, weighted_identity_loss -class PoseLoss(nn.Module): - def __init__(self, motion_extractor : MotionExtractorModule) -> None: +class MotionLoss(nn.Module): + def __init__(self, motion_extractor : MotionExtractorModule): super().__init__() self.motion_extractor = motion_extractor + self.pose_loss = PoseLoss() + self.expression_loss = ExpressionLoss() + + def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: + target_pose_features, target_expression = self.get_motion_features(target_tensor) + output_pose_features, output_expression = self.get_motion_features(output_tensor) + pose_loss, weighted_pose_loss = self.pose_loss(target_pose_features, output_pose_features) + expression_loss, weighted_expression_loss = self.expression_loss(target_expression, output_expression) + return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss + + def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], Tensor]: + input_tensor = (input_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) + pose_features = translation, scale, rotation, motion_points + return pose_features, expression + + +class ExpressionLoss(nn.Module): + def __init__(self) -> None: + super().__init__() + + def forward(self, target_expression : Tensor, output_expression : Tensor, ) -> Tuple[Tensor, Tensor]: + expression_weight = CONFIG.getfloat('training.losses', 'expression_weight') + expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() + weighted_expression_loss = expression_loss * expression_weight + return expression_loss, weighted_expression_loss + + +class PoseLoss(nn.Module): + def __init__(self) -> None: + super().__init__() self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + def forward(self, target_pose_features : Tensor, output_pose_features : Tensor, ) -> Tuple[Tensor, Tensor]: pose_weight = CONFIG.getfloat('training.losses', 'pose_weight') - output_motion_features = self.get_motion_features(output_tensor) - target_motion_features = self.get_motion_features(target_tensor) temp_tensors = [] - for target_motion_feature, output_motion_feature in zip(target_motion_features, output_motion_features): - temp_tensor = self.mse_loss(target_motion_feature, output_motion_feature) + for target_pose_feature, output_pose_feature in zip(target_pose_features, output_pose_features): + temp_tensor = self.mse_loss(target_pose_feature, output_pose_feature) temp_tensors.append(temp_tensor) pose_loss = torch.stack(temp_tensors).mean() weighted_pose_loss = pose_loss * pose_weight return pose_loss, weighted_pose_loss - def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tensor, Tensor, Tensor]: - input_tensor = (input_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, _ = self.motion_extractor(input_tensor) - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - return translation, scale, rotation - class GazeLoss(nn.Module): def __init__(self, gazer : GazerModule) -> None: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 90dab65..49b7612 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, PoseLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MotionLoss, ReconstructionLoss from .types import Batch, BatchMode, Embedding, OptimizerConfig, WarpTemplate warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -44,7 +44,7 @@ class FaceSwapperTrainer(lightning.LightningModule): self.attribute_loss = AttributeLoss() self.reconstruction_loss = ReconstructionLoss(self.embedder) self.identity_loss = IdentityLoss(self.embedder) - self.pose_loss = PoseLoss(self.motion_extractor) + self.motion_loss = MotionLoss(self.motion_extractor) self.gaze_loss = GazeLoss(self.gazer) self.automatic_optimization = False @@ -95,9 +95,9 @@ class FaceSwapperTrainer(lightning.LightningModule): attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) - pose_loss, weighted_pose_loss = self.pose_loss(target_tensor, generator_output_tensor) + pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss generator_optimizer.zero_grad() self.manual_backward(generator_loss) From df5895e26633a4034582db6a9e68bef32f0d3857 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 4 Mar 2025 16:43:36 +0530 Subject: [PATCH 223/394] changes --- face_swapper/src/models/loss.py | 60 +++++++++++++-------------------- 1 file changed, 24 insertions(+), 36 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 093e260..98f1941 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -110,52 +110,40 @@ class MotionLoss(nn.Module): def __init__(self, motion_extractor : MotionExtractorModule): super().__init__() self.motion_extractor = motion_extractor - self.pose_loss = PoseLoss() - self.expression_loss = ExpressionLoss() - - def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor, Tensor, Tensor]: - target_pose_features, target_expression = self.get_motion_features(target_tensor) - output_pose_features, output_expression = self.get_motion_features(output_tensor) - pose_loss, weighted_pose_loss = self.pose_loss(target_pose_features, output_pose_features) - expression_loss, weighted_expression_loss = self.expression_loss(target_expression, output_expression) - return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss - - def get_motion_features(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, Tensor, Tensor, Tensor], Tensor]: - input_tensor = (input_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - pose_features = translation, scale, rotation, motion_points - return pose_features, expression - - -class ExpressionLoss(nn.Module): - def __init__(self) -> None: - super().__init__() - - def forward(self, target_expression : Tensor, output_expression : Tensor, ) -> Tuple[Tensor, Tensor]: - expression_weight = CONFIG.getfloat('training.losses', 'expression_weight') - expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() - weighted_expression_loss = expression_loss * expression_weight - return expression_loss, weighted_expression_loss - - -class PoseLoss(nn.Module): - def __init__(self) -> None: - super().__init__() self.mse_loss = nn.MSELoss() - def forward(self, target_pose_features : Tensor, output_pose_features : Tensor, ) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, ...]: + target_poses, target_expression = self.get_motions(target_tensor) + output_poses, output_expression = self.get_motions(output_tensor) + pose_loss, weighted_pose_loss = self.calc_pose_loss(target_poses, output_poses) + expression_loss, weighted_expression_loss = self.calc_expression_loss(target_expression, output_expression) + return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss + + def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Tensor, Tensor]: pose_weight = CONFIG.getfloat('training.losses', 'pose_weight') temp_tensors = [] - for target_pose_feature, output_pose_feature in zip(target_pose_features, output_pose_features): - temp_tensor = self.mse_loss(target_pose_feature, output_pose_feature) + for target_pose, output_pose in zip(target_poses, output_poses): + temp_tensor = self.mse_loss(target_pose, output_pose) temp_tensors.append(temp_tensor) pose_loss = torch.stack(temp_tensors).mean() weighted_pose_loss = pose_loss * pose_weight return pose_loss, weighted_pose_loss + def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Tensor, Tensor]: + expression_weight = CONFIG.getfloat('training.losses', 'expression_weight') + expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() + weighted_expression_loss = expression_loss * expression_weight + return expression_loss, weighted_expression_loss + + def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: + input_tensor = (input_tensor + 1) * 0.5 + pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) + pose = translation, scale, rotation, motion_points + return pose, expression + class GazeLoss(nn.Module): def __init__(self, gazer : GazerModule) -> None: @@ -168,7 +156,7 @@ class GazeLoss(nn.Module): transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ]) ]) - def forward(self, target_tensor : Tensor, output_tensor : Tensor, ) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') output_pitch_tensor, output_yaw_tensor = self.detect_gaze(output_tensor) target_pitch_tensor, target_yaw_tensor = self.detect_gaze(target_tensor) From fea75ff9493f0f4fc89ffd6760ffc3ad7627b760 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 4 Mar 2025 12:17:59 +0100 Subject: [PATCH 224/394] Remove useless comma --- face_swapper/src/dataset.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 852648b..7464fb5 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -86,7 +86,7 @@ class AugmentTransform: albumentations.MotionBlur(p = 0.1), albumentations.MedianBlur(p = 0.1) ], p = 0.3), - albumentations.ColorJitter(p = 0.1), + albumentations.ColorJitter(p = 0.1) ]) @@ -97,4 +97,3 @@ class WarpTransform: def __call__(self, input_tensor : Tensor) -> Tensor: temp_tensor = input_tensor.unsqueeze(0) return warp_tensor(temp_tensor, self.warp_template).squeeze(0) - From 18a605e1a30f431de36b74bd447c1d1452598dc2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 4 Mar 2025 12:19:49 +0100 Subject: [PATCH 225/394] Change the weight config order --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 147245d..1a1e4d7 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -70,8 +70,8 @@ adversarial_weight = 1.5 attribute_weight = 10 reconstruction_weight = 20 identity_weight = 20 -pose_weight = 0 gaze_weight = 0 +pose_weight = 0 expression_weight = 0 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index db2437a..08d1df8 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -32,8 +32,8 @@ adversarial_weight = attribute_weight = reconstruction_weight = identity_weight = -pose_weight = gaze_weight = +pose_weight = expression_weight = [training.trainer] From 176dced1f628480fd14f420a1ce6fadba3d805db Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 4 Mar 2025 20:07:41 +0530 Subject: [PATCH 226/394] changes --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/dataset.py | 5 +++-- face_swapper/src/models/loss.py | 8 +++++++- face_swapper/src/networks/aad.py | 6 +++++- face_swapper/src/training.py | 3 ++- 6 files changed, 19 insertions(+), 5 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 1a1e4d7..2395ef2 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -31,6 +31,7 @@ file_pattern = .datasets/vggface2/**/*.jpg warp_template = vgg_face_hq_to_arcface_128_v2 batch_mode = equal batch_ratio = 0.2 +resolution = 256 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 08d1df8..eb2cbe2 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -26,6 +26,7 @@ num_filters = num_layers = num_discriminators = kernel_size = +resolution = [training.losses] adversarial_weight = diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 7464fb5..ce45a90 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -12,11 +12,12 @@ from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_mode : BatchMode, batch_ratio : float) -> None: + def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_mode : BatchMode, batch_ratio : float, resolution : int) -> None: self.file_paths = glob.glob(file_pattern) self.warp_template = warp_template self.batch_mode = batch_mode self.batch_ratio = batch_ratio + self.resolution = resolution self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: @@ -38,7 +39,7 @@ class DynamicDataset(Dataset[Tensor]): [ AugmentTransform(), transforms.ToPILImage(), - transforms.Resize((256, 256), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.Resize((self.resolution, self.resolution), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), WarpTransform(self.warp_template), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 98f1941..9792bf4 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -169,7 +169,13 @@ class GazeLoss(nn.Module): return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - crop_tensor = input_tensor[:, :, 60: 224, 16: 205] + resolution = CONFIG.getint('training.dataset', 'resolution') + scale_factor = resolution / 256 + y_min = int(60 * scale_factor) + y_max = int(224 * scale_factor) + x_min = int(16 * scale_factor) + x_max = int(205 * scale_factor) + crop_tensor = input_tensor[:, :, y_min: y_max, x_min: x_max] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) crop_tensor = nn.functional.interpolate(crop_tensor, size = (448, 448), mode = 'bicubic') diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index ac5b252..7db1761 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -28,8 +28,9 @@ class AAD(nn.Module): temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): + temp_shape = target_attributes[index + 1].shape[2:] temp_tensor = layer(temp_tensors, target_attributes[index], source_embedding) - temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) + temp_tensors = nn.functional.interpolate(temp_tensor, temp_shape, mode = 'bilinear', align_corners = False) temp_tensors = self.layers[-1](temp_tensors, target_attributes[-1], source_embedding) output_tensor = torch.tanh(temp_tensors) @@ -113,6 +114,9 @@ class FeatureModulation(nn.Module): def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: temp_tensor = self.instance_norm(input_tensor) + if attribute_embedding.shape[2:] != temp_tensor.shape[2:]: + attribute_embedding = nn.functional.interpolate(attribute_embedding, size = temp_tensor.shape[2:], mode = 'bilinear') + attribute_scale = self.conv1(attribute_embedding) attribute_shift = self.conv2(attribute_embedding) attribute_modulation = attribute_scale * temp_tensor + attribute_shift diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 49b7612..821c492 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -200,12 +200,13 @@ def train() -> None: dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) dataset_batch_mode = cast(BatchMode, CONFIG.get('training.dataset', 'batch_mode')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') + dataset_resolution = CONFIG.getint('training.dataset', 'resolution') output_resume_path = CONFIG.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_mode, dataset_batch_ratio) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_mode, dataset_batch_ratio, dataset_resolution) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From 430c71d031fd51d871f1464b245822efa3231ae9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 4 Mar 2025 16:24:24 +0100 Subject: [PATCH 227/394] Adjust config and namings --- face_swapper/README.md | 3 ++- face_swapper/config.ini | 3 ++- face_swapper/src/dataset.py | 6 +++--- face_swapper/src/helper.py | 2 +- face_swapper/src/models/loss.py | 8 ++++---- face_swapper/src/networks/aad.py | 9 ++++----- face_swapper/src/training.py | 4 ++-- 7 files changed, 18 insertions(+), 17 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 2395ef2..0e5e413 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -29,9 +29,9 @@ This `config.ini` utilizes the MegaFace dataset to train the Face Swapper model. [training.dataset] file_pattern = .datasets/vggface2/**/*.jpg warp_template = vgg_face_hq_to_arcface_128_v2 +transform_size = 256 batch_mode = equal batch_ratio = 0.2 -resolution = 256 ``` ``` @@ -72,6 +72,7 @@ attribute_weight = 10 reconstruction_weight = 20 identity_weight = 20 gaze_weight = 0 +gaze_scale_factor = 1 pose_weight = 0 expression_weight = 0 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index eb2cbe2..052fbe0 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -1,6 +1,7 @@ [training.dataset] file_pattern = warp_template = +transform_size = batch_mode = batch_ratio = @@ -26,7 +27,6 @@ num_filters = num_layers = num_discriminators = kernel_size = -resolution = [training.losses] adversarial_weight = @@ -34,6 +34,7 @@ attribute_weight = reconstruction_weight = identity_weight = gaze_weight = +gaze_scale_factor = pose_weight = expression_weight = diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index ce45a90..393a934 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -12,12 +12,12 @@ from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, warp_template : WarpTemplate, batch_mode : BatchMode, batch_ratio : float, resolution : int) -> None: + def __init__(self, file_pattern : str, warp_template : WarpTemplate, transform_size : int, batch_mode : BatchMode, batch_ratio : float) -> None: self.file_paths = glob.glob(file_pattern) self.warp_template = warp_template + self.transform_size = transform_size self.batch_mode = batch_mode self.batch_ratio = batch_ratio - self.resolution = resolution self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: @@ -39,7 +39,7 @@ class DynamicDataset(Dataset[Tensor]): [ AugmentTransform(), transforms.ToPILImage(), - transforms.Resize((self.resolution, self.resolution), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.Resize((self.transform_size, self.transform_size), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), WarpTransform(self.warp_template), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 557fcbe..97d4cfc 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -27,7 +27,7 @@ def warp_tensor(input_tensor : Tensor, warp_template : WarpTemplate) -> Tensor: def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : Padding) -> Embedding: crop_tensor = warp_tensor(input_tensor, 'arcface_128_v2_to_arcface_112_v2') - crop_tensor = nn.functional.interpolate(crop_tensor, size = (112, 112), mode = 'area') + crop_tensor = nn.functional.interpolate(crop_tensor, size = 112, mode = 'area') crop_tensor[:, :, :padding[0], :] = 0 crop_tensor[:, :, 112 - padding[1]:, :] = 0 crop_tensor[:, :, :, :padding[2]] = 0 diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 9792bf4..d2b6f40 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -169,15 +169,15 @@ class GazeLoss(nn.Module): return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - resolution = CONFIG.getint('training.dataset', 'resolution') - scale_factor = resolution / 256 + scale_factor = CONFIG.getint('training.losses', 'gaze_scale_factor') y_min = int(60 * scale_factor) y_max = int(224 * scale_factor) x_min = int(16 * scale_factor) x_max = int(205 * scale_factor) - crop_tensor = input_tensor[:, :, y_min: y_max, x_min: x_max] + + crop_tensor = input_tensor[:, :, y_min:y_max, x_min:x_max] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) - crop_tensor = nn.functional.interpolate(crop_tensor, size = (448, 448), mode = 'bicubic') + crop_tensor = nn.functional.interpolate(crop_tensor, size = 448, mode = 'bicubic') pitch_tensor, yaw_tensor = self.gazer(crop_tensor) return pitch_tensor, yaw_tensor diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 7db1761..a06a3c4 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -28,9 +28,9 @@ class AAD(nn.Module): temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): - temp_shape = target_attributes[index + 1].shape[2:] temp_tensor = layer(temp_tensors, target_attributes[index], source_embedding) - temp_tensors = nn.functional.interpolate(temp_tensor, temp_shape, mode = 'bilinear', align_corners = False) + temp_size = target_attributes[index + 1].shape[2:] + temp_tensors = nn.functional.interpolate(temp_tensor, temp_size, mode = 'bilinear', align_corners = False) temp_tensors = self.layers[-1](temp_tensors, target_attributes[-1], source_embedding) output_tensor = torch.tanh(temp_tensors) @@ -113,10 +113,9 @@ class FeatureModulation(nn.Module): def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: temp_tensor = self.instance_norm(input_tensor) + temp_size = temp_tensor.shape[2:] - if attribute_embedding.shape[2:] != temp_tensor.shape[2:]: - attribute_embedding = nn.functional.interpolate(attribute_embedding, size = temp_tensor.shape[2:], mode = 'bilinear') - + attribute_embedding = nn.functional.interpolate(attribute_embedding, size = temp_size, mode = 'bilinear') attribute_scale = self.conv1(attribute_embedding) attribute_shift = self.conv2(attribute_embedding) attribute_modulation = attribute_scale * temp_tensor + attribute_shift diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 821c492..d5e0245 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -198,15 +198,15 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) + dataset_transform_size = CONFIG.getint('training.dataset', 'transform_size') dataset_batch_mode = cast(BatchMode, CONFIG.get('training.dataset', 'batch_mode')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') - dataset_resolution = CONFIG.getint('training.dataset', 'resolution') output_resume_path = CONFIG.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_batch_mode, dataset_batch_ratio, dataset_resolution) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_transform_size, dataset_batch_mode, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From 5056b8df756755d86405fee16adfe387380aa564 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 4 Mar 2025 22:13:37 +0100 Subject: [PATCH 228/394] Variable AAD layer according to output size --- face_swapper/README.md | 2 + face_swapper/config.ini | 2 + face_swapper/src/exporting.py | 3 +- face_swapper/src/models/generator.py | 3 +- face_swapper/src/networks/aad.py | 87 ++++++++++++++++++---------- face_swapper/src/networks/nld.py | 23 ++++---- 6 files changed, 77 insertions(+), 43 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 0e5e413..d48ea8b 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -53,6 +53,7 @@ motion_extractor_path = .models/motion_extractor.pt encoder_type = unet-pro identity_channels = 512 output_channels = 4096 +output_size = 256 num_blocks = 2 ``` @@ -97,6 +98,7 @@ resume_path = .outputs/last.ckpt directory_path = .exports source_path = .outputs/last.ckpt target_path = .exports/face_swapper.onnx +target_size = 256 ir_version = 10 opset_version = 15 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 052fbe0..2f17749 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -19,6 +19,7 @@ motion_extractor_path = encoder_type = identity_channels = output_channels = +output_size = num_blocks = [training.model.discriminator] @@ -53,6 +54,7 @@ resume_path = directory_path = source_path = target_path = +target_size = ir_version = opset_version = diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index c0c74e1..8e64c8b 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -13,6 +13,7 @@ def export() -> None: directory_path = CONFIG.get('exporting', 'directory_path') source_path = CONFIG.get('exporting', 'source_path') target_path = CONFIG.get('exporting', 'target_path') + target_size = CONFIG.getint('exporting', 'target_size') ir_version = CONFIG.getint('exporting', 'ir_version') opset_version = CONFIG.getint('exporting', 'opset_version') @@ -21,5 +22,5 @@ def export() -> None: model.eval() model.ir_version = torch.tensor(ir_version) source_tensor = torch.randn(1, 512) - target_tensor = torch.randn(1, 3, 256, 256) + target_tensor = torch.randn(1, 3, target_size, target_size) torch.onnx.export(model, (source_tensor, target_tensor), target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = opset_version) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index a8ae9ff..4c057cf 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -16,13 +16,14 @@ class Generator(nn.Module): encoder_type = CONFIG.get('training.model.generator', 'encoder_type') identity_channels = CONFIG.getint('training.model.generator', 'identity_channels') output_channels = CONFIG.getint('training.model.generator', 'output_channels') + output_size = CONFIG.getint('training.model.generator', 'output_size') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') if encoder_type == 'unet': self.encoder = UNet() if encoder_type == 'unet-pro': self.encoder = UNetPro() - self.generator = AAD(identity_channels, output_channels, num_blocks) + self.generator = AAD(identity_channels, output_channels, output_size, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index a06a3c4..1f12986 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -5,32 +5,58 @@ from ..types import Attributes, Embedding class AAD(nn.Module): - def __init__(self, identity_channels : int, output_channels : int, num_blocks : int) -> None: + def __init__(self, identity_channels : int, output_channels : int, output_size : int, num_blocks : int) -> None: super().__init__() + self.identity_channels = identity_channels + self.output_channels = output_channels + self.output_size = output_size + self.num_blocks = num_blocks self.pixel_shuffle_up_sample = PixelShuffleUpSample(identity_channels, output_channels) - self.layers = self.create_layers(identity_channels, num_blocks) + self.layers = self.create_layers() - @staticmethod - def create_layers(identity_channels : int, num_blocks : int) -> nn.ModuleList: - return nn.ModuleList( + def create_layers(self) -> nn.ModuleList: + layers = nn.ModuleList( [ - AdaptiveFeatureModulation(1024, 1024, 1024, identity_channels, num_blocks), - AdaptiveFeatureModulation(1024, 1024, 2048, identity_channels, num_blocks), - AdaptiveFeatureModulation(1024, 1024, 1024, identity_channels, num_blocks), - AdaptiveFeatureModulation(1024, 512, 512, identity_channels, num_blocks), - AdaptiveFeatureModulation(512, 256, 256, identity_channels, num_blocks), - AdaptiveFeatureModulation(256, 128, 128, identity_channels, num_blocks), - AdaptiveFeatureModulation(128, 64, 64, identity_channels, num_blocks), - AdaptiveFeatureModulation(64, 3, 64, identity_channels, num_blocks) + AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 1024, 2048, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(512, 256, 256, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(256, 128, 128, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks), ]) + if self.output_size in [ 384, 512, 768, 1024 ]: + layers.append(AdaptiveFeatureModulation(64, 32, 32, self.identity_channels, self.num_blocks)) + + if self.output_size in [ 512, 768, 1024 ]: + layers.append(AdaptiveFeatureModulation(32, 16, 16, self.identity_channels, self.num_blocks)) + + if self.output_size in [ 768, 1024 ]: + layers.append(AdaptiveFeatureModulation(16, 8, 8, self.identity_channels, self.num_blocks)) + + if self.output_size == 1024: + layers.append(AdaptiveFeatureModulation(8, 4, 4, self.identity_channels, self.num_blocks)) + + if self.output_size == 256: + layers.append(AdaptiveFeatureModulation(64, 3, 64, self.identity_channels, self.num_blocks)) + if self.output_size == 384: + layers.append(AdaptiveFeatureModulation(32, 3, 32, self.identity_channels, self.num_blocks)) + if self.output_size == 512: + layers.append(AdaptiveFeatureModulation(16, 3, 16, self.identity_channels, self.num_blocks)) + if self.output_size == 768: + layers.append(AdaptiveFeatureModulation(8, 3, 8, self.identity_channels, self.num_blocks)) + if self.output_size == 1024: + layers.append(AdaptiveFeatureModulation(4, 3, 4, self.identity_channels, self.num_blocks)) + + return layers + def forward(self, source_embedding : Embedding, target_attributes : Attributes) -> Tensor: temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): temp_tensor = layer(temp_tensors, target_attributes[index], source_embedding) - temp_size = target_attributes[index + 1].shape[2:] - temp_tensors = nn.functional.interpolate(temp_tensor, temp_size, mode = 'bilinear', align_corners = False) + temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) temp_tensors = self.layers[-1](temp_tensors, target_attributes[-1], source_embedding) output_tensor = torch.tanh(temp_tensors) @@ -42,37 +68,38 @@ class AdaptiveFeatureModulation(nn.Module): super().__init__() self.input_channels = input_channels self.output_channels = output_channels - self.primary_layers = self.create_primary_layers(input_channels, output_channels, attribute_channels, identity_channels, num_blocks) - self.shortcut_layers = self.create_shortcut_layers(input_channels, output_channels, attribute_channels, identity_channels) + self.attribute_channels = attribute_channels + self.identity_channels = identity_channels + self.num_blocks = num_blocks + self.primary_layers = self.create_primary_layers() + self.shortcut_layers = self.create_shortcut_layers() - @staticmethod - def create_primary_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> nn.ModuleList: + def create_primary_layers(self) -> nn.ModuleList: primary_layers = nn.ModuleList() - for index in range(num_blocks): + for index in range(self.num_blocks): primary_layers.extend( [ - FeatureModulation(input_channels, attribute_channels, identity_channels), + FeatureModulation(self.input_channels, self.attribute_channels, self.identity_channels), nn.ReLU(inplace = True) ]) - if index < num_blocks - 1: - primary_layers.append(nn.Conv2d(input_channels, input_channels, kernel_size = 3, padding = 1, bias = False)) + if index < self.num_blocks - 1: + primary_layers.append(nn.Conv2d(self.input_channels, self.input_channels, kernel_size = 3, padding = 1, bias = False)) else: - primary_layers.append(nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False)) + primary_layers.append(nn.Conv2d(self.input_channels, self.output_channels, kernel_size = 3, padding = 1, bias = False)) return primary_layers - @staticmethod - def create_shortcut_layers(input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int) -> nn.ModuleList: + def create_shortcut_layers(self) -> nn.ModuleList: shortcut_layers = nn.ModuleList() - if input_channels > output_channels: + if self.input_channels > self.output_channels: shortcut_layers.extend( [ - FeatureModulation(input_channels, attribute_channels, identity_channels), + FeatureModulation(self.input_channels, self.attribute_channels, self.identity_channels), nn.ReLU(inplace = True), - nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) + nn.Conv2d(self.input_channels, self.output_channels, kernel_size = 3, padding = 1, bias = False) ]) return shortcut_layers @@ -113,9 +140,7 @@ class FeatureModulation(nn.Module): def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: temp_tensor = self.instance_norm(input_tensor) - temp_size = temp_tensor.shape[2:] - attribute_embedding = nn.functional.interpolate(attribute_embedding, size = temp_size, mode = 'bilinear') attribute_scale = self.conv1(attribute_embedding) attribute_shift = self.conv2(attribute_embedding) attribute_modulation = attribute_scale * temp_tensor + attribute_shift diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index 015d612..2ef6865 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -6,25 +6,28 @@ from torch import Tensor, nn class NLD(nn.Module): def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: super().__init__() - self.layers = self.create_layers(input_channels, num_filters, num_layers, kernel_size) + self.input_channels = input_channels + self.num_filters = num_filters + self.num_layers = num_layers + self.kernel_size = kernel_size + self.layers = self.create_layers() self.sequences = nn.Sequential(*self.layers) - @staticmethod - def create_layers(input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> nn.ModuleList: - padding = math.ceil((kernel_size - 1) / 2) - current_filters = num_filters + def create_layers(self) -> nn.ModuleList: + padding = math.ceil((self.kernel_size - 1) / 2) + current_filters = self.num_filters layers = nn.ModuleList( [ - nn.Conv2d(input_channels, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), + nn.Conv2d(self.input_channels, current_filters, kernel_size = self.kernel_size, stride = 2, padding = padding), nn.LeakyReLU(0.2, True) ]) - for _ in range(1, num_layers): + for _ in range(1, self.num_layers): previous_filters = current_filters current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, stride = 2, padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.kernel_size, stride = 2, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) ] @@ -33,10 +36,10 @@ class NLD(nn.Module): current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = kernel_size, padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.kernel_size, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True), - nn.Conv2d(current_filters, 1, kernel_size = kernel_size, padding = padding) + nn.Conv2d(current_filters, 1, kernel_size = self.kernel_size, padding = padding) ] return layers From dcc5ccccd7467ce0fc0705be216a9595a631ca33 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 10:25:37 +0100 Subject: [PATCH 229/394] Extend Unet with more layers --- face_swapper/src/models/generator.py | 4 +- face_swapper/src/networks/aad.py | 5 +-- face_swapper/src/networks/unet.py | 55 ++++++++++++++++++++++------ 3 files changed, 46 insertions(+), 18 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 4c057cf..369edc2 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -20,9 +20,9 @@ class Generator(nn.Module): num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') if encoder_type == 'unet': - self.encoder = UNet() + self.encoder = UNet(output_size) if encoder_type == 'unet-pro': - self.encoder = UNetPro() + self.encoder = UNetPro(output_size) self.generator = AAD(identity_channels, output_channels, output_size, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 1f12986..53c9c8b 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -23,18 +23,15 @@ class AAD(nn.Module): AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(512, 256, 256, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(256, 128, 128, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks) ]) if self.output_size in [ 384, 512, 768, 1024 ]: layers.append(AdaptiveFeatureModulation(64, 32, 32, self.identity_channels, self.num_blocks)) - if self.output_size in [ 512, 768, 1024 ]: layers.append(AdaptiveFeatureModulation(32, 16, 16, self.identity_channels, self.num_blocks)) - if self.output_size in [ 768, 1024 ]: layers.append(AdaptiveFeatureModulation(16, 8, 8, self.identity_channels, self.num_blocks)) - if self.output_size == 1024: layers.append(AdaptiveFeatureModulation(8, 4, 4, self.identity_channels, self.num_blocks)) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 2d6147f..2f185bd 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -7,14 +7,14 @@ from torchvision.models import ResNet34_Weights class UNet(nn.Module): - def __init__(self) -> None: + def __init__(self, output_size : int) -> None: super().__init__() - self.down_samples = self.create_down_samples(self) + self.output_size = output_size + self.down_samples = self.create_down_samples() self.up_samples = self.create_up_samples() - @staticmethod - def create_down_samples(self : nn.Module) -> nn.ModuleList: - return nn.ModuleList( + def create_down_samples(self) -> nn.ModuleList: + down_samples = nn.ModuleList( [ DownSample(3, 32), DownSample(32, 64), @@ -25,9 +25,19 @@ class UNet(nn.Module): DownSample(1024, 1024) ]) - @staticmethod - def create_up_samples() -> nn.ModuleList: - return nn.ModuleList( + if self.output_size in [ 384, 512, 768, 1024 ]: + down_samples.append(DownSample(1024, 2048)) + if self.output_size in [ 512, 768, 1024 ]: + down_samples.append(DownSample(2048, 4096)) + if self.output_size in [ 768, 1024 ]: + down_samples.append(DownSample(4096, 8192)) + if self.output_size == 1024: + down_samples.append(DownSample(8192, 16384)) + + return down_samples + + def create_up_samples(self) -> nn.ModuleList: + up_samples = nn.ModuleList( [ UpSample(1024, 1024), UpSample(2048, 512), @@ -37,6 +47,17 @@ class UNet(nn.Module): UpSample(128, 32) ]) + if self.output_size in [ 384, 512, 768, 1024 ]: + up_samples.append(UpSample(32, 16)) + if self.output_size in [ 512, 768, 1024 ]: + up_samples.append(UpSample(16, 8)) + if self.output_size in [ 768, 1024 ]: + up_samples.append(UpSample(8, 4)) + if self.output_size == 1024: + up_samples.append(UpSample(4, 2)) + + return up_samples + def forward(self, target_tensor : Tensor) -> Tuple[Tensor, ...]: down_features = [] up_features = [] @@ -62,12 +83,11 @@ class UNetPro(UNet): def __init__(self) -> None: super(UNet, self).__init__() self.resnet = models.resnet34(weights = ResNet34_Weights.DEFAULT) - self.down_samples = self.create_down_samples(self) + self.down_samples = self.create_down_samples() self.up_samples = self.create_up_samples() - @staticmethod - def create_down_samples(self : nn.Module) -> nn.ModuleList: - return nn.ModuleList( + def create_down_samples(self) -> nn.ModuleList: + down_samples = nn.ModuleList( [ nn.Sequential( self.resnet.conv1, @@ -85,6 +105,17 @@ class UNetPro(UNet): DownSample(1024, 1024) ]) + if self.output_size in [ 384, 512, 768, 1024 ]: + down_samples.append(DownSample(1024, 2048)) + if self.output_size in [ 512, 768, 1024 ]: + down_samples.append(DownSample(2048, 4096)) + if self.output_size in [ 768, 1024 ]: + down_samples.append(DownSample(4096, 8192)) + if self.output_size == 1024: + down_samples.append(DownSample(8192, 16384)) + + return down_samples + class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: From 786adf73a2acfd0c7d63ba7a584810b281028c49 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 10:27:55 +0100 Subject: [PATCH 230/394] Fix UnetPro --- face_swapper/src/networks/unet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 2f185bd..7a6ebd6 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -80,8 +80,8 @@ class UNet(nn.Module): class UNetPro(UNet): - def __init__(self) -> None: - super(UNet, self).__init__() + def __init__(self, output_size : int) -> None: + super().__init__(output_size) self.resnet = models.resnet34(weights = ResNet34_Weights.DEFAULT) self.down_samples = self.create_down_samples() self.up_samples = self.create_up_samples() From 72371b9f11958f03ebb2921f97839f16c04d6155 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 12:53:24 +0100 Subject: [PATCH 231/394] Add basic test for aad and unet --- .github/workflows/ci.yml | 19 +++++++++++++++++-- face_swapper/tests/test_networks.py | 19 +++++++++++++++++++ 2 files changed, 36 insertions(+), 2 deletions(-) create mode 100644 face_swapper/tests/test_networks.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 8bbeef5..d22407d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -8,12 +8,27 @@ jobs: steps: - name: Checkout uses: actions/checkout@v4 - - name: Set up Python 3.10 + - name: Set up Python 3.12 uses: actions/setup-python@v5 with: - python-version: '3.10' + python-version: '3.12' - run: pip install flake8 - run: pip install flake8-import-order - run: pip install mypy - run: flake8 embedding_converter face_swapper - run: mypy embedding_converter face_swapper + test: + strategy: + matrix: + os: [ macos-latest, ubuntu-latest, windows-latest ] + runs-on: ${{ matrix.os }} + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: '3.12' + - run: pip install torch torchvision + - run: pip install pytest + - run: pytest diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py new file mode 100644 index 0000000..e1d4381 --- /dev/null +++ b/face_swapper/tests/test_networks.py @@ -0,0 +1,19 @@ +import torch +import pytest + +from face_swapper.src.networks.aad import AAD +from face_swapper.src.networks.unet import UNet + + +@pytest.mark.parametrize('output_size', [ 256 ]) +def test_aad_with_unet(output_size : int) -> None: + generator = AAD(512, 4096, output_size, 2).eval() + encoder = UNet(output_size).eval() + + source_tensor = torch.randn(1, 512) + target_tensor = torch.randn(1, 3, output_size, output_size) + + target_attributes = encoder(target_tensor) + output_tensor = generator(source_tensor, target_attributes) + + assert output_tensor.shape == (1, 3, output_size, output_size) From 6fa8d6b6eb5c770ecfd5f1c666fd67bdfb540ecd Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 12:57:15 +0100 Subject: [PATCH 232/394] Add basic test for aad and unet --- .github/workflows/ci.yml | 2 +- face_swapper/tests/test_networks.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d22407d..b6ced3e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,4 +31,4 @@ jobs: python-version: '3.12' - run: pip install torch torchvision - run: pip install pytest - - run: pytest + - run: pytest face_swapper/tests diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index e1d4381..18ce42d 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,8 +1,8 @@ import torch import pytest -from face_swapper.src.networks.aad import AAD -from face_swapper.src.networks.unet import UNet +from ..src.networks.aad import AAD +from ..src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) From d5c51a90e890e8473b34231a6e403188185c41d1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:10:14 +0100 Subject: [PATCH 233/394] Add basic test for aad and unet --- face_swapper/tests/test_networks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 18ce42d..e1d4381 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,8 +1,8 @@ import torch import pytest -from ..src.networks.aad import AAD -from ..src.networks.unet import UNet +from face_swapper.src.networks.aad import AAD +from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) From 13d15029b7934692ffa6fc99cb1f11e49a8afa61 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:26:44 +0100 Subject: [PATCH 234/394] Add basic test for aad and unet --- face_swapper/tests/test_networks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index e1d4381..b00e2b0 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,5 +1,5 @@ -import torch import pytest +import torch from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet From daeec46e36026a2550993e0a37eb213d3b461ab0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:28:08 +0100 Subject: [PATCH 235/394] Add basic test for aad and unet --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b6ced3e..3db5d8a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,4 +31,4 @@ jobs: python-version: '3.12' - run: pip install torch torchvision - run: pip install pytest - - run: pytest face_swapper/tests + - run: PYTHONPATH=face_swapper pytest From 64ebfa7b84fddff6834ada276a4660ae2beaa6fd Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:43:47 +0100 Subject: [PATCH 236/394] Add basic test for aad and unet --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3db5d8a..d22407d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,4 +31,4 @@ jobs: python-version: '3.12' - run: pip install torch torchvision - run: pip install pytest - - run: PYTHONPATH=face_swapper pytest + - run: pytest From 2148e9b70177d6fde8e71276ea9e39af44f60b0d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:50:23 +0100 Subject: [PATCH 237/394] Prepare test for 512 --- face_swapper/tests/test_networks.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index b00e2b0..37ccc7a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -7,7 +7,14 @@ from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) def test_aad_with_unet(output_size : int) -> None: - generator = AAD(512, 4096, output_size, 2).eval() + identity_channels = 512 + if output_size == 256: + output_channels = 4096 + if output_size == 512: + output_channels = 8192 + num_blocks = 2 + + generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() encoder = UNet(output_size).eval() source_tensor = torch.randn(1, 512) From dbf5687bcd0a19fe36686cc58ac9cf64f70a8a84 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 13:59:21 +0100 Subject: [PATCH 238/394] Crazy fix --- face_swapper/tests/test_networks.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 37ccc7a..dbc1773 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,6 +1,10 @@ +import sys + import pytest import torch +sys.path.append('..') + from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet From 3c5554c1c5b09ecc42d703fbd164c5bedb43e723 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:32:27 +0100 Subject: [PATCH 239/394] Crazy fix --- face_swapper/tests/test_networks.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index dbc1773..5f74f83 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,9 +1,13 @@ +import os import sys import pytest import torch -sys.path.append('..') +package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append(package_directory) + +print(package_directory) from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet From 4a2559c866c363cc876a3906ac6815682f48c113 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:34:51 +0100 Subject: [PATCH 240/394] Crazy fix --- face_swapper/tests/test_networks.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 5f74f83..085fc6d 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -7,10 +7,8 @@ import torch package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(package_directory) -print(package_directory) - -from face_swapper.src.networks.aad import AAD -from face_swapper.src.networks.unet import UNet +from src.networks.aad import AAD +from src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) From 0e8207ccc8800d91a7107b8ffbee2230ef2b91cb Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:36:07 +0100 Subject: [PATCH 241/394] Crazy fix --- face_swapper/tests/test_networks.py | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 085fc6d..82c192f 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -29,4 +29,5 @@ def test_aad_with_unet(output_size : int) -> None: target_attributes = encoder(target_tensor) output_tensor = generator(source_tensor, target_attributes) + assert package_directory == None assert output_tensor.shape == (1, 3, output_size, output_size) From f678aa8f7e4a4ee42140b50edc873a2fed357783 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:41:15 +0100 Subject: [PATCH 242/394] Crazy fix --- .github/workflows/ci.yml | 2 +- face_swapper/tests/test_networks.py | 12 ++---------- 2 files changed, 3 insertions(+), 11 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d22407d..e3ba555 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,4 +31,4 @@ jobs: python-version: '3.12' - run: pip install torch torchvision - run: pip install pytest - - run: pytest + - run: PYTHONPATH=/Users/runner/work/facefusion-labs/facefusion-labs pytest diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 82c192f..f848884 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,15 +1,8 @@ -import os -import sys - import pytest import torch -package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(package_directory) - -from src.networks.aad import AAD -from src.networks.unet import UNet - +from face_swapper.src.networks.aad import AAD +from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) def test_aad_with_unet(output_size : int) -> None: @@ -29,5 +22,4 @@ def test_aad_with_unet(output_size : int) -> None: target_attributes = encoder(target_tensor) output_tensor = generator(source_tensor, target_attributes) - assert package_directory == None assert output_tensor.shape == (1, 3, output_size, output_size) From 5f053f9f69e23972d8b95a22a6ac7d2881003e66 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:42:21 +0100 Subject: [PATCH 243/394] Crazy fix --- face_swapper/tests/test_networks.py | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index f848884..37ccc7a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -4,6 +4,7 @@ import torch from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet + @pytest.mark.parametrize('output_size', [ 256 ]) def test_aad_with_unet(output_size : int) -> None: identity_channels = 512 From de72e502337317ff123b36e330c7be01db9b0523 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:44:58 +0100 Subject: [PATCH 244/394] Only ubuntu needed for test --- .github/workflows/ci.yml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e3ba555..563a723 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,10 +18,7 @@ jobs: - run: flake8 embedding_converter face_swapper - run: mypy embedding_converter face_swapper test: - strategy: - matrix: - os: [ macos-latest, ubuntu-latest, windows-latest ] - runs-on: ${{ matrix.os }} + runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v4 From 94ad33cb1ee6fcdb9e17875e5d858e38447f06c5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:50:50 +0100 Subject: [PATCH 245/394] Debug path --- face_swapper/tests/test_networks.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 37ccc7a..9593f2a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,6 +1,12 @@ +import os +import sys + import pytest import torch +package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append(package_directory) + from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet @@ -23,4 +29,5 @@ def test_aad_with_unet(output_size : int) -> None: target_attributes = encoder(target_tensor) output_tensor = generator(source_tensor, target_attributes) + assert package_directory == None assert output_tensor.shape == (1, 3, output_size, output_size) From 866019d44f61fc18ca299fea7a705db958c7388d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 14:59:59 +0100 Subject: [PATCH 246/394] Debug path --- face_swapper/tests/test_networks.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 9593f2a..f4d9b8b 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -7,8 +7,8 @@ import torch package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(package_directory) -from face_swapper.src.networks.aad import AAD -from face_swapper.src.networks.unet import UNet +#from face_swapper.src.networks.aad import AAD +#from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) @@ -20,14 +20,14 @@ def test_aad_with_unet(output_size : int) -> None: output_channels = 8192 num_blocks = 2 - generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() - encoder = UNet(output_size).eval() + #generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() + #encoder = UNet(output_size).eval() source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, output_size, output_size) - target_attributes = encoder(target_tensor) - output_tensor = generator(source_tensor, target_attributes) + #target_attributes = encoder(target_tensor) + #output_tensor = generator(source_tensor, target_attributes) assert package_directory == None - assert output_tensor.shape == (1, 3, output_size, output_size) + #assert output_tensor.shape == (1, 3, output_size, output_size) From f791178ded4c6895130e28346fc15cfd2666e0d8 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 15:02:12 +0100 Subject: [PATCH 247/394] Fix CI --- .github/workflows/ci.yml | 2 +- face_swapper/tests/test_networks.py | 21 +++++++-------------- 2 files changed, 8 insertions(+), 15 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 563a723..c2e3cd2 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -28,4 +28,4 @@ jobs: python-version: '3.12' - run: pip install torch torchvision - run: pip install pytest - - run: PYTHONPATH=/Users/runner/work/facefusion-labs/facefusion-labs pytest + - run: PYTHONPATH=/home/runner/work/facefusion-labs/facefusion-labs pytest diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index f4d9b8b..37ccc7a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,14 +1,8 @@ -import os -import sys - import pytest import torch -package_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -sys.path.append(package_directory) - -#from face_swapper.src.networks.aad import AAD -#from face_swapper.src.networks.unet import UNet +from face_swapper.src.networks.aad import AAD +from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 256 ]) @@ -20,14 +14,13 @@ def test_aad_with_unet(output_size : int) -> None: output_channels = 8192 num_blocks = 2 - #generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() - #encoder = UNet(output_size).eval() + generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() + encoder = UNet(output_size).eval() source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, output_size, output_size) - #target_attributes = encoder(target_tensor) - #output_tensor = generator(source_tensor, target_attributes) + target_attributes = encoder(target_tensor) + output_tensor = generator(source_tensor, target_attributes) - assert package_directory == None - #assert output_tensor.shape == (1, 3, output_size, output_size) + assert output_tensor.shape == (1, 3, output_size, output_size) From 3e69d5a9a929722c37d8b4bc4640b4969525c0f0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 15:23:16 +0100 Subject: [PATCH 248/394] Adjust networks for 512 --- face_swapper/src/networks/unet.py | 37 ++++++++++++++--------------- face_swapper/tests/test_networks.py | 3 ++- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 7a6ebd6..bf8550e 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -25,21 +25,29 @@ class UNet(nn.Module): DownSample(1024, 1024) ]) - if self.output_size in [ 384, 512, 768, 1024 ]: - down_samples.append(DownSample(1024, 2048)) - if self.output_size in [ 512, 768, 1024 ]: - down_samples.append(DownSample(2048, 4096)) - if self.output_size in [ 768, 1024 ]: - down_samples.append(DownSample(4096, 8192)) - if self.output_size == 1024: - down_samples.append(DownSample(8192, 16384)) + if self.output_size == 512: + down_samples.append(DownSample(2048, 2048)) return down_samples def create_up_samples(self) -> nn.ModuleList: - up_samples = nn.ModuleList( + up_samples = nn.ModuleList() + + if self.output_size == 256: + up_samples.extend( + [ + UpSample(1024, 1024) + ]) + + if self.output_size == 512: + up_samples.extend( + [ + UpSample(2048, 2048), + UpSample(4096, 1024) + ]) + + up_samples.extend( [ - UpSample(1024, 1024), UpSample(2048, 512), UpSample(1024, 256), UpSample(512, 128), @@ -47,15 +55,6 @@ class UNet(nn.Module): UpSample(128, 32) ]) - if self.output_size in [ 384, 512, 768, 1024 ]: - up_samples.append(UpSample(32, 16)) - if self.output_size in [ 512, 768, 1024 ]: - up_samples.append(UpSample(16, 8)) - if self.output_size in [ 768, 1024 ]: - up_samples.append(UpSample(8, 4)) - if self.output_size == 1024: - up_samples.append(UpSample(4, 2)) - return up_samples def forward(self, target_tensor : Tensor) -> Tuple[Tensor, ...]: diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 37ccc7a..b9da7b2 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -5,9 +5,10 @@ from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet -@pytest.mark.parametrize('output_size', [ 256 ]) +@pytest.mark.parametrize('output_size', [ 256, 512 ]) def test_aad_with_unet(output_size : int) -> None: identity_channels = 512 + output_channels = 2048 if output_size == 256: output_channels = 4096 if output_size == 512: From 7cc893c32ec5eb60beab99a45926fa5f08bc2db9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 15:23:31 +0100 Subject: [PATCH 249/394] Adjust networks for 512 --- face_swapper/src/networks/aad.py | 47 +++++++++++++++----------------- 1 file changed, 22 insertions(+), 25 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 53c9c8b..dc1b73c 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -15,37 +15,34 @@ class AAD(nn.Module): self.layers = self.create_layers() def create_layers(self) -> nn.ModuleList: - layers = nn.ModuleList( + layers = nn.ModuleList() + + if self.output_size == 256: + layers.extend( + [ + AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 1024, 2048, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks) + ]) + + if self.output_size == 512: + layers.extend( + [ + AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(2048, 2048, 4096, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(2048, 1024, 1024, self.identity_channels, self.num_blocks) + ]) + + layers.extend( [ - AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 1024, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(512, 256, 256, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(256, 128, 128, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(64, 3, 64, self.identity_channels, self.num_blocks) ]) - if self.output_size in [ 384, 512, 768, 1024 ]: - layers.append(AdaptiveFeatureModulation(64, 32, 32, self.identity_channels, self.num_blocks)) - if self.output_size in [ 512, 768, 1024 ]: - layers.append(AdaptiveFeatureModulation(32, 16, 16, self.identity_channels, self.num_blocks)) - if self.output_size in [ 768, 1024 ]: - layers.append(AdaptiveFeatureModulation(16, 8, 8, self.identity_channels, self.num_blocks)) - if self.output_size == 1024: - layers.append(AdaptiveFeatureModulation(8, 4, 4, self.identity_channels, self.num_blocks)) - - if self.output_size == 256: - layers.append(AdaptiveFeatureModulation(64, 3, 64, self.identity_channels, self.num_blocks)) - if self.output_size == 384: - layers.append(AdaptiveFeatureModulation(32, 3, 32, self.identity_channels, self.num_blocks)) - if self.output_size == 512: - layers.append(AdaptiveFeatureModulation(16, 3, 16, self.identity_channels, self.num_blocks)) - if self.output_size == 768: - layers.append(AdaptiveFeatureModulation(8, 3, 8, self.identity_channels, self.num_blocks)) - if self.output_size == 1024: - layers.append(AdaptiveFeatureModulation(4, 3, 4, self.identity_channels, self.num_blocks)) - return layers def forward(self, source_embedding : Embedding, target_attributes : Attributes) -> Tensor: From 6d805438ad47077bc3b9b9e4a0ccb24b71af2876 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 15:39:26 +0100 Subject: [PATCH 250/394] Adjust networks for 512 --- face_swapper/src/networks/unet.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index bf8550e..8ab7707 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -20,13 +20,23 @@ class UNet(nn.Module): DownSample(32, 64), DownSample(64, 128), DownSample(128, 256), - DownSample(256, 512), - DownSample(512, 1024), - DownSample(1024, 1024) + DownSample(256, 512) ]) + if self.output_size == 256: + down_samples.extend( + [ + DownSample(512, 1024), + DownSample(1024, 1024) + ]) + if self.output_size == 512: - down_samples.append(DownSample(2048, 2048)) + down_samples.extend( + [ + DownSample(512, 1024), + DownSample(1024, 2048), + DownSample(2048, 2048) + ]) return down_samples @@ -104,14 +114,8 @@ class UNetPro(UNet): DownSample(1024, 1024) ]) - if self.output_size in [ 384, 512, 768, 1024 ]: - down_samples.append(DownSample(1024, 2048)) - if self.output_size in [ 512, 768, 1024 ]: - down_samples.append(DownSample(2048, 4096)) - if self.output_size in [ 768, 1024 ]: - down_samples.append(DownSample(4096, 8192)) - if self.output_size == 1024: - down_samples.append(DownSample(8192, 16384)) + if self.output_size == 512: + down_samples.append(DownSample(2048, 2048)) return down_samples From f9de4ce78abb869650134ad150eaef5636b1e916 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 17:05:38 +0100 Subject: [PATCH 251/394] Remove UnetPro, make float values visible in README --- face_swapper/config.ini | 1 - face_swapper/src/models/generator.py | 8 ++----- face_swapper/src/models/loss.py | 2 +- face_swapper/src/networks/unet.py | 35 ---------------------------- face_swapper/src/training.py | 2 +- 5 files changed, 4 insertions(+), 44 deletions(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 2f17749..ad69109 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -16,7 +16,6 @@ gazer_path = motion_extractor_path = [training.model.generator] -encoder_type = identity_channels = output_channels = output_size = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 369edc2..7e1ec08 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -3,7 +3,7 @@ import configparser from torch import Tensor, nn from ..networks.aad import AAD -from ..networks.unet import UNet, UNetPro +from ..networks.unet import UNet from ..types import Attributes, Embedding CONFIG = configparser.ConfigParser() @@ -13,16 +13,12 @@ CONFIG.read('config.ini') class Generator(nn.Module): def __init__(self) -> None: super().__init__() - encoder_type = CONFIG.get('training.model.generator', 'encoder_type') identity_channels = CONFIG.getint('training.model.generator', 'identity_channels') output_channels = CONFIG.getint('training.model.generator', 'output_channels') output_size = CONFIG.getint('training.model.generator', 'output_size') num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - if encoder_type == 'unet': - self.encoder = UNet(output_size) - if encoder_type == 'unet-pro': - self.encoder = UNetPro(output_size) + self.encoder = UNet(output_size) self.generator = AAD(identity_channels, output_channels, output_size, num_blocks) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index d2b6f40..6272ba9 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -169,7 +169,7 @@ class GazeLoss(nn.Module): return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - scale_factor = CONFIG.getint('training.losses', 'gaze_scale_factor') + scale_factor = CONFIG.getfloat('training.losses', 'gaze_scale_factor') y_min = int(60 * scale_factor) y_max = int(224 * scale_factor) x_min = int(16 * scale_factor) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 8ab7707..187cbe6 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -2,9 +2,6 @@ from typing import Tuple import torch from torch import Tensor, nn -from torchvision import models -from torchvision.models import ResNet34_Weights - class UNet(nn.Module): def __init__(self, output_size : int) -> None: @@ -88,38 +85,6 @@ class UNet(nn.Module): return bottleneck_tensor, *up_features, output_tensor -class UNetPro(UNet): - def __init__(self, output_size : int) -> None: - super().__init__(output_size) - self.resnet = models.resnet34(weights = ResNet34_Weights.DEFAULT) - self.down_samples = self.create_down_samples() - self.up_samples = self.create_up_samples() - - def create_down_samples(self) -> nn.ModuleList: - down_samples = nn.ModuleList( - [ - nn.Sequential( - self.resnet.conv1, - self.resnet.bn1, - self.resnet.relu, - nn.Conv2d(64, 32, kernel_size = 1, bias = False), - nn.BatchNorm2d(32), - nn.LeakyReLU(0.1, inplace = True) - ), - DownSample(32, 64), - self.resnet.layer2, - self.resnet.layer3, - self.resnet.layer4, - DownSample(512, 1024), - DownSample(1024, 1024) - ]) - - if self.output_size == 512: - down_samples.append(DownSample(2048, 2048)) - - return down_samples - - class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index d5e0245..fd35e34 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -80,7 +80,7 @@ class FaceSwapperTrainer(lightning.LightningModule): return generator_config, discriminator_config def training_step(self, batch : Batch, batch_index : int) -> Tensor: - preview_frequency = CONFIG.getfloat('training.trainer', 'preview_frequency') + preview_frequency = CONFIG.getint('training.trainer', 'preview_frequency') source_tensor, target_tensor = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] From aedaa20d7898befac142f5b2ad194b0b72590358 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 17:06:04 +0100 Subject: [PATCH 252/394] Remove UnetPro, make float values visible in README --- face_swapper/README.md | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index d48ea8b..65d47ed 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -50,7 +50,6 @@ motion_extractor_path = .models/motion_extractor.pt ``` [training.model.generator] -encoder_type = unet-pro identity_channels = 512 output_channels = 4096 output_size = 256 @@ -68,14 +67,14 @@ kernel_size = 4 ``` [training.losses] -adversarial_weight = 1.5 -attribute_weight = 10 -reconstruction_weight = 20 -identity_weight = 20 -gaze_weight = 0 -gaze_scale_factor = 1 -pose_weight = 0 -expression_weight = 0 +adversarial_weight = 1.0 +attribute_weight = 10.0 +reconstruction_weight = 20.0 +identity_weight = 20.0 +gaze_weight = 0.0 +gaze_scale_factor = 1.0 +pose_weight = 0.0 +expression_weight = 0.0 ``` ``` From 6388727262b5243a7c35c8ad07a2ec139df02568 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 17:08:07 +0100 Subject: [PATCH 253/394] Fix CI --- face_swapper/src/networks/unet.py | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 187cbe6..7f0683e 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -3,6 +3,7 @@ from typing import Tuple import torch from torch import Tensor, nn + class UNet(nn.Module): def __init__(self, output_size : int) -> None: super().__init__() From 7cf5609c1f79dd5f1192358cca9d0667bb40d771 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 5 Mar 2025 22:21:26 +0530 Subject: [PATCH 254/394] changes --- face_swapper/README.md | 1 - face_swapper/config.ini | 1 - face_swapper/src/models/loss.py | 10 +++------- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 65d47ed..7772c88 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -72,7 +72,6 @@ attribute_weight = 10.0 reconstruction_weight = 20.0 identity_weight = 20.0 gaze_weight = 0.0 -gaze_scale_factor = 1.0 pose_weight = 0.0 expression_weight = 0.0 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index ad69109..0f3c702 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -34,7 +34,6 @@ attribute_weight = reconstruction_weight = identity_weight = gaze_weight = -gaze_scale_factor = pose_weight = expression_weight = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 6272ba9..bf6ba01 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -169,13 +169,9 @@ class GazeLoss(nn.Module): return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - scale_factor = CONFIG.getfloat('training.losses', 'gaze_scale_factor') - y_min = int(60 * scale_factor) - y_max = int(224 * scale_factor) - x_min = int(16 * scale_factor) - x_max = int(205 * scale_factor) - - crop_tensor = input_tensor[:, :, y_min:y_max, x_min:x_max] + transform_size = CONFIG.getint('training.dataset', 'transform_size') + crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * transform_size).int() + crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) crop_tensor = nn.functional.interpolate(crop_tensor, size = 448, mode = 'bicubic') From abdc770892eb101b925fa48a703a606e1c9cfe03 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 18:59:23 +0100 Subject: [PATCH 255/394] Improve UNet --- face_swapper/src/networks/unet.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 7f0683e..226659f 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -18,20 +18,19 @@ class UNet(nn.Module): DownSample(32, 64), DownSample(64, 128), DownSample(128, 256), - DownSample(256, 512) + DownSample(256, 512), + DownSample(512, 1024) ]) if self.output_size == 256: down_samples.extend( [ - DownSample(512, 1024), DownSample(1024, 1024) ]) if self.output_size == 512: down_samples.extend( [ - DownSample(512, 1024), DownSample(1024, 2048), DownSample(2048, 2048) ]) From e9ea9cd9e57fc0239d56de52b828b40b668ff175 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 19:19:09 +0100 Subject: [PATCH 256/394] Clean GazeLoss --- face_swapper/src/models/loss.py | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index bf6ba01..9c8e192 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -149,22 +149,17 @@ class GazeLoss(nn.Module): def __init__(self, gazer : GazerModule) -> None: super().__init__() self.gazer = gazer - self.mae_loss = nn.L1Loss() - self.transform = transforms.Compose( - [ - transforms.Resize(448), - transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ]) - ]) + self.l1_loss = nn.L1Loss() def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') - output_pitch_tensor, output_yaw_tensor = self.detect_gaze(output_tensor) - target_pitch_tensor, target_yaw_tensor = self.detect_gaze(target_tensor) + output_pitch, output_yaw = self.detect_gaze(output_tensor) + target_pitch, target_yaw = self.detect_gaze(target_tensor) - pitch_gaze_loss = self.mae_loss(output_pitch_tensor, target_pitch_tensor) - yaw_gaze_loss = self.mae_loss(output_yaw_tensor, target_yaw_tensor) + pitch_loss = self.l1_loss(output_pitch, target_pitch) + yaw_loss = self.l1_loss(output_yaw, target_yaw) - gaze_loss = (pitch_gaze_loss + yaw_gaze_loss) * 0.5 + gaze_loss = (pitch_loss + yaw_loss) * 0.5 weighted_gaze_loss = gaze_loss * gaze_weight return gaze_loss, weighted_gaze_loss @@ -175,5 +170,5 @@ class GazeLoss(nn.Module): crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) crop_tensor = nn.functional.interpolate(crop_tensor, size = 448, mode = 'bicubic') - pitch_tensor, yaw_tensor = self.gazer(crop_tensor) - return pitch_tensor, yaw_tensor + pitch, yaw = self.gazer(crop_tensor) + return pitch, yaw From b63562abad560d966285a3a076c923c346feb5dc Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 5 Mar 2025 22:43:09 +0100 Subject: [PATCH 257/394] Simplify infer() --- face_swapper/src/inferencing.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index d1112db..47ade09 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -12,12 +12,6 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -def run_swap(generator : GeneratorModule, embedder : EmbedderModule, source_tensor : Tensor, target_tensor : Tensor) -> Tensor: - source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) - output_tensor = generator(source_embedding, target_tensor)[0] - return output_tensor - - def infer() -> None: generator_path = CONFIG.get('inferencing', 'generator_path') embedder_path = CONFIG.get('inferencing', 'embedder_path') @@ -34,5 +28,6 @@ def infer() -> None: source_tensor = io.read_image(source_path) target_tensor = io.read_image(target_path) - output_tensor = run_swap(generator, embedder, source_tensor, target_tensor) + source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) + output_tensor = generator(source_embedding, target_tensor)[0] io.write_jpeg(output_tensor, output_path) From dfd018a8979377c02aa02d2b43364bc5e1af3c4f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 08:41:55 +0100 Subject: [PATCH 258/394] Fix CI --- face_swapper/src/inferencing.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 47ade09..52c5f80 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -1,12 +1,10 @@ import configparser import torch -from torch import Tensor from torchvision import io from .helper import calc_embedding from .models.generator import Generator -from .types import EmbedderModule, GeneratorModule CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') From 61f48d9246f15703b7235ae60cb76265f4000a7b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 6 Mar 2025 15:00:42 +0530 Subject: [PATCH 259/394] changes --- face_swapper/src/models/discriminator.py | 4 ++-- face_swapper/src/models/loss.py | 14 +++++++------- face_swapper/src/training.py | 4 ++-- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index d9f0d33..9f7bf49 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -30,12 +30,12 @@ class Discriminator(nn.Module): return discriminators - def forward(self, input_tensor : Tensor) -> List[List[Tensor]]: + def forward(self, input_tensor : Tensor) -> List[Tensor]: temp_tensor = input_tensor output_tensors = [] for discriminator in self.discriminators: - output_tensors.append([ discriminator(temp_tensor) ]) + output_tensors.append(discriminator(temp_tensor)) temp_tensor = self.avg_pool(temp_tensor) return output_tensors diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 9c8e192..0ded689 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -21,12 +21,12 @@ class DiscriminatorLoss(nn.Module): positive_tensors = [] negative_tensors = [] - for discriminator_output_tensor in discriminator_output_tensors: - positive_tensor = torch.relu(discriminator_output_tensor[0] + 1).mean(dim = [ 1, 2, 3 ]) + for discriminator_source_tensor in discriminator_source_tensors: + positive_tensor = torch.relu(discriminator_source_tensor + 1).mean(dim = [ 1, 2, 3 ]) positive_tensors.append(positive_tensor) - for discriminator_source_tensor in discriminator_source_tensors: - negative_tensor = torch.relu(1 - discriminator_source_tensor[0]).mean(dim = [ 1, 2, 3 ]) + for discriminator_output_tensor in discriminator_output_tensors: + negative_tensor = torch.relu(1 - discriminator_output_tensor).mean(dim = [ 1, 2, 3 ]) negative_tensors.append(negative_tensor) positive_loss = torch.stack(positive_tensors).mean() @@ -44,7 +44,7 @@ class AdversarialLoss(nn.Module): temp_tensors = [] for discriminator_output_tensor in discriminator_output_tensors: - temp_tensor = torch.relu(1 - discriminator_output_tensor[0]).mean(dim = [ 1, 2, 3 ]).mean() + temp_tensor = torch.relu(1 - discriminator_output_tensor).mean(dim = [ 1, 2, 3 ]).mean() temp_tensors.append(temp_tensor) adversarial_loss = torch.stack(temp_tensors).mean() @@ -164,8 +164,8 @@ class GazeLoss(nn.Module): return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - transform_size = CONFIG.getint('training.dataset', 'transform_size') - crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * transform_size).int() + output_size = CONFIG.getint('training.model.generator', 'output_size') + crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * output_size).int() crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index fd35e34..6472c90 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -198,15 +198,15 @@ def create_trainer() -> Trainer: def train() -> None: dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) - dataset_transform_size = CONFIG.getint('training.dataset', 'transform_size') dataset_batch_mode = cast(BatchMode, CONFIG.get('training.dataset', 'batch_mode')) dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') output_resume_path = CONFIG.get('training.output', 'resume_path') + output_size = CONFIG.getint('training.model.generator', 'output_size') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, dataset_transform_size, dataset_batch_mode, dataset_batch_ratio) + dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, output_size, dataset_batch_mode, dataset_batch_ratio) training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer() trainer = create_trainer() From a89e51c2f8675708194d2055dd25a75e5cfc4384 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 6 Mar 2025 15:24:53 +0530 Subject: [PATCH 260/394] remove grad for inference only models --- face_swapper/src/helper.py | 4 +++- face_swapper/src/models/loss.py | 8 ++++++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 97d4cfc..a51fada 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -32,6 +32,8 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P crop_tensor[:, :, 112 - padding[1]:, :] = 0 crop_tensor[:, :, :, :padding[2]] = 0 crop_tensor[:, :, :, 112 - padding[3]:] = 0 - embedding = embedder(crop_tensor) + + with torch.no_grad(): + embedding = embedder(crop_tensor) embedding = nn.functional.normalize(embedding, p = 2) return embedding diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 0ded689..71a20ea 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -139,7 +139,9 @@ class MotionLoss(nn.Module): def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: input_tensor = (input_tensor + 1) * 0.5 - pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) + + with torch.no_grad(): + pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) rotation = torch.cat([ pitch, yaw, roll ], dim = 1) pose = translation, scale, rotation, motion_points return pose, expression @@ -170,5 +172,7 @@ class GazeLoss(nn.Module): crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) crop_tensor = nn.functional.interpolate(crop_tensor, size = 448, mode = 'bicubic') - pitch, yaw = self.gazer(crop_tensor) + + with torch.no_grad(): + pitch, yaw = self.gazer(crop_tensor) return pitch, yaw From e428ae04e300766d48be5e70b0892b09792126c9 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 6 Mar 2025 15:47:39 +0530 Subject: [PATCH 261/394] add 128px --- face_swapper/src/networks/aad.py | 15 ++++++++++++--- face_swapper/src/networks/unet.py | 24 +++++++++++++++++++----- face_swapper/tests/test_networks.py | 6 ++++-- 3 files changed, 35 insertions(+), 10 deletions(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index dc1b73c..8810ee8 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -17,12 +17,21 @@ class AAD(nn.Module): def create_layers(self) -> nn.ModuleList: layers = nn.ModuleList() + if self.output_size == 128: + layers.extend( + [ + AdaptiveFeatureModulation(512, 512, 512, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(512, 512, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(512, 512, 512, self.identity_channels, self.num_blocks), + ]) + if self.output_size == 256: layers.extend( [ AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(1024, 1024, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks) ]) if self.output_size == 512: @@ -31,12 +40,12 @@ class AAD(nn.Module): AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(2048, 2048, 4096, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(2048, 1024, 1024, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(2048, 1024, 1024, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks) ]) layers.extend( [ - AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(512, 256, 256, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(256, 128, 128, self.identity_channels, self.num_blocks), AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks), diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 226659f..1e0494c 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -18,19 +18,26 @@ class UNet(nn.Module): DownSample(32, 64), DownSample(64, 128), DownSample(128, 256), - DownSample(256, 512), - DownSample(512, 1024) + DownSample(256, 512) ]) + if self.output_size == 128: + down_samples.extend( + [ + DownSample(512, 512) + ]) + if self.output_size == 256: down_samples.extend( [ + DownSample(512, 1024), DownSample(1024, 1024) ]) if self.output_size == 512: down_samples.extend( [ + DownSample(512, 1024), DownSample(1024, 2048), DownSample(2048, 2048) ]) @@ -40,22 +47,29 @@ class UNet(nn.Module): def create_up_samples(self) -> nn.ModuleList: up_samples = nn.ModuleList() + if self.output_size == 128: + up_samples.extend( + [ + UpSample(512, 512) + ]) + if self.output_size == 256: up_samples.extend( [ - UpSample(1024, 1024) + UpSample(1024, 1024), + UpSample(2048, 512) ]) if self.output_size == 512: up_samples.extend( [ UpSample(2048, 2048), - UpSample(4096, 1024) + UpSample(4096, 1024), + UpSample(2048, 512) ]) up_samples.extend( [ - UpSample(2048, 512), UpSample(1024, 256), UpSample(512, 128), UpSample(256, 64), diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index b9da7b2..e33f5ca 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -5,10 +5,12 @@ from face_swapper.src.networks.aad import AAD from face_swapper.src.networks.unet import UNet -@pytest.mark.parametrize('output_size', [ 256, 512 ]) +@pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) def test_aad_with_unet(output_size : int) -> None: identity_channels = 512 - output_channels = 2048 + output_channels = 1024 + if output_size == 128: + output_channels = 2048 if output_size == 256: output_channels = 4096 if output_size == 512: From c8953ce8a100d00dd503041867f4a39ed2a29a71 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 08:40:15 +0100 Subject: [PATCH 262/394] Apply new config approach for embedding converter --- embedding_converter/src/dataset.py | 7 +-- embedding_converter/src/exporting.py | 21 +++++---- embedding_converter/src/training.py | 66 +++++++++++++++++----------- embedding_converter/src/types.py | 3 +- 4 files changed, 59 insertions(+), 38 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 5680d59..77b5ab1 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -4,12 +4,13 @@ from torch import Tensor from torch.utils.data import Dataset from torchvision import io, transforms -from .types import Batch +from .types import Batch, Config class StaticDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str) -> None: - self.file_paths = glob.glob(file_pattern) + def __init__(self, config : Config) -> None: + self.config = config + self.file_paths = glob.glob(self.config.get('file_pattern')) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 9102c71..b9f048d 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -10,15 +10,18 @@ CONFIG.read('config.ini') def export() -> None: - directory_path = CONFIG.get('exporting', 'directory_path') - source_path = CONFIG.get('exporting', 'source_path') - target_path = CONFIG.get('exporting', 'target_path') - ir_version = CONFIG.getint('exporting', 'ir_version') - opset_version = CONFIG.getint('exporting', 'opset_version') + config =\ + { + 'directory_path': CONFIG.get('exporting', 'directory_path'), + 'source_path': CONFIG.get('exporting', 'source_path'), + 'target_path': CONFIG.get('exporting', 'target_path'), + 'ir_version': CONFIG.getint('exporting', 'ir_version'), + 'opset_version': CONFIG.getint('exporting', 'opset_version') + } - makedirs(directory_path, exist_ok = True) - model = EmbeddingConverterTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + makedirs(config.get('directory_path'), exist_ok = True) + model = EmbeddingConverterTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') model.eval() - model.ir_version = torch.tensor(ir_version) + model.ir_version = torch.tensor(config.get('ir_version')) input_tensor = torch.randn(1, 512) - torch.onnx.export(model, input_tensor, target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = opset_version) + torch.onnx.export(model, input_tensor, config.get('target_path'), input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config.get('opset_version')) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 1fa0009..f30c22f 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -22,12 +22,16 @@ CONFIG.read('config.ini') class EmbeddingConverterTrainer(lightning.LightningModule): def __init__(self) -> None: super(EmbeddingConverterTrainer, self).__init__() - source_path = CONFIG.get('training.model', 'source_path') - target_path = CONFIG.get('training.model', 'target_path') + self.config =\ + { + 'source_path': CONFIG.get('training.model', 'source_path'), + 'target_path': CONFIG.get('training.model', 'target_path'), + 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') + } self.embedding_converter = EmbeddingConverter() - self.source_embedder = torch.jit.load(source_path, map_location = 'cpu') # type:ignore[no-untyped-call] - self.target_embedder = torch.jit.load(target_path, map_location = 'cpu') # type:ignore[no-untyped-call] + self.source_embedder = torch.jit.load(self.config.get('source_path'), map_location = 'cpu') # type:ignore[no-untyped-call] + self.target_embedder = torch.jit.load(self.config.get('target_path'), map_location = 'cpu') # type:ignore[no-untyped-call] self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: @@ -51,8 +55,7 @@ class EmbeddingConverterTrainer(lightning.LightningModule): return validation_score def configure_optimizers(self) -> OptimizerConfig: - learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') - optimizer = torch.optim.Adam(self.parameters(), lr = learning_rate) + optimizer = torch.optim.Adam(self.parameters(), lr = self.config.get('learning_rate')) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) config =\ { @@ -70,42 +73,52 @@ class EmbeddingConverterTrainer(lightning.LightningModule): def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: - batch_size = CONFIG.getint('training.loader', 'batch_size') - num_workers = CONFIG.getint('training.loader', 'num_workers') + config =\ + { + 'batch_size': CONFIG.getint('training.loader', 'batch_size'), + 'num_workers': CONFIG.getint('training.loader', 'num_workers') + } training_dataset, validate_dataset = split_dataset(dataset) - training_loader = StatefulDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = StatefulDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = config.get('batch_size'), shuffle = True, num_workers = config.get('num_workers'), drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = config.get('batch_size'), shuffle = False, num_workers = config.get('num_workers'), pin_memory = True, persistent_workers = True) return training_loader, validation_loader def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') + config =\ + { + 'split_ratio': CONFIG.getfloat('training.loader', 'split_ratio') + } + dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * split_ratio) + training_size = int(dataset_size * config.get('split_ratio')) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset def create_trainer() -> Trainer: - trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') - output_directory_path = CONFIG.get('training.output', 'directory_path') - output_file_pattern = CONFIG.get('training.output', 'file_pattern') - trainer_precision = CONFIG.get('training.trainer', 'precision') + config =\ + { + 'max_epochs': CONFIG.getint('training.trainer', 'max_epochs'), + 'directory_path': CONFIG.get('training.output', 'directory_path'), + 'file_pattern': CONFIG.get('training.output', 'file_pattern'), + 'precision': CONFIG.get('training.trainer', 'precision') + } logger = TensorBoardLogger('.logs', name = 'embedding_converter') return Trainer( logger = logger, log_every_n_steps = 10, - max_epochs = trainer_max_epochs, - precision = trainer_precision, # type:ignore[arg-type] + max_epochs = config.get('max_epochs'), + precision = config.get('precision'), # type:ignore[arg-type] callbacks = [ ModelCheckpoint( monitor = 'training_loss', - dirpath = output_directory_path, - filename = output_file_pattern, + dirpath = config.get('directory_path'), + filename = config.get('file_pattern'), every_n_epochs = 1, save_top_k = 3, save_last = True @@ -115,18 +128,21 @@ def create_trainer() -> Trainer: def train() -> None: - dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - output_resume_path = CONFIG.get('training.output', 'resume_path') + config =\ + { + 'file_pattern': CONFIG.get('training.dataset', 'file_pattern'), + 'resume_path': CONFIG.get('training.output', 'resume_path') + } if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = StaticDataset(dataset_file_pattern) + dataset = StaticDataset(config) training_loader, validation_loader = create_loaders(dataset) embedding_converter_trainer = EmbeddingConverterTrainer() trainer = create_trainer() - if os.path.exists(output_resume_path): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = output_resume_path) + if os.path.exists(config.get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 7f41a40..0c89451 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,8 +1,9 @@ -from typing import Any, TypeAlias +from typing import Any, TypeAlias, Dict from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor +Config : TypeAlias = Dict[str, Any] OptimizerConfig : TypeAlias = Any From e61e470432c3caa2b2087fff737cb091b959a461 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 08:44:02 +0100 Subject: [PATCH 263/394] Fix CI --- embedding_converter/src/training.py | 4 ++-- embedding_converter/src/types.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index f30c22f..a6ca04f 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -13,7 +13,7 @@ from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Embedding, OptimizerConfig +from .types import Batch, Embedding, OptimizerSet CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -54,7 +54,7 @@ class EmbeddingConverterTrainer(lightning.LightningModule): self.log('validation_score', validation_score, prog_bar = True) return validation_score - def configure_optimizers(self) -> OptimizerConfig: + def configure_optimizers(self) -> OptimizerSet: optimizer = torch.optim.Adam(self.parameters(), lr = self.config.get('learning_rate')) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) config =\ diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 0c89451..a893b5f 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, TypeAlias, Dict +from typing import Any, Dict, TypeAlias from torch import Tensor @@ -6,4 +6,4 @@ Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor Config : TypeAlias = Dict[str, Any] -OptimizerConfig : TypeAlias = Any +OptimizerSet : TypeAlias = Any From 368da824aa971a29a22f9597ed8da6b6a1becfc6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 08:47:59 +0100 Subject: [PATCH 264/394] Fix CI --- embedding_converter/src/exporting.py | 2 +- face_swapper/src/training.py | 4 ++-- face_swapper/src/types.py | 3 ++- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index b9f048d..0a39d69 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -19,7 +19,7 @@ def export() -> None: 'opset_version': CONFIG.getint('exporting', 'opset_version') } - makedirs(config.get('directory_path'), exist_ok = True) + makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] model = EmbeddingConverterTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') model.eval() model.ir_version = torch.tensor(config.get('ir_version')) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 6472c90..4873913 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -18,7 +18,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MotionLoss, ReconstructionLoss -from .types import Batch, BatchMode, Embedding, OptimizerConfig, WarpTemplate +from .types import Batch, BatchMode, Embedding, OptimizerSet, WarpTemplate warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -52,7 +52,7 @@ class FaceSwapperTrainer(lightning.LightningModule): output_tensor = self.generator(source_embedding, target_tensor) return output_tensor - def configure_optimizers(self) -> Tuple[OptimizerConfig, OptimizerConfig]: + def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 5aed9bd..34ebe12 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -17,7 +17,8 @@ EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module -OptimizerConfig : TypeAlias = Any +Config : TypeAlias = Dict[str, Any] +OptimizerSet : TypeAlias = Any WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] WarpTemplateSet : TypeAlias = Dict[WarpTemplate, Tensor] From b59e172fa3f264e91dacbfbf94aa2bf135b4b3c4 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 09:22:14 +0100 Subject: [PATCH 265/394] Always config injection --- embedding_converter/src/training.py | 35 ++++++++++++++--------------- 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index a6ca04f..b52057a 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -2,9 +2,8 @@ import configparser import os from typing import Tuple -import lightning import torch -from lightning import Trainer +from lightning import Trainer, LightningModule from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn @@ -13,22 +12,16 @@ from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Embedding, OptimizerSet +from .types import Batch, Embedding, OptimizerSet, Config CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class EmbeddingConverterTrainer(lightning.LightningModule): - def __init__(self) -> None: - super(EmbeddingConverterTrainer, self).__init__() - self.config =\ - { - 'source_path': CONFIG.get('training.model', 'source_path'), - 'target_path': CONFIG.get('training.model', 'target_path'), - 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') - } - +class EmbeddingConverterTrainer(LightningModule): + def __init__(self, config : Config) -> None: + super().__init__() + self.config = config self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(self.config.get('source_path'), map_location = 'cpu') # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(self.config.get('target_path'), map_location = 'cpu') # type:ignore[no-untyped-call] @@ -128,21 +121,27 @@ def create_trainer() -> Trainer: def train() -> None: - config =\ + config_dataset =\ { 'file_pattern': CONFIG.get('training.dataset', 'file_pattern'), 'resume_path': CONFIG.get('training.output', 'resume_path') } + config_trainer =\ + { + 'source_path': CONFIG.get('training.model', 'source_path'), + 'target_path': CONFIG.get('training.model', 'target_path'), + 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') + } if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = StaticDataset(config) + dataset = StaticDataset(config_dataset) training_loader, validation_loader = create_loaders(dataset) - embedding_converter_trainer = EmbeddingConverterTrainer() + embedding_converter_trainer = EmbeddingConverterTrainer(config_trainer) trainer = create_trainer() - if os.path.exists(config.get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) + if os.path.exists(config_dataset.get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_dataset.get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) From 8f1f63f2eff83d6d3ae32b452ea9a261d92a55b8 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 09:25:38 +0100 Subject: [PATCH 266/394] Fix CI --- face_swapper/src/training.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4873913..56f8632 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -3,10 +3,9 @@ import os import warnings from typing import Tuple, cast -import lightning import torch import torchvision -from lightning import Trainer +from lightning import Trainer, LightningModule from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn @@ -26,7 +25,7 @@ CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') -class FaceSwapperTrainer(lightning.LightningModule): +class FaceSwapperTrainer(LightningModule): def __init__(self) -> None: super().__init__() embedder_path = CONFIG.get('training.model', 'embedder_path') From d944d95bfdfccf73f680c3f295f7ab078f6d81ee Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 09:28:19 +0100 Subject: [PATCH 267/394] Fix CI --- embedding_converter/src/training.py | 4 ++-- face_swapper/src/training.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index b52057a..4198673 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -3,7 +3,7 @@ import os from typing import Tuple import torch -from lightning import Trainer, LightningModule +from lightning import LightningModule, Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn @@ -12,7 +12,7 @@ from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Embedding, OptimizerSet, Config +from .types import Batch, Config, Embedding, OptimizerSet CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 56f8632..a99f89f 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -5,7 +5,7 @@ from typing import Tuple, cast import torch import torchvision -from lightning import Trainer, LightningModule +from lightning import LightningModule, Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn From d3b0051912c8bbf57b72be917ccc41249739b35c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:00:06 +0100 Subject: [PATCH 268/394] More adjustments --- embedding_converter/src/training.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 4198673..9dc28c8 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -50,7 +50,7 @@ class EmbeddingConverterTrainer(LightningModule): def configure_optimizers(self) -> OptimizerSet: optimizer = torch.optim.Adam(self.parameters(), lr = self.config.get('learning_rate')) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) - config =\ + optimizer_set =\ { 'optimizer': optimizer, 'lr_scheduler': @@ -62,7 +62,7 @@ class EmbeddingConverterTrainer(LightningModule): } } - return config + return optimizer_set def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: @@ -124,13 +124,16 @@ def train() -> None: config_dataset =\ { 'file_pattern': CONFIG.get('training.dataset', 'file_pattern'), - 'resume_path': CONFIG.get('training.output', 'resume_path') } config_trainer =\ { 'source_path': CONFIG.get('training.model', 'source_path'), 'target_path': CONFIG.get('training.model', 'target_path'), - 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') + 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate'), + } + config_common =\ + { + 'resume_path': CONFIG.get('training.output', 'resume_path') } if torch.cuda.is_available(): @@ -141,7 +144,7 @@ def train() -> None: embedding_converter_trainer = EmbeddingConverterTrainer(config_trainer) trainer = create_trainer() - if os.path.exists(config_dataset.get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_dataset.get('resume_path')) + if os.path.exists(config_common.get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_common.get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) From 57aad5204ef5750efd4fdaeecf1108ed07e639f0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:02:47 +0100 Subject: [PATCH 269/394] More adjustments --- embedding_converter/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 9dc28c8..5ee8dde 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -129,7 +129,7 @@ def train() -> None: { 'source_path': CONFIG.get('training.model', 'source_path'), 'target_path': CONFIG.get('training.model', 'target_path'), - 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate'), + 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') } config_common =\ { From 847579f92556e69d2244b24f449be2cbf04b59a5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:08:29 +0100 Subject: [PATCH 270/394] More adjustments --- embedding_converter/src/training.py | 42 ++++++++++++++++------------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 5ee8dde..4bf5b78 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -95,9 +95,9 @@ def create_trainer() -> Trainer: config =\ { 'max_epochs': CONFIG.getint('training.trainer', 'max_epochs'), + 'precision': CONFIG.get('training.trainer', 'precision'), 'directory_path': CONFIG.get('training.output', 'directory_path'), - 'file_pattern': CONFIG.get('training.output', 'file_pattern'), - 'precision': CONFIG.get('training.trainer', 'precision') + 'file_pattern': CONFIG.get('training.output', 'file_pattern') } logger = TensorBoardLogger('.logs', name = 'embedding_converter') @@ -105,7 +105,7 @@ def create_trainer() -> Trainer: logger = logger, log_every_n_steps = 10, max_epochs = config.get('max_epochs'), - precision = config.get('precision'), # type:ignore[arg-type] + precision = config.get('precision'), callbacks = [ ModelCheckpoint( @@ -121,30 +121,34 @@ def create_trainer() -> Trainer: def train() -> None: - config_dataset =\ + config =\ { - 'file_pattern': CONFIG.get('training.dataset', 'file_pattern'), - } - config_trainer =\ - { - 'source_path': CONFIG.get('training.model', 'source_path'), - 'target_path': CONFIG.get('training.model', 'target_path'), - 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') - } - config_common =\ - { - 'resume_path': CONFIG.get('training.output', 'resume_path') + 'dataset': + { + 'file_pattern': CONFIG.get('training.dataset', 'file_pattern') + }, + 'trainer': + { + 'source_path': CONFIG.get('training.model', 'source_path'), + 'target_path': CONFIG.get('training.model', 'target_path'), + 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') + }, + 'common': + { + 'resume_path': CONFIG.get('training.output', 'resume_path') + } } + if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = StaticDataset(config_dataset) + dataset = StaticDataset(config.get('dataset')) training_loader, validation_loader = create_loaders(dataset) - embedding_converter_trainer = EmbeddingConverterTrainer(config_trainer) + embedding_converter_trainer = EmbeddingConverterTrainer(config.get('trainer')) trainer = create_trainer() - if os.path.exists(config_common.get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_common.get('resume_path')) + if os.path.exists(config.get('common').get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('common').get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) From 5bacb048dd03d82a671d3731843fe6e23c70b4b0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:10:20 +0100 Subject: [PATCH 271/394] More adjustments --- embedding_converter/src/training.py | 1 - 1 file changed, 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 4bf5b78..6012936 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -139,7 +139,6 @@ def train() -> None: } } - if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') From ff9b777b2831484c417a5dda8e14afd48da8d326 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:12:26 +0100 Subject: [PATCH 272/394] More adjustments --- embedding_converter/src/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index a893b5f..98da9f9 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -5,5 +5,5 @@ from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor -Config : TypeAlias = Dict[str, Any] +Config : TypeAlias = Any OptimizerSet : TypeAlias = Any From 6a11603e7ef67cb3891203ca2abdd3c996931e50 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:13:07 +0100 Subject: [PATCH 273/394] More adjustments --- embedding_converter/src/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 98da9f9..fd6235b 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, TypeAlias +from typing import Any, TypeAlias from torch import Tensor From 6a5f81e5fed6985abeaaaf7b20e3343d8eb693a5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:16:01 +0100 Subject: [PATCH 274/394] More adjustments --- embedding_converter/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 6012936..02d093a 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -121,7 +121,7 @@ def create_trainer() -> Trainer: def train() -> None: - config =\ + config : Config =\ { 'dataset': { From 7ab7efbbf4af3a26d9b31b08a73a21c11e96b879 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:18:52 +0100 Subject: [PATCH 275/394] More adjustments --- embedding_converter/src/training.py | 4 ++-- embedding_converter/src/types.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 02d093a..a492df9 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -12,7 +12,7 @@ from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Config, Embedding, OptimizerSet +from .types import Batch, Config, ConfigSet, Embedding, OptimizerSet CONFIG = configparser.ConfigParser() CONFIG.read('config.ini') @@ -121,7 +121,7 @@ def create_trainer() -> Trainer: def train() -> None: - config : Config =\ + config : ConfigSet =\ { 'dataset': { diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index fd6235b..38a5708 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,9 +1,10 @@ -from typing import Any, TypeAlias +from typing import Any, Dict, TypeAlias from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor -Config : TypeAlias = Any +Config : TypeAlias = Dict[str, Any] +ConfigSet : TypeAlias = Dict[str, Config] OptimizerSet : TypeAlias = Any From f4a1e18ca9b2db4418e9ecf0fdadb49c08222c2e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:20:04 +0100 Subject: [PATCH 276/394] More adjustments --- embedding_converter/src/training.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index a492df9..91aea51 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -121,7 +121,7 @@ def create_trainer() -> Trainer: def train() -> None: - config : ConfigSet =\ + config_set : ConfigSet =\ { 'dataset': { @@ -133,7 +133,7 @@ def train() -> None: 'target_path': CONFIG.get('training.model', 'target_path'), 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') }, - 'common': + 'output': { 'resume_path': CONFIG.get('training.output', 'resume_path') } @@ -142,12 +142,12 @@ def train() -> None: if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = StaticDataset(config.get('dataset')) + dataset = StaticDataset(config_set.get('dataset')) training_loader, validation_loader = create_loaders(dataset) - embedding_converter_trainer = EmbeddingConverterTrainer(config.get('trainer')) + embedding_converter_trainer = EmbeddingConverterTrainer(config_set.get('trainer')) trainer = create_trainer() - if os.path.exists(config.get('common').get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('common').get('resume_path')) + if os.path.exists(config_set.get('output').get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_set.get('output').get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) From cf26f66e364a5071260f1c82131cbc6297ac2330 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:31:42 +0100 Subject: [PATCH 277/394] More adjustments --- embedding_converter/src/types.py | 1 + 1 file changed, 1 insertion(+) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index 38a5708..e16bd5e 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -7,4 +7,5 @@ Embedding : TypeAlias = Tensor Config : TypeAlias = Dict[str, Any] ConfigSet : TypeAlias = Dict[str, Config] + OptimizerSet : TypeAlias = Any From a2a9b78dac7db12e50596897adbbec31b8858eca Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 13:34:29 +0100 Subject: [PATCH 278/394] More adjustments --- face_swapper/src/types.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 34ebe12..a1f041b 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -18,6 +18,8 @@ GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module Config : TypeAlias = Dict[str, Any] +ConfigSet : TypeAlias = Dict[str, Config] + OptimizerSet : TypeAlias = Any WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] From 01278d679ffd208cd24c7ec89916c45525838526 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 14:11:56 +0100 Subject: [PATCH 279/394] Forward config parser --- embedding_converter/src/dataset.py | 10 +++-- embedding_converter/src/exporting.py | 14 +++---- embedding_converter/src/training.py | 56 ++++++++++++---------------- embedding_converter/src/types.py | 5 +-- 4 files changed, 39 insertions(+), 46 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 77b5ab1..79c25a4 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -1,15 +1,19 @@ import glob +from configparser import ConfigParser from torch import Tensor from torch.utils.data import Dataset from torchvision import io, transforms -from .types import Batch, Config +from .types import Batch class StaticDataset(Dataset[Tensor]): - def __init__(self, config : Config) -> None: - self.config = config + def __init__(self, config : ConfigParser) -> None: + self.config =\ + { + 'file_pattern': config.get('training.dataset', 'file_pattern') + } self.file_paths = glob.glob(self.config.get('file_pattern')) self.transforms = self.compose_transforms() diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 0a39d69..4222295 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -5,18 +5,18 @@ import torch from .training import EmbeddingConverterTrainer -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +CONFIG_PARSER = configparser.ConfigParser() +CONFIG_PARSER.read('config.ini') def export() -> None: config =\ { - 'directory_path': CONFIG.get('exporting', 'directory_path'), - 'source_path': CONFIG.get('exporting', 'source_path'), - 'target_path': CONFIG.get('exporting', 'target_path'), - 'ir_version': CONFIG.getint('exporting', 'ir_version'), - 'opset_version': CONFIG.getint('exporting', 'opset_version') + 'directory_path': CONFIG_PARSER.get('exporting', 'directory_path'), + 'source_path': CONFIG_PARSER.get('exporting', 'source_path'), + 'target_path': CONFIG_PARSER.get('exporting', 'target_path'), + 'ir_version': CONFIG_PARSER.getint('exporting', 'ir_version'), + 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') } makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 91aea51..4d3024d 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -3,6 +3,7 @@ import os from typing import Tuple import torch +from configparser import ConfigParser from lightning import LightningModule, Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger @@ -12,16 +13,20 @@ from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import StaticDataset from .models.embedding_converter import EmbeddingConverter -from .types import Batch, Config, ConfigSet, Embedding, OptimizerSet - -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +from .types import Batch, Embedding, OptimizerSet +CONFIG_PARSER = ConfigParser() +CONFIG_PARSER.read('config.ini') class EmbeddingConverterTrainer(LightningModule): - def __init__(self, config : Config) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config = config + self.config_parser =\ + { + 'source_path': config_parser.get('training.model', 'source_path'), + 'target_path': config_parser.get('training.model', 'target_path'), + 'learning_rate': config_parser.getfloat('training.trainer', 'learning_rate') + } self.embedding_converter = EmbeddingConverter() self.source_embedder = torch.jit.load(self.config.get('source_path'), map_location = 'cpu') # type:ignore[no-untyped-call] self.target_embedder = torch.jit.load(self.config.get('target_path'), map_location = 'cpu') # type:ignore[no-untyped-call] @@ -68,8 +73,8 @@ class EmbeddingConverterTrainer(LightningModule): def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: config =\ { - 'batch_size': CONFIG.getint('training.loader', 'batch_size'), - 'num_workers': CONFIG.getint('training.loader', 'num_workers') + 'batch_size': CONFIG_PARSER.getint('training.loader', 'batch_size'), + 'num_workers': CONFIG_PARSER.getint('training.loader', 'num_workers') } training_dataset, validate_dataset = split_dataset(dataset) @@ -81,7 +86,7 @@ def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: config =\ { - 'split_ratio': CONFIG.getfloat('training.loader', 'split_ratio') + 'split_ratio': CONFIG_PARSER.getfloat('training.loader', 'split_ratio') } dataset_size = len(dataset) # type:ignore[arg-type] @@ -94,10 +99,10 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T def create_trainer() -> Trainer: config =\ { - 'max_epochs': CONFIG.getint('training.trainer', 'max_epochs'), - 'precision': CONFIG.get('training.trainer', 'precision'), - 'directory_path': CONFIG.get('training.output', 'directory_path'), - 'file_pattern': CONFIG.get('training.output', 'file_pattern') + 'max_epochs': CONFIG_PARSER.getint('training.trainer', 'max_epochs'), + 'precision': CONFIG_PARSER.get('training.trainer', 'precision'), + 'directory_path': CONFIG_PARSER.get('training.output', 'directory_path'), + 'file_pattern': CONFIG_PARSER.get('training.output', 'file_pattern') } logger = TensorBoardLogger('.logs', name = 'embedding_converter') @@ -121,33 +126,20 @@ def create_trainer() -> Trainer: def train() -> None: - config_set : ConfigSet =\ + config =\ { - 'dataset': - { - 'file_pattern': CONFIG.get('training.dataset', 'file_pattern') - }, - 'trainer': - { - 'source_path': CONFIG.get('training.model', 'source_path'), - 'target_path': CONFIG.get('training.model', 'target_path'), - 'learning_rate': CONFIG.getfloat('training.trainer', 'learning_rate') - }, - 'output': - { - 'resume_path': CONFIG.get('training.output', 'resume_path') - } + 'resume_path': CONFIG_PARSER.get('training.output', 'resume_path') } if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = StaticDataset(config_set.get('dataset')) + dataset = StaticDataset(CONFIG_PARSER) training_loader, validation_loader = create_loaders(dataset) - embedding_converter_trainer = EmbeddingConverterTrainer(config_set.get('trainer')) + embedding_converter_trainer = EmbeddingConverterTrainer(CONFIG_PARSER) trainer = create_trainer() - if os.path.exists(config_set.get('output').get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_set.get('output').get('resume_path')) + if os.path.exists(config.get('resume_path')): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) diff --git a/embedding_converter/src/types.py b/embedding_converter/src/types.py index e16bd5e..0522b39 100644 --- a/embedding_converter/src/types.py +++ b/embedding_converter/src/types.py @@ -1,11 +1,8 @@ -from typing import Any, Dict, TypeAlias +from typing import Any, TypeAlias from torch import Tensor Batch : TypeAlias = Tensor Embedding : TypeAlias = Tensor -Config : TypeAlias = Dict[str, Any] -ConfigSet : TypeAlias = Dict[str, Config] - OptimizerSet : TypeAlias = Any From d215b6f98bf508395579225c1af9fef49d7e93d6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 14:21:47 +0100 Subject: [PATCH 280/394] Fix CI --- embedding_converter/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 4d3024d..a9e945a 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -1,9 +1,8 @@ -import configparser import os +from configparser import ConfigParser from typing import Tuple import torch -from configparser import ConfigParser from lightning import LightningModule, Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger @@ -18,6 +17,7 @@ from .types import Batch, Embedding, OptimizerSet CONFIG_PARSER = ConfigParser() CONFIG_PARSER.read('config.ini') + class EmbeddingConverterTrainer(LightningModule): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() From a0c42bedbeba013284ca552a4d40bef66dc555d5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 14:24:55 +0100 Subject: [PATCH 281/394] Improve imports --- embedding_converter/src/dataset.py | 4 ++-- embedding_converter/src/exporting.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 79c25a4..21057a4 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -9,10 +9,10 @@ from .types import Batch class StaticDataset(Dataset[Tensor]): - def __init__(self, config : ConfigParser) -> None: + def __init__(self, config_parser : ConfigParser) -> None: self.config =\ { - 'file_pattern': config.get('training.dataset', 'file_pattern') + 'file_pattern': config_parser.get('training.dataset', 'file_pattern') } self.file_paths = glob.glob(self.config.get('file_pattern')) self.transforms = self.compose_transforms() diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 4222295..3c76a4f 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -1,11 +1,11 @@ -import configparser -from os import makedirs +import os +from configparser import ConfigParser import torch from .training import EmbeddingConverterTrainer -CONFIG_PARSER = configparser.ConfigParser() +CONFIG_PARSER = ConfigParser() CONFIG_PARSER.read('config.ini') @@ -19,7 +19,7 @@ def export() -> None: 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') } - makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] + os.makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] model = EmbeddingConverterTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') model.eval() model.ir_version = torch.tensor(config.get('ir_version')) From bfa9924b408c32eccb9e97c30b94114b0d49d803 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 14:26:03 +0100 Subject: [PATCH 282/394] Remove types --- face_swapper/src/types.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index a1f041b..9f0c526 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -17,9 +17,6 @@ EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module -Config : TypeAlias = Dict[str, Any] -ConfigSet : TypeAlias = Dict[str, Config] - OptimizerSet : TypeAlias = Any WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] From a5d99c139e979013624359f1fc364d61502cf231 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 16:32:39 +0100 Subject: [PATCH 283/394] Fix bug --- embedding_converter/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index a9e945a..522bd59 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -21,7 +21,7 @@ CONFIG_PARSER.read('config.ini') class EmbeddingConverterTrainer(LightningModule): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config_parser =\ + self.config =\ { 'source_path': config_parser.get('training.model', 'source_path'), 'target_path': config_parser.get('training.model', 'target_path'), From ab3b69912420558483615ae0bd529b02bf2daed3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 16:32:07 +0100 Subject: [PATCH 284/394] Rework on config --- face_swapper/src/dataset.py | 39 ++++---- face_swapper/src/exporting.py | 34 +++---- face_swapper/src/inferencing.py | 27 +++--- face_swapper/src/models/discriminator.py | 28 +++--- face_swapper/src/models/generator.py | 16 +--- face_swapper/src/models/loss.py | 14 +-- face_swapper/src/networks/nld.py | 28 +++--- face_swapper/src/networks/unet.py | 20 +++-- face_swapper/src/training.py | 109 ++++++++++++----------- 9 files changed, 169 insertions(+), 146 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 393a934..99232be 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -1,6 +1,8 @@ import glob import os import random +from configparser import ConfigParser +from typing import cast import albumentations from torch import Tensor @@ -8,25 +10,29 @@ from torch.utils.data import Dataset from torchvision import io, transforms from .helper import warp_tensor -from .types import Batch, BatchMode, WarpTemplate +from .types import Batch, WarpTemplate, BatchMode class DynamicDataset(Dataset[Tensor]): - def __init__(self, file_pattern : str, warp_template : WarpTemplate, transform_size : int, batch_mode : BatchMode, batch_ratio : float) -> None: - self.file_paths = glob.glob(file_pattern) - self.warp_template = warp_template - self.transform_size = transform_size - self.batch_mode = batch_mode - self.batch_ratio = batch_ratio + def __init__(self, config_parser : ConfigParser) -> None: + self.config =\ + { + 'file_pattern': config_parser.get('training.dataset', 'file_pattern'), + 'transform_size': config_parser.get('training.dataset', 'transform_size'), + 'batch_mode': cast(BatchMode, config_parser.get('training.dataset', 'batch_mode')), + 'batch_ratio': config_parser.getfloat('training.dataset', 'batch_ratio'), + } + self.config_parser = config_parser + self.file_paths = glob.glob(self.config.get('file_pattern')) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: file_path = self.file_paths[index] - if random.random() < self.batch_ratio: - if self.batch_mode == 'equal': + if random.random() < self.config.get('batch_ratio'): + if self.config.get('batch_mode') == 'equal': return self.prepare_equal_batch(file_path) - if self.batch_mode == 'same': + if self.config.get('batch_mode') == 'same': return self.prepare_same_batch(file_path) return self.prepare_different_batch(file_path) @@ -39,9 +45,9 @@ class DynamicDataset(Dataset[Tensor]): [ AugmentTransform(), transforms.ToPILImage(), - transforms.Resize((self.transform_size, self.transform_size), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.Resize((self.config.get('transform_size'), self.config.get('transform_size')), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), - WarpTransform(self.warp_template), + WarpTransform(self.config_parser), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) @@ -92,9 +98,12 @@ class AugmentTransform: class WarpTransform: - def __init__(self, warp_template : WarpTemplate) -> None: - self.warp_template = warp_template + def __init__(self, config_parser : ConfigParser) -> None: + self.config =\ + { + 'warp_template': cast(WarpTemplate, config_parser.get('training.dataset', 'warp_template')) + } def __call__(self, input_tensor : Tensor) -> Tensor: temp_tensor = input_tensor.unsqueeze(0) - return warp_tensor(temp_tensor, self.warp_template).squeeze(0) + return warp_tensor(temp_tensor, self.config.get('warp_template')).squeeze(0) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 8e64c8b..a24aa0d 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -1,26 +1,30 @@ -import configparser -from os import makedirs +import os +from configparser import ConfigParser + import torch from .training import FaceSwapperTrainer -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +CONFIG_PARSER = ConfigParser() +CONFIG_PARSER.read('config.ini') def export() -> None: - directory_path = CONFIG.get('exporting', 'directory_path') - source_path = CONFIG.get('exporting', 'source_path') - target_path = CONFIG.get('exporting', 'target_path') - target_size = CONFIG.getint('exporting', 'target_size') - ir_version = CONFIG.getint('exporting', 'ir_version') - opset_version = CONFIG.getint('exporting', 'opset_version') + config =\ + { + 'directory_path': CONFIG_PARSER.get('exporting', 'directory_path'), + 'source_path': CONFIG_PARSER.get('exporting', 'source_path'), + 'target_path': CONFIG_PARSER.get('exporting', 'target_path'), + 'target_size': CONFIG_PARSER.getint('exporting', 'target_size'), + 'ir_version': CONFIG_PARSER.getint('exporting', 'ir_version'), + 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') + } - makedirs(directory_path, exist_ok = True) - model = FaceSwapperTrainer.load_from_checkpoint(source_path, map_location = 'cpu') + os.makedirs(config.get('directory_path'), exist_ok = True) + model = FaceSwapperTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') model.eval() - model.ir_version = torch.tensor(ir_version) + model.ir_version = torch.tensor(config.get('ir_version')) source_tensor = torch.randn(1, 512) - target_tensor = torch.randn(1, 3, target_size, target_size) - torch.onnx.export(model, (source_tensor, target_tensor), target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = opset_version) + target_tensor = torch.randn(1, 3, config.get('target_size'), config.get('target_size')) + torch.onnx.export(model, (source_tensor, target_tensor), config.get('target_path'), input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = config.get('opset_version')) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 52c5f80..3f718b3 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -6,26 +6,29 @@ from torchvision import io from .helper import calc_embedding from .models.generator import Generator -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +CONFIG_PARSER = configparser.ConfigParser() +CONFIG_PARSER.read('config.ini') def infer() -> None: - generator_path = CONFIG.get('inferencing', 'generator_path') - embedder_path = CONFIG.get('inferencing', 'embedder_path') - source_path = CONFIG.get('inferencing', 'source_path') - target_path = CONFIG.get('inferencing', 'target_path') - output_path = CONFIG.get('inferencing', 'output_path') + config =\ + { + 'generator_path': CONFIG_PARSER.get('inferencing', 'generator_path'), + 'embedder_path': CONFIG_PARSER.get('inferencing', 'embedder_path'), + 'source_path': CONFIG_PARSER.get('inferencing', 'source_path'), + 'target_path': CONFIG_PARSER.get('inferencing', 'target_path'), + 'output_path': CONFIG_PARSER.get('inferencing', 'output_path') + } - state_dict = torch.load(generator_path).get('state_dict').get('generator') - generator = Generator() + state_dict = torch.load(config.get('generator_path')).get('state_dict').get('generator') + generator = Generator(CONFIG_PARSER) generator.load_state_dict(state_dict) generator.eval() embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] embedder.eval() - source_tensor = io.read_image(source_path) - target_tensor = io.read_image(target_path) + source_tensor = io.read_image(config.get('source_path')) + target_tensor = io.read_image(config.get('target_path')) source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) output_tensor = generator(source_embedding, target_tensor)[0] - io.write_jpeg(output_tensor, output_path) + io.write_jpeg(output_tensor, config.get('output_path')) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 9f7bf49..4ccb88f 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -1,31 +1,28 @@ -import configparser +from configparser import ConfigParser from typing import List from torch import Tensor, nn from ..networks.nld import NLD -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - class Discriminator(nn.Module): - def __init__(self) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() + self.config =\ + { + 'num_discriminators': config_parser.getint('training.model.discriminator', 'num_discriminators') + } + self.config_parser = config_parser self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() - @staticmethod - def create_discriminators() -> nn.ModuleList: - num_discriminators = CONFIG.getint('training.model.discriminator', 'num_discriminators') - input_channels = CONFIG.getint('training.model.discriminator', 'input_channels') - num_filters = CONFIG.getint('training.model.discriminator', 'num_filters') - kernel_size = CONFIG.getint('training.model.discriminator', 'kernel_size') - num_layers = CONFIG.getint('training.model.discriminator', 'num_layers') + + def create_discriminators(self) -> nn.ModuleList: discriminators = nn.ModuleList() - for _ in range(num_discriminators): - discriminator = NLD(input_channels, num_filters, num_layers, kernel_size).sequences + for _ in range(self.config.get('num_discriminators')): + discriminator = NLD(self.config_parser).sequences discriminators.append(discriminator) return discriminators @@ -35,7 +32,8 @@ class Discriminator(nn.Module): output_tensors = [] for discriminator in self.discriminators: - output_tensors.append(discriminator(temp_tensor)) + output_tensor = discriminator(temp_tensor) + output_tensors.append(output_tensor) temp_tensor = self.avg_pool(temp_tensor) return output_tensors diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 7e1ec08..d02be0b 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,4 +1,4 @@ -import configparser +from configparser import ConfigParser from torch import Tensor, nn @@ -6,20 +6,12 @@ from ..networks.aad import AAD from ..networks.unet import UNet from ..types import Attributes, Embedding -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - class Generator(nn.Module): - def __init__(self) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - identity_channels = CONFIG.getint('training.model.generator', 'identity_channels') - output_channels = CONFIG.getint('training.model.generator', 'output_channels') - output_size = CONFIG.getint('training.model.generator', 'output_size') - num_blocks = CONFIG.getint('training.model.generator', 'num_blocks') - - self.encoder = UNet(output_size) - self.generator = AAD(identity_channels, output_channels, output_size, num_blocks) + self.encoder = UNet(config_parser) + self.generator = AAD(config_parser) self.encoder.apply(init_weight) self.generator.apply(init_weight) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 71a20ea..85e85e1 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -1,4 +1,4 @@ -import configparser +from configparser import ConfigParser from typing import List, Tuple import torch @@ -9,9 +9,6 @@ from torchvision import transforms from ..helper import calc_embedding from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') - class DiscriminatorLoss(nn.Module): def __init__(self) -> None: @@ -36,11 +33,14 @@ class DiscriminatorLoss(nn.Module): class AdversarialLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() + self.config =\ + { + 'adversarial_weight': config_parser.getfloat('training.losses', 'adversarial_weight') + } def forward(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: - adversarial_weight = CONFIG.getfloat('training.losses', 'adversarial_weight') temp_tensors = [] for discriminator_output_tensor in discriminator_output_tensors: @@ -48,7 +48,7 @@ class AdversarialLoss(nn.Module): temp_tensors.append(temp_tensor) adversarial_loss = torch.stack(temp_tensors).mean() - weighted_adversarial_loss = adversarial_loss * adversarial_weight + weighted_adversarial_loss = adversarial_loss * self.config.get('adversarial_weight') return adversarial_loss, weighted_adversarial_loss diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index 2ef6865..d0347f8 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -1,33 +1,37 @@ import math +from configparser import ConfigParser from torch import Tensor, nn class NLD(nn.Module): - def __init__(self, input_channels : int, num_filters : int, num_layers : int, kernel_size : int) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.input_channels = input_channels - self.num_filters = num_filters - self.num_layers = num_layers - self.kernel_size = kernel_size + self.config =\ + { + 'input_channels': config_parser.getint('training.model.discriminator', 'input_channels'), + 'num_filters': config_parser.getint('training.model.discriminator', 'num_filters'), + 'kernel_size': config_parser.getint('training.model.discriminator', 'kernel_size'), + 'num_layers': config_parser.getint('training.model.discriminator', 'num_layers') + } self.layers = self.create_layers() self.sequences = nn.Sequential(*self.layers) def create_layers(self) -> nn.ModuleList: - padding = math.ceil((self.kernel_size - 1) / 2) - current_filters = self.num_filters + padding = math.ceil((self.config.get('kernel_size') - 1) / 2) + current_filters = self.config.get('num_filters') layers = nn.ModuleList( [ - nn.Conv2d(self.input_channels, current_filters, kernel_size = self.kernel_size, stride = 2, padding = padding), + nn.Conv2d(self.config.get('input_channels'), current_filters, kernel_size = self.config.get('kernel_size'), stride = 2, padding = padding), nn.LeakyReLU(0.2, True) ]) - for _ in range(1, self.num_layers): + for _ in range(1, self.config.get('num_layers')): previous_filters = current_filters current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = self.kernel_size, stride = 2, padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.config.get('kernel_size'), stride = 2, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) ] @@ -36,10 +40,10 @@ class NLD(nn.Module): current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = self.kernel_size, padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.config.get('kernel_size'), padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True), - nn.Conv2d(current_filters, 1, kernel_size = self.kernel_size, padding = padding) + nn.Conv2d(current_filters, 1, kernel_size = self.config.get('kernel_size'), padding = padding) ] return layers diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 1e0494c..250c7b0 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -1,3 +1,4 @@ +from configparser import ConfigParser from typing import Tuple import torch @@ -5,9 +6,12 @@ from torch import Tensor, nn class UNet(nn.Module): - def __init__(self, output_size : int) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.output_size = output_size + self.config =\ + { + 'output_size': config_parser.getint('training.model.generator', 'output_size') + } self.down_samples = self.create_down_samples() self.up_samples = self.create_up_samples() @@ -21,20 +25,20 @@ class UNet(nn.Module): DownSample(256, 512) ]) - if self.output_size == 128: + if self.config.get('output_size') == 128: down_samples.extend( [ DownSample(512, 512) ]) - if self.output_size == 256: + if self.config.get('output_size') == 256: down_samples.extend( [ DownSample(512, 1024), DownSample(1024, 1024) ]) - if self.output_size == 512: + if self.config.get('output_size') == 512: down_samples.extend( [ DownSample(512, 1024), @@ -47,20 +51,20 @@ class UNet(nn.Module): def create_up_samples(self) -> nn.ModuleList: up_samples = nn.ModuleList() - if self.output_size == 128: + if self.config.get('output_size') == 128: up_samples.extend( [ UpSample(512, 512) ]) - if self.output_size == 256: + if self.config.get('output_size') == 256: up_samples.extend( [ UpSample(1024, 1024), UpSample(2048, 512) ]) - if self.output_size == 512: + if self.config.get('output_size') == 512: up_samples.extend( [ UpSample(2048, 2048), diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a99f89f..a375dad 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,5 +1,5 @@ -import configparser import os +from configparser import ConfigParser import warnings from typing import Tuple, cast @@ -21,30 +21,34 @@ from .types import Batch, BatchMode, Embedding, OptimizerSet, WarpTemplate warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') -CONFIG = configparser.ConfigParser() -CONFIG.read('config.ini') +CONFIG_PARSER = ConfigParser() +CONFIG_PARSER.read('config.ini') class FaceSwapperTrainer(LightningModule): - def __init__(self) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - embedder_path = CONFIG.get('training.model', 'embedder_path') - gazer_path = CONFIG.get('training.model', 'gazer_path') - motion_extractor_path = CONFIG.get('training.model', 'motion_extractor_path') + self.config =\ + { + 'embedder_path': config_parser.get('training.model', 'embedder_path'), + 'gazer_path': config_parser.get('training.model', 'gazer_path'), + 'motion_extractor_path': config_parser.get('training.model', 'motion_extractor_path'), + 'learning_rate': config_parser.getfloat('training.trainer', 'learning_rate'), + 'preview_frequency': config_parser.getint('training.trainer', 'preview_frequency') + } + self.embedder = torch.jit.load(self.config.get('embedder_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.gazer = torch.jit.load(self.config.get('gazer_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.motion_extractor = torch.jit.load(self.config.get('motion_extractor_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.embedder = torch.jit.load(embedder_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.gazer = torch.jit.load(gazer_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(motion_extractor_path, map_location = 'cpu').eval() # type:ignore[no-untyped-call] - - self.generator = Generator() - self.discriminator = Discriminator() - self.discriminator_loss = DiscriminatorLoss() - self.adversarial_loss = AdversarialLoss() - self.attribute_loss = AttributeLoss() - self.reconstruction_loss = ReconstructionLoss(self.embedder) - self.identity_loss = IdentityLoss(self.embedder) - self.motion_loss = MotionLoss(self.motion_extractor) - self.gaze_loss = GazeLoss(self.gazer) + self.generator = Generator(config_parser) + self.discriminator = Discriminator(config_parser) + self.discriminator_loss = DiscriminatorLoss(config_parser) + self.adversarial_loss = AdversarialLoss(config_parser) + self.attribute_loss = AttributeLoss(config_parser) + self.reconstruction_loss = ReconstructionLoss(config_parser, self.embedder) + self.identity_loss = IdentityLoss(config_parser, self.embedder) + self.motion_loss = MotionLoss(config_parser, self.motion_extractor) + self.gaze_loss = GazeLoss(config_parser, self.gazer) self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: @@ -52,9 +56,8 @@ class FaceSwapperTrainer(LightningModule): return output_tensor def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: - learning_rate = CONFIG.getfloat('training.trainer', 'learning_rate') - generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config.get('learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config.get('learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) @@ -79,8 +82,6 @@ class FaceSwapperTrainer(LightningModule): return generator_config, discriminator_config def training_step(self, batch : Batch, batch_index : int) -> Tensor: - preview_frequency = CONFIG.getint('training.trainer', 'preview_frequency') - source_tensor, target_tensor = batch generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) @@ -113,7 +114,7 @@ class FaceSwapperTrainer(LightningModule): discriminator_optimizer.step() self.untoggle_optimizer(discriminator_optimizer) - if self.global_step % preview_frequency == 0: + if self.global_step % self.config.get('preview_frequency') == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor) self.log('generator_loss', generator_loss, prog_bar = True) @@ -149,42 +150,52 @@ class FaceSwapperTrainer(LightningModule): def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: - batch_size = CONFIG.getint('training.loader', 'batch_size') - num_workers = CONFIG.getint('training.loader', 'num_workers') + config =\ + { + 'batch_size': CONFIG_PARSER.getint('training.loader', 'batch_size'), + 'num_workers': CONFIG_PARSER.getint('training.loader', 'num_workers') + } training_dataset, validate_dataset = split_dataset(dataset) - training_loader = StatefulDataLoader(training_dataset, batch_size = batch_size, shuffle = True, num_workers = num_workers, drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = StatefulDataLoader(validate_dataset, batch_size = batch_size, shuffle = False, num_workers = num_workers, pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = config.get('batch_size'), shuffle = True, num_workers = config.get('num_workers'), drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = config.get('batch_size'), shuffle = False, num_workers = config.get('num_workers'), pin_memory = True, persistent_workers = True) return training_loader, validation_loader def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - split_ratio = CONFIG.getfloat('training.loader', 'split_ratio') + config =\ + { + 'split_ratio': CONFIG_PARSER.getfloat('training.loader', 'split_ratio') + } + dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * split_ratio) + training_size = int(dataset_size * config.get('split_ratio')) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset def create_trainer() -> Trainer: - trainer_max_epochs = CONFIG.getint('training.trainer', 'max_epochs') - output_directory_path = CONFIG.get('training.output', 'directory_path') - output_file_pattern = CONFIG.get('training.output', 'file_pattern') - trainer_precision = CONFIG.get('training.trainer', 'precision') + config =\ + { + 'max_epochs': CONFIG_PARSER.getint('training.trainer', 'max_epochs'), + 'precision': CONFIG_PARSER.get('training.trainer', 'precision'), + 'directory_path': CONFIG_PARSER.get('training.output', 'directory_path'), + 'file_pattern': CONFIG_PARSER.get('training.output', 'file_pattern') + } logger = TensorBoardLogger('.logs', name = 'face_swapper') return Trainer( logger = logger, log_every_n_steps = 10, - max_epochs = trainer_max_epochs, - precision = trainer_precision, # type:ignore[arg-type] + max_epochs = config.get('max_epochs'), + precision = config.get('precision'), callbacks = [ ModelCheckpoint( monitor = 'generator_loss', - dirpath = output_directory_path, - filename = output_file_pattern, + dirpath = config.get('directory_path'), + filename = config.get('file_pattern'), every_n_train_steps = 1000, save_top_k = 3, save_last = True @@ -195,22 +206,20 @@ def create_trainer() -> Trainer: def train() -> None: - dataset_file_pattern = CONFIG.get('training.dataset', 'file_pattern') - dataset_warp_template = cast(WarpTemplate, CONFIG.get('training.dataset', 'warp_template')) - dataset_batch_mode = cast(BatchMode, CONFIG.get('training.dataset', 'batch_mode')) - dataset_batch_ratio = CONFIG.getfloat('training.dataset', 'batch_ratio') - output_resume_path = CONFIG.get('training.output', 'resume_path') - output_size = CONFIG.getint('training.model.generator', 'output_size') + config =\ + { + 'resume_path': CONFIG_PARSER.get('training.output', 'resume_path') + } if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(dataset_file_pattern, dataset_warp_template, output_size, dataset_batch_mode, dataset_batch_ratio) + dataset = DynamicDataset(CONFIG_PARSER) training_loader, validation_loader = create_loaders(dataset) - face_swapper_trainer = FaceSwapperTrainer() + face_swapper_trainer = FaceSwapperTrainer(CONFIG_PARSER) trainer = create_trainer() - if os.path.isfile(output_resume_path): - trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = output_resume_path) + if os.path.isfile(config.get('resume_path')): + trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) else: trainer.fit(face_swapper_trainer, training_loader, validation_loader) From b829d5e42c4e2effee7b607f033d86e09ccce671 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 18:27:56 +0100 Subject: [PATCH 285/394] Migrate most to self.config and self.context --- face_swapper/src/models/loss.py | 57 ++++++++++------- face_swapper/src/networks/aad.py | 96 ++++++++++++++++------------- face_swapper/src/networks/unet.py | 4 +- face_swapper/src/training.py | 3 +- face_swapper/tests/test_networks.py | 23 +++---- 5 files changed, 105 insertions(+), 78 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 85e85e1..9c0525d 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -53,31 +53,37 @@ class AdversarialLoss(nn.Module): class AttributeLoss(nn.Module): - def __init__(self) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() + self.config =\ + { + 'batch_size': config_parser.getint('training.loader', 'batch_size'), + 'attribute_weight': config_parser.getfloat('training.losses', 'attribute_weight') + } def forward(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: - batch_size = CONFIG.getint('training.loader', 'batch_size') - attribute_weight = CONFIG.getfloat('training.losses', 'attribute_weight') temp_tensors = [] for target_attribute, output_attribute in zip(target_attributes, output_attributes): - temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(batch_size, -1), dim = 1).mean() + temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(self.config.get('batch_size'), -1), dim = 1).mean() temp_tensors.append(temp_tensor) attribute_loss = torch.stack(temp_tensors).mean() * 0.5 - weighted_attribute_loss = attribute_loss * attribute_weight + weighted_attribute_loss = attribute_loss * self.config.get('attribute_weight') return attribute_loss, weighted_attribute_loss class ReconstructionLoss(nn.Module): - def __init__(self, embedder : EmbedderModule) -> None: + def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None: super().__init__() + self.config =\ + { + 'reconstruction_weight': config_parser.getfloat('training.losses', 'reconstruction_weight') + } self.embedder = embedder self.mse_loss = nn.MSELoss() def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - reconstruction_weight = CONFIG.getfloat('training.losses', 'reconstruction_weight') source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) has_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 @@ -88,27 +94,35 @@ class ReconstructionLoss(nn.Module): data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) visual_loss = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() reconstruction_loss = (reconstruction_loss + visual_loss) * 0.5 - weighted_reconstruction_loss = reconstruction_loss * reconstruction_weight + weighted_reconstruction_loss = reconstruction_loss * self.config.get('reconstruction_weight') return reconstruction_loss, weighted_reconstruction_loss class IdentityLoss(nn.Module): - def __init__(self, embedder : EmbedderModule) -> None: + def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None: super().__init__() + self.config =\ + { + 'identity_weight': config_parser.getfloat('training.losses', 'identity_weight') + } self.embedder = embedder def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - identity_weight = CONFIG.getfloat('training.losses', 'identity_weight') output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() - weighted_identity_loss = identity_loss * identity_weight + weighted_identity_loss = identity_loss * self.config.get('identity_weight') return identity_loss, weighted_identity_loss class MotionLoss(nn.Module): - def __init__(self, motion_extractor : MotionExtractorModule): + def __init__(self, config_parser : ConfigParser, motion_extractor : MotionExtractorModule): super().__init__() + self.config =\ + { + 'pose_weight': config_parser.getfloat('training.losses', 'pose_weight'), + 'expression_weight': config_parser.getfloat('training.losses', 'expression_weight') + } self.motion_extractor = motion_extractor self.mse_loss = nn.MSELoss() @@ -120,7 +134,6 @@ class MotionLoss(nn.Module): return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Tensor, Tensor]: - pose_weight = CONFIG.getfloat('training.losses', 'pose_weight') temp_tensors = [] for target_pose, output_pose in zip(target_poses, output_poses): @@ -128,13 +141,12 @@ class MotionLoss(nn.Module): temp_tensors.append(temp_tensor) pose_loss = torch.stack(temp_tensors).mean() - weighted_pose_loss = pose_loss * pose_weight + weighted_pose_loss = pose_loss * self.config.get('pose_weight') return pose_loss, weighted_pose_loss def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Tensor, Tensor]: - expression_weight = CONFIG.getfloat('training.losses', 'expression_weight') expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() - weighted_expression_loss = expression_loss * expression_weight + weighted_expression_loss = expression_loss * self.config.get('expression_weight') return expression_loss, weighted_expression_loss def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: @@ -148,13 +160,17 @@ class MotionLoss(nn.Module): class GazeLoss(nn.Module): - def __init__(self, gazer : GazerModule) -> None: + def __init__(self, config_parser : ConfigParser, gazer : GazerModule) -> None: super().__init__() + self.config =\ + { + 'gaze_weight': config_parser.getfloat('training.losses', 'gaze_weight'), + 'output_size': config_parser.getint('training.model.generator', 'output_size') + } self.gazer = gazer self.l1_loss = nn.L1Loss() def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - gaze_weight = CONFIG.getfloat('training.losses', 'gaze_weight') output_pitch, output_yaw = self.detect_gaze(output_tensor) target_pitch, target_yaw = self.detect_gaze(target_tensor) @@ -162,12 +178,11 @@ class GazeLoss(nn.Module): yaw_loss = self.l1_loss(output_yaw, target_yaw) gaze_loss = (pitch_loss + yaw_loss) * 0.5 - weighted_gaze_loss = gaze_loss * gaze_weight + weighted_gaze_loss = gaze_loss * self.config.get('gaze_weight') return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - output_size = CONFIG.getint('training.model.generator', 'output_size') - crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * output_size).int() + crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * self.config.get('output_size')).int() crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 8810ee8..8bf261e 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -1,3 +1,5 @@ +from configparser import ConfigParser + import torch from torch import Tensor, nn @@ -5,51 +7,55 @@ from ..types import Attributes, Embedding class AAD(nn.Module): - def __init__(self, identity_channels : int, output_channels : int, output_size : int, num_blocks : int) -> None: + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.identity_channels = identity_channels - self.output_channels = output_channels - self.output_size = output_size - self.num_blocks = num_blocks - self.pixel_shuffle_up_sample = PixelShuffleUpSample(identity_channels, output_channels) + self.config =\ + { + 'identity_channels': config_parser.getint('training.model.generator', 'identity_channels'), + 'output_channels': config_parser.getint('training.model.generator', 'output_channels'), + 'output_size': config_parser.getint('training.model.generator', 'output_size'), + 'num_blocks': config_parser.getint('training.model.generator', 'num_blocks') + } + self.config_parser = config_parser + self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config.get('identity_channels'), self.config.get('output_channels')) self.layers = self.create_layers() def create_layers(self) -> nn.ModuleList: layers = nn.ModuleList() - if self.output_size == 128: + if self.config.get('output_size') == 128: layers.extend( [ - AdaptiveFeatureModulation(512, 512, 512, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(512, 512, 1024, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(512, 512, 512, self.identity_channels, self.num_blocks), + AdaptiveFeatureModulation(512, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(512, 512, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(512, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) ]) - if self.output_size == 256: + if self.config.get('output_size') == 256: layers.extend( [ - AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 1024, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 1024, 1024, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(1024, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(1024, 1024, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(1024, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(1024, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) ]) - if self.output_size == 512: + if self.config.get('output_size') == 512: layers.extend( [ - AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(2048, 2048, 4096, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(2048, 2048, 2048, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(2048, 1024, 1024, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(1024, 512, 512, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(2048, 2048, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(2048, 2048, 4096, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(2048, 2048, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(2048, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(1024, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) ]) layers.extend( [ - AdaptiveFeatureModulation(512, 256, 256, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(256, 128, 128, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(128, 64, 64, self.identity_channels, self.num_blocks), - AdaptiveFeatureModulation(64, 3, 64, self.identity_channels, self.num_blocks) + AdaptiveFeatureModulation(512, 256, 256, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(256, 128, 128, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(128, 64, 64, self.config.get('identity_channels'), self.config.get('num_blocks')), + AdaptiveFeatureModulation(64, 3, 64, self.config.get('identity_channels'), self.config.get('num_blocks')) ]) return layers @@ -69,40 +75,43 @@ class AAD(nn.Module): class AdaptiveFeatureModulation(nn.Module): def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> None: super().__init__() - self.input_channels = input_channels - self.output_channels = output_channels - self.attribute_channels = attribute_channels - self.identity_channels = identity_channels - self.num_blocks = num_blocks + self.context =\ + { + 'input_channels': input_channels, + 'output_channels': output_channels, + 'attribute_channels': attribute_channels, + 'identity_channels': identity_channels, + 'num_blocks': num_blocks + } self.primary_layers = self.create_primary_layers() self.shortcut_layers = self.create_shortcut_layers() def create_primary_layers(self) -> nn.ModuleList: primary_layers = nn.ModuleList() - for index in range(self.num_blocks): + for index in range(self.context.get('num_blocks')): primary_layers.extend( [ - FeatureModulation(self.input_channels, self.attribute_channels, self.identity_channels), + FeatureModulation(self.context.get('input_channels'), self.context.get('attribute_channels'), self.context.get('identity_channels')), nn.ReLU(inplace = True) ]) - if index < self.num_blocks - 1: - primary_layers.append(nn.Conv2d(self.input_channels, self.input_channels, kernel_size = 3, padding = 1, bias = False)) + if index < self.context.get('num_blocks') - 1: + primary_layers.append(nn.Conv2d(self.context.get('input_channels'), self.context.get('input_channels'), kernel_size = 3, padding = 1, bias = False)) else: - primary_layers.append(nn.Conv2d(self.input_channels, self.output_channels, kernel_size = 3, padding = 1, bias = False)) + primary_layers.append(nn.Conv2d(self.context.get('input_channels'), self.context.get('output_channels'), kernel_size = 3, padding = 1, bias = False)) return primary_layers def create_shortcut_layers(self) -> nn.ModuleList: shortcut_layers = nn.ModuleList() - if self.input_channels > self.output_channels: + if self.context.get('input_channels') > self.context.get('output_channels'): shortcut_layers.extend( [ - FeatureModulation(self.input_channels, self.attribute_channels, self.identity_channels), + FeatureModulation(self.context.get('input_channels'), self.context.get('attribute_channels'), self.context.get('identity_channels')), nn.ReLU(inplace = True), - nn.Conv2d(self.input_channels, self.output_channels, kernel_size = 3, padding = 1, bias = False) + nn.Conv2d(self.context.get('input_channels'), self.context.get('output_channels'), kernel_size = 3, padding = 1, bias = False) ]) return shortcut_layers @@ -116,7 +125,7 @@ class AdaptiveFeatureModulation(nn.Module): else: primary_tensor = primary_layer(primary_tensor) - if self.input_channels > self.output_channels: + if self.context.get('input_channels') > self.context.get('output_channels'): shortcut_tensor = input_tensor for shortcut_layer in self.shortcut_layers: @@ -133,7 +142,10 @@ class AdaptiveFeatureModulation(nn.Module): class FeatureModulation(nn.Module): def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: super().__init__() - self.input_channels = input_channels + self.context =\ + { + 'input_channels': input_channels + } self.conv1 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) self.conv2 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) self.conv3 = nn.Conv2d(input_channels, 1, kernel_size = 1) @@ -148,8 +160,8 @@ class FeatureModulation(nn.Module): attribute_shift = self.conv2(attribute_embedding) attribute_modulation = attribute_scale * temp_tensor + attribute_shift - identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.input_channels, 1, 1).expand_as(temp_tensor) - identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.input_channels, 1, 1).expand_as(temp_tensor) + identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.context.get('input_channels'), 1, 1).expand_as(temp_tensor) + identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.context.get('input_channels'), 1, 1).expand_as(temp_tensor) identity_modulation = identity_scale * temp_tensor + identity_shift temp_mask = torch.sigmoid(self.conv3(temp_tensor)) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 250c7b0..892a593 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -106,7 +106,7 @@ class UNet(nn.Module): class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv_transpose = nn.ConvTranspose2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.conv_transpose = nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) @@ -121,7 +121,7 @@ class UpSample(nn.Module): class DownSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv = nn.Conv2d(in_channels = input_channels, out_channels = output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) + self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) self.batch_norm = nn.BatchNorm2d(output_channels) self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a375dad..c9ffde4 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -39,10 +39,9 @@ class FaceSwapperTrainer(LightningModule): self.embedder = torch.jit.load(self.config.get('embedder_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] self.gazer = torch.jit.load(self.config.get('gazer_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] self.motion_extractor = torch.jit.load(self.config.get('motion_extractor_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) - self.discriminator_loss = DiscriminatorLoss(config_parser) + self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) self.attribute_loss = AttributeLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.embedder) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index e33f5ca..16305e8 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -1,3 +1,5 @@ +from configparser import ConfigParser + import pytest import torch @@ -7,18 +9,17 @@ from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) def test_aad_with_unet(output_size : int) -> None: - identity_channels = 512 - output_channels = 1024 - if output_size == 128: - output_channels = 2048 - if output_size == 256: - output_channels = 4096 - if output_size == 512: - output_channels = 8192 - num_blocks = 2 + config_parser = ConfigParser() + config_parser['training.model.generator'] =\ + { + 'identity_channels': '512', + 'output_channels': str(output_size * 16), + 'output_size': str(output_size), + 'num_blocks': '2' + } - generator = AAD(identity_channels, output_channels, output_size, num_blocks).eval() - encoder = UNet(output_size).eval() + generator = AAD(config_parser).eval() + encoder = UNet(config_parser).eval() source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, output_size, output_size) From 6bc44ad3d8f15da3b3d9b8fb95a366bf04c7ae9b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 18:32:20 +0100 Subject: [PATCH 286/394] Fix CI --- face_swapper/src/dataset.py | 2 +- face_swapper/src/inferencing.py | 2 +- face_swapper/src/models/discriminator.py | 1 - face_swapper/src/training.py | 6 +++--- 4 files changed, 5 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 99232be..ff438b8 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -10,7 +10,7 @@ from torch.utils.data import Dataset from torchvision import io, transforms from .helper import warp_tensor -from .types import Batch, WarpTemplate, BatchMode +from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 3f718b3..b20535b 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -24,7 +24,7 @@ def infer() -> None: generator = Generator(CONFIG_PARSER) generator.load_state_dict(state_dict) generator.eval() - embedder = torch.jit.load(embedder_path, map_location = 'cpu') # type:ignore[no-untyped-call] + embedder = torch.jit.load(config.get('embedder_path'), map_location = 'cpu') # type:ignore[no-untyped-call] embedder.eval() source_tensor = io.read_image(config.get('source_path')) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index 4ccb88f..f60b07f 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -17,7 +17,6 @@ class Discriminator(nn.Module): self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() - def create_discriminators(self) -> nn.ModuleList: discriminators = nn.ModuleList() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index c9ffde4..f08d65d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,7 +1,7 @@ import os -from configparser import ConfigParser import warnings -from typing import Tuple, cast +from configparser import ConfigParser +from typing import Tuple import torch import torchvision @@ -17,7 +17,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MotionLoss, ReconstructionLoss -from .types import Batch, BatchMode, Embedding, OptimizerSet, WarpTemplate +from .types import Batch, Embedding, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') From c7d55d0d1784b06ae22b7f9be5f0ccb4999de1ab Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 19:42:55 +0100 Subject: [PATCH 287/394] Use a lot of ignores --- face_swapper/src/dataset.py | 8 ++++---- face_swapper/src/exporting.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index ff438b8..ecd0d78 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -29,13 +29,13 @@ class DynamicDataset(Dataset[Tensor]): def __getitem__(self, index : int) -> Batch: file_path = self.file_paths[index] - if random.random() < self.config.get('batch_ratio'): + if random.random() < self.config.get('batch_ratio'): # type:ignore[operator] if self.config.get('batch_mode') == 'equal': - return self.prepare_equal_batch(file_path) + return self.prepare_equal_batch(file_path) # type:ignore[arg-type] if self.config.get('batch_mode') == 'same': - return self.prepare_same_batch(file_path) + return self.prepare_same_batch(file_path) # type:ignore[arg-type] - return self.prepare_different_batch(file_path) + return self.prepare_different_batch(file_path) # type:ignore[arg-type] def __len__(self) -> int: return len(self.file_paths) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index a24aa0d..0c4f5ac 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -21,7 +21,7 @@ def export() -> None: 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') } - os.makedirs(config.get('directory_path'), exist_ok = True) + os.makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] model = FaceSwapperTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') model.eval() model.ir_version = torch.tensor(config.get('ir_version')) From 7e938c2ec9bca345ef628f200cdf14c285615a58 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 19:44:31 +0100 Subject: [PATCH 288/394] Use a lot of ignores --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index ecd0d78..cd31d2c 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -23,7 +23,7 @@ class DynamicDataset(Dataset[Tensor]): 'batch_ratio': config_parser.getfloat('training.dataset', 'batch_ratio'), } self.config_parser = config_parser - self.file_paths = glob.glob(self.config.get('file_pattern')) + self.file_paths = glob.glob(self.config.get('file_pattern')) # type:ignore[type-var] self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: From e5f983b2bf1bb9a918d7cba845ce2ac7af457bf2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 22:28:54 +0100 Subject: [PATCH 289/394] Minor adjustment for test --- face_swapper/tests/test_networks.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 16305e8..96f1c6a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -10,13 +10,16 @@ from face_swapper.src.networks.unet import UNet @pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) def test_aad_with_unet(output_size : int) -> None: config_parser = ConfigParser() - config_parser['training.model.generator'] =\ + config_parser.read_dict( { - 'identity_channels': '512', - 'output_channels': str(output_size * 16), - 'output_size': str(output_size), - 'num_blocks': '2' - } + 'training.model.generator': + { + 'identity_channels': '512', + 'output_channels': str(output_size * 16), + 'output_size': str(output_size), + 'num_blocks': '2' + } + }) generator = AAD(config_parser).eval() encoder = UNet(config_parser).eval() From 1dfd230fc5fdce1702e871a23e9ea1f9e7452db7 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Thu, 6 Mar 2025 23:28:11 +0100 Subject: [PATCH 290/394] Revert the config dicts --- embedding_converter/src/dataset.py | 7 +-- embedding_converter/src/exporting.py | 21 ++++---- embedding_converter/src/training.py | 61 +++++++++--------------- face_swapper/src/models/discriminator.py | 7 +-- face_swapper/src/models/loss.py | 18 +++---- face_swapper/src/networks/nld.py | 25 +++++----- face_swapper/src/networks/unet.py | 17 +++---- 7 files changed, 60 insertions(+), 96 deletions(-) diff --git a/embedding_converter/src/dataset.py b/embedding_converter/src/dataset.py index 21057a4..c3b5503 100644 --- a/embedding_converter/src/dataset.py +++ b/embedding_converter/src/dataset.py @@ -10,11 +10,8 @@ from .types import Batch class StaticDataset(Dataset[Tensor]): def __init__(self, config_parser : ConfigParser) -> None: - self.config =\ - { - 'file_pattern': config_parser.get('training.dataset', 'file_pattern') - } - self.file_paths = glob.glob(self.config.get('file_pattern')) + self.config_file_pattern = config_parser.get('training.dataset', 'file_pattern') + self.file_paths = glob.glob(self.config_file_pattern) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 3c76a4f..5194ae6 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -10,18 +10,15 @@ CONFIG_PARSER.read('config.ini') def export() -> None: - config =\ - { - 'directory_path': CONFIG_PARSER.get('exporting', 'directory_path'), - 'source_path': CONFIG_PARSER.get('exporting', 'source_path'), - 'target_path': CONFIG_PARSER.get('exporting', 'target_path'), - 'ir_version': CONFIG_PARSER.getint('exporting', 'ir_version'), - 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') - } + config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path') + config_source_path = CONFIG_PARSER.get('exporting', 'source_path') + config_target_path = CONFIG_PARSER.get('exporting', 'target_path') + config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version') + config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') - os.makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] - model = EmbeddingConverterTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') + os.makedirs(config_directory_path, exist_ok = True) + model = EmbeddingConverterTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu') model.eval() - model.ir_version = torch.tensor(config.get('ir_version')) + model.ir_version = torch.tensor(config_ir_version) input_tensor = torch.randn(1, 512) - torch.onnx.export(model, input_tensor, config.get('target_path'), input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config.get('opset_version')) + torch.onnx.export(model, input_tensor, config_target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config_opset_version) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 522bd59..4c91b5c 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -21,15 +21,12 @@ CONFIG_PARSER.read('config.ini') class EmbeddingConverterTrainer(LightningModule): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'source_path': config_parser.get('training.model', 'source_path'), - 'target_path': config_parser.get('training.model', 'target_path'), - 'learning_rate': config_parser.getfloat('training.trainer', 'learning_rate') - } + self.config_source_path = config_parser.get('training.model', 'source_path') + self.config_target_path = config_parser.get('training.model', 'target_path') + self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() - self.source_embedder = torch.jit.load(self.config.get('source_path'), map_location = 'cpu') # type:ignore[no-untyped-call] - self.target_embedder = torch.jit.load(self.config.get('target_path'), map_location = 'cpu') # type:ignore[no-untyped-call] + self.source_embedder = torch.jit.load(self.config_source_path, map_location = 'cpu') + self.target_embedder = torch.jit.load(self.config_target_path, map_location = 'cpu') self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: @@ -53,7 +50,7 @@ class EmbeddingConverterTrainer(LightningModule): return validation_score def configure_optimizers(self) -> OptimizerSet: - optimizer = torch.optim.Adam(self.parameters(), lr = self.config.get('learning_rate')) + optimizer = torch.optim.Adam(self.parameters(), lr = self.config_learning_rate) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer) optimizer_set =\ { @@ -71,52 +68,43 @@ class EmbeddingConverterTrainer(LightningModule): def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: - config =\ - { - 'batch_size': CONFIG_PARSER.getint('training.loader', 'batch_size'), - 'num_workers': CONFIG_PARSER.getint('training.loader', 'num_workers') - } + config_batch_size = CONFIG_PARSER.getint('training.loader', 'batch_size') + config_num_workers = CONFIG_PARSER.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) - training_loader = StatefulDataLoader(training_dataset, batch_size = config.get('batch_size'), shuffle = True, num_workers = config.get('num_workers'), drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = StatefulDataLoader(validate_dataset, batch_size = config.get('batch_size'), shuffle = False, num_workers = config.get('num_workers'), pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = config_batch_size, shuffle = True, num_workers = config_num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = config_batch_size, shuffle = False, num_workers = config_num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - config =\ - { - 'split_ratio': CONFIG_PARSER.getfloat('training.loader', 'split_ratio') - } + config_split_ratio = CONFIG_PARSER.getfloat('training.loader', 'split_ratio') dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * config.get('split_ratio')) + training_size = int(dataset_size * config_split_ratio) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset def create_trainer() -> Trainer: - config =\ - { - 'max_epochs': CONFIG_PARSER.getint('training.trainer', 'max_epochs'), - 'precision': CONFIG_PARSER.get('training.trainer', 'precision'), - 'directory_path': CONFIG_PARSER.get('training.output', 'directory_path'), - 'file_pattern': CONFIG_PARSER.get('training.output', 'file_pattern') - } + config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') + config_precision = CONFIG_PARSER.get('training.trainer', 'precision') + config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') + config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') logger = TensorBoardLogger('.logs', name = 'embedding_converter') return Trainer( logger = logger, log_every_n_steps = 10, - max_epochs = config.get('max_epochs'), - precision = config.get('precision'), + max_epochs = config_max_epochs, + precision = config_precision, callbacks = [ ModelCheckpoint( monitor = 'training_loss', - dirpath = config.get('directory_path'), - filename = config.get('file_pattern'), + dirpath = config_directory_path, + filename = config_file_pattern, every_n_epochs = 1, save_top_k = 3, save_last = True @@ -126,10 +114,7 @@ def create_trainer() -> Trainer: def train() -> None: - config =\ - { - 'resume_path': CONFIG_PARSER.get('training.output', 'resume_path') - } + config_resume_path = CONFIG_PARSER.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') @@ -139,7 +124,7 @@ def train() -> None: embedding_converter_trainer = EmbeddingConverterTrainer(CONFIG_PARSER) trainer = create_trainer() - if os.path.exists(config.get('resume_path')): - trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) + if os.path.exists(config_resume_path): + trainer.fit(embedding_converter_trainer, training_loader, validation_loader, ckpt_path = config_resume_path) else: trainer.fit(embedding_converter_trainer, training_loader, validation_loader) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index f60b07f..c36b82b 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -9,10 +9,7 @@ from ..networks.nld import NLD class Discriminator(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'num_discriminators': config_parser.getint('training.model.discriminator', 'num_discriminators') - } + self.config_num_discriminators = config_parser.getint('training.model.discriminator', 'num_discriminators') self.config_parser = config_parser self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() @@ -20,7 +17,7 @@ class Discriminator(nn.Module): def create_discriminators(self) -> nn.ModuleList: discriminators = nn.ModuleList() - for _ in range(self.config.get('num_discriminators')): + for _ in range(self.config_num_discriminators): discriminator = NLD(self.config_parser).sequences discriminators.append(discriminator) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 9c0525d..99eb754 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -101,28 +101,22 @@ class ReconstructionLoss(nn.Module): class IdentityLoss(nn.Module): def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None: super().__init__() - self.config =\ - { - 'identity_weight': config_parser.getfloat('training.losses', 'identity_weight') - } + self.config_identity_weight = config_parser.getfloat('training.losses', 'identity_weight') self.embedder = embedder def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() - weighted_identity_loss = identity_loss * self.config.get('identity_weight') + weighted_identity_loss = identity_loss * self.config_identity_weight return identity_loss, weighted_identity_loss class MotionLoss(nn.Module): def __init__(self, config_parser : ConfigParser, motion_extractor : MotionExtractorModule): super().__init__() - self.config =\ - { - 'pose_weight': config_parser.getfloat('training.losses', 'pose_weight'), - 'expression_weight': config_parser.getfloat('training.losses', 'expression_weight') - } + self.config_pose_weight = config_parser.getfloat('training.losses', 'pose_weight') + self.expression_weight = config_parser.getfloat('training.losses', 'expression_weight') self.motion_extractor = motion_extractor self.mse_loss = nn.MSELoss() @@ -141,12 +135,12 @@ class MotionLoss(nn.Module): temp_tensors.append(temp_tensor) pose_loss = torch.stack(temp_tensors).mean() - weighted_pose_loss = pose_loss * self.config.get('pose_weight') + weighted_pose_loss = pose_loss * self.config_pose_weight return pose_loss, weighted_pose_loss def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Tensor, Tensor]: expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() - weighted_expression_loss = expression_loss * self.config.get('expression_weight') + weighted_expression_loss = expression_loss * self.config_expression_weight return expression_loss, weighted_expression_loss def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: diff --git a/face_swapper/src/networks/nld.py b/face_swapper/src/networks/nld.py index d0347f8..cf2ffd9 100644 --- a/face_swapper/src/networks/nld.py +++ b/face_swapper/src/networks/nld.py @@ -7,31 +7,28 @@ from torch import Tensor, nn class NLD(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'input_channels': config_parser.getint('training.model.discriminator', 'input_channels'), - 'num_filters': config_parser.getint('training.model.discriminator', 'num_filters'), - 'kernel_size': config_parser.getint('training.model.discriminator', 'kernel_size'), - 'num_layers': config_parser.getint('training.model.discriminator', 'num_layers') - } + self.config_input_channels = config_parser.getint('training.model.discriminator', 'input_channels') + self.config_num_filters = config_parser.getint('training.model.discriminator', 'num_filters') + self.config_kernel_size = config_parser.getint('training.model.discriminator', 'kernel_size') + self.config_num_layers = config_parser.getint('training.model.discriminator', 'num_layers') self.layers = self.create_layers() self.sequences = nn.Sequential(*self.layers) def create_layers(self) -> nn.ModuleList: - padding = math.ceil((self.config.get('kernel_size') - 1) / 2) - current_filters = self.config.get('num_filters') + padding = math.ceil((self.config_kernel_size - 1) / 2) + current_filters = self.config_num_filters layers = nn.ModuleList( [ - nn.Conv2d(self.config.get('input_channels'), current_filters, kernel_size = self.config.get('kernel_size'), stride = 2, padding = padding), + nn.Conv2d(self.config_input_channels, current_filters, kernel_size = self.config_kernel_size, stride = 2, padding = padding), nn.LeakyReLU(0.2, True) ]) - for _ in range(1, self.config.get('num_layers')): + for _ in range(1, self.config_num_layers): previous_filters = current_filters current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = self.config.get('kernel_size'), stride = 2, padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.config_kernel_size, stride = 2, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True) ] @@ -40,10 +37,10 @@ class NLD(nn.Module): current_filters = min(current_filters * 2, 512) layers +=\ [ - nn.Conv2d(previous_filters, current_filters, kernel_size = self.config.get('kernel_size'), padding = padding), + nn.Conv2d(previous_filters, current_filters, kernel_size = self.config_kernel_size, padding = padding), nn.InstanceNorm2d(current_filters), nn.LeakyReLU(0.2, True), - nn.Conv2d(current_filters, 1, kernel_size = self.config.get('kernel_size'), padding = padding) + nn.Conv2d(current_filters, 1, kernel_size = self.config_kernel_size, padding = padding) ] return layers diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 892a593..4aaa653 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -8,10 +8,7 @@ from torch import Tensor, nn class UNet(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'output_size': config_parser.getint('training.model.generator', 'output_size') - } + self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.down_samples = self.create_down_samples() self.up_samples = self.create_up_samples() @@ -25,20 +22,20 @@ class UNet(nn.Module): DownSample(256, 512) ]) - if self.config.get('output_size') == 128: + if self.config_output_size == 128: down_samples.extend( [ DownSample(512, 512) ]) - if self.config.get('output_size') == 256: + if self.config_output_size == 256: down_samples.extend( [ DownSample(512, 1024), DownSample(1024, 1024) ]) - if self.config.get('output_size') == 512: + if self.config_output_size == 512: down_samples.extend( [ DownSample(512, 1024), @@ -51,20 +48,20 @@ class UNet(nn.Module): def create_up_samples(self) -> nn.ModuleList: up_samples = nn.ModuleList() - if self.config.get('output_size') == 128: + if self.config_output_size == 128: up_samples.extend( [ UpSample(512, 512) ]) - if self.config.get('output_size') == 256: + if self.config_output_size == 256: up_samples.extend( [ UpSample(1024, 1024), UpSample(2048, 512) ]) - if self.config.get('output_size') == 512: + if self.config_output_size == 512: up_samples.extend( [ UpSample(2048, 2048), From 8101b15e1ced6c60a0f054cf1ff85b0d418f33d2 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 7 Mar 2025 00:30:06 +0100 Subject: [PATCH 291/394] Revert the config dicts --- embedding_converter/src/exporting.py | 3 +- embedding_converter/src/training.py | 4 +- face_swapper/src/dataset.py | 34 +++++------ face_swapper/src/exporting.py | 26 ++++---- face_swapper/src/inferencing.py | 24 +++----- face_swapper/src/models/loss.py | 36 ++++------- face_swapper/src/networks/aad.py | 91 +++++++++++++--------------- face_swapper/src/training.py | 71 +++++++++------------- 8 files changed, 119 insertions(+), 170 deletions(-) diff --git a/embedding_converter/src/exporting.py b/embedding_converter/src/exporting.py index 5194ae6..f7e7549 100644 --- a/embedding_converter/src/exporting.py +++ b/embedding_converter/src/exporting.py @@ -17,8 +17,7 @@ def export() -> None: config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') os.makedirs(config_directory_path, exist_ok = True) - model = EmbeddingConverterTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu') - model.eval() + model = EmbeddingConverterTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu').eval() model.ir_version = torch.tensor(config_ir_version) input_tensor = torch.randn(1, 512) torch.onnx.export(model, input_tensor, config_target_path, input_names = [ 'input' ], output_names = [ 'output' ], opset_version = config_opset_version) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 4c91b5c..50d42d2 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -25,8 +25,8 @@ class EmbeddingConverterTrainer(LightningModule): self.config_target_path = config_parser.get('training.model', 'target_path') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.embedding_converter = EmbeddingConverter() - self.source_embedder = torch.jit.load(self.config_source_path, map_location = 'cpu') - self.target_embedder = torch.jit.load(self.config_target_path, map_location = 'cpu') + self.source_embedder = torch.jit.load(self.config_source_path, map_location = 'cpu').eval() + self.target_embedder = torch.jit.load(self.config_target_path, map_location = 'cpu').eval() self.mse_loss = nn.MSELoss() def forward(self, source_embedding : Embedding) -> Embedding: diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index cd31d2c..7219ac5 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -15,27 +15,24 @@ from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): def __init__(self, config_parser : ConfigParser) -> None: - self.config =\ - { - 'file_pattern': config_parser.get('training.dataset', 'file_pattern'), - 'transform_size': config_parser.get('training.dataset', 'transform_size'), - 'batch_mode': cast(BatchMode, config_parser.get('training.dataset', 'batch_mode')), - 'batch_ratio': config_parser.getfloat('training.dataset', 'batch_ratio'), - } + self.config_file_pattern = config_parser.get('training.dataset', 'file_pattern') + self.config_transform_size = config_parser.get('training.dataset', 'transform_size') + self.config_batch_mode = cast(BatchMode, config_parser.get('training.dataset', 'batch_mode')) + self.config_batch_ratio = config_parser.getfloat('training.dataset', 'batch_ratio') self.config_parser = config_parser - self.file_paths = glob.glob(self.config.get('file_pattern')) # type:ignore[type-var] + self.file_paths = glob.glob(self.config_file_pattern) self.transforms = self.compose_transforms() def __getitem__(self, index : int) -> Batch: file_path = self.file_paths[index] - if random.random() < self.config.get('batch_ratio'): # type:ignore[operator] - if self.config.get('batch_mode') == 'equal': - return self.prepare_equal_batch(file_path) # type:ignore[arg-type] - if self.config.get('batch_mode') == 'same': - return self.prepare_same_batch(file_path) # type:ignore[arg-type] + if random.random() < self.config_batch_ratio: + if self.config_batch_mode == 'equal': + return self.prepare_equal_batch(file_path) + if self.config_batch_mode == 'same': + return self.prepare_same_batch(file_path) - return self.prepare_different_batch(file_path) # type:ignore[arg-type] + return self.prepare_different_batch(file_path) def __len__(self) -> int: return len(self.file_paths) @@ -45,7 +42,7 @@ class DynamicDataset(Dataset[Tensor]): [ AugmentTransform(), transforms.ToPILImage(), - transforms.Resize((self.config.get('transform_size'), self.config.get('transform_size')), interpolation = transforms.InterpolationMode.BICUBIC), + transforms.Resize((self.config_transform_size, self.config_transform_size), interpolation = transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), WarpTransform(self.config_parser), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) @@ -99,11 +96,8 @@ class AugmentTransform: class WarpTransform: def __init__(self, config_parser : ConfigParser) -> None: - self.config =\ - { - 'warp_template': cast(WarpTemplate, config_parser.get('training.dataset', 'warp_template')) - } + self.config_warp_template = cast(WarpTemplate, config_parser.get('training.dataset', 'warp_template')) def __call__(self, input_tensor : Tensor) -> Tensor: temp_tensor = input_tensor.unsqueeze(0) - return warp_tensor(temp_tensor, self.config.get('warp_template')).squeeze(0) + return warp_tensor(temp_tensor, self.config_warp_template).squeeze(0) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 0c4f5ac..ae0cc4d 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -11,20 +11,16 @@ CONFIG_PARSER.read('config.ini') def export() -> None: - config =\ - { - 'directory_path': CONFIG_PARSER.get('exporting', 'directory_path'), - 'source_path': CONFIG_PARSER.get('exporting', 'source_path'), - 'target_path': CONFIG_PARSER.get('exporting', 'target_path'), - 'target_size': CONFIG_PARSER.getint('exporting', 'target_size'), - 'ir_version': CONFIG_PARSER.getint('exporting', 'ir_version'), - 'opset_version': CONFIG_PARSER.getint('exporting', 'opset_version') - } + config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path') + config_source_path = CONFIG_PARSER.get('exporting', 'source_path') + config_target_path = CONFIG_PARSER.get('exporting', 'target_path') + config_target_size = CONFIG_PARSER.getint('exporting', 'target_size') + config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version') + config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') - os.makedirs(config.get('directory_path'), exist_ok = True) # type:ignore[arg-type] - model = FaceSwapperTrainer.load_from_checkpoint(config.get('source_path'), map_location = 'cpu') - model.eval() - model.ir_version = torch.tensor(config.get('ir_version')) + os.makedirs(config_directory_path, exist_ok = True) + model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu').eval() + model.ir_version = torch.tensor(config_ir_version) source_tensor = torch.randn(1, 512) - target_tensor = torch.randn(1, 3, config.get('target_size'), config.get('target_size')) - torch.onnx.export(model, (source_tensor, target_tensor), config.get('target_path'), input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = config.get('opset_version')) + target_tensor = torch.randn(1, 3, config_target_size, config_target_size) + torch.onnx.export(model, (source_tensor, target_tensor), config_target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = config_opset_version) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index b20535b..5182e00 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -11,24 +11,20 @@ CONFIG_PARSER.read('config.ini') def infer() -> None: - config =\ - { - 'generator_path': CONFIG_PARSER.get('inferencing', 'generator_path'), - 'embedder_path': CONFIG_PARSER.get('inferencing', 'embedder_path'), - 'source_path': CONFIG_PARSER.get('inferencing', 'source_path'), - 'target_path': CONFIG_PARSER.get('inferencing', 'target_path'), - 'output_path': CONFIG_PARSER.get('inferencing', 'output_path') - } + config_generator_path = CONFIG_PARSER.get('inferencing', 'generator_path') + config_embedder_path = CONFIG_PARSER.get('inferencing', 'embedder_path') + config_source_path = CONFIG_PARSER.get('inferencing', 'source_path') + config_target_path = CONFIG_PARSER.get('inferencing', 'target_path') + config_output_path = CONFIG_PARSER.get('inferencing', 'output_path') - state_dict = torch.load(config.get('generator_path')).get('state_dict').get('generator') + state_dict = torch.load(config_generator_path).get('state_dict').get('generator') generator = Generator(CONFIG_PARSER) generator.load_state_dict(state_dict) generator.eval() - embedder = torch.jit.load(config.get('embedder_path'), map_location = 'cpu') # type:ignore[no-untyped-call] - embedder.eval() + embedder = torch.jit.load(config_embedder_path, map_location = 'cpu').eval() - source_tensor = io.read_image(config.get('source_path')) - target_tensor = io.read_image(config.get('target_path')) + source_tensor = io.read_image(config_source_path) + target_tensor = io.read_image(config_target_path) source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) output_tensor = generator(source_embedding, target_tensor)[0] - io.write_jpeg(output_tensor, config.get('output_path')) + io.write_jpeg(output_tensor, config_output_path) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 99eb754..1248e1b 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -35,10 +35,7 @@ class DiscriminatorLoss(nn.Module): class AdversarialLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'adversarial_weight': config_parser.getfloat('training.losses', 'adversarial_weight') - } + self.config_adversarial_weight = config_parser.getfloat('training.losses', 'adversarial_weight') def forward(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: temp_tensors = [] @@ -48,38 +45,32 @@ class AdversarialLoss(nn.Module): temp_tensors.append(temp_tensor) adversarial_loss = torch.stack(temp_tensors).mean() - weighted_adversarial_loss = adversarial_loss * self.config.get('adversarial_weight') + weighted_adversarial_loss = adversarial_loss * self.config_adversarial_weight return adversarial_loss, weighted_adversarial_loss class AttributeLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'batch_size': config_parser.getint('training.loader', 'batch_size'), - 'attribute_weight': config_parser.getfloat('training.losses', 'attribute_weight') - } + self.config_batch_size = config_parser.getint('training.loader', 'batch_size') + self.config_attribute_weight = config_parser.getfloat('training.losses', 'attribute_weight') def forward(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: temp_tensors = [] for target_attribute, output_attribute in zip(target_attributes, output_attributes): - temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(self.config.get('batch_size'), -1), dim = 1).mean() + temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(self.config_batch_size, -1), dim = 1).mean() temp_tensors.append(temp_tensor) attribute_loss = torch.stack(temp_tensors).mean() * 0.5 - weighted_attribute_loss = attribute_loss * self.config.get('attribute_weight') + weighted_attribute_loss = attribute_loss * self.config_attribute_weight return attribute_loss, weighted_attribute_loss class ReconstructionLoss(nn.Module): def __init__(self, config_parser : ConfigParser, embedder : EmbedderModule) -> None: super().__init__() - self.config =\ - { - 'reconstruction_weight': config_parser.getfloat('training.losses', 'reconstruction_weight') - } + self.config_reconstruction_weight = config_parser.getfloat('training.losses', 'reconstruction_weight') self.embedder = embedder self.mse_loss = nn.MSELoss() @@ -94,7 +85,7 @@ class ReconstructionLoss(nn.Module): data_range = float(torch.max(output_tensor) - torch.min(output_tensor)) visual_loss = 1 - ssim(output_tensor, target_tensor, data_range = data_range).mean() reconstruction_loss = (reconstruction_loss + visual_loss) * 0.5 - weighted_reconstruction_loss = reconstruction_loss * self.config.get('reconstruction_weight') + weighted_reconstruction_loss = reconstruction_loss * self.config_reconstruction_weight return reconstruction_loss, weighted_reconstruction_loss @@ -156,11 +147,8 @@ class MotionLoss(nn.Module): class GazeLoss(nn.Module): def __init__(self, config_parser : ConfigParser, gazer : GazerModule) -> None: super().__init__() - self.config =\ - { - 'gaze_weight': config_parser.getfloat('training.losses', 'gaze_weight'), - 'output_size': config_parser.getint('training.model.generator', 'output_size') - } + self.config_gaze_weight = config_parser.getfloat('training.losses', 'gaze_weight') + self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.gazer = gazer self.l1_loss = nn.L1Loss() @@ -172,11 +160,11 @@ class GazeLoss(nn.Module): yaw_loss = self.l1_loss(output_yaw, target_yaw) gaze_loss = (pitch_loss + yaw_loss) * 0.5 - weighted_gaze_loss = gaze_loss * self.config.get('gaze_weight') + weighted_gaze_loss = gaze_loss * self.config_gaze_weight return gaze_loss, weighted_gaze_loss def detect_gaze(self, input_tensor : Tensor) -> Gaze: - crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * self.config.get('output_size')).int() + crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * self.config_output_size).int() crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]] crop_tensor = (crop_tensor + 1) * 0.5 crop_tensor = transforms.Normalize(mean = [ 0.485, 0.456, 0.406 ], std = [ 0.229, 0.224, 0.225 ])(crop_tensor) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 8bf261e..357d662 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -9,53 +9,50 @@ from ..types import Attributes, Embedding class AAD(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'identity_channels': config_parser.getint('training.model.generator', 'identity_channels'), - 'output_channels': config_parser.getint('training.model.generator', 'output_channels'), - 'output_size': config_parser.getint('training.model.generator', 'output_size'), - 'num_blocks': config_parser.getint('training.model.generator', 'num_blocks') - } + self.config_identity_channels = config_parser.getint('training.model.generator', 'identity_channels') + self.config_output_channels = config_parser.getint('training.model.generator', 'output_channels') + self.config_output_size = config_parser.getint('training.model.generator', 'output_size') + self.config_num_blocks = config_parser.getint('training.model.generator', 'num_blocks') self.config_parser = config_parser - self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config.get('identity_channels'), self.config.get('output_channels')) + self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config_identity_channels, self.config_output_channels) self.layers = self.create_layers() def create_layers(self) -> nn.ModuleList: layers = nn.ModuleList() - if self.config.get('output_size') == 128: + if self.config_output_size == 128: layers.extend( [ - AdaptiveFeatureModulation(512, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(512, 512, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(512, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) + AdaptiveFeatureModulation(512, 512, 512, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(512, 512, 1024, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(512, 512, 512, self.config_identity_channels, self.config_num_blocks) ]) - if self.config.get('output_size') == 256: + if self.config_output_size == 256: layers.extend( [ - AdaptiveFeatureModulation(1024, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(1024, 1024, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(1024, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(1024, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) + AdaptiveFeatureModulation(1024, 1024, 1024, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 1024, 2048, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 1024, 1024, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.config_identity_channels, self.config_num_blocks) ]) - if self.config.get('output_size') == 512: + if self.config_output_size == 512: layers.extend( [ - AdaptiveFeatureModulation(2048, 2048, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(2048, 2048, 4096, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(2048, 2048, 2048, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(2048, 1024, 1024, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(1024, 512, 512, self.config.get('identity_channels'), self.config.get('num_blocks')) + AdaptiveFeatureModulation(2048, 2048, 2048, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 2048, 4096, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 2048, 2048, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 1024, 1024, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.config_identity_channels, self.config_num_blocks) ]) layers.extend( [ - AdaptiveFeatureModulation(512, 256, 256, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(256, 128, 128, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(128, 64, 64, self.config.get('identity_channels'), self.config.get('num_blocks')), - AdaptiveFeatureModulation(64, 3, 64, self.config.get('identity_channels'), self.config.get('num_blocks')) + AdaptiveFeatureModulation(512, 256, 256, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(256, 128, 128, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(128, 64, 64, self.config_identity_channels, self.config_num_blocks), + AdaptiveFeatureModulation(64, 3, 64, self.config_identity_channels, self.config_num_blocks) ]) return layers @@ -75,43 +72,40 @@ class AAD(nn.Module): class AdaptiveFeatureModulation(nn.Module): def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> None: super().__init__() - self.context =\ - { - 'input_channels': input_channels, - 'output_channels': output_channels, - 'attribute_channels': attribute_channels, - 'identity_channels': identity_channels, - 'num_blocks': num_blocks - } + self.context_input_channels = input_channels + self.context_output_channels = output_channels + self.context_attribute_channels = attribute_channels + self.context_identity_channels = identity_channels + self.context_num_blocks = num_blocks self.primary_layers = self.create_primary_layers() self.shortcut_layers = self.create_shortcut_layers() def create_primary_layers(self) -> nn.ModuleList: primary_layers = nn.ModuleList() - for index in range(self.context.get('num_blocks')): + for index in range(self.context_num_blocks): primary_layers.extend( [ - FeatureModulation(self.context.get('input_channels'), self.context.get('attribute_channels'), self.context.get('identity_channels')), + FeatureModulation(self.context_input_channels, self.context_attribute_channels, self.context_identity_channels), nn.ReLU(inplace = True) ]) - if index < self.context.get('num_blocks') - 1: - primary_layers.append(nn.Conv2d(self.context.get('input_channels'), self.context.get('input_channels'), kernel_size = 3, padding = 1, bias = False)) + if index < self.context_num_blocks - 1: + primary_layers.append(nn.Conv2d(self.context_input_channels, self.context_input_channels, kernel_size = 3, padding = 1, bias = False)) else: - primary_layers.append(nn.Conv2d(self.context.get('input_channels'), self.context.get('output_channels'), kernel_size = 3, padding = 1, bias = False)) + primary_layers.append(nn.Conv2d(self.context_input_channels, self.context_output_channels, kernel_size = 3, padding = 1, bias = False)) return primary_layers def create_shortcut_layers(self) -> nn.ModuleList: shortcut_layers = nn.ModuleList() - if self.context.get('input_channels') > self.context.get('output_channels'): + if self.context_input_channels > self.context_output_channels: shortcut_layers.extend( [ - FeatureModulation(self.context.get('input_channels'), self.context.get('attribute_channels'), self.context.get('identity_channels')), + FeatureModulation(self.context_input_channels, self.context_attribute_channels, self.context_identity_channels), nn.ReLU(inplace = True), - nn.Conv2d(self.context.get('input_channels'), self.context.get('output_channels'), kernel_size = 3, padding = 1, bias = False) + nn.Conv2d(self.context_input_channels, self.context_output_channels, kernel_size = 3, padding = 1, bias = False) ]) return shortcut_layers @@ -125,7 +119,7 @@ class AdaptiveFeatureModulation(nn.Module): else: primary_tensor = primary_layer(primary_tensor) - if self.context.get('input_channels') > self.context.get('output_channels'): + if self.context_input_channels > self.context_output_channels: shortcut_tensor = input_tensor for shortcut_layer in self.shortcut_layers: @@ -142,10 +136,7 @@ class AdaptiveFeatureModulation(nn.Module): class FeatureModulation(nn.Module): def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: super().__init__() - self.context =\ - { - 'input_channels': input_channels - } + self.context_input_channels = input_channels self.conv1 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) self.conv2 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) self.conv3 = nn.Conv2d(input_channels, 1, kernel_size = 1) @@ -160,8 +151,8 @@ class FeatureModulation(nn.Module): attribute_shift = self.conv2(attribute_embedding) attribute_modulation = attribute_scale * temp_tensor + attribute_shift - identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.context.get('input_channels'), 1, 1).expand_as(temp_tensor) - identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.context.get('input_channels'), 1, 1).expand_as(temp_tensor) + identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) + identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) identity_modulation = identity_scale * temp_tensor + identity_shift temp_mask = torch.sigmoid(self.conv3(temp_tensor)) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f08d65d..005753e 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -28,17 +28,14 @@ CONFIG_PARSER.read('config.ini') class FaceSwapperTrainer(LightningModule): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config =\ - { - 'embedder_path': config_parser.get('training.model', 'embedder_path'), - 'gazer_path': config_parser.get('training.model', 'gazer_path'), - 'motion_extractor_path': config_parser.get('training.model', 'motion_extractor_path'), - 'learning_rate': config_parser.getfloat('training.trainer', 'learning_rate'), - 'preview_frequency': config_parser.getint('training.trainer', 'preview_frequency') - } - self.embedder = torch.jit.load(self.config.get('embedder_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.gazer = torch.jit.load(self.config.get('gazer_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] - self.motion_extractor = torch.jit.load(self.config.get('motion_extractor_path'), map_location = 'cpu').eval() # type:ignore[no-untyped-call] + self.config_embedder_path = config_parser.get('training.model', 'embedder_path') + self.config_gazer_path = config_parser.get('training.model', 'gazer_path') + self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') + self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') + self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') + self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() + self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() + self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() @@ -55,8 +52,8 @@ class FaceSwapperTrainer(LightningModule): return output_tensor def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: - generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config.get('learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) - discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config.get('learning_rate'), betas = (0.0, 0.999), weight_decay = 1e-4) + generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) @@ -113,7 +110,7 @@ class FaceSwapperTrainer(LightningModule): discriminator_optimizer.step() self.untoggle_optimizer(discriminator_optimizer) - if self.global_step % self.config.get('preview_frequency') == 0: + if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor) self.log('generator_loss', generator_loss, prog_bar = True) @@ -149,52 +146,43 @@ class FaceSwapperTrainer(LightningModule): def create_loaders(dataset : Dataset[Tensor]) -> Tuple[StatefulDataLoader[Tensor], StatefulDataLoader[Tensor]]: - config =\ - { - 'batch_size': CONFIG_PARSER.getint('training.loader', 'batch_size'), - 'num_workers': CONFIG_PARSER.getint('training.loader', 'num_workers') - } + config_batch_size = CONFIG_PARSER.getint('training.loader', 'batch_size') + config_num_workers = CONFIG_PARSER.getint('training.loader', 'num_workers') training_dataset, validate_dataset = split_dataset(dataset) - training_loader = StatefulDataLoader(training_dataset, batch_size = config.get('batch_size'), shuffle = True, num_workers = config.get('num_workers'), drop_last = True, pin_memory = True, persistent_workers = True) - validation_loader = StatefulDataLoader(validate_dataset, batch_size = config.get('batch_size'), shuffle = False, num_workers = config.get('num_workers'), pin_memory = True, persistent_workers = True) + training_loader = StatefulDataLoader(training_dataset, batch_size = config_batch_size, shuffle = True, num_workers = config_num_workers, drop_last = True, pin_memory = True, persistent_workers = True) + validation_loader = StatefulDataLoader(validate_dataset, batch_size = config_batch_size, shuffle = False, num_workers = config_num_workers, pin_memory = True, persistent_workers = True) return training_loader, validation_loader def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[Tensor]]: - config =\ - { - 'split_ratio': CONFIG_PARSER.getfloat('training.loader', 'split_ratio') - } + config_split_ratio = CONFIG_PARSER.getfloat('training.loader', 'split_ratio') dataset_size = len(dataset) # type:ignore[arg-type] - training_size = int(dataset_size * config.get('split_ratio')) + training_size = int(dataset_size * config_split_ratio) validation_size = int(dataset_size - training_size) training_dataset, validate_dataset = random_split(dataset, [ training_size, validation_size ]) return training_dataset, validate_dataset def create_trainer() -> Trainer: - config =\ - { - 'max_epochs': CONFIG_PARSER.getint('training.trainer', 'max_epochs'), - 'precision': CONFIG_PARSER.get('training.trainer', 'precision'), - 'directory_path': CONFIG_PARSER.get('training.output', 'directory_path'), - 'file_pattern': CONFIG_PARSER.get('training.output', 'file_pattern') - } + config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') + config_precision = CONFIG_PARSER.get('training.trainer', 'precision') + config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') + config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') logger = TensorBoardLogger('.logs', name = 'face_swapper') return Trainer( logger = logger, log_every_n_steps = 10, - max_epochs = config.get('max_epochs'), - precision = config.get('precision'), + max_epochs = config_max_epochs, + precision = config_precision, callbacks = [ ModelCheckpoint( monitor = 'generator_loss', - dirpath = config.get('directory_path'), - filename = config.get('file_pattern'), + dirpath = config_directory_path, + filename = config_file_pattern, every_n_train_steps = 1000, save_top_k = 3, save_last = True @@ -205,10 +193,7 @@ def create_trainer() -> Trainer: def train() -> None: - config =\ - { - 'resume_path': CONFIG_PARSER.get('training.output', 'resume_path') - } + config_resume_path = CONFIG_PARSER.get('training.output', 'resume_path') if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') @@ -218,7 +203,7 @@ def train() -> None: face_swapper_trainer = FaceSwapperTrainer(CONFIG_PARSER) trainer = create_trainer() - if os.path.isfile(config.get('resume_path')): - trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = config.get('resume_path')) + if os.path.isfile(config_resume_path): + trainer.fit(face_swapper_trainer, training_loader, validation_loader, ckpt_path = config_resume_path) else: trainer.fit(face_swapper_trainer, training_loader, validation_loader) From 862dce7bc6b5e3c721134f98bcf4b66f9c3a984a Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 7 Mar 2025 00:35:46 +0100 Subject: [PATCH 292/394] Revert the config dicts --- face_swapper/src/networks/aad.py | 1 - 1 file changed, 1 deletion(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 357d662..a912319 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -13,7 +13,6 @@ class AAD(nn.Module): self.config_output_channels = config_parser.getint('training.model.generator', 'output_channels') self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.config_num_blocks = config_parser.getint('training.model.generator', 'num_blocks') - self.config_parser = config_parser self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config_identity_channels, self.config_output_channels) self.layers = self.create_layers() From dbe931e9504a1c2910c77b41235a953c923272a0 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 7 Mar 2025 14:04:24 +0100 Subject: [PATCH 293/394] Fix naming --- face_swapper/src/models/loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 1248e1b..6d7a0e5 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -107,7 +107,7 @@ class MotionLoss(nn.Module): def __init__(self, config_parser : ConfigParser, motion_extractor : MotionExtractorModule): super().__init__() self.config_pose_weight = config_parser.getfloat('training.losses', 'pose_weight') - self.expression_weight = config_parser.getfloat('training.losses', 'expression_weight') + self.config_expression_weight = config_parser.getfloat('training.losses', 'expression_weight') self.motion_extractor = motion_extractor self.mse_loss = nn.MSELoss() From 9ff30a026882b2fd04ec131b14df49f67e1ad375 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 7 Mar 2025 14:07:00 +0100 Subject: [PATCH 294/394] Use getint --- face_swapper/src/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 7219ac5..ebf7270 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -16,7 +16,7 @@ from .types import Batch, BatchMode, WarpTemplate class DynamicDataset(Dataset[Tensor]): def __init__(self, config_parser : ConfigParser) -> None: self.config_file_pattern = config_parser.get('training.dataset', 'file_pattern') - self.config_transform_size = config_parser.get('training.dataset', 'transform_size') + self.config_transform_size = config_parser.getint('training.dataset', 'transform_size') self.config_batch_mode = cast(BatchMode, config_parser.get('training.dataset', 'batch_mode')) self.config_batch_ratio = config_parser.getfloat('training.dataset', 'batch_ratio') self.config_parser = config_parser From 4af22832db8d2d48c492d6f0c2aaf19696a8f032 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sat, 8 Mar 2025 15:11:55 +0530 Subject: [PATCH 295/394] fix discriminator & restore grad for ID embedder --- face_swapper/src/helper.py | 3 +-- face_swapper/src/models/loss.py | 10 ++++++---- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index a51fada..8ea5b3a 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -33,7 +33,6 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P crop_tensor[:, :, :, :padding[2]] = 0 crop_tensor[:, :, :, 112 - padding[3]:] = 0 - with torch.no_grad(): - embedding = embedder(crop_tensor) + embedding = embedder(crop_tensor) embedding = nn.functional.normalize(embedding, p = 2) return embedding diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 6d7a0e5..52f049f 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -19,11 +19,11 @@ class DiscriminatorLoss(nn.Module): negative_tensors = [] for discriminator_source_tensor in discriminator_source_tensors: - positive_tensor = torch.relu(discriminator_source_tensor + 1).mean(dim = [ 1, 2, 3 ]) + positive_tensor = torch.relu(1 - discriminator_source_tensor).mean(dim = [ 1, 2, 3 ]) positive_tensors.append(positive_tensor) for discriminator_output_tensor in discriminator_output_tensors: - negative_tensor = torch.relu(1 - discriminator_output_tensor).mean(dim = [ 1, 2, 3 ]) + negative_tensor = torch.relu(discriminator_output_tensor + 1).mean(dim = [ 1, 2, 3 ]) negative_tensors.append(negative_tensor) positive_loss = torch.stack(positive_tensors).mean() @@ -75,8 +75,10 @@ class ReconstructionLoss(nn.Module): self.mse_loss = nn.MSELoss() def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) + + with torch.no_grad(): + source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) has_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) From 8f1f002c648b8381c86a987eba9f926a61515173 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sat, 8 Mar 2025 16:05:07 +0530 Subject: [PATCH 296/394] add masknet --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/models/generator.py | 9 ++- face_swapper/src/models/loss.py | 31 +++++++++- face_swapper/src/networks/masknet.py | 90 ++++++++++++++++++++++++++++ face_swapper/src/training.py | 19 +++--- face_swapper/src/types.py | 1 + 7 files changed, 142 insertions(+), 10 deletions(-) create mode 100644 face_swapper/src/networks/masknet.py diff --git a/face_swapper/README.md b/face_swapper/README.md index 7772c88..b7bf47f 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -74,6 +74,7 @@ identity_weight = 20.0 gaze_weight = 0.0 pose_weight = 0.0 expression_weight = 0.0 +mask_weight = 1.0 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 0f3c702..dc20073 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -36,6 +36,7 @@ identity_weight = gaze_weight = pose_weight = expression_weight = +mask_weight = [training.trainer] learning_rate = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index d02be0b..84a9303 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,8 +1,10 @@ from configparser import ConfigParser +from typing import Tuple from torch import Tensor, nn from ..networks.aad import AAD +from ..networks.masknet import MaskNet from ..networks.unet import UNet from ..types import Attributes, Embedding @@ -12,13 +14,16 @@ class Generator(nn.Module): super().__init__() self.encoder = UNet(config_parser) self.generator = AAD(config_parser) + self.masker = MaskNet(67, 1, 16) self.encoder.apply(init_weight) self.generator.apply(init_weight) + self.masker.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: target_attributes = self.get_attributes(target_tensor) output_tensor = self.generator(source_embedding, target_attributes) - return output_tensor + mask_tensor = self.masker(target_tensor, target_attributes[-1]) + return output_tensor, mask_tensor def get_attributes(self, input_tensor : Tensor) -> Attributes: return self.encoder(input_tensor) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 52f049f..3341d81 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule +from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule, ParserModule class DiscriminatorLoss(nn.Module): @@ -175,3 +175,32 @@ class GazeLoss(nn.Module): with torch.no_grad(): pitch, yaw = self.gazer(crop_tensor) return pitch, yaw + + +class MaskLoss(nn.Module): + def __init__(self, config_parser : ConfigParser, parser : ParserModule) -> None: + super().__init__() + self.config_mask_weight = config_parser.getfloat('training.losses', 'mask_weight') + self.config_output_size = config_parser.getint('training.model.generator', 'output_size') + self.parser = parser + self.mse_loss = nn.MSELoss() + + def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Tuple[Tensor, Tensor]: + target_mask = self.calc_mask(target_tensor) + target_mask = target_mask.view(-1, self.config_output_size, self.config_output_size) + mask_tensor = mask_tensor.view(-1, self.config_output_size, self.config_output_size) + mask_loss = self.mse_loss(target_mask, mask_tensor) + weighted_mask_loss = mask_loss * self.config_mask_weight + return mask_loss, weighted_mask_loss + + def calc_mask(self, target_tensor : Tensor) -> Tensor: + target_tensor = torch.nn.functional.interpolate(target_tensor, (512, 512), mode = 'bilinear') + face_indices = torch.tensor([ 1, 2, 3, 4, 5, 10, 11, 12, 13 ]).to(target_tensor.device) + + with torch.no_grad(): + output_tensor = self.parser(target_tensor)[0] + output_tensor = output_tensor.argmax(1) + output_tensor = torch.isin(output_tensor, face_indices).to(target_tensor.dtype) + output_tensor = output_tensor.view(-1, 1, 512, 512) + output_tensor = torch.nn.functional.interpolate(output_tensor, (self.config_output_size, self.config_output_size), mode = 'bilinear') + return output_tensor diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py new file mode 100644 index 0000000..a7d83d4 --- /dev/null +++ b/face_swapper/src/networks/masknet.py @@ -0,0 +1,90 @@ +import torch +from torch import Tensor, nn + + +class MaskNet(nn.Module): + def __init__(self, input_channels : int, output_channels : int, base_channels : int): + super().__init__() + self.down_samples = self.create_down_samples(input_channels, base_channels) + self.up_samples = self.create_up_samples(base_channels) + self.bottleneck = ResBlock(base_channels * 4) + self.conv = nn.Conv2d(base_channels, output_channels, kernel_size = 1) + self.sigmoid = nn.Sigmoid() + + def create_down_samples(self, input_channels : int, base_channels: int) -> nn.ModuleList: + down_samples = nn.ModuleList( + [ + DownSample(input_channels, base_channels), + DownSample(base_channels, base_channels * 2), + DownSample(base_channels * 2, base_channels * 4) + ]) + return down_samples + + def create_up_samples(self, base_channels : int) -> nn.ModuleList: + down_samples = nn.ModuleList( + [ + UpSample(base_channels * 4, base_channels * 2), + UpSample(base_channels * 2, base_channels), + UpSample(base_channels, base_channels) + ]) + return down_samples + + def forward(self, target_tensor : Tensor, target_attribute : Tensor) -> Tensor: + output_tensor = torch.cat([ target_tensor, target_attribute ], dim=1) + + for down_sample in self.down_samples: + output_tensor = down_sample(output_tensor) + output_tensor = self.bottleneck(output_tensor) + + for up_sample in self.up_samples: + output_tensor = up_sample(output_tensor) + output_tensor = self.conv(output_tensor) + output_tensor = self.activation(output_tensor) + return output_tensor + + +class ResBlock(nn.Module): + def __init__(self, channels: int): + super().__init__() + self.conv = nn.Sequential( + nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(channels), + nn.ReLU(inplace=True), + nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(channels), + nn.ReLU(inplace=True) + ) + self.relu = nn.ReLU(inplace=True) + + def forward(self, input_tensor: Tensor) -> Tensor: + output_tensor = self.conv(input_tensor) + input_tensor + output_tensor = self.relu(output_tensor) + return output_tensor + + +class UpSample(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super().__init__() + self.conv_transpose = nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 2, stride = 2) + self.relu = nn.ReLU(inplace=True) + + def forward(self, input_tensor : Tensor) -> Tensor: + output_tensor = self.conv_transpose(input_tensor) + output_tensor = self.relu(output_tensor) + return output_tensor + + +class DownSample(nn.Module): + def __init__(self, input_channels : int, output_channels : int) -> None: + super().__init__() + self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) + self.batch_norm = nn.BatchNorm2d(output_channels) + self.relu = nn.ReLU(inplace = True) + self.max_pool = nn.MaxPool2d(2) + + def forward(self, input_tensor : Tensor) -> Tensor: + output_tensor = self.conv(input_tensor) + output_tensor = self.batch_norm(output_tensor) + output_tensor = self.relu(output_tensor) + output_tensor = self.max_pool(output_tensor) + return output_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 005753e..ada30c0 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .types import Batch, Embedding, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -31,11 +31,13 @@ class FaceSwapperTrainer(LightningModule): self.config_embedder_path = config_parser.get('training.model', 'embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') + self.config_parser_path = config_parser.get('training.model', 'parser_path') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() + self.parser = torch.jit.load(self.config_parser_path, map_location = 'cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() @@ -45,11 +47,12 @@ class FaceSwapperTrainer(LightningModule): self.identity_loss = IdentityLoss(config_parser, self.embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) self.gaze_loss = GazeLoss(config_parser, self.gazer) + self.mask_loss = MaskLoss(config_parser, self.parser) self.automatic_optimization = False - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: - output_tensor = self.generator(source_embedding, target_tensor) - return output_tensor + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: + output_tensor, mask_tensor = self.generator(source_embedding, target_tensor) + return output_tensor, mask_tensor def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) @@ -82,7 +85,7 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) - generator_output_tensor = self.generator(source_embedding, target_tensor) + generator_output_tensor, generator_mask_tensor = self.generator(source_embedding, target_tensor) generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) @@ -93,7 +96,8 @@ class FaceSwapperTrainer(LightningModule): identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + mask_loss, weighted_mask_loss = self.mask_loss(target_tensor, generator_mask_tensor) + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + weighted_mask_loss generator_optimizer.zero_grad() self.manual_backward(generator_loss) @@ -121,12 +125,13 @@ class FaceSwapperTrainer(LightningModule): self.log('identity_loss', identity_loss) self.log('pose_loss', pose_loss) self.log('gaze_loss', gaze_loss) + self.log('mask_loss', mask_loss) return generator_loss def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - output_tensor = self.generator(source_embedding, target_tensor) + output_tensor, mask_tensor = self.generator(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, prog_bar = True) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 9f0c526..bce6a9e 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -16,6 +16,7 @@ GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module +ParserModule : TypeAlias = Module OptimizerSet : TypeAlias = Any From 1659805b0874c50d232cac02e88441c0895d8d0c Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 13:24:39 +0530 Subject: [PATCH 297/394] changes --- face_swapper/README.md | 7 ++++ face_swapper/config.ini | 5 +++ face_swapper/src/models/generator.py | 2 +- face_swapper/src/models/loss.py | 4 +- face_swapper/src/networks/masknet.py | 57 ++++++++++++++++------------ 5 files changed, 47 insertions(+), 28 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index b7bf47f..3abe315 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -65,6 +65,13 @@ num_discriminators = 3 kernel_size = 4 ``` +``` +[training.model.masker] +input_channels = 67 +output_channels = 1 +base_channels = 16 +``` + ``` [training.losses] adversarial_weight = 1.0 diff --git a/face_swapper/config.ini b/face_swapper/config.ini index dc20073..088b6be 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -28,6 +28,11 @@ num_layers = num_discriminators = kernel_size = +[training.model.masker] +input_channels = +output_channels = +base_channels = + [training.losses] adversarial_weight = attribute_weight = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 84a9303..615dcd1 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -14,7 +14,7 @@ class Generator(nn.Module): super().__init__() self.encoder = UNet(config_parser) self.generator = AAD(config_parser) - self.masker = MaskNet(67, 1, 16) + self.masker = MaskNet(config_parser) self.encoder.apply(init_weight) self.generator.apply(init_weight) self.masker.apply(init_weight) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 3341d81..aec44e3 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -195,12 +195,12 @@ class MaskLoss(nn.Module): def calc_mask(self, target_tensor : Tensor) -> Tensor: target_tensor = torch.nn.functional.interpolate(target_tensor, (512, 512), mode = 'bilinear') - face_indices = torch.tensor([ 1, 2, 3, 4, 5, 10, 11, 12, 13 ]).to(target_tensor.device) + face_mask_regions = torch.tensor([ 1, 2, 3, 4, 5, 10, 11, 12, 13 ]).to(target_tensor.device) with torch.no_grad(): output_tensor = self.parser(target_tensor)[0] output_tensor = output_tensor.argmax(1) - output_tensor = torch.isin(output_tensor, face_indices).to(target_tensor.dtype) + output_tensor = torch.isin(output_tensor, face_mask_regions).to(target_tensor.dtype) output_tensor = output_tensor.view(-1, 1, 512, 512) output_tensor = torch.nn.functional.interpolate(output_tensor, (self.config_output_size, self.config_output_size), mode = 'bilinear') return output_tensor diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index a7d83d4..859597f 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -1,62 +1,69 @@ +from configparser import ConfigParser + import torch from torch import Tensor, nn class MaskNet(nn.Module): - def __init__(self, input_channels : int, output_channels : int, base_channels : int): + def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.down_samples = self.create_down_samples(input_channels, base_channels) - self.up_samples = self.create_up_samples(base_channels) - self.bottleneck = ResBlock(base_channels * 4) - self.conv = nn.Conv2d(base_channels, output_channels, kernel_size = 1) + self.config_input_channels = config_parser.getint('training.model.masker', 'input_channels') + self.config_output_channels = config_parser.getint('training.model.masker', 'output_channels') + self.config_base_channels = config_parser.getint('training.model.masker', 'base_channels') + self.down_samples = self.create_down_samples(self.config_input_channels, self.config_base_channels) + self.up_samples = self.create_up_samples(self.config_base_channels) + self.bottleneck = BottleNeck(self.config_base_channels * 4) + self.conv = nn.Conv2d(self.config_base_channels, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() def create_down_samples(self, input_channels : int, base_channels: int) -> nn.ModuleList: down_samples = nn.ModuleList( - [ - DownSample(input_channels, base_channels), - DownSample(base_channels, base_channels * 2), - DownSample(base_channels * 2, base_channels * 4) - ]) + [ + DownSample(input_channels, base_channels), + DownSample(base_channels, base_channels * 2), + DownSample(base_channels * 2, base_channels * 4) + ]) return down_samples def create_up_samples(self, base_channels : int) -> nn.ModuleList: down_samples = nn.ModuleList( - [ - UpSample(base_channels * 4, base_channels * 2), - UpSample(base_channels * 2, base_channels), - UpSample(base_channels, base_channels) - ]) + [ + UpSample(base_channels * 4, base_channels * 2), + UpSample(base_channels * 2, base_channels), + UpSample(base_channels, base_channels) + ]) return down_samples def forward(self, target_tensor : Tensor, target_attribute : Tensor) -> Tensor: - output_tensor = torch.cat([ target_tensor, target_attribute ], dim=1) + output_tensor = torch.cat([ target_tensor, target_attribute ], dim = 1) for down_sample in self.down_samples: output_tensor = down_sample(output_tensor) + output_tensor = self.bottleneck(output_tensor) for up_sample in self.up_samples: output_tensor = up_sample(output_tensor) + output_tensor = self.conv(output_tensor) output_tensor = self.activation(output_tensor) return output_tensor -class ResBlock(nn.Module): - def __init__(self, channels: int): +class BottleNeck(nn.Module): + def __init__(self, channels : int): super().__init__() self.conv = nn.Sequential( - nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False), + nn.Conv2d(channels, channels, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(channels), - nn.ReLU(inplace=True), - nn.Conv2d(channels, channels, kernel_size=3, padding=1, bias=False), + nn.ReLU(inplace = True), + nn.Conv2d(channels, channels, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(channels), - nn.ReLU(inplace=True) + nn.ReLU(inplace = True) ) - self.relu = nn.ReLU(inplace=True) + self.relu = nn.ReLU(inplace = True) - def forward(self, input_tensor: Tensor) -> Tensor: + def forward(self, input_tensor : Tensor) -> Tensor: output_tensor = self.conv(input_tensor) + input_tensor output_tensor = self.relu(output_tensor) return output_tensor @@ -66,7 +73,7 @@ class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() self.conv_transpose = nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 2, stride = 2) - self.relu = nn.ReLU(inplace=True) + self.relu = nn.ReLU(inplace = True) def forward(self, input_tensor : Tensor) -> Tensor: output_tensor = self.conv_transpose(input_tensor) From a3ac4d5ddd219307f268fd0445f2310a221298c4 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 13:43:52 +0530 Subject: [PATCH 298/394] changes --- face_swapper/README.md | 1 - face_swapper/config.ini | 1 - face_swapper/src/models/generator.py | 9 ++----- face_swapper/src/models/loss.py | 6 ++--- face_swapper/src/training.py | 35 ++++++++++++++++++++++------ 5 files changed, 32 insertions(+), 20 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 3abe315..be3f497 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -81,7 +81,6 @@ identity_weight = 20.0 gaze_weight = 0.0 pose_weight = 0.0 expression_weight = 0.0 -mask_weight = 1.0 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 088b6be..022c809 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -41,7 +41,6 @@ identity_weight = gaze_weight = pose_weight = expression_weight = -mask_weight = [training.trainer] learning_rate = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 615dcd1..d02be0b 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,10 +1,8 @@ from configparser import ConfigParser -from typing import Tuple from torch import Tensor, nn from ..networks.aad import AAD -from ..networks.masknet import MaskNet from ..networks.unet import UNet from ..types import Attributes, Embedding @@ -14,16 +12,13 @@ class Generator(nn.Module): super().__init__() self.encoder = UNet(config_parser) self.generator = AAD(config_parser) - self.masker = MaskNet(config_parser) self.encoder.apply(init_weight) self.generator.apply(init_weight) - self.masker.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: target_attributes = self.get_attributes(target_tensor) output_tensor = self.generator(source_embedding, target_attributes) - mask_tensor = self.masker(target_tensor, target_attributes[-1]) - return output_tensor, mask_tensor + return output_tensor def get_attributes(self, input_tensor : Tensor) -> Attributes: return self.encoder(input_tensor) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index aec44e3..7e8f871 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -180,18 +180,16 @@ class GazeLoss(nn.Module): class MaskLoss(nn.Module): def __init__(self, config_parser : ConfigParser, parser : ParserModule) -> None: super().__init__() - self.config_mask_weight = config_parser.getfloat('training.losses', 'mask_weight') self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.parser = parser self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: target_mask = self.calc_mask(target_tensor) target_mask = target_mask.view(-1, self.config_output_size, self.config_output_size) mask_tensor = mask_tensor.view(-1, self.config_output_size, self.config_output_size) mask_loss = self.mse_loss(target_mask, mask_tensor) - weighted_mask_loss = mask_loss * self.config_mask_weight - return mask_loss, weighted_mask_loss + return mask_loss def calc_mask(self, target_tensor : Tensor) -> Tensor: target_tensor = torch.nn.functional.interpolate(target_tensor, (512, 512), mode = 'bilinear') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ada30c0..6ba9604 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,6 +17,7 @@ from .helper import calc_embedding from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .networks.masknet import MaskNet from .types import Batch, Embedding, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -40,6 +41,7 @@ class FaceSwapperTrainer(LightningModule): self.parser = torch.jit.load(self.config_parser_path, map_location = 'cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) + self.masker = MaskNet(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) self.attribute_loss = AttributeLoss(config_parser) @@ -54,11 +56,13 @@ class FaceSwapperTrainer(LightningModule): output_tensor, mask_tensor = self.generator(source_embedding, target_tensor) return output_tensor, mask_tensor - def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: + def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet, OptimizerSet]: generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) + masker_optimizer = torch.optim.AdamW(self.masker.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) + masker_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(masker_optimizer, T_0 = 300, T_mult = 2) generator_config =\ { @@ -78,14 +82,23 @@ class FaceSwapperTrainer(LightningModule): 'interval': 'step' } } - return generator_config, discriminator_config + masker_config =\ + { + 'optimizer': masker_optimizer, + 'lr_scheduler': + { + 'scheduler': masker_scheduler, + 'interval': 'step' + } + } + return generator_config, discriminator_config, masker_config def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch - generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] + generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) - generator_output_tensor, generator_mask_tensor = self.generator(source_embedding, target_tensor) + generator_output_tensor = self.generator(source_embedding, target_tensor) generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) @@ -96,14 +109,22 @@ class FaceSwapperTrainer(LightningModule): identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - mask_loss, weighted_mask_loss = self.mask_loss(target_tensor, generator_mask_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + weighted_mask_loss + generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss generator_optimizer.zero_grad() self.manual_backward(generator_loss) generator_optimizer.step() self.untoggle_optimizer(generator_optimizer) + self.toggle_optimizer(masker_optimizer) + mask_tensor = self.masker(target_tensor, target_attributes[-1].detach()) + mask_loss = self.mask_loss(target_tensor, mask_tensor) + + masker_optimizer.zero_grad() + self.manual_backward(mask_loss) + masker_optimizer.step() + self.untoggle_optimizer(masker_optimizer) + self.toggle_optimizer(discriminator_optimizer) discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) @@ -131,7 +152,7 @@ class FaceSwapperTrainer(LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - output_tensor, mask_tensor = self.generator(source_embedding, target_tensor) + output_tensor = self.generator(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, prog_bar = True) From 606bf42089b549a592c088836016ebd321e31f4c Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 13:49:50 +0530 Subject: [PATCH 299/394] fix --- face_swapper/config.ini | 1 + face_swapper/src/networks/masknet.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 022c809..881f72e 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -14,6 +14,7 @@ split_ratio = embedder_path = gazer_path = motion_extractor_path = +parser_path = [training.model.generator] identity_channels = diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 859597f..7d26633 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -46,7 +46,7 @@ class MaskNet(nn.Module): output_tensor = up_sample(output_tensor) output_tensor = self.conv(output_tensor) - output_tensor = self.activation(output_tensor) + output_tensor = self.sigmoid(output_tensor) return output_tensor From 64464b6f1c698a9d5ce36d193c1c22052ecdcd2c Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 13:50:45 +0530 Subject: [PATCH 300/394] fix --- face_swapper/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/README.md b/face_swapper/README.md index be3f497..67a1bd9 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -46,6 +46,7 @@ split_ratio = 0.9995 embedder_path = .models/arcface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt +parser_path .models/parser.pt ``` ``` From d2efb2fd08026475071e326293529d87bc7e231e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 10 Mar 2025 11:04:24 +0100 Subject: [PATCH 301/394] Fix README.md --- face_swapper/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 67a1bd9..d46b69c 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -46,7 +46,7 @@ split_ratio = 0.9995 embedder_path = .models/arcface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt -parser_path .models/parser.pt +parser_path = .models/parser.pt ``` ``` From f9d105ea2b05789b09153c674391233ec865ec70 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 10 Mar 2025 11:09:50 +0100 Subject: [PATCH 302/394] Some code review --- face_swapper/src/networks/masknet.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 7d26633..5b8305d 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -16,23 +16,23 @@ class MaskNet(nn.Module): self.conv = nn.Conv2d(self.config_base_channels, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() - def create_down_samples(self, input_channels : int, base_channels: int) -> nn.ModuleList: - down_samples = nn.ModuleList( + @staticmethod + def create_down_samples(input_channels : int, base_channels : int) -> nn.ModuleList: + return nn.ModuleList( [ DownSample(input_channels, base_channels), DownSample(base_channels, base_channels * 2), DownSample(base_channels * 2, base_channels * 4) ]) - return down_samples - def create_up_samples(self, base_channels : int) -> nn.ModuleList: - down_samples = nn.ModuleList( + @staticmethod + def create_up_samples(base_channels : int) -> nn.ModuleList: + return nn.ModuleList( [ UpSample(base_channels * 4, base_channels * 2), UpSample(base_channels * 2, base_channels), UpSample(base_channels, base_channels) ]) - return down_samples def forward(self, target_tensor : Tensor, target_attribute : Tensor) -> Tensor: output_tensor = torch.cat([ target_tensor, target_attribute ], dim = 1) @@ -51,14 +51,14 @@ class MaskNet(nn.Module): class BottleNeck(nn.Module): - def __init__(self, channels : int): + def __init__(self, base_channels : int): super().__init__() self.conv = nn.Sequential( - nn.Conv2d(channels, channels, kernel_size = 3, padding = 1, bias = False), - nn.BatchNorm2d(channels), + nn.Conv2d(base_channels, base_channels, kernel_size = 3, padding = 1, bias = False), + nn.BatchNorm2d(base_channels), nn.ReLU(inplace = True), - nn.Conv2d(channels, channels, kernel_size = 3, padding = 1, bias = False), - nn.BatchNorm2d(channels), + nn.Conv2d(base_channels, base_channels, kernel_size = 3, padding = 1, bias = False), + nn.BatchNorm2d(base_channels), nn.ReLU(inplace = True) ) self.relu = nn.ReLU(inplace = True) From c2a639229fa756cca9c16d64778992d2b87703cb Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 22:54:23 +0530 Subject: [PATCH 303/394] changes --- face_swapper/src/networks/masknet.py | 49 +++++++++++++++------------- face_swapper/tests/test_networks.py | 24 ++++++++++++++ 2 files changed, 50 insertions(+), 23 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 5b8305d..e2ddfab 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -9,29 +9,29 @@ class MaskNet(nn.Module): super().__init__() self.config_input_channels = config_parser.getint('training.model.masker', 'input_channels') self.config_output_channels = config_parser.getint('training.model.masker', 'output_channels') - self.config_base_channels = config_parser.getint('training.model.masker', 'base_channels') - self.down_samples = self.create_down_samples(self.config_input_channels, self.config_base_channels) - self.up_samples = self.create_up_samples(self.config_base_channels) - self.bottleneck = BottleNeck(self.config_base_channels * 4) - self.conv = nn.Conv2d(self.config_base_channels, self.config_output_channels, kernel_size = 1) + self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters') + self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters) + self.up_samples = self.create_up_samples(self.config_num_filters) + self.bottleneck = BottleNeck(self.config_num_filters * 4) + self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() @staticmethod - def create_down_samples(input_channels : int, base_channels : int) -> nn.ModuleList: + def create_down_samples(input_channels : int, num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ - DownSample(input_channels, base_channels), - DownSample(base_channels, base_channels * 2), - DownSample(base_channels * 2, base_channels * 4) + DownSample(input_channels, num_filters), + DownSample(num_filters, num_filters * 2), + DownSample(num_filters * 2, num_filters * 4) ]) @staticmethod - def create_up_samples(base_channels : int) -> nn.ModuleList: + def create_up_samples(num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ - UpSample(base_channels * 4, base_channels * 2), - UpSample(base_channels * 2, base_channels), - UpSample(base_channels, base_channels) + UpSample(num_filters * 4, num_filters * 2), + UpSample(num_filters * 2, num_filters), + UpSample(num_filters, num_filters) ]) def forward(self, target_tensor : Tensor, target_attribute : Tensor) -> Tensor: @@ -51,20 +51,23 @@ class MaskNet(nn.Module): class BottleNeck(nn.Module): - def __init__(self, base_channels : int): + def __init__(self, num_filters : int): super().__init__() - self.conv = nn.Sequential( - nn.Conv2d(base_channels, base_channels, kernel_size = 3, padding = 1, bias = False), - nn.BatchNorm2d(base_channels), - nn.ReLU(inplace = True), - nn.Conv2d(base_channels, base_channels, kernel_size = 3, padding = 1, bias = False), - nn.BatchNorm2d(base_channels), - nn.ReLU(inplace = True) - ) + self.layers = self.create_layers(num_filters) self.relu = nn.ReLU(inplace = True) + def create_layers(self, num_filters : int) -> nn.Sequential: + return nn.Sequential( + nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), + nn.BatchNorm2d(num_filters), + nn.ReLU(inplace = True), + nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), + nn.BatchNorm2d(num_filters), + nn.ReLU(inplace = True) + ) + def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.conv(input_tensor) + input_tensor + output_tensor = self.layers(input_tensor) + input_tensor output_tensor = self.relu(output_tensor) return output_tensor diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 96f1c6a..7ad12dc 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -4,6 +4,7 @@ import pytest import torch from face_swapper.src.networks.aad import AAD +from face_swapper.src.networks.masknet import MaskNet from face_swapper.src.networks.unet import UNet @@ -31,3 +32,26 @@ def test_aad_with_unet(output_size : int) -> None: output_tensor = generator(source_tensor, target_attributes) assert output_tensor.shape == (1, 3, output_size, output_size) + + +@pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) +def test_mask_net(output_size : int) -> None: + config_parser = ConfigParser() + config_parser.read_dict( + { + 'training.model.masker': + { + 'input_channels': '67', + 'output_channels': '1', + 'num_filters': '16', + } + }) + + masker = MaskNet(config_parser).eval() + + target_tensor = torch.randn(1, 3, output_size, output_size) + target_attribute = torch.randn(1, 64, output_size, output_size) + + output_tensor = masker(target_tensor, target_attribute) + + assert output_tensor.shape == (1, 1, output_size, output_size) From 27440ef023bc16c7247cf4569070f52c138bf427 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 22:54:49 +0530 Subject: [PATCH 304/394] changes --- face_swapper/config.ini | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 881f72e..07d282d 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -32,7 +32,7 @@ kernel_size = [training.model.masker] input_channels = output_channels = -base_channels = +num_filters = [training.losses] adversarial_weight = From e267f9ffd59efc92f1bcb05ed7f649af19e17161 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 10 Mar 2025 22:59:23 +0530 Subject: [PATCH 305/394] reduce layer --- face_swapper/src/networks/masknet.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index e2ddfab..8e5dad5 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -12,7 +12,7 @@ class MaskNet(nn.Module): self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters') self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters) self.up_samples = self.create_up_samples(self.config_num_filters) - self.bottleneck = BottleNeck(self.config_num_filters * 4) + self.bottleneck = BottleNeck(self.config_num_filters * 2) self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() @@ -22,14 +22,12 @@ class MaskNet(nn.Module): [ DownSample(input_channels, num_filters), DownSample(num_filters, num_filters * 2), - DownSample(num_filters * 2, num_filters * 4) ]) @staticmethod def create_up_samples(num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ - UpSample(num_filters * 4, num_filters * 2), UpSample(num_filters * 2, num_filters), UpSample(num_filters, num_filters) ]) From 54923abf7f5a4fa9757a4218c9cbeafb5d34f0c1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 10 Mar 2025 18:46:25 +0100 Subject: [PATCH 306/394] Follow layers and sequences pattern --- face_swapper/README.md | 2 +- face_swapper/src/networks/masknet.py | 11 +++++++---- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index d46b69c..098755e 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -70,7 +70,7 @@ kernel_size = 4 [training.model.masker] input_channels = 67 output_channels = 1 -base_channels = 16 +num_filters = 16 ``` ``` diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 8e5dad5..03b9940 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -52,20 +52,23 @@ class BottleNeck(nn.Module): def __init__(self, num_filters : int): super().__init__() self.layers = self.create_layers(num_filters) + self.sequences = nn.Sequential(*self.layers) self.relu = nn.ReLU(inplace = True) - def create_layers(self, num_filters : int) -> nn.Sequential: - return nn.Sequential( + @staticmethod + def create_layers(num_filters : int) -> nn.ModuleList: + return nn.ModuleList( + [ nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(num_filters), nn.ReLU(inplace = True), nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(num_filters), nn.ReLU(inplace = True) - ) + ]) def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.layers(input_tensor) + input_tensor + output_tensor = self.sequences(input_tensor) + input_tensor output_tensor = self.relu(output_tensor) return output_tensor From 7f16d0a10eab8a04c6efbbf6a6dee2d9e0b2b6c9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 10 Mar 2025 18:50:54 +0100 Subject: [PATCH 307/394] Follow sequences pattern --- face_swapper/src/networks/masknet.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 03b9940..bbe4e55 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -51,21 +51,19 @@ class MaskNet(nn.Module): class BottleNeck(nn.Module): def __init__(self, num_filters : int): super().__init__() - self.layers = self.create_layers(num_filters) - self.sequences = nn.Sequential(*self.layers) + self.sequences = self.create_sequences(num_filters) self.relu = nn.ReLU(inplace = True) @staticmethod - def create_layers(num_filters : int) -> nn.ModuleList: - return nn.ModuleList( - [ + def create_sequences(num_filters : int) -> nn.Sequential: + return nn.Sequential( nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(num_filters), nn.ReLU(inplace = True), nn.Conv2d(num_filters, num_filters, kernel_size = 3, padding = 1, bias = False), nn.BatchNorm2d(num_filters), nn.ReLU(inplace = True) - ]) + ) def forward(self, input_tensor : Tensor) -> Tensor: output_tensor = self.sequences(input_tensor) + input_tensor From 90cb6afe107f968942c3d8452a3016d4de58caa6 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 10 Mar 2025 18:53:37 +0100 Subject: [PATCH 308/394] Minor adjustment --- face_swapper/tests/test_networks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 7ad12dc..20c645e 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -43,7 +43,7 @@ def test_mask_net(output_size : int) -> None: { 'input_channels': '67', 'output_channels': '1', - 'num_filters': '16', + 'num_filters': '16' } }) From d809f6621698fdf672698e458ac725477763d147 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 12:48:44 +0530 Subject: [PATCH 309/394] changes --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/training.py | 23 +++++++++++++++++------ 3 files changed, 19 insertions(+), 6 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 098755e..e2b53f9 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -86,6 +86,7 @@ expression_weight = 0.0 ``` [training.trainer] +accumulate_size = 4 learning_rate = 0.0004 max_epochs = 50 precision = 16-mixed diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 07d282d..271cab6 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -44,6 +44,7 @@ pose_weight = expression_weight = [training.trainer] +accumulate_size = learning_rate = max_epochs = precision = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 6ba9604..7348d8e 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -33,6 +33,7 @@ class FaceSwapperTrainer(LightningModule): self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') self.config_parser_path = config_parser.get('training.model', 'parser_path') + self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() @@ -95,6 +96,7 @@ class FaceSwapperTrainer(LightningModule): def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch + is_accumulation_step = (batch_index + 1) % self.config_accumulate_size == 0 generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) @@ -111,18 +113,24 @@ class FaceSwapperTrainer(LightningModule): gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss - generator_optimizer.zero_grad() self.manual_backward(generator_loss) - generator_optimizer.step() + + if is_accumulation_step: + generator_optimizer.step() + generator_optimizer.zero_grad() + self.untoggle_optimizer(generator_optimizer) self.toggle_optimizer(masker_optimizer) mask_tensor = self.masker(target_tensor, target_attributes[-1].detach()) mask_loss = self.mask_loss(target_tensor, mask_tensor) - masker_optimizer.zero_grad() self.manual_backward(mask_loss) - masker_optimizer.step() + + if is_accumulation_step: + masker_optimizer.step() + masker_optimizer.zero_grad() + self.untoggle_optimizer(masker_optimizer) self.toggle_optimizer(discriminator_optimizer) @@ -130,9 +138,12 @@ class FaceSwapperTrainer(LightningModule): discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) - discriminator_optimizer.zero_grad() self.manual_backward(discriminator_loss) - discriminator_optimizer.step() + + if is_accumulation_step: + discriminator_optimizer.step() + discriminator_optimizer.zero_grad() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: From b6e131e4c1b835dce5b901a1169fa53d2c12b786 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 12:59:52 +0530 Subject: [PATCH 310/394] changes --- face_swapper/src/training.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7348d8e..11b95a1 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -96,7 +96,7 @@ class FaceSwapperTrainer(LightningModule): def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch - is_accumulation_step = (batch_index + 1) % self.config_accumulate_size == 0 + do_update = (batch_index + 1) % self.config_accumulate_size == 0 generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) @@ -115,7 +115,7 @@ class FaceSwapperTrainer(LightningModule): self.manual_backward(generator_loss) - if is_accumulation_step: + if do_update: generator_optimizer.step() generator_optimizer.zero_grad() @@ -127,7 +127,7 @@ class FaceSwapperTrainer(LightningModule): self.manual_backward(mask_loss) - if is_accumulation_step: + if do_update: masker_optimizer.step() masker_optimizer.zero_grad() @@ -140,7 +140,7 @@ class FaceSwapperTrainer(LightningModule): self.manual_backward(discriminator_loss) - if is_accumulation_step: + if do_update: discriminator_optimizer.step() discriminator_optimizer.zero_grad() From 2322b6539f5d36aaf8aab19dfda813297c8dd61a Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 14:10:39 +0530 Subject: [PATCH 311/394] changes --- face_swapper/src/training.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 11b95a1..f0b5cf7 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -147,7 +147,7 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: - self.generate_preview(source_tensor, target_tensor, generator_output_tensor) + self.generate_preview(source_tensor, target_tensor, generator_output_tensor, mask_tensor) self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) @@ -169,12 +169,13 @@ class FaceSwapperTrainer(LightningModule): self.log('validation_score', validation_score, prog_bar = True) return validation_score - def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> None: + def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, mask_tensor : Tensor) -> None: preview_limit = 8 preview_cells = [] + mask_tensor = (mask_tensor.repeat(1, 3, 1, 1) - 0.5) * 2 - for source_tensor, target_tensor, output_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit]): - preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor] , dim = 2) + for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], mask_tensor[:preview_limit]): + preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, mask_tensor] , dim = 2) preview_cells.append(preview_cell) preview_cells = torch.cat(preview_cells, dim = 1).unsqueeze(0) From 0304b5dd918de95a64aac5cff01ad2ba123ce433 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 09:52:23 +0100 Subject: [PATCH 312/394] Fix spacing --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f0b5cf7..7040725 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -175,7 +175,7 @@ class FaceSwapperTrainer(LightningModule): mask_tensor = (mask_tensor.repeat(1, 3, 1, 1) - 0.5) * 2 for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], mask_tensor[:preview_limit]): - preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, mask_tensor] , dim = 2) + preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, mask_tensor ], dim = 2) preview_cells.append(preview_cell) preview_cells = torch.cat(preview_cells, dim = 1).unsqueeze(0) From 52b98b5be5af373ad0ade5c11d496cb1c223b2c0 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 17:37:00 +0530 Subject: [PATCH 313/394] change mask preview --- face_swapper/src/helper.py | 8 ++++++++ face_swapper/src/training.py | 4 ++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 8ea5b3a..7fc5e9e 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -36,3 +36,11 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P embedding = embedder(crop_tensor) embedding = nn.functional.normalize(embedding, p = 2) return embedding + + +def overlay_mask(target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: + color_tensor = torch.zeros(*list(target_tensor.shape), dtype = target_tensor.dtype, device = target_tensor.device) + color_tensor[:, 2, :, :] = 1 + mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) + output_tensor = target_tensor * (1 - mask_tensor) + color_tensor * mask_tensor + return output_tensor diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7040725..7bda57b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -13,7 +13,7 @@ from torch.utils.data import Dataset, random_split from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import DynamicDataset -from .helper import calc_embedding +from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss @@ -172,7 +172,7 @@ class FaceSwapperTrainer(LightningModule): def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, mask_tensor : Tensor) -> None: preview_limit = 8 preview_cells = [] - mask_tensor = (mask_tensor.repeat(1, 3, 1, 1) - 0.5) * 2 + mask_tensor = overlay_mask(target_tensor, mask_tensor) for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], mask_tensor[:preview_limit]): preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, mask_tensor ], dim = 2) From a7a21cd684e7d9859c99339ffe77984b93004623 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 17:42:29 +0530 Subject: [PATCH 314/394] changes --- face_swapper/src/helper.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 7fc5e9e..015ba43 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -39,7 +39,7 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P def overlay_mask(target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: - color_tensor = torch.zeros(*list(target_tensor.shape), dtype = target_tensor.dtype, device = target_tensor.device) + color_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) color_tensor[:, 2, :, :] = 1 mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) output_tensor = target_tensor * (1 - mask_tensor) + color_tensor * mask_tensor From 66d1573f4bb7ce4c9c75ac8c0726579610cd0a0c Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 17:43:08 +0530 Subject: [PATCH 315/394] changes --- face_swapper/src/helper.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 015ba43..0e223bc 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -39,8 +39,8 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P def overlay_mask(target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: - color_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) - color_tensor[:, 2, :, :] = 1 + temp_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) + temp_tensor[:, 2, :, :] = 1 mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) - output_tensor = target_tensor * (1 - mask_tensor) + color_tensor * mask_tensor + output_tensor = target_tensor * (1 - mask_tensor) + temp_tensor * mask_tensor return output_tensor From 8f76d96bb2d4b8f39067a6b106e27a2093b68493 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 13:17:01 +0100 Subject: [PATCH 316/394] Name color tensor -> overlay tensor --- face_swapper/src/helper.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 0e223bc..352cfd4 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -39,8 +39,8 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P def overlay_mask(target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: - temp_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) - temp_tensor[:, 2, :, :] = 1 + overlay_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) + overlay_tensor[:, 2, :, :] = 1 mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) - output_tensor = target_tensor * (1 - mask_tensor) + temp_tensor * mask_tensor + output_tensor = target_tensor * (1 - mask_tensor) + overlay_tensor * mask_tensor return output_tensor From f90fd73b54c4555eab55f30dd8e8b24f54fdbc63 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 18:16:36 +0530 Subject: [PATCH 317/394] add strategy to config --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/training.py | 2 ++ 3 files changed, 4 insertions(+) diff --git a/face_swapper/README.md b/face_swapper/README.md index e2b53f9..33f3e6d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -91,6 +91,7 @@ learning_rate = 0.0004 max_epochs = 50 precision = 16-mixed preview_frequency = 250 +strategy = auto ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 271cab6..1566739 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -49,6 +49,7 @@ learning_rate = max_epochs = precision = preview_frequency = +strategy = [training.output] directory_path = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7bda57b..a79d142 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -206,6 +206,7 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T def create_trainer() -> Trainer: config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') config_precision = CONFIG_PARSER.get('training.trainer', 'precision') + config_strategy = CONFIG_PARSER.get('training.trainer', 'strategy') config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') logger = TensorBoardLogger('.logs', name = 'face_swapper') @@ -215,6 +216,7 @@ def create_trainer() -> Trainer: log_every_n_steps = 10, max_epochs = config_max_epochs, precision = config_precision, + strategy = config_strategy, callbacks = [ ModelCheckpoint( From e758eb3e196fb0d21cf049efcb2de99c016442b7 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 08:04:14 +0100 Subject: [PATCH 318/394] Use sequential whenever possible --- face_swapper/src/models/discriminator.py | 2 +- face_swapper/src/models/loss.py | 5 +++- face_swapper/src/networks/aad.py | 14 +++++++--- face_swapper/src/networks/masknet.py | 34 +++++++++++++++--------- face_swapper/src/networks/unet.py | 32 +++++++++++++--------- 5 files changed, 56 insertions(+), 31 deletions(-) diff --git a/face_swapper/src/models/discriminator.py b/face_swapper/src/models/discriminator.py index c36b82b..b44305d 100644 --- a/face_swapper/src/models/discriminator.py +++ b/face_swapper/src/models/discriminator.py @@ -11,8 +11,8 @@ class Discriminator(nn.Module): super().__init__() self.config_num_discriminators = config_parser.getint('training.model.discriminator', 'num_discriminators') self.config_parser = config_parser - self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) self.discriminators = self.create_discriminators() + self.avg_pool = nn.AvgPool2d(kernel_size = 3, stride = 2, padding = (1, 1), count_include_pad = False) def create_discriminators(self) -> nn.ModuleList: discriminators = nn.ModuleList() diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 7e8f871..ac53975 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -75,10 +75,10 @@ class ReconstructionLoss(nn.Module): self.mse_loss = nn.MSELoss() def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: - with torch.no_grad(): source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) + has_similar_identity = torch.cosine_similarity(source_embedding, target_embedding) > 0.8 reconstruction_loss = torch.mean((source_tensor - target_tensor) ** 2, dim = (1, 2, 3)) @@ -141,6 +141,7 @@ class MotionLoss(nn.Module): with torch.no_grad(): pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) + rotation = torch.cat([ pitch, yaw, roll ], dim = 1) pose = translation, scale, rotation, motion_points return pose, expression @@ -174,6 +175,7 @@ class GazeLoss(nn.Module): with torch.no_grad(): pitch, yaw = self.gazer(crop_tensor) + return pitch, yaw @@ -201,4 +203,5 @@ class MaskLoss(nn.Module): output_tensor = torch.isin(output_tensor, face_mask_regions).to(target_tensor.dtype) output_tensor = output_tensor.view(-1, 1, 512, 512) output_tensor = torch.nn.functional.interpolate(output_tensor, (self.config_output_size, self.config_output_size), mode = 'bilinear') + return output_tensor diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index a912319..aeb1969 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -162,10 +162,16 @@ class FeatureModulation(nn.Module): class PixelShuffleUpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1) - self.pixel_shuffle = nn.PixelShuffle(upscale_factor = 2) + self.sequences = self.create_sequences(input_channels, output_channels) + + @staticmethod + def create_sequences(input_channels : int, output_channels : int) -> nn.Sequential: + return nn.Sequential( + nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1), + nn.PixelShuffle(upscale_factor = 2) + ) def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.conv(input_tensor.view(input_tensor.shape[0], -1, 1, 1)) - output_tensor = self.pixel_shuffle(output_tensor) + temp_tensor = input_tensor.view(input_tensor.shape[0], -1, 1, 1) + output_tensor = self.sequences(temp_tensor) return output_tensor diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index bbe4e55..0a207aa 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -21,7 +21,7 @@ class MaskNet(nn.Module): return nn.ModuleList( [ DownSample(input_channels, num_filters), - DownSample(num_filters, num_filters * 2), + DownSample(num_filters, num_filters * 2) ]) @staticmethod @@ -74,26 +74,34 @@ class BottleNeck(nn.Module): class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv_transpose = nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 2, stride = 2) - self.relu = nn.ReLU(inplace = True) + self.sequences = self.create_sequences(input_channels, output_channels) + + @staticmethod + def create_sequences(input_channels : int, output_channels : int) -> nn.Sequential: + return nn.Sequential( + nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 2, stride = 2), + nn.ReLU(inplace = True) + ) def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.conv_transpose(input_tensor) - output_tensor = self.relu(output_tensor) + output_tensor = self.sequences(input_tensor) return output_tensor class DownSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.relu = nn.ReLU(inplace = True) - self.max_pool = nn.MaxPool2d(2) + self.sequences = self.create_sequences(input_channels, output_channels) + + @staticmethod + def create_sequences(input_channels : int, output_channels : int) -> nn.Sequential: + return nn.Sequential( + nn.Conv2d(input_channels, output_channels, kernel_size = 3, padding = 1, bias = False), + nn.BatchNorm2d(output_channels), + nn.ReLU(inplace = True), + nn.MaxPool2d(2) + ) def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.conv(input_tensor) - output_tensor = self.batch_norm(output_tensor) - output_tensor = self.relu(output_tensor) - output_tensor = self.max_pool(output_tensor) + output_tensor = self.sequences(input_tensor) return output_tensor diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 4aaa653..1a27fe4 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -103,14 +103,18 @@ class UNet(nn.Module): class UpSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv_transpose = nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + self.sequences = self.create_sequences(input_channels, output_channels) + + @staticmethod + def create_sequences(input_channels : int, output_channels : int) -> nn.Sequential: + return nn.Sequential( + nn.ConvTranspose2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False), + nn.BatchNorm2d(output_channels), + nn.LeakyReLU(0.1, inplace = True) + ) def forward(self, input_tensor : Tensor, skip_tensor : Tensor) -> Tensor: - output_tensor = self.conv_transpose(input_tensor) - output_tensor = self.batch_norm(output_tensor) - output_tensor = self.leaky_relu(output_tensor) + output_tensor = self.sequences(input_tensor) output_tensor = torch.cat((output_tensor, skip_tensor), dim = 1) return output_tensor @@ -118,12 +122,16 @@ class UpSample(nn.Module): class DownSample(nn.Module): def __init__(self, input_channels : int, output_channels : int) -> None: super().__init__() - self.conv = nn.Conv2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False) - self.batch_norm = nn.BatchNorm2d(output_channels) - self.leaky_relu = nn.LeakyReLU(0.1, inplace = True) + self.sequences = self.create_sequences(input_channels, output_channels) + + @staticmethod + def create_sequences(input_channels : int, output_channels : int) -> nn.Sequential: + return nn.Sequential( + nn.Conv2d(input_channels, output_channels, kernel_size = 4, stride = 2, padding = 1, bias = False), + nn.BatchNorm2d(output_channels), + nn.LeakyReLU(0.1, inplace = True) + ) def forward(self, input_tensor : Tensor) -> Tensor: - output_tensor = self.conv(input_tensor) - output_tensor = self.batch_norm(output_tensor) - output_tensor = self.leaky_relu(output_tensor) + output_tensor = self.sequences(input_tensor) return output_tensor From afab997ffca6687f5ed7df011778b5d12c83cc95 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 12:51:32 +0100 Subject: [PATCH 319/394] Rename parser to face parser --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/models/loss.py | 8 ++++---- face_swapper/src/training.py | 6 +++--- face_swapper/src/types.py | 2 +- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 33f3e6d..5776b31 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -46,7 +46,7 @@ split_ratio = 0.9995 embedder_path = .models/arcface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt -parser_path = .models/parser.pt +face_parser_path = .models/face_parser.pt ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 1566739..1ff4100 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -14,7 +14,7 @@ split_ratio = embedder_path = gazer_path = motion_extractor_path = -parser_path = +face_parser_path = [training.model.generator] identity_channels = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ac53975..ec84563 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule, ParserModule +from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule, FaceParserModule class DiscriminatorLoss(nn.Module): @@ -180,10 +180,10 @@ class GazeLoss(nn.Module): class MaskLoss(nn.Module): - def __init__(self, config_parser : ConfigParser, parser : ParserModule) -> None: + def __init__(self, config_parser : ConfigParser, face_parser : FaceParserModule) -> None: super().__init__() self.config_output_size = config_parser.getint('training.model.generator', 'output_size') - self.parser = parser + self.face_parser = face_parser self.mse_loss = nn.MSELoss() def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: @@ -198,7 +198,7 @@ class MaskLoss(nn.Module): face_mask_regions = torch.tensor([ 1, 2, 3, 4, 5, 10, 11, 12, 13 ]).to(target_tensor.device) with torch.no_grad(): - output_tensor = self.parser(target_tensor)[0] + output_tensor = self.face_parser(target_tensor)[0] output_tensor = output_tensor.argmax(1) output_tensor = torch.isin(output_tensor, face_mask_regions).to(target_tensor.dtype) output_tensor = output_tensor.view(-1, 1, 512, 512) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a79d142..0f8ff94 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -32,14 +32,14 @@ class FaceSwapperTrainer(LightningModule): self.config_embedder_path = config_parser.get('training.model', 'embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') - self.config_parser_path = config_parser.get('training.model', 'parser_path') + self.config_face_parser_path = config_parser.get('training.model', 'parser_path') self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() - self.parser = torch.jit.load(self.config_parser_path, map_location = 'cpu').eval() + self.face_parser = torch.jit.load(self.config_face_parser_path, map_location ='cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) self.masker = MaskNet(config_parser) @@ -50,7 +50,7 @@ class FaceSwapperTrainer(LightningModule): self.identity_loss = IdentityLoss(config_parser, self.embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) self.gaze_loss = GazeLoss(config_parser, self.gazer) - self.mask_loss = MaskLoss(config_parser, self.parser) + self.mask_loss = MaskLoss(config_parser, self.face_parser) self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index bce6a9e..27708b7 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -16,7 +16,7 @@ GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module -ParserModule : TypeAlias = Module +FaceParserModule : TypeAlias = Module OptimizerSet : TypeAlias = Any From 09432d9214611577d8c3eded54856f9bd782c577 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 12:54:35 +0100 Subject: [PATCH 320/394] Rename parser to face parser --- face_swapper/src/models/loss.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ec84563..5c6a33a 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, Gaze, GazerModule, MotionExtractorModule, FaceParserModule +from ..types import Attributes, EmbedderModule, FaceParserModule, Gaze, GazerModule, MotionExtractorModule class DiscriminatorLoss(nn.Module): From af09ee7ff3c0c020e32082772674e9b99dbcac65 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 14:39:47 +0100 Subject: [PATCH 321/394] Fix config --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 0f8ff94..3d25597 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -32,7 +32,7 @@ class FaceSwapperTrainer(LightningModule): self.config_embedder_path = config_parser.get('training.model', 'embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') - self.config_face_parser_path = config_parser.get('training.model', 'parser_path') + self.config_face_parser_path = config_parser.get('training.model', 'face_parser_path') self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') From 4f67e045a07d8ecef18cef4acaff0f56a6347ccd Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 19:37:05 +0530 Subject: [PATCH 322/394] move generator optimizer toggle --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 3d25597..927510b 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -98,13 +98,13 @@ class FaceSwapperTrainer(LightningModule): source_tensor, target_tensor = batch do_update = (batch_index + 1) % self.config_accumulate_size == 0 generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] + + self.toggle_optimizer(generator_optimizer) source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_attributes = self.generator.get_attributes(target_tensor) generator_output_tensor = self.generator(source_embedding, target_tensor) generator_output_attributes = self.generator.get_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) - - self.toggle_optimizer(generator_optimizer) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) From 99931def84e02798b47f148518e9362e5606b0e8 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 22:57:08 +0530 Subject: [PATCH 323/394] fix --- face_swapper/src/exporting.py | 8 ++++---- face_swapper/src/training.py | 7 ++++++- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index ae0cc4d..3241ad3 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -14,13 +14,13 @@ def export() -> None: config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path') config_source_path = CONFIG_PARSER.get('exporting', 'source_path') config_target_path = CONFIG_PARSER.get('exporting', 'target_path') - config_target_size = CONFIG_PARSER.getint('exporting', 'target_size') + config_output_size = CONFIG_PARSER.getint('training.model.generator', 'output_size') config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version') config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') os.makedirs(config_directory_path, exist_ok = True) - model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, map_location = 'cpu').eval() + model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, config_parser = CONFIG_PARSER, map_location = 'cpu').eval() model.ir_version = torch.tensor(config_ir_version) source_tensor = torch.randn(1, 512) - target_tensor = torch.randn(1, 3, config_target_size, config_target_size) - torch.onnx.export(model, (source_tensor, target_tensor), config_target_path, input_names = [ 'source', 'target' ], output_names = [ 'output' ], opset_version = config_opset_version) + target_tensor = torch.randn(1, 3, config_output_size, config_output_size) + torch.onnx.export(model, (source_tensor, target_tensor), config_target_path, input_names = [ 'source', 'target' ], output_names = [ 'output', 'mask' ], opset_version = config_opset_version) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 927510b..1c8f1ee 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -54,7 +54,12 @@ class FaceSwapperTrainer(LightningModule): self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: - output_tensor, mask_tensor = self.generator(source_embedding, target_tensor) + + with torch.no_grad(): + output_tensor = self.generator(source_embedding, target_tensor) + target_attributes = self.generator.get_attributes(target_tensor) + mask_tensor = self.masker(target_tensor, target_attributes[-1]) + return output_tensor, mask_tensor def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet, OptimizerSet]: From 5567b49a6d386f063466c5d7bf5259878b424eac Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 11 Mar 2025 23:02:46 +0530 Subject: [PATCH 324/394] fix --- face_swapper/src/exporting.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 3241ad3..31fc078 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -14,7 +14,7 @@ def export() -> None: config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path') config_source_path = CONFIG_PARSER.get('exporting', 'source_path') config_target_path = CONFIG_PARSER.get('exporting', 'target_path') - config_output_size = CONFIG_PARSER.getint('training.model.generator', 'output_size') + config_target_size = CONFIG_PARSER.getint('exporting', 'target_size') config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version') config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') @@ -22,5 +22,5 @@ def export() -> None: model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, config_parser = CONFIG_PARSER, map_location = 'cpu').eval() model.ir_version = torch.tensor(config_ir_version) source_tensor = torch.randn(1, 512) - target_tensor = torch.randn(1, 3, config_output_size, config_output_size) + target_tensor = torch.randn(1, 3, config_target_size, config_target_size) torch.onnx.export(model, (source_tensor, target_tensor), config_target_path, input_names = [ 'source', 'target' ], output_names = [ 'output', 'mask' ], opset_version = config_opset_version) From 31303c1c6c5c7cc3356e06af23fb0459299f51be Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 19:12:26 +0100 Subject: [PATCH 325/394] Add Attributes, polish names --- face_swapper/src/networks/masknet.py | 4 +++- face_swapper/src/training.py | 8 ++++---- face_swapper/src/types.py | 3 ++- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 0a207aa..b80655e 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -3,6 +3,8 @@ from configparser import ConfigParser import torch from torch import Tensor, nn +from face_swapper.src.types import Attribute + class MaskNet(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: @@ -32,7 +34,7 @@ class MaskNet(nn.Module): UpSample(num_filters, num_filters) ]) - def forward(self, target_tensor : Tensor, target_attribute : Tensor) -> Tensor: + def forward(self, target_tensor : Tensor, target_attribute : Attribute) -> Tensor: output_tensor = torch.cat([ target_tensor, target_attribute ], dim = 1) for down_sample in self.down_samples: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1c8f1ee..0baea47 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -54,11 +54,10 @@ class FaceSwapperTrainer(LightningModule): self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: - with torch.no_grad(): output_tensor = self.generator(source_embedding, target_tensor) - target_attributes = self.generator.get_attributes(target_tensor) - mask_tensor = self.masker(target_tensor, target_attributes[-1]) + target_attribute = self.generator.get_attributes(target_tensor)[-1] + mask_tensor = self.masker(target_tensor, target_attribute) return output_tensor, mask_tensor @@ -127,7 +126,8 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(generator_optimizer) self.toggle_optimizer(masker_optimizer) - mask_tensor = self.masker(target_tensor, target_attributes[-1].detach()) + target_attribute = target_attributes[-1].detach() + mask_tensor = self.masker(target_tensor, target_attribute) mask_loss = self.mask_loss(target_tensor, mask_tensor) self.manual_backward(mask_loss) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 27708b7..b3304a3 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -6,7 +6,8 @@ from torch.nn import Module Batch : TypeAlias = Tuple[Tensor, Tensor] BatchMode = Literal['equal', 'same'] -Attributes : TypeAlias = Tuple[Tensor, ...] +Attribute : TypeAlias = Tensor +Attributes : TypeAlias = Tuple[Attribute, ...] Embedding : TypeAlias = Tensor Gaze : TypeAlias = Tuple[Tensor, Tensor] From 70ac772a34fba663fb60ef7313606d8ce5a9785b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 19:16:17 +0100 Subject: [PATCH 326/394] Remove Attributes --- face_swapper/src/models/generator.py | 5 +++-- face_swapper/src/models/loss.py | 4 ++-- face_swapper/src/networks/aad.py | 5 +++-- face_swapper/src/types.py | 1 - 4 files changed, 8 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index d02be0b..3ff125f 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -1,10 +1,11 @@ from configparser import ConfigParser +from typing import Tuple from torch import Tensor, nn from ..networks.aad import AAD from ..networks.unet import UNet -from ..types import Attributes, Embedding +from ..types import Embedding, Attribute class Generator(nn.Module): @@ -20,7 +21,7 @@ class Generator(nn.Module): output_tensor = self.generator(source_embedding, target_attributes) return output_tensor - def get_attributes(self, input_tensor : Tensor) -> Attributes: + def get_attributes(self, input_tensor : Tensor) -> Tuple[Attribute, ...]: return self.encoder(input_tensor) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 5c6a33a..4584535 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attributes, EmbedderModule, FaceParserModule, Gaze, GazerModule, MotionExtractorModule +from ..types import Attribute, EmbedderModule, FaceParserModule, Gaze, GazerModule, MotionExtractorModule class DiscriminatorLoss(nn.Module): @@ -55,7 +55,7 @@ class AttributeLoss(nn.Module): self.config_batch_size = config_parser.getint('training.loader', 'batch_size') self.config_attribute_weight = config_parser.getfloat('training.losses', 'attribute_weight') - def forward(self, target_attributes : Attributes, output_attributes : Attributes) -> Tuple[Tensor, Tensor]: + def forward(self, target_attributes : Tuple[Attribute, ...], output_attributes : Tuple[Attribute, ...]) -> Tuple[Tensor, Tensor]: temp_tensors = [] for target_attribute, output_attribute in zip(target_attributes, output_attributes): diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index aeb1969..671ff14 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -1,9 +1,10 @@ from configparser import ConfigParser +from typing import Tuple import torch from torch import Tensor, nn -from ..types import Attributes, Embedding +from ..types import Attribute, Embedding class AAD(nn.Module): @@ -56,7 +57,7 @@ class AAD(nn.Module): return layers - def forward(self, source_embedding : Embedding, target_attributes : Attributes) -> Tensor: + def forward(self, source_embedding : Embedding, target_attributes : Tuple[Attribute, ...]) -> Tensor: temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index b3304a3..2b39c79 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -7,7 +7,6 @@ Batch : TypeAlias = Tuple[Tensor, Tensor] BatchMode = Literal['equal', 'same'] Attribute : TypeAlias = Tensor -Attributes : TypeAlias = Tuple[Attribute, ...] Embedding : TypeAlias = Tensor Gaze : TypeAlias = Tuple[Tensor, Tensor] From 0991745753e85a0852ba982cce33e82d0090aeee Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 19:17:26 +0100 Subject: [PATCH 327/394] Remove Attributes --- face_swapper/src/models/generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 3ff125f..8953354 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -5,7 +5,7 @@ from torch import Tensor, nn from ..networks.aad import AAD from ..networks.unet import UNet -from ..types import Embedding, Attribute +from ..types import Attribute, Embedding class Generator(nn.Module): From 564cc7b127df17d5120fb280b8561c003f649c79 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 08:38:49 +0100 Subject: [PATCH 328/394] Introduce Loss type, Remove Gaze type --- face_swapper/src/models/loss.py | 26 +++++++++++++------------- face_swapper/src/types.py | 4 ++-- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 4584535..8520a61 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attribute, EmbedderModule, FaceParserModule, Gaze, GazerModule, MotionExtractorModule +from ..types import Attribute, EmbedderModule, FaceParserModule, GazerModule, Loss, MotionExtractorModule class DiscriminatorLoss(nn.Module): @@ -55,7 +55,7 @@ class AttributeLoss(nn.Module): self.config_batch_size = config_parser.getint('training.loader', 'batch_size') self.config_attribute_weight = config_parser.getfloat('training.losses', 'attribute_weight') - def forward(self, target_attributes : Tuple[Attribute, ...], output_attributes : Tuple[Attribute, ...]) -> Tuple[Tensor, Tensor]: + def forward(self, target_attributes : Tuple[Attribute, ...], output_attributes : Tuple[Attribute, ...]) -> Tuple[Loss, Loss]: temp_tensors = [] for target_attribute, output_attribute in zip(target_attributes, output_attributes): @@ -74,7 +74,7 @@ class ReconstructionLoss(nn.Module): self.embedder = embedder self.mse_loss = nn.MSELoss() - def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Loss, Loss]: with torch.no_grad(): source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) target_embedding = calc_embedding(self.embedder, target_tensor, (0, 0, 0, 0)) @@ -97,7 +97,7 @@ class IdentityLoss(nn.Module): self.config_identity_weight = config_parser.getfloat('training.losses', 'identity_weight') self.embedder = embedder - def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_tensor : Tensor, output_tensor : Tensor) -> Tuple[Loss, Loss]: output_embedding = calc_embedding(self.embedder, output_tensor, (30, 0, 10, 10)) source_embedding = calc_embedding(self.embedder, source_tensor, (30, 0, 10, 10)) identity_loss = (1 - torch.cosine_similarity(source_embedding, output_embedding)).mean() @@ -113,14 +113,14 @@ class MotionLoss(nn.Module): self.motion_extractor = motion_extractor self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, ...]: - target_poses, target_expression = self.get_motions(target_tensor) - output_poses, output_expression = self.get_motions(output_tensor) + def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Loss, Loss, Loss, Loss]: + target_poses, target_expression = self.detect_motions(target_tensor) + output_poses, output_expression = self.detect_motions(output_tensor) pose_loss, weighted_pose_loss = self.calc_pose_loss(target_poses, output_poses) expression_loss, weighted_expression_loss = self.calc_expression_loss(target_expression, output_expression) return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss - def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Tensor, Tensor]: + def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Loss, Loss]: temp_tensors = [] for target_pose, output_pose in zip(target_poses, output_poses): @@ -131,12 +131,12 @@ class MotionLoss(nn.Module): weighted_pose_loss = pose_loss * self.config_pose_weight return pose_loss, weighted_pose_loss - def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Tensor, Tensor]: + def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Loss, Loss]: expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() weighted_expression_loss = expression_loss * self.config_expression_weight return expression_loss, weighted_expression_loss - def get_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: + def detect_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: input_tensor = (input_tensor + 1) * 0.5 with torch.no_grad(): @@ -155,7 +155,7 @@ class GazeLoss(nn.Module): self.gazer = gazer self.l1_loss = nn.L1Loss() - def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Loss, Loss]: output_pitch, output_yaw = self.detect_gaze(output_tensor) target_pitch, target_yaw = self.detect_gaze(target_tensor) @@ -166,7 +166,7 @@ class GazeLoss(nn.Module): weighted_gaze_loss = gaze_loss * self.config_gaze_weight return gaze_loss, weighted_gaze_loss - def detect_gaze(self, input_tensor : Tensor) -> Gaze: + def detect_gaze(self, input_tensor : Tensor) -> Tuple[Tensor, Tensor]: crop_sizes = (torch.tensor([ 0.235, 0.875, 0.0625, 0.8 ]) * self.config_output_size).int() crop_tensor = input_tensor[:, :, crop_sizes[0]:crop_sizes[1], crop_sizes[2]:crop_sizes[3]] crop_tensor = (crop_tensor + 1) * 0.5 @@ -186,7 +186,7 @@ class MaskLoss(nn.Module): self.face_parser = face_parser self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: + def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Loss: target_mask = self.calc_mask(target_tensor) target_mask = target_mask.view(-1, self.config_output_size, self.config_output_size) mask_tensor = mask_tensor.view(-1, self.config_output_size, self.config_output_size) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 2b39c79..a794ee1 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -4,11 +4,11 @@ from torch import Tensor from torch.nn import Module Batch : TypeAlias = Tuple[Tensor, Tensor] -BatchMode = Literal['equal', 'same'] +BatchMode = Literal['equal', 'same', 'different'] Attribute : TypeAlias = Tensor Embedding : TypeAlias = Tensor -Gaze : TypeAlias = Tuple[Tensor, Tensor] +Loss : TypeAlias = Tensor Padding : TypeAlias = Tuple[int, int, int, int] From 738e00d59e9ae876031c9505ce5eea9aa0adf465 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 08:42:38 +0100 Subject: [PATCH 329/394] Add more Loss types --- face_swapper/src/models/loss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 8520a61..e340924 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -14,7 +14,7 @@ class DiscriminatorLoss(nn.Module): def __init__(self) -> None: super().__init__() - def forward(self, discriminator_source_tensors : List[Tensor], discriminator_output_tensors : List[Tensor]) -> Tensor: + def forward(self, discriminator_source_tensors : List[Tensor], discriminator_output_tensors : List[Tensor]) -> Loss: positive_tensors = [] negative_tensors = [] @@ -37,7 +37,7 @@ class AdversarialLoss(nn.Module): super().__init__() self.config_adversarial_weight = config_parser.getfloat('training.losses', 'adversarial_weight') - def forward(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Tensor, Tensor]: + def forward(self, discriminator_output_tensors : List[Tensor]) -> Tuple[Loss, Loss]: temp_tensors = [] for discriminator_output_tensor in discriminator_output_tensors: From 569df9e96d74e2c9adab9a18cb1b94ae54464a9c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 15:33:31 +0100 Subject: [PATCH 330/394] Let the generator return target attributes --- face_swapper/src/models/generator.py | 9 +++------ face_swapper/src/training.py | 15 +++++++-------- 2 files changed, 10 insertions(+), 14 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 8953354..6fd0097 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -16,13 +16,10 @@ class Generator(nn.Module): self.encoder.apply(init_weight) self.generator.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: - target_attributes = self.get_attributes(target_tensor) + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Attribute, ...]]: + target_attributes = self.encoder(target_tensor) output_tensor = self.generator(source_embedding, target_attributes) - return output_tensor - - def get_attributes(self, input_tensor : Tensor) -> Tuple[Attribute, ...]: - return self.encoder(input_tensor) + return output_tensor, target_attributes def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 0baea47..29a5000 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -55,8 +55,8 @@ class FaceSwapperTrainer(LightningModule): def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: with torch.no_grad(): - output_tensor = self.generator(source_embedding, target_tensor) - target_attribute = self.generator.get_attributes(target_tensor)[-1] + output_tensor, target_attributes = self.generator(source_embedding, target_tensor) + target_attribute = target_attributes[-1] mask_tensor = self.masker(target_tensor, target_attribute) return output_tensor, mask_tensor @@ -105,12 +105,11 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(generator_optimizer) source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - target_attributes = self.generator.get_attributes(target_tensor) - generator_output_tensor = self.generator(source_embedding, target_tensor) - generator_output_attributes = self.generator.get_attributes(generator_output_tensor) + _, generator_target_attributes = self.generator(target_tensor) + generator_output_tensor, generator_output_attributes = self.generator(source_embedding, target_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) - attribute_loss, weighted_attribute_loss = self.attribute_loss(target_attributes, generator_output_attributes) + attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) @@ -126,7 +125,7 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(generator_optimizer) self.toggle_optimizer(masker_optimizer) - target_attribute = target_attributes[-1].detach() + target_attribute = generator_target_attributes[-1].detach() mask_tensor = self.masker(target_tensor, target_attribute) mask_loss = self.mask_loss(target_tensor, mask_tensor) @@ -168,7 +167,7 @@ class FaceSwapperTrainer(LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - output_tensor = self.generator(source_embedding, target_tensor) + output_tensor, _ = self.generator(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, prog_bar = True) From d212e2fe124af7d0974024bc2c354a287c5f4aaf Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 15:48:53 +0100 Subject: [PATCH 331/394] Fix import --- face_swapper/src/networks/masknet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index b80655e..0c43c4d 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -3,7 +3,7 @@ from configparser import ConfigParser import torch from torch import Tensor, nn -from face_swapper.src.types import Attribute +from ..types import Attribute class MaskNet(nn.Module): From 8b465fce038442a0fbf8e47926a1042ad97c4175 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 15:52:56 +0100 Subject: [PATCH 332/394] Fix generator call --- face_swapper/src/training.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 29a5000..68a0aad 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -105,11 +105,10 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(generator_optimizer) source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - _, generator_target_attributes = self.generator(target_tensor) generator_output_tensor, generator_output_attributes = self.generator(source_embedding, target_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) - attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_target_attributes, generator_output_attributes) + attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_output_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) @@ -125,7 +124,7 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(generator_optimizer) self.toggle_optimizer(masker_optimizer) - target_attribute = generator_target_attributes[-1].detach() + target_attribute = generator_output_attributes[-1].detach() mask_tensor = self.masker(target_tensor, target_attribute) mask_loss = self.mask_loss(target_tensor, mask_tensor) From 2af4f2c8ab550f3f7ed71cf82f3ed631b6c692c5 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 16:21:47 +0100 Subject: [PATCH 333/394] Extend config --- embedding_converter/README.md | 3 +++ embedding_converter/config.ini | 3 +++ embedding_converter/src/training.py | 6 +++++- face_swapper/README.md | 6 ++++-- face_swapper/config.ini | 6 ++++-- face_swapper/src/training.py | 8 +++++--- 6 files changed, 24 insertions(+), 8 deletions(-) diff --git a/embedding_converter/README.md b/embedding_converter/README.md index 502670f..3f940c1 100644 --- a/embedding_converter/README.md +++ b/embedding_converter/README.md @@ -47,7 +47,10 @@ target_path = .models/arcface_simswap.pt [training.trainer] learning_rate = 0.001 max_epochs = 4096 +strategy = auto precision = 16-mixed +logger_path = .logs +logger_name = arcface_converter_simswap ``` ``` diff --git a/embedding_converter/config.ini b/embedding_converter/config.ini index cb24d3f..a040687 100644 --- a/embedding_converter/config.ini +++ b/embedding_converter/config.ini @@ -13,7 +13,10 @@ target_path = [training.trainer] learning_rate = max_epochs = +strategy = precision = +logger_path = +logger_name = [training.output] directory_path = diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 50d42d2..bf577ee 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -89,15 +89,19 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T def create_trainer() -> Trainer: config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') + config_strategy = CONFIG_PARSER.get('training.trainer', 'strategy') config_precision = CONFIG_PARSER.get('training.trainer', 'precision') + config_logger_path = CONFIG_PARSER.get('training.trainer', 'logger_path') + config_logger_name = CONFIG_PARSER.get('training.trainer', 'logger_name') config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') - logger = TensorBoardLogger('.logs', name = 'embedding_converter') + logger = TensorBoardLogger(config_logger_path, name = config_logger_name) return Trainer( logger = logger, log_every_n_steps = 10, max_epochs = config_max_epochs, + strategy = config_strategy, precision = config_precision, callbacks = [ diff --git a/face_swapper/README.md b/face_swapper/README.md index 5776b31..fa85087 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -89,9 +89,11 @@ expression_weight = 0.0 accumulate_size = 4 learning_rate = 0.0004 max_epochs = 50 -precision = 16-mixed -preview_frequency = 250 strategy = auto +precision = 16-mixed +logger_path = .logs +logger_name = face_swapper +preview_frequency = 250 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 1ff4100..79ac834 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -47,9 +47,11 @@ expression_weight = accumulate_size = learning_rate = max_epochs = -precision = -preview_frequency = strategy = +precision = +logger_path = +logger_name = +preview_frequency = [training.output] directory_path = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 68a0aad..754d6b7 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -208,18 +208,20 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T def create_trainer() -> Trainer: config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') - config_precision = CONFIG_PARSER.get('training.trainer', 'precision') config_strategy = CONFIG_PARSER.get('training.trainer', 'strategy') + config_precision = CONFIG_PARSER.get('training.trainer', 'precision') + config_logger_path = CONFIG_PARSER.get('training.trainer', 'logger_path') + config_logger_name = CONFIG_PARSER.get('training.trainer', 'logger_name') config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') - logger = TensorBoardLogger('.logs', name = 'face_swapper') + logger = TensorBoardLogger(config_logger_path, name = config_logger_name) return Trainer( logger = logger, log_every_n_steps = 10, max_epochs = config_max_epochs, - precision = config_precision, strategy = config_strategy, + precision = config_precision, callbacks = [ ModelCheckpoint( From 20aa5114e10f0aec5f64f106f4b8587466614863 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 16:24:03 +0100 Subject: [PATCH 334/394] Extend config --- embedding_converter/src/training.py | 2 +- face_swapper/src/training.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index bf577ee..8333fc8 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -95,7 +95,7 @@ def create_trainer() -> Trainer: config_logger_name = CONFIG_PARSER.get('training.trainer', 'logger_name') config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') - logger = TensorBoardLogger(config_logger_path, name = config_logger_name) + logger = TensorBoardLogger(config_logger_path, config_logger_name) return Trainer( logger = logger, diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 754d6b7..f5a62b0 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -214,7 +214,7 @@ def create_trainer() -> Trainer: config_logger_name = CONFIG_PARSER.get('training.trainer', 'logger_name') config_directory_path = CONFIG_PARSER.get('training.output', 'directory_path') config_file_pattern = CONFIG_PARSER.get('training.output', 'file_pattern') - logger = TensorBoardLogger(config_logger_path, name = config_logger_name) + logger = TensorBoardLogger(config_logger_path, config_logger_name) return Trainer( logger = logger, From 5550c89f43b561bfa03963e77d0b2b3548324efa Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 19:40:43 +0100 Subject: [PATCH 335/394] Fix AAD naming, Attribute and Embedding --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/networks/aad.py | 84 ++++++++++++++++---------------- 3 files changed, 45 insertions(+), 43 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index fa85087..f18a7ae 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -51,7 +51,7 @@ face_parser_path = .models/face_parser.pt ``` [training.model.generator] -identity_channels = 512 +source_channels = 512 output_channels = 4096 output_size = 256 num_blocks = 2 diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 79ac834..eec38bd 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -17,7 +17,7 @@ motion_extractor_path = face_parser_path = [training.model.generator] -identity_channels = +source_channels = output_channels = output_size = num_blocks = diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 671ff14..9e24b03 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -10,11 +10,11 @@ from ..types import Attribute, Embedding class AAD(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config_identity_channels = config_parser.getint('training.model.generator', 'identity_channels') + self.config_source_channels = config_parser.getint('training.model.generator', 'source_channels') self.config_output_channels = config_parser.getint('training.model.generator', 'output_channels') self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.config_num_blocks = config_parser.getint('training.model.generator', 'num_blocks') - self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config_identity_channels, self.config_output_channels) + self.pixel_shuffle_up_sample = PixelShuffleUpSample(self.config_source_channels, self.config_output_channels) self.layers = self.create_layers() def create_layers(self) -> nn.ModuleList: @@ -23,36 +23,36 @@ class AAD(nn.Module): if self.config_output_size == 128: layers.extend( [ - AdaptiveFeatureModulation(512, 512, 512, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(512, 512, 1024, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(512, 512, 512, self.config_identity_channels, self.config_num_blocks) + AdaptiveFeatureModulation(512, 512, 512, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(512, 512, 1024, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(512, 512, 512, self.config_source_channels, self.config_num_blocks) ]) if self.config_output_size == 256: layers.extend( [ - AdaptiveFeatureModulation(1024, 1024, 1024, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(1024, 1024, 2048, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(1024, 1024, 1024, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(1024, 512, 512, self.config_identity_channels, self.config_num_blocks) + AdaptiveFeatureModulation(1024, 1024, 1024, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 1024, 2048, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 1024, 1024, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.config_source_channels, self.config_num_blocks) ]) if self.config_output_size == 512: layers.extend( [ - AdaptiveFeatureModulation(2048, 2048, 2048, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(2048, 2048, 4096, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(2048, 2048, 2048, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(2048, 1024, 1024, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(1024, 512, 512, self.config_identity_channels, self.config_num_blocks) + AdaptiveFeatureModulation(2048, 2048, 2048, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 2048, 4096, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 2048, 2048, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 1024, 1024, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.config_source_channels, self.config_num_blocks) ]) layers.extend( [ - AdaptiveFeatureModulation(512, 256, 256, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(256, 128, 128, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(128, 64, 64, self.config_identity_channels, self.config_num_blocks), - AdaptiveFeatureModulation(64, 3, 64, self.config_identity_channels, self.config_num_blocks) + AdaptiveFeatureModulation(512, 256, 256, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(256, 128, 128, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(128, 64, 64, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(64, 3, 64, self.config_source_channels, self.config_num_blocks) ]) return layers @@ -61,21 +61,23 @@ class AAD(nn.Module): temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): - temp_tensor = layer(temp_tensors, target_attributes[index], source_embedding) + target_attribute = target_attributes[index] + temp_tensor = layer(temp_tensors, source_embedding, target_attribute) temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) - temp_tensors = self.layers[-1](temp_tensors, target_attributes[-1], source_embedding) + target_attribute = target_attributes[-1], + temp_tensors = self.layers[-1](temp_tensors, source_embedding, target_attribute) output_tensor = torch.tanh(temp_tensors) return output_tensor class AdaptiveFeatureModulation(nn.Module): - def __init__(self, input_channels : int, output_channels : int, attribute_channels : int, identity_channels : int, num_blocks : int) -> None: + def __init__(self, input_channels : int, output_channels : int, target_channels : int, source_channels : int, num_blocks : int) -> None: super().__init__() self.context_input_channels = input_channels self.context_output_channels = output_channels - self.context_attribute_channels = attribute_channels - self.context_identity_channels = identity_channels + self.context_target_channels = target_channels + self.context_source_channels = source_channels self.context_num_blocks = num_blocks self.primary_layers = self.create_primary_layers() self.shortcut_layers = self.create_shortcut_layers() @@ -86,7 +88,7 @@ class AdaptiveFeatureModulation(nn.Module): for index in range(self.context_num_blocks): primary_layers.extend( [ - FeatureModulation(self.context_input_channels, self.context_attribute_channels, self.context_identity_channels), + FeatureModulation(self.context_input_channels, self.context_target_channels, self.context_source_channels), nn.ReLU(inplace = True) ]) @@ -103,19 +105,19 @@ class AdaptiveFeatureModulation(nn.Module): if self.context_input_channels > self.context_output_channels: shortcut_layers.extend( [ - FeatureModulation(self.context_input_channels, self.context_attribute_channels, self.context_identity_channels), + FeatureModulation(self.context_input_channels, self.context_target_channels, self.context_source_channels), nn.ReLU(inplace = True), nn.Conv2d(self.context_input_channels, self.context_output_channels, kernel_size = 3, padding = 1, bias = False) ]) return shortcut_layers - def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_attribute : Attribute) -> Tensor: primary_tensor = input_tensor for primary_layer in self.primary_layers: if isinstance(primary_layer, FeatureModulation): - primary_tensor = primary_layer(primary_tensor, attribute_embedding, identity_embedding) + primary_tensor = primary_layer(primary_tensor, source_embedding, target_attribute) else: primary_tensor = primary_layer(primary_tensor) @@ -124,7 +126,7 @@ class AdaptiveFeatureModulation(nn.Module): for shortcut_layer in self.shortcut_layers: if isinstance(shortcut_layer, FeatureModulation): - shortcut_tensor = shortcut_layer(shortcut_tensor, attribute_embedding, identity_embedding) + shortcut_tensor = shortcut_layer(shortcut_tensor, source_embedding, target_attribute) else: shortcut_tensor = shortcut_layer(shortcut_tensor) @@ -134,29 +136,29 @@ class AdaptiveFeatureModulation(nn.Module): class FeatureModulation(nn.Module): - def __init__(self, input_channels : int, attribute_channels : int, identity_channels : int) -> None: + def __init__(self, input_channels : int, target_channels : int, source_channels : int) -> None: super().__init__() self.context_input_channels = input_channels - self.conv1 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) - self.conv2 = nn.Conv2d(attribute_channels, input_channels, kernel_size = 1) + self.conv1 = nn.Conv2d(target_channels, input_channels, kernel_size = 1) + self.conv2 = nn.Conv2d(target_channels, input_channels, kernel_size = 1) self.conv3 = nn.Conv2d(input_channels, 1, kernel_size = 1) - self.linear1 = nn.Linear(identity_channels, input_channels) - self.linear2 = nn.Linear(identity_channels, input_channels) + self.linear1 = nn.Linear(source_channels, input_channels) + self.linear2 = nn.Linear(source_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels) - def forward(self, input_tensor : Tensor, attribute_embedding : Embedding, identity_embedding : Embedding) -> Tensor: + def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_attribute : Attribute) -> Tensor: temp_tensor = self.instance_norm(input_tensor) - attribute_scale = self.conv1(attribute_embedding) - attribute_shift = self.conv2(attribute_embedding) - attribute_modulation = attribute_scale * temp_tensor + attribute_shift + source_scale = self.linear2(source_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) + source_shift = self.linear1(source_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) + source_modulation = source_scale * temp_tensor + source_shift - identity_scale = self.linear2(identity_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) - identity_shift = self.linear1(identity_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) - identity_modulation = identity_scale * temp_tensor + identity_shift + target_scale = self.conv1(target_attribute) + target_shift = self.conv2(target_attribute) + target_modulation = target_scale * temp_tensor + target_shift temp_mask = torch.sigmoid(self.conv3(temp_tensor)) - output_tensor = (1 - temp_mask) * attribute_modulation + temp_mask * identity_modulation + output_tensor = (1 - temp_mask) * target_modulation + temp_mask * source_modulation return output_tensor From ecc5b1a1d5da2a397364959f0f0a2b4abc2f39d4 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 19:43:24 +0100 Subject: [PATCH 336/394] Fix test --- face_swapper/tests/test_networks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 20c645e..37a9c1a 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -15,7 +15,7 @@ def test_aad_with_unet(output_size : int) -> None: { 'training.model.generator': { - 'identity_channels': '512', + 'source_channels': '512', 'output_channels': str(output_size * 16), 'output_size': str(output_size), 'num_blocks': '2' From f989df39e9b2183e4693f1aff692fca628698d45 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Tue, 11 Mar 2025 23:03:22 +0100 Subject: [PATCH 337/394] Fix test --- face_swapper/src/networks/aad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 9e24b03..7babfbe 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -65,7 +65,7 @@ class AAD(nn.Module): temp_tensor = layer(temp_tensors, source_embedding, target_attribute) temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) - target_attribute = target_attributes[-1], + target_attribute = target_attributes[-1] temp_tensors = self.layers[-1](temp_tensors, source_embedding, target_attribute) output_tensor = torch.tanh(temp_tensors) return output_tensor From 0732924f1e8aa63774f7247990965bfdee276f2c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 20:18:33 +0100 Subject: [PATCH 338/394] Minor change --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index f5a62b0..4b9a261 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -124,8 +124,8 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(generator_optimizer) self.toggle_optimizer(masker_optimizer) - target_attribute = generator_output_attributes[-1].detach() - mask_tensor = self.masker(target_tensor, target_attribute) + target_attribute = generator_output_attributes[-1] + mask_tensor = self.masker(target_tensor, target_attribute.detach()) mask_loss = self.mask_loss(target_tensor, mask_tensor) self.manual_backward(mask_loss) From cf0bd938140ac9013b94df2d39ef2b63d22dcb0f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 21:35:07 +0100 Subject: [PATCH 339/394] Train MaskNet based on the output --- face_swapper/src/helper.py | 6 ++-- face_swapper/src/networks/masknet.py | 4 +-- face_swapper/src/training.py | 48 ++++++++++++---------------- 3 files changed, 26 insertions(+), 32 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 352cfd4..98eb971 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -38,9 +38,9 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P return embedding -def overlay_mask(target_tensor : Tensor, mask_tensor : Tensor) -> Tensor: - overlay_tensor = torch.zeros(*target_tensor.shape, dtype = target_tensor.dtype, device = target_tensor.device) +def overlay_mask(input_tensor : Tensor, mask_tensor : Tensor) -> Tensor: + overlay_tensor = torch.zeros(*input_tensor.shape, dtype = input_tensor.dtype, device = input_tensor.device) overlay_tensor[:, 2, :, :] = 1 mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) - output_tensor = target_tensor * (1 - mask_tensor) + overlay_tensor * mask_tensor + output_tensor = input_tensor * (1 - mask_tensor) + overlay_tensor * mask_tensor return output_tensor diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 0c43c4d..a26d56b 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -34,8 +34,8 @@ class MaskNet(nn.Module): UpSample(num_filters, num_filters) ]) - def forward(self, target_tensor : Tensor, target_attribute : Attribute) -> Tensor: - output_tensor = torch.cat([ target_tensor, target_attribute ], dim = 1) + def forward(self, input_tensor : Tensor, input_attribute : Attribute) -> Tensor: + output_tensor = torch.cat([ input_tensor, input_attribute ], dim = 1) for down_sample in self.down_samples: output_tensor = down_sample(output_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 4b9a261..fca3105 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -103,7 +103,6 @@ class FaceSwapperTrainer(LightningModule): do_update = (batch_index + 1) % self.config_accumulate_size == 0 generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] - self.toggle_optimizer(generator_optimizer) source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) generator_output_tensor, generator_output_attributes = self.generator(source_embedding, target_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) @@ -115,40 +114,35 @@ class FaceSwapperTrainer(LightningModule): gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss - self.manual_backward(generator_loss) - - if do_update: - generator_optimizer.step() - generator_optimizer.zero_grad() - - self.untoggle_optimizer(generator_optimizer) - - self.toggle_optimizer(masker_optimizer) - target_attribute = generator_output_attributes[-1] - mask_tensor = self.masker(target_tensor, target_attribute.detach()) - mask_loss = self.mask_loss(target_tensor, mask_tensor) - - self.manual_backward(mask_loss) - - if do_update: - masker_optimizer.step() - masker_optimizer.zero_grad() - - self.untoggle_optimizer(masker_optimizer) - - self.toggle_optimizer(discriminator_optimizer) discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) - self.manual_backward(discriminator_loss) + generator_output_attribute = generator_output_attributes[-1] + mask_tensor = self.masker(generator_output_tensor.detach(), generator_output_attribute.detach()) + mask_loss = self.mask_loss(generator_output_tensor.detach(), mask_tensor) + self.toggle_optimizer(generator_optimizer) + self.manual_backward(generator_loss, retain_graph = True) + if do_update: + generator_optimizer.step() + generator_optimizer.zero_grad() + self.untoggle_optimizer(generator_optimizer) + + self.toggle_optimizer(discriminator_optimizer) + self.manual_backward(discriminator_loss) if do_update: discriminator_optimizer.step() discriminator_optimizer.zero_grad() - self.untoggle_optimizer(discriminator_optimizer) + self.toggle_optimizer(masker_optimizer) + self.manual_backward(mask_loss) + if do_update: + masker_optimizer.step() + masker_optimizer.zero_grad() + self.untoggle_optimizer(masker_optimizer) + if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, mask_tensor) @@ -175,10 +169,10 @@ class FaceSwapperTrainer(LightningModule): def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, mask_tensor : Tensor) -> None: preview_limit = 8 preview_cells = [] - mask_tensor = overlay_mask(target_tensor, mask_tensor) + overlay_tensor = overlay_mask(output_tensor, mask_tensor) for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], mask_tensor[:preview_limit]): - preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, mask_tensor ], dim = 2) + preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, overlay_tensor ], dim = 2) preview_cells.append(preview_cell) preview_cells = torch.cat(preview_cells, dim = 1).unsqueeze(0) From 72591fbed1d7e7fd32358101b2f098bc6632c881 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 21:42:08 +0100 Subject: [PATCH 340/394] Train MaskNet based on the output --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index fca3105..ab880fb 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -120,7 +120,7 @@ class FaceSwapperTrainer(LightningModule): generator_output_attribute = generator_output_attributes[-1] mask_tensor = self.masker(generator_output_tensor.detach(), generator_output_attribute.detach()) - mask_loss = self.mask_loss(generator_output_tensor.detach(), mask_tensor) + mask_loss = self.mask_loss(target_tensor, mask_tensor) self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss, retain_graph = True) From c0aaae93582e94b6eff4d2cd2f887557392a84b3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 21:55:26 +0100 Subject: [PATCH 341/394] Hotfix --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ab880fb..64648dd 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -171,7 +171,7 @@ class FaceSwapperTrainer(LightningModule): preview_cells = [] overlay_tensor = overlay_mask(output_tensor, mask_tensor) - for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], mask_tensor[:preview_limit]): + for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], overlay_tensor[:preview_limit]): preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, overlay_tensor ], dim = 2) preview_cells.append(preview_cell) From 431df7cff80ad6a17ba1222b88989c5c3bae471c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 21:58:29 +0100 Subject: [PATCH 342/394] Hotfix --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 64648dd..14bb67e 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -171,7 +171,7 @@ class FaceSwapperTrainer(LightningModule): preview_cells = [] overlay_tensor = overlay_mask(output_tensor, mask_tensor) - for source_tensor, target_tensor, output_tensor, mask_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], overlay_tensor[:preview_limit]): + for source_tensor, target_tensor, output_tensor, overlay_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], overlay_tensor[:preview_limit]): preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, overlay_tensor ], dim = 2) preview_cells.append(preview_cell) From 8f0ee4935b158024560d6cbe569716112ca42898 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 22:07:39 +0100 Subject: [PATCH 343/394] Remove flag --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 14bb67e..5ebde71 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -123,7 +123,7 @@ class FaceSwapperTrainer(LightningModule): mask_loss = self.mask_loss(target_tensor, mask_tensor) self.toggle_optimizer(generator_optimizer) - self.manual_backward(generator_loss, retain_graph = True) + self.manual_backward(generator_loss) if do_update: generator_optimizer.step() generator_optimizer.zero_grad() From 0d73bcf918d85fee3df1c072f126a8c6fb8acc10 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 22:37:06 +0100 Subject: [PATCH 344/394] Fix attribute loss --- face_swapper/src/models/generator.py | 9 ++++++--- face_swapper/src/training.py | 3 ++- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 6fd0097..2ddf748 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -16,10 +16,13 @@ class Generator(nn.Module): self.encoder.apply(init_weight) self.generator.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Attribute, ...]]: - target_attributes = self.encoder(target_tensor) + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: + target_attributes = self.encode_attributes(target_tensor) output_tensor = self.generator(source_embedding, target_attributes) - return output_tensor, target_attributes + return output_tensor + + def encode_attributes(self, input_tensor : Tensor) -> Tuple[Attribute, ...]: + return self.encoder(input_tensor) def init_weight(module : nn.Module) -> None: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 5ebde71..eaacfa3 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -104,10 +104,11 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) + generator_target_attributes = self.generator.encode_attributes(target_tensor) generator_output_tensor, generator_output_attributes = self.generator(source_embedding, target_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) - attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_output_attributes, generator_output_attributes) + attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_target_attributes, generator_output_attributes) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) From 7c75b0d898465e039b69bf3cf5da1b7b85d6fe60 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 22:41:28 +0100 Subject: [PATCH 345/394] Fix attribute loss --- face_swapper/src/models/generator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 2ddf748..d083614 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -16,10 +16,10 @@ class Generator(nn.Module): self.encoder.apply(init_weight) self.generator.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tensor: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Attribute, ...]]: target_attributes = self.encode_attributes(target_tensor) output_tensor = self.generator(source_embedding, target_attributes) - return output_tensor + return output_tensor, target_attributes def encode_attributes(self, input_tensor : Tensor) -> Tuple[Attribute, ...]: return self.encoder(input_tensor) From bdd7fd0d86c5a7e3804238f61981102f9c1971be Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 22:54:20 +0100 Subject: [PATCH 346/394] Fix attribute loss --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index eaacfa3..ae7addd 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -104,8 +104,8 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - generator_target_attributes = self.generator.encode_attributes(target_tensor) - generator_output_tensor, generator_output_attributes = self.generator(source_embedding, target_tensor) + generator_output_tensor, generator_target_attributes = self.generator(source_embedding, target_tensor) + generator_output_attributes = self.generator.encode_attributes(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_target_attributes, generator_output_attributes) From b5efcbe44aba825c3ed373748dc15a740e2d65e7 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Wed, 12 Mar 2025 23:02:26 +0100 Subject: [PATCH 347/394] Adjust the weights in README --- face_swapper/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index f18a7ae..399b69e 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -77,11 +77,11 @@ num_filters = 16 [training.losses] adversarial_weight = 1.0 attribute_weight = 10.0 -reconstruction_weight = 20.0 +reconstruction_weight = 10.0 identity_weight = 20.0 -gaze_weight = 0.0 -pose_weight = 0.0 -expression_weight = 0.0 +gaze_weight = 0.05 +pose_weight = 0.05 +expression_weight = 0.05 ``` ``` From 5234874bc70a36bb38ec66bdc9800643393f8afd Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Mar 2025 08:09:47 +0100 Subject: [PATCH 348/394] Mask typing and naming related updates --- face_swapper/src/helper.py | 8 ++++---- face_swapper/src/models/loss.py | 8 ++++---- face_swapper/src/networks/masknet.py | 24 +++++++++++++----------- face_swapper/src/training.py | 16 ++++++++-------- face_swapper/src/types.py | 1 + 5 files changed, 30 insertions(+), 27 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 98eb971..b1377d5 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -1,7 +1,7 @@ import torch from torch import Tensor, nn -from .types import EmbedderModule, Embedding, Padding, WarpTemplate, WarpTemplateSet +from .types import EmbedderModule, Embedding, Mask, Padding, WarpTemplate, WarpTemplateSet WARP_TEMPLATE_SET : WarpTemplateSet =\ { @@ -38,9 +38,9 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P return embedding -def overlay_mask(input_tensor : Tensor, mask_tensor : Tensor) -> Tensor: +def overlay_mask(input_tensor : Tensor, input_mask : Mask) -> Tensor: overlay_tensor = torch.zeros(*input_tensor.shape, dtype = input_tensor.dtype, device = input_tensor.device) overlay_tensor[:, 2, :, :] = 1 - mask_tensor = mask_tensor.repeat(1, 3, 1, 1).clamp(0, 0.8) - output_tensor = input_tensor * (1 - mask_tensor) + overlay_tensor * mask_tensor + input_mask = input_mask.repeat(1, 3, 1, 1).clamp(0, 0.8) + output_tensor = input_tensor * (1 - input_mask) + overlay_tensor * input_mask return output_tensor diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index e340924..08835f6 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attribute, EmbedderModule, FaceParserModule, GazerModule, Loss, MotionExtractorModule +from ..types import Attribute, EmbedderModule, FaceParserModule, GazerModule, Loss, Mask, MotionExtractorModule class DiscriminatorLoss(nn.Module): @@ -186,11 +186,11 @@ class MaskLoss(nn.Module): self.face_parser = face_parser self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, mask_tensor : Tensor) -> Loss: + def forward(self, target_tensor : Tensor, output_mask : Mask) -> Loss: target_mask = self.calc_mask(target_tensor) target_mask = target_mask.view(-1, self.config_output_size, self.config_output_size) - mask_tensor = mask_tensor.view(-1, self.config_output_size, self.config_output_size) - mask_loss = self.mse_loss(target_mask, mask_tensor) + output_mask = output_mask.view(-1, self.config_output_size, self.config_output_size) + mask_loss = self.mse_loss(target_mask, output_mask) return mask_loss def calc_mask(self, target_tensor : Tensor) -> Tensor: diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index a26d56b..0449fca 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -3,7 +3,7 @@ from configparser import ConfigParser import torch from torch import Tensor, nn -from ..types import Attribute +from ..types import Attribute, Mask class MaskNet(nn.Module): @@ -14,7 +14,7 @@ class MaskNet(nn.Module): self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters') self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters) self.up_samples = self.create_up_samples(self.config_num_filters) - self.bottleneck = BottleNeck(self.config_num_filters * 2) + self.bottleneck = BottleNeck(self.config_num_filters * 4) self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() @@ -23,31 +23,33 @@ class MaskNet(nn.Module): return nn.ModuleList( [ DownSample(input_channels, num_filters), - DownSample(num_filters, num_filters * 2) + DownSample(num_filters, num_filters * 2), + DownSample(num_filters, num_filters * 4) ]) @staticmethod def create_up_samples(num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ + UpSample(num_filters * 4, num_filters), UpSample(num_filters * 2, num_filters), UpSample(num_filters, num_filters) ]) - def forward(self, input_tensor : Tensor, input_attribute : Attribute) -> Tensor: - output_tensor = torch.cat([ input_tensor, input_attribute ], dim = 1) + def forward(self, input_tensor : Tensor, input_attribute : Attribute) -> Mask: + output_mask = torch.cat([ input_tensor, input_attribute ], dim = 1) for down_sample in self.down_samples: - output_tensor = down_sample(output_tensor) + output_mask = down_sample(output_mask) - output_tensor = self.bottleneck(output_tensor) + output_mask = self.bottleneck(output_mask) for up_sample in self.up_samples: - output_tensor = up_sample(output_tensor) + output_mask = up_sample(output_mask) - output_tensor = self.conv(output_tensor) - output_tensor = self.sigmoid(output_tensor) - return output_tensor + output_mask = self.conv(output_mask) + output_mask = self.sigmoid(output_mask) + return output_mask class BottleNeck(nn.Module): diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ae7addd..16f1790 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -18,7 +18,7 @@ from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .networks.masknet import MaskNet -from .types import Batch, Embedding, OptimizerSet +from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -57,9 +57,9 @@ class FaceSwapperTrainer(LightningModule): with torch.no_grad(): output_tensor, target_attributes = self.generator(source_embedding, target_tensor) target_attribute = target_attributes[-1] - mask_tensor = self.masker(target_tensor, target_attribute) + output_mask = self.masker(target_tensor, target_attribute) - return output_tensor, mask_tensor + return output_tensor, output_mask def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet, OptimizerSet]: generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) @@ -120,8 +120,8 @@ class FaceSwapperTrainer(LightningModule): discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) generator_output_attribute = generator_output_attributes[-1] - mask_tensor = self.masker(generator_output_tensor.detach(), generator_output_attribute.detach()) - mask_loss = self.mask_loss(target_tensor, mask_tensor) + generator_output_mask = self.masker(generator_output_tensor.detach(), generator_output_attribute.detach()) + mask_loss = self.mask_loss(target_tensor, generator_output_mask) self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss) @@ -145,7 +145,7 @@ class FaceSwapperTrainer(LightningModule): self.untoggle_optimizer(masker_optimizer) if self.global_step % self.config_preview_frequency == 0: - self.generate_preview(source_tensor, target_tensor, generator_output_tensor, mask_tensor) + self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) @@ -167,10 +167,10 @@ class FaceSwapperTrainer(LightningModule): self.log('validation_score', validation_score, prog_bar = True) return validation_score - def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, mask_tensor : Tensor) -> None: + def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, output_mask : Mask) -> None: preview_limit = 8 preview_cells = [] - overlay_tensor = overlay_mask(output_tensor, mask_tensor) + overlay_tensor = overlay_mask(output_tensor, output_mask) for source_tensor, target_tensor, output_tensor, overlay_tensor in zip(source_tensor[:preview_limit], target_tensor[:preview_limit], output_tensor[:preview_limit], overlay_tensor[:preview_limit]): preview_cell = torch.cat([ source_tensor, target_tensor, output_tensor, overlay_tensor ], dim = 2) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index a794ee1..3789995 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -8,6 +8,7 @@ BatchMode = Literal['equal', 'same', 'different'] Attribute : TypeAlias = Tensor Embedding : TypeAlias = Tensor +Mask : TypeAlias = Tensor Loss : TypeAlias = Tensor Padding : TypeAlias = Tuple[int, int, int, int] From 33d00ac9410ed94758d342c3b28f411d722ca016 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Mar 2025 08:13:21 +0100 Subject: [PATCH 349/394] Mask typing and naming related updates --- face_swapper/src/networks/masknet.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 0449fca..0a465e4 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -14,7 +14,7 @@ class MaskNet(nn.Module): self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters') self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters) self.up_samples = self.create_up_samples(self.config_num_filters) - self.bottleneck = BottleNeck(self.config_num_filters * 4) + self.bottleneck = BottleNeck(self.config_num_filters * 2) self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() @@ -23,15 +23,13 @@ class MaskNet(nn.Module): return nn.ModuleList( [ DownSample(input_channels, num_filters), - DownSample(num_filters, num_filters * 2), - DownSample(num_filters, num_filters * 4) + DownSample(num_filters, num_filters * 2) ]) @staticmethod def create_up_samples(num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ - UpSample(num_filters * 4, num_filters), UpSample(num_filters * 2, num_filters), UpSample(num_filters, num_filters) ]) From 904a447e06f917fab291fe8007a70a80680af13e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Fri, 14 Mar 2025 08:30:55 +0100 Subject: [PATCH 350/394] Mask typing and naming related updates --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 16f1790..502c907 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -53,7 +53,7 @@ class FaceSwapperTrainer(LightningModule): self.mask_loss = MaskLoss(config_parser, self.face_parser) self.automatic_optimization = False - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tensor]: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: with torch.no_grad(): output_tensor, target_attributes = self.generator(source_embedding, target_tensor) target_attribute = target_attributes[-1] From 94571c567673396ede7b1bf78ea7dc7c948d8669 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 08:39:35 +0100 Subject: [PATCH 351/394] Rename attribute to feature --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/models/generator.py | 12 ++++++------ face_swapper/src/models/loss.py | 18 +++++++++--------- face_swapper/src/networks/aad.py | 24 ++++++++++++------------ face_swapper/src/networks/masknet.py | 6 +++--- face_swapper/src/networks/unet.py | 22 ++++++++++++---------- face_swapper/src/training.py | 24 ++++++++++++------------ face_swapper/src/types.py | 2 +- face_swapper/tests/test_networks.py | 10 +++++----- 10 files changed, 62 insertions(+), 60 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 399b69e..e95d853 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -76,7 +76,7 @@ num_filters = 16 ``` [training.losses] adversarial_weight = 1.0 -attribute_weight = 10.0 +feature_weight = 10.0 reconstruction_weight = 10.0 identity_weight = 20.0 gaze_weight = 0.05 diff --git a/face_swapper/config.ini b/face_swapper/config.ini index eec38bd..113a0e1 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -36,7 +36,7 @@ num_filters = [training.losses] adversarial_weight = -attribute_weight = +feature_weight = reconstruction_weight = identity_weight = gaze_weight = diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index d083614..0084d1e 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -5,7 +5,7 @@ from torch import Tensor, nn from ..networks.aad import AAD from ..networks.unet import UNet -from ..types import Attribute, Embedding +from ..types import Feature, Embedding class Generator(nn.Module): @@ -16,12 +16,12 @@ class Generator(nn.Module): self.encoder.apply(init_weight) self.generator.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Attribute, ...]]: - target_attributes = self.encode_attributes(target_tensor) - output_tensor = self.generator(source_embedding, target_attributes) - return output_tensor, target_attributes + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Feature, ...]]: + target_features = self.encode_features(target_tensor) + output_tensor = self.generator(source_embedding, target_features) + return output_tensor, target_features - def encode_attributes(self, input_tensor : Tensor) -> Tuple[Attribute, ...]: + def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]: return self.encoder(input_tensor) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 08835f6..37034c5 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Attribute, EmbedderModule, FaceParserModule, GazerModule, Loss, Mask, MotionExtractorModule +from ..types import Feature, EmbedderModule, FaceParserModule, GazerModule, Loss, Mask, MotionExtractorModule class DiscriminatorLoss(nn.Module): @@ -49,22 +49,22 @@ class AdversarialLoss(nn.Module): return adversarial_loss, weighted_adversarial_loss -class AttributeLoss(nn.Module): +class FeautureLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') - self.config_attribute_weight = config_parser.getfloat('training.losses', 'attribute_weight') + self.config_feature_weight = config_parser.getfloat('training.losses', 'feature_weight') - def forward(self, target_attributes : Tuple[Attribute, ...], output_attributes : Tuple[Attribute, ...]) -> Tuple[Loss, Loss]: + def forward(self, target_features : Tuple[Feature, ...], output_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: temp_tensors = [] - for target_attribute, output_attribute in zip(target_attributes, output_attributes): - temp_tensor = torch.mean(torch.pow(output_attribute - target_attribute, 2).reshape(self.config_batch_size, -1), dim = 1).mean() + for target_feature, output_feature in zip(target_features, output_features): + temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean() temp_tensors.append(temp_tensor) - attribute_loss = torch.stack(temp_tensors).mean() * 0.5 - weighted_attribute_loss = attribute_loss * self.config_attribute_weight - return attribute_loss, weighted_attribute_loss + feature_loss = torch.stack(temp_tensors).mean() * 0.5 + weighted_feature_loss = feature_loss * self.config_feature_weight + return feature_loss, weighted_feature_loss class ReconstructionLoss(nn.Module): diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 7babfbe..b2211e2 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -4,7 +4,7 @@ from typing import Tuple import torch from torch import Tensor, nn -from ..types import Attribute, Embedding +from ..types import Feature, Embedding class AAD(nn.Module): @@ -57,16 +57,16 @@ class AAD(nn.Module): return layers - def forward(self, source_embedding : Embedding, target_attributes : Tuple[Attribute, ...]) -> Tensor: + def forward(self, source_embedding : Embedding, target_features : Tuple[Feature, ...]) -> Tensor: temp_tensors = self.pixel_shuffle_up_sample(source_embedding) for index, layer in enumerate(self.layers[:-1]): - target_attribute = target_attributes[index] - temp_tensor = layer(temp_tensors, source_embedding, target_attribute) + target_feature = target_features[index] + temp_tensor = layer(temp_tensors, source_embedding, target_feature) temp_tensors = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) - target_attribute = target_attributes[-1] - temp_tensors = self.layers[-1](temp_tensors, source_embedding, target_attribute) + target_feature = target_features[-1] + temp_tensors = self.layers[-1](temp_tensors, source_embedding, target_feature) output_tensor = torch.tanh(temp_tensors) return output_tensor @@ -112,12 +112,12 @@ class AdaptiveFeatureModulation(nn.Module): return shortcut_layers - def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_attribute : Attribute) -> Tensor: + def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_feature : Feature) -> Tensor: primary_tensor = input_tensor for primary_layer in self.primary_layers: if isinstance(primary_layer, FeatureModulation): - primary_tensor = primary_layer(primary_tensor, source_embedding, target_attribute) + primary_tensor = primary_layer(primary_tensor, source_embedding, target_feature) else: primary_tensor = primary_layer(primary_tensor) @@ -126,7 +126,7 @@ class AdaptiveFeatureModulation(nn.Module): for shortcut_layer in self.shortcut_layers: if isinstance(shortcut_layer, FeatureModulation): - shortcut_tensor = shortcut_layer(shortcut_tensor, source_embedding, target_attribute) + shortcut_tensor = shortcut_layer(shortcut_tensor, source_embedding, target_feature) else: shortcut_tensor = shortcut_layer(shortcut_tensor) @@ -146,15 +146,15 @@ class FeatureModulation(nn.Module): self.linear2 = nn.Linear(source_channels, input_channels) self.instance_norm = nn.InstanceNorm2d(input_channels) - def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_attribute : Attribute) -> Tensor: + def forward(self, input_tensor : Tensor, source_embedding : Embedding, target_feature : Feature) -> Tensor: temp_tensor = self.instance_norm(input_tensor) source_scale = self.linear2(source_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) source_shift = self.linear1(source_embedding).reshape(temp_tensor.shape[0], self.context_input_channels, 1, 1).expand_as(temp_tensor) source_modulation = source_scale * temp_tensor + source_shift - target_scale = self.conv1(target_attribute) - target_shift = self.conv2(target_attribute) + target_scale = self.conv1(target_feature) + target_shift = self.conv2(target_feature) target_modulation = target_scale * temp_tensor + target_shift temp_mask = torch.sigmoid(self.conv3(temp_tensor)) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 0a465e4..2b66467 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -3,7 +3,7 @@ from configparser import ConfigParser import torch from torch import Tensor, nn -from ..types import Attribute, Mask +from ..types import Feature, Mask class MaskNet(nn.Module): @@ -34,8 +34,8 @@ class MaskNet(nn.Module): UpSample(num_filters, num_filters) ]) - def forward(self, input_tensor : Tensor, input_attribute : Attribute) -> Mask: - output_mask = torch.cat([ input_tensor, input_attribute ], dim = 1) + def forward(self, input_tensor : Tensor, input_feature : Feature) -> Mask: + output_mask = torch.cat([ input_tensor, input_feature ], dim = 1) for down_sample in self.down_samples: output_mask = down_sample(output_mask) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index 1a27fe4..c102b18 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -4,6 +4,8 @@ from typing import Tuple import torch from torch import Tensor, nn +from face_swapper.src.types import Feature + class UNet(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: @@ -79,25 +81,25 @@ class UNet(nn.Module): return up_samples - def forward(self, target_tensor : Tensor) -> Tuple[Tensor, ...]: + def forward(self, target_tensor : Tensor) -> Tuple[Feature, ...]: down_features = [] up_features = [] - temp_tensor = target_tensor + temp_feature = target_tensor for down_sample in self.down_samples: - temp_tensor = down_sample(temp_tensor) - down_features.append(temp_tensor) + temp_feature = down_sample(temp_feature) + down_features.append(temp_feature) - bottleneck_tensor = down_features[-1] - temp_tensor = bottleneck_tensor + bottleneck_feature = down_features[-1] + temp_feature = bottleneck_feature for index, up_sample in enumerate(self.up_samples): skip_tensor = down_features[-(index + 2)] - temp_tensor = up_sample(temp_tensor, skip_tensor) - up_features.append(temp_tensor) + temp_feature = up_sample(temp_feature, skip_tensor) + up_features.append(temp_feature) - output_tensor = nn.functional.interpolate(temp_tensor, scale_factor = 2, mode = 'bilinear', align_corners = False) - return bottleneck_tensor, *up_features, output_tensor + final_feature = nn.functional.interpolate(temp_feature, scale_factor = 2, mode = 'bilinear', align_corners = False) + return bottleneck_feature, *up_features, final_feature class UpSample(nn.Module): diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 502c907..02df9aa 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, AttributeLoss, DiscriminatorLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, DiscriminatorLoss, FeautureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .networks.masknet import MaskNet from .types import Batch, Embedding, Mask, OptimizerSet @@ -45,7 +45,7 @@ class FaceSwapperTrainer(LightningModule): self.masker = MaskNet(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) - self.attribute_loss = AttributeLoss(config_parser) + self.feature_loss = FeautureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.embedder) self.identity_loss = IdentityLoss(config_parser, self.embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) @@ -55,9 +55,9 @@ class FaceSwapperTrainer(LightningModule): def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: with torch.no_grad(): - output_tensor, target_attributes = self.generator(source_embedding, target_tensor) - target_attribute = target_attributes[-1] - output_mask = self.masker(target_tensor, target_attribute) + output_tensor, target_features = self.generator(source_embedding, target_tensor) + target_feature = target_features[-1] + output_mask = self.masker(target_tensor, target_feature) return output_tensor, output_mask @@ -104,23 +104,23 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - generator_output_tensor, generator_target_attributes = self.generator(source_embedding, target_tensor) - generator_output_attributes = self.generator.encode_attributes(generator_output_tensor) + generator_output_tensor, generator_target_features = self.generator(source_embedding, target_tensor) + generator_output_features = self.generator.encode_features(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) - attribute_loss, weighted_attribute_loss = self.attribute_loss(generator_target_attributes, generator_output_attributes) + feature_loss, weighted_feature_loss = self.feature_loss(generator_target_features, generator_output_features) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - generator_loss = weighted_adversarial_loss + weighted_attribute_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + generator_loss = weighted_adversarial_loss + weighted_feature_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) - generator_output_attribute = generator_output_attributes[-1] - generator_output_mask = self.masker(generator_output_tensor.detach(), generator_output_attribute.detach()) + generator_output_feature = generator_output_features[-1] + generator_output_mask = self.masker(generator_output_tensor.detach(), generator_output_feature.detach()) mask_loss = self.mask_loss(target_tensor, generator_output_mask) self.toggle_optimizer(generator_optimizer) @@ -150,7 +150,7 @@ class FaceSwapperTrainer(LightningModule): self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) self.log('adversarial_loss', adversarial_loss) - self.log('attribute_loss', attribute_loss) + self.log('feature_loss', feature_loss) self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) self.log('pose_loss', pose_loss) diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 3789995..915741d 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -6,7 +6,7 @@ from torch.nn import Module Batch : TypeAlias = Tuple[Tensor, Tensor] BatchMode = Literal['equal', 'same', 'different'] -Attribute : TypeAlias = Tensor +Feature : TypeAlias = Tensor Embedding : TypeAlias = Tensor Mask : TypeAlias = Tensor Loss : TypeAlias = Tensor diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 37a9c1a..c0551be 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -22,14 +22,14 @@ def test_aad_with_unet(output_size : int) -> None: } }) - generator = AAD(config_parser).eval() encoder = UNet(config_parser).eval() + generator = AAD(config_parser).eval() source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, output_size, output_size) - target_attributes = encoder(target_tensor) - output_tensor = generator(source_tensor, target_attributes) + target_features = encoder(target_tensor) + output_tensor = generator(source_tensor, target_features) assert output_tensor.shape == (1, 3, output_size, output_size) @@ -50,8 +50,8 @@ def test_mask_net(output_size : int) -> None: masker = MaskNet(config_parser).eval() target_tensor = torch.randn(1, 3, output_size, output_size) - target_attribute = torch.randn(1, 64, output_size, output_size) + target_feature = torch.randn(1, 64, output_size, output_size) - output_tensor = masker(target_tensor, target_attribute) + output_tensor = masker(target_tensor, target_feature) assert output_tensor.shape == (1, 1, output_size, output_size) From eefc69a8207cfc5512b6dd53abcee30daa5220dc Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 08:41:31 +0100 Subject: [PATCH 352/394] Fix CI --- face_swapper/src/models/generator.py | 2 +- face_swapper/src/models/loss.py | 2 +- face_swapper/src/networks/aad.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 0084d1e..2d9183d 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -5,7 +5,7 @@ from torch import Tensor, nn from ..networks.aad import AAD from ..networks.unet import UNet -from ..types import Feature, Embedding +from ..types import Embedding, Feature class Generator(nn.Module): diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 37034c5..ef7a296 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import Feature, EmbedderModule, FaceParserModule, GazerModule, Loss, Mask, MotionExtractorModule +from ..types import EmbedderModule, FaceParserModule, Feature, GazerModule, Loss, Mask, MotionExtractorModule class DiscriminatorLoss(nn.Module): diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index b2211e2..880065e 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -4,7 +4,7 @@ from typing import Tuple import torch from torch import Tensor, nn -from ..types import Feature, Embedding +from ..types import Embedding, Feature class AAD(nn.Module): From bc1b04a107ce0680273c089dc1d284c48f3f9174 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 08:59:08 +0100 Subject: [PATCH 353/394] Hotfix imprt --- face_swapper/src/networks/unet.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index c102b18..c12f3da 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -4,7 +4,7 @@ from typing import Tuple import torch from torch import Tensor, nn -from face_swapper.src.types import Feature +from ..types import Feature class UNet(nn.Module): From 803902c8bb01b16bf4fc03bc6b9017da62dddb14 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 09:01:40 +0100 Subject: [PATCH 354/394] Add sync_dist to validation score logging --- embedding_converter/src/training.py | 2 +- face_swapper/src/training.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/embedding_converter/src/training.py b/embedding_converter/src/training.py index 8333fc8..fbd5e39 100644 --- a/embedding_converter/src/training.py +++ b/embedding_converter/src/training.py @@ -46,7 +46,7 @@ class EmbeddingConverterTrainer(LightningModule): source_embedding = self.source_embedder(batch) output_embedding = self(source_embedding) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 - self.log('validation_score', validation_score, prog_bar = True) + self.log('validation_score', validation_score, sync_dist = True, prog_bar = True) return validation_score def configure_optimizers(self) -> OptimizerSet: diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 02df9aa..7ab2936 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -164,7 +164,7 @@ class FaceSwapperTrainer(LightningModule): output_tensor, _ = self.generator(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 - self.log('validation_score', validation_score, prog_bar = True) + self.log('validation_score', validation_score, sync_dist = True, prog_bar = True) return validation_score def generate_preview(self, source_tensor : Tensor, target_tensor : Tensor, output_tensor : Tensor, output_mask : Mask) -> None: From ad675ae633c5db73eed81b67fa2f4e8d8989f80b Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 12:23:08 +0100 Subject: [PATCH 355/394] Join MaskNet to guide generator --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/helper.py | 1 + face_swapper/src/inferencing.py | 2 +- face_swapper/src/models/generator.py | 11 +++++--- face_swapper/src/models/loss.py | 6 +++-- face_swapper/src/training.py | 40 ++++++---------------------- 7 files changed, 24 insertions(+), 38 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index e95d853..e80e484 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -82,6 +82,7 @@ identity_weight = 20.0 gaze_weight = 0.05 pose_weight = 0.05 expression_weight = 0.05 +mask_weight = 0.5 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 113a0e1..29443e6 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -42,6 +42,7 @@ identity_weight = gaze_weight = pose_weight = expression_weight = +mask_weight = [training.trainer] accumulate_size = diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index b1377d5..695d541 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -39,6 +39,7 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P def overlay_mask(input_tensor : Tensor, input_mask : Mask) -> Tensor: + input_mask = input_mask.mean(dim = 1, keepdim = True) overlay_tensor = torch.zeros(*input_tensor.shape, dtype = input_tensor.dtype, device = input_tensor.device) overlay_tensor[:, 2, :, :] = 1 input_mask = input_mask.repeat(1, 3, 1, 1).clamp(0, 0.8) diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 5182e00..2643195 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -26,5 +26,5 @@ def infer() -> None: source_tensor = io.read_image(config_source_path) target_tensor = io.read_image(config_target_path) source_embedding = calc_embedding(embedder, source_tensor, (0, 0, 0, 0)) - output_tensor = generator(source_embedding, target_tensor)[0] + output_tensor, _ = generator(source_embedding, target_tensor) io.write_jpeg(output_tensor, config_output_path) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 2d9183d..73d4fea 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -5,7 +5,8 @@ from torch import Tensor, nn from ..networks.aad import AAD from ..networks.unet import UNet -from ..types import Embedding, Feature +from ..networks.masknet import MaskNet +from ..types import Embedding, Feature, Mask class Generator(nn.Module): @@ -13,13 +14,17 @@ class Generator(nn.Module): super().__init__() self.encoder = UNet(config_parser) self.generator = AAD(config_parser) + self.masker = MaskNet(config_parser) self.encoder.apply(init_weight) self.generator.apply(init_weight) + self.masker.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Tuple[Feature, ...]]: + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: target_features = self.encode_features(target_tensor) output_tensor = self.generator(source_embedding, target_features) - return output_tensor, target_features + target_feature = target_features[-1] + output_mask = self.masker(target_tensor, target_feature) + return output_tensor, output_mask def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]: return self.encoder(input_tensor) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ef7a296..2b1a36e 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -182,16 +182,18 @@ class GazeLoss(nn.Module): class MaskLoss(nn.Module): def __init__(self, config_parser : ConfigParser, face_parser : FaceParserModule) -> None: super().__init__() + self.config_mask_weight = config_parser.getfloat('training.losses', 'mask_weight') self.config_output_size = config_parser.getint('training.model.generator', 'output_size') self.face_parser = face_parser self.mse_loss = nn.MSELoss() - def forward(self, target_tensor : Tensor, output_mask : Mask) -> Loss: + def forward(self, target_tensor : Tensor, output_mask : Mask) -> Tuple[Loss, Loss]: target_mask = self.calc_mask(target_tensor) target_mask = target_mask.view(-1, self.config_output_size, self.config_output_size) output_mask = output_mask.view(-1, self.config_output_size, self.config_output_size) mask_loss = self.mse_loss(target_mask, output_mask) - return mask_loss + weighted_mask_loss = mask_loss * self.config_mask_weight + return mask_loss, weighted_mask_loss def calc_mask(self, target_tensor : Tensor) -> Tensor: target_tensor = torch.nn.functional.interpolate(target_tensor, (512, 512), mode = 'bilinear') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7ab2936..a1ef480 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -17,7 +17,6 @@ from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator from .models.loss import AdversarialLoss, DiscriminatorLoss, FeautureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss -from .networks.masknet import MaskNet from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -42,7 +41,6 @@ class FaceSwapperTrainer(LightningModule): self.face_parser = torch.jit.load(self.config_face_parser_path, map_location ='cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) - self.masker = MaskNet(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) self.feature_loss = FeautureLoss(config_parser) @@ -55,19 +53,15 @@ class FaceSwapperTrainer(LightningModule): def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: with torch.no_grad(): - output_tensor, target_features = self.generator(source_embedding, target_tensor) - target_feature = target_features[-1] - output_mask = self.masker(target_tensor, target_feature) + output_tensor, output_mask = self.generator(source_embedding, target_tensor) return output_tensor, output_mask - def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet, OptimizerSet]: + def configure_optimizers(self) -> Tuple[OptimizerSet, OptimizerSet]: generator_optimizer = torch.optim.AdamW(self.generator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) discriminator_optimizer = torch.optim.AdamW(self.discriminator.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) - masker_optimizer = torch.optim.AdamW(self.masker.parameters(), lr = self.config_learning_rate, betas = (0.0, 0.999), weight_decay = 1e-4) generator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(generator_optimizer, T_0 = 300, T_mult = 2) discriminator_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(discriminator_optimizer, T_0 = 300, T_mult = 2) - masker_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(masker_optimizer, T_0 = 300, T_mult = 2) generator_config =\ { @@ -87,24 +81,16 @@ class FaceSwapperTrainer(LightningModule): 'interval': 'step' } } - masker_config =\ - { - 'optimizer': masker_optimizer, - 'lr_scheduler': - { - 'scheduler': masker_scheduler, - 'interval': 'step' - } - } - return generator_config, discriminator_config, masker_config + return generator_config, discriminator_config def training_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch do_update = (batch_index + 1) % self.config_accumulate_size == 0 - generator_optimizer, discriminator_optimizer, masker_optimizer = self.optimizers() #type:ignore[attr-defined] + generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - generator_output_tensor, generator_target_features = self.generator(source_embedding, target_tensor) + generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor) + generator_target_features = self.generator.encode_features(target_tensor) generator_output_features = self.generator.encode_features(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) @@ -113,16 +99,13 @@ class FaceSwapperTrainer(LightningModule): identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) - generator_loss = weighted_adversarial_loss + weighted_feature_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + mask_loss, weighted_mask_loss = self.mask_loss(target_tensor, generator_output_mask) + generator_loss = weighted_adversarial_loss + weighted_feature_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + weighted_mask_loss discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) discriminator_loss = self.discriminator_loss(discriminator_source_tensors, discriminator_output_tensors) - generator_output_feature = generator_output_features[-1] - generator_output_mask = self.masker(generator_output_tensor.detach(), generator_output_feature.detach()) - mask_loss = self.mask_loss(target_tensor, generator_output_mask) - self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss) if do_update: @@ -137,13 +120,6 @@ class FaceSwapperTrainer(LightningModule): discriminator_optimizer.zero_grad() self.untoggle_optimizer(discriminator_optimizer) - self.toggle_optimizer(masker_optimizer) - self.manual_backward(mask_loss) - if do_update: - masker_optimizer.step() - masker_optimizer.zero_grad() - self.untoggle_optimizer(masker_optimizer) - if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) From c45fcbba84411b0e47316daa6242241d6028525f Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 12:24:08 +0100 Subject: [PATCH 356/394] Join MaskNet to guide generator --- face_swapper/src/models/generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 73d4fea..5ed2821 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -4,8 +4,8 @@ from typing import Tuple from torch import Tensor, nn from ..networks.aad import AAD -from ..networks.unet import UNet from ..networks.masknet import MaskNet +from ..networks.unet import UNet from ..types import Embedding, Feature, Mask From 24f2e14a95bcb365e5eae8d84588ebc5e02e7c81 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 12:29:17 +0100 Subject: [PATCH 357/394] Join MaskNet to guide generator --- face_swapper/README.md | 2 +- face_swapper/src/helper.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index e80e484..7a74819 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -94,7 +94,7 @@ strategy = auto precision = 16-mixed logger_path = .logs logger_name = face_swapper -preview_frequency = 250 +preview_frequency = 100 ``` ``` diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 695d541..b1377d5 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -39,7 +39,6 @@ def calc_embedding(embedder : EmbedderModule, input_tensor : Tensor, padding : P def overlay_mask(input_tensor : Tensor, input_mask : Mask) -> Tensor: - input_mask = input_mask.mean(dim = 1, keepdim = True) overlay_tensor = torch.zeros(*input_tensor.shape, dtype = input_tensor.dtype, device = input_tensor.device) overlay_tensor[:, 2, :, :] = 1 input_mask = input_mask.repeat(1, 3, 1, 1).clamp(0, 0.8) From 798ff877369a699637b803e4029e6c0bc23636b9 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 16 Mar 2025 15:18:28 +0100 Subject: [PATCH 358/394] Adjust naming --- face_swapper/tests/test_networks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index c0551be..654a172 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -52,6 +52,6 @@ def test_mask_net(output_size : int) -> None: target_tensor = torch.randn(1, 3, output_size, output_size) target_feature = torch.randn(1, 64, output_size, output_size) - output_tensor = masker(target_tensor, target_feature) + output_mask = masker(target_tensor, target_feature) - assert output_tensor.shape == (1, 1, output_size, output_size) + assert output_mask.shape == (1, 1, output_size, output_size) From 10b6f801d1d27a5d085aea312d5890b8a291e4cc Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 18 Mar 2025 21:43:49 +0530 Subject: [PATCH 359/394] add mask blend --- face_swapper/src/models/generator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 5ed2821..4f5a2f5 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -24,6 +24,7 @@ class Generator(nn.Module): output_tensor = self.generator(source_embedding, target_features) target_feature = target_features[-1] output_mask = self.masker(target_tensor, target_feature) + output_tensor = output_tensor * output_mask + target_tensor * (1 - output_mask) return output_tensor, output_mask def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]: From b6a2734622526bafd677ca4256cd68934f750f0b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 19 Mar 2025 19:27:09 +0530 Subject: [PATCH 360/394] add masknet layer --- face_swapper/src/networks/masknet.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/networks/masknet.py b/face_swapper/src/networks/masknet.py index 2b66467..023b767 100644 --- a/face_swapper/src/networks/masknet.py +++ b/face_swapper/src/networks/masknet.py @@ -14,7 +14,7 @@ class MaskNet(nn.Module): self.config_num_filters = config_parser.getint('training.model.masker', 'num_filters') self.down_samples = self.create_down_samples(self.config_input_channels, self.config_num_filters) self.up_samples = self.create_up_samples(self.config_num_filters) - self.bottleneck = BottleNeck(self.config_num_filters * 2) + self.bottleneck = BottleNeck(self.config_num_filters * 4) self.conv = nn.Conv2d(self.config_num_filters, self.config_output_channels, kernel_size = 1) self.sigmoid = nn.Sigmoid() @@ -23,13 +23,15 @@ class MaskNet(nn.Module): return nn.ModuleList( [ DownSample(input_channels, num_filters), - DownSample(num_filters, num_filters * 2) + DownSample(num_filters, num_filters * 2), + DownSample(num_filters * 2, num_filters * 4) ]) @staticmethod def create_up_samples(num_filters : int) -> nn.ModuleList: return nn.ModuleList( [ + UpSample(num_filters * 4, num_filters * 2), UpSample(num_filters * 2, num_filters), UpSample(num_filters, num_filters) ]) From 35afb426b7b8f00889767bda8e42fd8041116fdc Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 20 Mar 2025 11:01:54 +0530 Subject: [PATCH 361/394] add expression loss to log --- face_swapper/src/training.py | 1 + 1 file changed, 1 insertion(+) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index a1ef480..b53feac 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -130,6 +130,7 @@ class FaceSwapperTrainer(LightningModule): self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) self.log('pose_loss', pose_loss) + self.log('expression_loss', expression_loss) self.log('gaze_loss', gaze_loss) self.log('mask_loss', mask_loss) return generator_loss From 9153b4ce8ff12860e4a376c5dee9265c009b6d08 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sat, 22 Mar 2025 14:14:37 +0530 Subject: [PATCH 362/394] add 'ffhq_to_arcface_128_v2' template --- face_swapper/src/helper.py | 5 +++++ face_swapper/src/types.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index b1377d5..0839a40 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -10,6 +10,11 @@ WARP_TEMPLATE_SET : WarpTemplateSet =\ [ 1.01305414, -0.00140513, -0.00585911 ], [ 0.00140513, 1.01305414, 0.11169602 ] ]), + 'ffhq_to_arcface_128_v2': torch.tensor( + [ + [ 8.50048894e-01, -1.29486822e-04, 1.90956388e-03 ], + [ 1.29486822e-04, 8.50048894e-01, 9.56254653e-02 ] + ]), 'arcface_128_v2_to_arcface_112_v2': torch.tensor( [ [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 915741d..417f6b7 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -21,5 +21,5 @@ FaceParserModule : TypeAlias = Module OptimizerSet : TypeAlias = Any -WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] +WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'ffhq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] WarpTemplateSet : TypeAlias = Dict[WarpTemplate, Tensor] From 583d09e666844fcde3bc1273acb83037cb1e3483 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:01:38 +0530 Subject: [PATCH 363/394] change face-parser --- face_swapper/src/models/loss.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2b1a36e..2549af0 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -196,14 +196,12 @@ class MaskLoss(nn.Module): return mask_loss, weighted_mask_loss def calc_mask(self, target_tensor : Tensor) -> Tensor: - target_tensor = torch.nn.functional.interpolate(target_tensor, (512, 512), mode = 'bilinear') - face_mask_regions = torch.tensor([ 1, 2, 3, 4, 5, 10, 11, 12, 13 ]).to(target_tensor.device) + target_tensor = torch.nn.functional.interpolate(target_tensor, (256, 256), mode = 'bilinear') + target_tensor = (target_tensor.clip(-1, 1) + 1) * 0.5 with torch.no_grad(): - output_tensor = self.face_parser(target_tensor)[0] - output_tensor = output_tensor.argmax(1) - output_tensor = torch.isin(output_tensor, face_mask_regions).to(target_tensor.dtype) - output_tensor = output_tensor.view(-1, 1, 512, 512) + output_tensor = self.face_parser(target_tensor) + output_tensor = output_tensor.clamp(0, 1) output_tensor = torch.nn.functional.interpolate(output_tensor, (self.config_output_size, self.config_output_size), mode = 'bilinear') return output_tensor From c1f39a73dd4eab8eece21142504bd5d1ed9d9496 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:18:10 +0530 Subject: [PATCH 364/394] change face-parser to face-masker --- face_swapper/README.md | 2 +- face_swapper/config.ini | 2 +- face_swapper/src/models/loss.py | 8 ++++---- face_swapper/src/training.py | 6 +++--- face_swapper/src/types.py | 2 +- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 7a74819..4cdc960 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -46,7 +46,7 @@ split_ratio = 0.9995 embedder_path = .models/arcface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt -face_parser_path = .models/face_parser.pt +face_masker_path = .models/face_masker.pt ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 29443e6..7377124 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -14,7 +14,7 @@ split_ratio = embedder_path = gazer_path = motion_extractor_path = -face_parser_path = +face_masker_path = [training.model.generator] source_channels = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2549af0..6f30ba3 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import EmbedderModule, FaceParserModule, Feature, GazerModule, Loss, Mask, MotionExtractorModule +from ..types import EmbedderModule, FaceMaskerModule, Feature, GazerModule, Loss, Mask, MotionExtractorModule class DiscriminatorLoss(nn.Module): @@ -180,11 +180,11 @@ class GazeLoss(nn.Module): class MaskLoss(nn.Module): - def __init__(self, config_parser : ConfigParser, face_parser : FaceParserModule) -> None: + def __init__(self, config_parser : ConfigParser, face_masker : FaceMaskerModule) -> None: super().__init__() self.config_mask_weight = config_parser.getfloat('training.losses', 'mask_weight') self.config_output_size = config_parser.getint('training.model.generator', 'output_size') - self.face_parser = face_parser + self.face_masker = face_masker self.mse_loss = nn.MSELoss() def forward(self, target_tensor : Tensor, output_mask : Mask) -> Tuple[Loss, Loss]: @@ -200,7 +200,7 @@ class MaskLoss(nn.Module): target_tensor = (target_tensor.clip(-1, 1) + 1) * 0.5 with torch.no_grad(): - output_tensor = self.face_parser(target_tensor) + output_tensor = self.face_masker(target_tensor) output_tensor = output_tensor.clamp(0, 1) output_tensor = torch.nn.functional.interpolate(output_tensor, (self.config_output_size, self.config_output_size), mode = 'bilinear') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index b53feac..c105652 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -31,14 +31,14 @@ class FaceSwapperTrainer(LightningModule): self.config_embedder_path = config_parser.get('training.model', 'embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') - self.config_face_parser_path = config_parser.get('training.model', 'face_parser_path') + self.config_face_masker_path = config_parser.get('training.model', 'face_masker_path') self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() - self.face_parser = torch.jit.load(self.config_face_parser_path, map_location ='cpu').eval() + self.face_masker = torch.jit.load(self.config_face_masker_path, map_location ='cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() @@ -48,7 +48,7 @@ class FaceSwapperTrainer(LightningModule): self.identity_loss = IdentityLoss(config_parser, self.embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) self.gaze_loss = GazeLoss(config_parser, self.gazer) - self.mask_loss = MaskLoss(config_parser, self.face_parser) + self.mask_loss = MaskLoss(config_parser, self.face_masker) self.automatic_optimization = False def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 417f6b7..7c0c1c9 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -17,7 +17,7 @@ GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module MotionExtractorModule : TypeAlias = Module -FaceParserModule : TypeAlias = Module +FaceMaskerModule : TypeAlias = Module OptimizerSet : TypeAlias = Any From c85c755e00777d113d100e29437b0d39d180f40e Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:34:23 +0530 Subject: [PATCH 365/394] changes --- face_swapper/src/models/generator.py | 5 ++--- face_swapper/src/training.py | 7 ++++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 4f5a2f5..81c81d9 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -19,13 +19,12 @@ class Generator(nn.Module): self.generator.apply(init_weight) self.masker.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: - target_features = self.encode_features(target_tensor) + def forward(self, source_embedding : Embedding, target_tensor : Tensor, target_features : Tuple[Feature, ...]) -> Tuple[Tensor, Mask, ]: output_tensor = self.generator(source_embedding, target_features) target_feature = target_features[-1] output_mask = self.masker(target_tensor, target_feature) output_tensor = output_tensor * output_mask + target_tensor * (1 - output_mask) - return output_tensor, output_mask + return output_tensor, output_mask, def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]: return self.encoder(input_tensor) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index c105652..19502cd 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -53,7 +53,8 @@ class FaceSwapperTrainer(LightningModule): def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: with torch.no_grad(): - output_tensor, output_mask = self.generator(source_embedding, target_tensor) + generator_target_features = self.generator.encode_features(target_tensor) + output_tensor, output_mask = self.generator(source_embedding, target_tensor, generator_target_features) return output_tensor, output_mask @@ -89,8 +90,8 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor) generator_target_features = self.generator.encode_features(target_tensor) + generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features) generator_output_features = self.generator.encode_features(generator_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) @@ -138,7 +139,7 @@ class FaceSwapperTrainer(LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - output_tensor, _ = self.generator(source_embedding, target_tensor) + output_tensor, _ = self(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, sync_dist = True, prog_bar = True) From 9ede8a2a7def5b021f8f71f6362587418110a823 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:38:48 +0530 Subject: [PATCH 366/394] changes --- face_swapper/src/models/generator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/generator.py b/face_swapper/src/models/generator.py index 81c81d9..ab76154 100644 --- a/face_swapper/src/models/generator.py +++ b/face_swapper/src/models/generator.py @@ -19,12 +19,12 @@ class Generator(nn.Module): self.generator.apply(init_weight) self.masker.apply(init_weight) - def forward(self, source_embedding : Embedding, target_tensor : Tensor, target_features : Tuple[Feature, ...]) -> Tuple[Tensor, Mask, ]: + def forward(self, source_embedding : Embedding, target_tensor : Tensor, target_features : Tuple[Feature, ...]) -> Tuple[Tensor, Mask]: output_tensor = self.generator(source_embedding, target_features) target_feature = target_features[-1] output_mask = self.masker(target_tensor, target_feature) output_tensor = output_tensor * output_mask + target_tensor * (1 - output_mask) - return output_tensor, output_mask, + return output_tensor, output_mask def encode_features(self, input_tensor : Tensor) -> Tuple[Feature, ...]: return self.encoder(input_tensor) From 602e890af2ceed43fe59b6319878240ee2b86cca Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:45:35 +0530 Subject: [PATCH 367/394] changes --- face_swapper/src/training.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 19502cd..b928679 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -139,7 +139,7 @@ class FaceSwapperTrainer(LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) - output_tensor, _ = self(source_embedding, target_tensor) + output_tensor, _ = self.forward(source_embedding, target_tensor) output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, sync_dist = True, prog_bar = True) From 99a8527e247bbdd5042fc7ecbe03133ab31a532b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Sun, 23 Mar 2025 18:48:45 +0530 Subject: [PATCH 368/394] changes --- face_swapper/src/exporting.py | 1 - face_swapper/src/inferencing.py | 7 ++----- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 31fc078..4b2f4f6 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -1,7 +1,6 @@ import os from configparser import ConfigParser - import torch from .training import FaceSwapperTrainer diff --git a/face_swapper/src/inferencing.py b/face_swapper/src/inferencing.py index 2643195..6f81b2d 100644 --- a/face_swapper/src/inferencing.py +++ b/face_swapper/src/inferencing.py @@ -4,7 +4,7 @@ import torch from torchvision import io from .helper import calc_embedding -from .models.generator import Generator +from .training import FaceSwapperTrainer CONFIG_PARSER = configparser.ConfigParser() CONFIG_PARSER.read('config.ini') @@ -17,10 +17,7 @@ def infer() -> None: config_target_path = CONFIG_PARSER.get('inferencing', 'target_path') config_output_path = CONFIG_PARSER.get('inferencing', 'output_path') - state_dict = torch.load(config_generator_path).get('state_dict').get('generator') - generator = Generator(CONFIG_PARSER) - generator.load_state_dict(state_dict) - generator.eval() + generator = FaceSwapperTrainer.load_from_checkpoint(config_generator_path, config_parser = CONFIG_PARSER, map_location = 'cpu').eval() embedder = torch.jit.load(config_embedder_path, map_location = 'cpu').eval() source_tensor = io.read_image(config_source_path) From 4ebdeee634275b21d7d1c1e45d213b63539d0325 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Mar 2025 16:21:40 +0100 Subject: [PATCH 369/394] Alphabetical order for warp templates --- face_swapper/src/helper.py | 12 ++++++------ face_swapper/src/types.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/helper.py b/face_swapper/src/helper.py index 0839a40..ff9d4d2 100644 --- a/face_swapper/src/helper.py +++ b/face_swapper/src/helper.py @@ -5,20 +5,20 @@ from .types import EmbedderModule, Embedding, Mask, Padding, WarpTemplate, WarpT WARP_TEMPLATE_SET : WarpTemplateSet =\ { - 'vgg_face_hq_to_arcface_128_v2': torch.tensor( + 'arcface_128_v2_to_arcface_112_v2': torch.tensor( [ - [ 1.01305414, -0.00140513, -0.00585911 ], - [ 0.00140513, 1.01305414, 0.11169602 ] + [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], + [ 1.07193451e-08, 8.75000016e-01, -1.25000007e-01 ] ]), 'ffhq_to_arcface_128_v2': torch.tensor( [ [ 8.50048894e-01, -1.29486822e-04, 1.90956388e-03 ], [ 1.29486822e-04, 8.50048894e-01, 9.56254653e-02 ] ]), - 'arcface_128_v2_to_arcface_112_v2': torch.tensor( + 'vgg_face_hq_to_arcface_128_v2': torch.tensor( [ - [ 8.75000016e-01, -1.07193451e-08, 3.80446920e-10 ], - [ 1.07193451e-08, 8.75000016e-01, -1.25000007e-01 ] + [ 1.01305414, -0.00140513, -0.00585911 ], + [ 0.00140513, 1.01305414, 0.11169602 ] ]) } diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 7c0c1c9..342dc6f 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -21,5 +21,5 @@ FaceMaskerModule : TypeAlias = Module OptimizerSet : TypeAlias = Any -WarpTemplate = Literal['vgg_face_hq_to_arcface_128_v2', 'ffhq_to_arcface_128_v2', 'arcface_128_v2_to_arcface_112_v2'] +WarpTemplate = Literal['arcface_128_v2_to_arcface_112_v2', 'ffhq_to_arcface_128_v2', 'vgg_face_hq_to_arcface_128_v2'] WarpTemplateSet : TypeAlias = Dict[WarpTemplate, Tensor] From 4f4057fc5423e86a671a411b8b8e7e3c168eafc3 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Mar 2025 16:33:25 +0100 Subject: [PATCH 370/394] Adjust suggested defaults --- face_swapper/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 4cdc960..13889e6 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -43,7 +43,7 @@ split_ratio = 0.9995 ``` [training.model] -embedder_path = .models/arcface.pt +embedder_path = .models/blendface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt face_masker_path = .models/face_masker.pt @@ -82,7 +82,7 @@ identity_weight = 20.0 gaze_weight = 0.05 pose_weight = 0.05 expression_weight = 0.05 -mask_weight = 0.5 +mask_weight = 5.0 ``` ``` From 0743b99347aad6dd6b1457fb0135d48f53fe31b1 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 23 Mar 2025 21:36:03 +0100 Subject: [PATCH 371/394] Add HorizontalFlip to AugmentTransform --- face_swapper/README.md | 2 +- face_swapper/src/dataset.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 13889e6..7fd5f9e 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -1,7 +1,7 @@ Face Swapper ============ -> Face shape and feature aware identity transfer. +> Face shape and occlusion aware identity transfer. ![License](https://img.shields.io/badge/license-ResearchRAIL--MS-red) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index ebf7270..183bb41 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -84,12 +84,13 @@ class AugmentTransform: def compose_transforms() -> albumentations.Compose: return albumentations.Compose( [ - albumentations.RandomBrightnessContrast(p = 0.3), + albumentations.HorizontalFlip(), albumentations.OneOf( [ albumentations.MotionBlur(p = 0.1), albumentations.MedianBlur(p = 0.1) ], p = 0.3), + albumentations.RandomBrightnessContrast(p = 0.3), albumentations.ColorJitter(p = 0.1) ]) From d2be8a386aa831c45f425a6a1615d6854ff5284c Mon Sep 17 00:00:00 2001 From: Henry Ruhs Date: Mon, 24 Mar 2025 09:09:29 +0100 Subject: [PATCH 372/394] Update dependencies --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 103dce8..c354c4c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,8 @@ --extra-index-url https://download.pytorch.org/whl/cu124 albumentations==2.0.5 -lightning==2.5.0 +lightning==2.5.1 onnx==1.17.0 -onnxruntime==1.20.1 +onnxruntime==1.21.0 pytorch-msssim==1.0.0 torch==2.6.0 torchdata==0.11.0 From 117a9d0fc9fe9f338fd5959bb79519a4f99c0a4d Mon Sep 17 00:00:00 2001 From: henryruhs Date: Mon, 24 Mar 2025 15:08:59 +0100 Subject: [PATCH 373/394] Remove outdated file --- .flake8 | 1 - 1 file changed, 1 deletion(-) diff --git a/.flake8 b/.flake8 index 8363652..544f9ff 100644 --- a/.flake8 +++ b/.flake8 @@ -3,4 +3,3 @@ select = E22, E23, E24, E27, E3, E4, E7, F, I1, I2 plugins = flake8-import-order application_import_names = embedding_converter, face_swapper import-order-style = pycharm -per-file-ignores = preparing.py:E402 From 80e600cbb58076f747e09e018e878dee9c977224 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 26 Mar 2025 16:51:38 +0530 Subject: [PATCH 374/394] changes --- face_swapper/src/training.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index b928679..7c72136 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -9,7 +9,7 @@ from lightning import LightningModule, Trainer from lightning.pytorch.callbacks import ModelCheckpoint from lightning.pytorch.loggers import TensorBoardLogger from torch import Tensor, nn -from torch.utils.data import Dataset, random_split +from torch.utils.data import ConcatDataset, Dataset, random_split from torchdata.stateful_dataloader import StatefulDataLoader from .dataset import DynamicDataset @@ -179,6 +179,23 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T return training_dataset, validate_dataset +def combine_datasets() -> Dataset[Tensor]: + datasets = [] + + for config_section in CONFIG_PARSER.sections(): + + if config_section.startswith('training.dataset'): + dataset_config = ConfigParser() + dataset_config.add_section('training.dataset') + + for key, value in CONFIG_PARSER.items(config_section): + dataset_config.set('training.dataset', key, value) + datasets.append(DynamicDataset(dataset_config)) + + combined_dataset = ConcatDataset(datasets) + return combined_dataset + + def create_trainer() -> Trainer: config_max_epochs = CONFIG_PARSER.getint('training.trainer', 'max_epochs') config_strategy = CONFIG_PARSER.get('training.trainer', 'strategy') @@ -216,8 +233,8 @@ def train() -> None: if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - dataset = DynamicDataset(CONFIG_PARSER) - training_loader, validation_loader = create_loaders(dataset) + combine_dataset = combine_datasets() + training_loader, validation_loader = create_loaders(combine_dataset) face_swapper_trainer = FaceSwapperTrainer(CONFIG_PARSER) trainer = create_trainer() From 9df29f8a229b48fe23688df629984e3f755e7934 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 26 Mar 2025 16:54:05 +0530 Subject: [PATCH 375/394] changes --- face_swapper/src/training.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7c72136..7507f91 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -192,8 +192,8 @@ def combine_datasets() -> Dataset[Tensor]: dataset_config.set('training.dataset', key, value) datasets.append(DynamicDataset(dataset_config)) - combined_dataset = ConcatDataset(datasets) - return combined_dataset + combine_dataset = ConcatDataset(datasets) + return combine_dataset def create_trainer() -> Trainer: From cc6a99f305a69eb7a61943cf549d3f1cfa9b134a Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 26 Mar 2025 17:13:44 +0530 Subject: [PATCH 376/394] changes --- face_swapper/src/training.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 7507f91..026f2a8 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -1,7 +1,7 @@ import os import warnings from configparser import ConfigParser -from typing import Tuple +from typing import List, Tuple import torch import torchvision @@ -179,21 +179,21 @@ def split_dataset(dataset : Dataset[Tensor]) -> Tuple[Dataset[Tensor], Dataset[T return training_dataset, validate_dataset -def combine_datasets() -> Dataset[Tensor]: +def prepare_datasets(config_parser : ConfigParser) -> List[Dataset[Tensor]]: datasets = [] - for config_section in CONFIG_PARSER.sections(): + for config_section in config_parser.sections(): if config_section.startswith('training.dataset'): - dataset_config = ConfigParser() - dataset_config.add_section('training.dataset') + current_config_parser = ConfigParser() + current_config_parser.add_section('training.dataset') - for key, value in CONFIG_PARSER.items(config_section): - dataset_config.set('training.dataset', key, value) - datasets.append(DynamicDataset(dataset_config)) + for key, value in config_parser.items(config_section): + current_config_parser.set('training.dataset', key, value) - combine_dataset = ConcatDataset(datasets) - return combine_dataset + datasets.append(DynamicDataset(current_config_parser)) + + return datasets def create_trainer() -> Trainer: @@ -233,8 +233,8 @@ def train() -> None: if torch.cuda.is_available(): torch.set_float32_matmul_precision('high') - combine_dataset = combine_datasets() - training_loader, validation_loader = create_loaders(combine_dataset) + dataset = ConcatDataset(prepare_datasets(CONFIG_PARSER)) + training_loader, validation_loader = create_loaders(dataset) face_swapper_trainer = FaceSwapperTrainer(CONFIG_PARSER) trainer = create_trainer() From 4b851a173de5939a6bc3d00f1e2fdab6b8ce44c8 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 28 Mar 2025 16:32:44 +0530 Subject: [PATCH 377/394] changes --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/exporting.py | 22 ++++++++++++++++++++++ 3 files changed, 24 insertions(+) diff --git a/face_swapper/README.md b/face_swapper/README.md index 7fd5f9e..2ef47cb 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -112,6 +112,7 @@ target_path = .exports/face_swapper.onnx target_size = 256 ir_version = 10 opset_version = 15 +precision = fp16 ``` ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 7377124..dade0ca 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -66,6 +66,7 @@ target_path = target_size = ir_version = opset_version = +precision = [inferencing] generator_path = diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index 4b2f4f6..cdcbd29 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -1,14 +1,31 @@ import os from configparser import ConfigParser +from typing import Tuple import torch +from torch import Tensor, nn from .training import FaceSwapperTrainer +from .types import Embedding, Mask, Module CONFIG_PARSER = ConfigParser() CONFIG_PARSER.read('config.ini') +class HalfPrecisionModel(nn.Module): + def __init__(self, model : Module) -> None: + super().__init__() + self.model = model.half() + + def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]: + source_embedding = source_embedding.half() + target_tensor = target_tensor.half() + output_tensor, output_mask = self.model(source_embedding, target_tensor) + output_tensor = output_tensor.float() + output_mask = output_mask.float() + return output_tensor, output_mask + + def export() -> None: config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path') config_source_path = CONFIG_PARSER.get('exporting', 'source_path') @@ -16,9 +33,14 @@ def export() -> None: config_target_size = CONFIG_PARSER.getint('exporting', 'target_size') config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version') config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version') + config_precision = CONFIG_PARSER.get('exporting', 'precision') os.makedirs(config_directory_path, exist_ok = True) model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, config_parser = CONFIG_PARSER, map_location = 'cpu').eval() + + if config_precision == 'fp16': + model = HalfPrecisionModel(model).eval() + model.ir_version = torch.tensor(config_ir_version) source_tensor = torch.randn(1, 512) target_tensor = torch.randn(1, 3, config_target_size, config_target_size) From 7845dd8522539ac9719db46b8b06a3f16f465e3e Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sat, 29 Mar 2025 09:56:23 +0100 Subject: [PATCH 378/394] Adjust like reviewed --- face_swapper/README.md | 2 +- face_swapper/src/exporting.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 2ef47cb..99cfe2b 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -112,7 +112,7 @@ target_path = .exports/face_swapper.onnx target_size = 256 ir_version = 10 opset_version = 15 -precision = fp16 +precision = full ``` ``` diff --git a/face_swapper/src/exporting.py b/face_swapper/src/exporting.py index cdcbd29..34916a0 100644 --- a/face_swapper/src/exporting.py +++ b/face_swapper/src/exporting.py @@ -12,7 +12,7 @@ CONFIG_PARSER = ConfigParser() CONFIG_PARSER.read('config.ini') -class HalfPrecisionModel(nn.Module): +class HalfPrecision(nn.Module): def __init__(self, model : Module) -> None: super().__init__() self.model = model.half() @@ -38,8 +38,8 @@ def export() -> None: os.makedirs(config_directory_path, exist_ok = True) model = FaceSwapperTrainer.load_from_checkpoint(config_source_path, config_parser = CONFIG_PARSER, map_location = 'cpu').eval() - if config_precision == 'fp16': - model = HalfPrecisionModel(model).eval() + if config_precision == 'half': + model = HalfPrecision(model).eval() model.ir_version = torch.tensor(config_ir_version) source_tensor = torch.randn(1, 512) From 4f5ac00a7bc37ae57efc722beaf234d82af24cfa Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 8 Apr 2025 12:29:07 +0530 Subject: [PATCH 379/394] changes --- face_swapper/README.md | 3 ++- face_swapper/config.ini | 3 ++- face_swapper/src/training.py | 16 +++++++++------- 3 files changed, 13 insertions(+), 9 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 99cfe2b..98b8375 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -43,7 +43,8 @@ split_ratio = 0.9995 ``` [training.model] -embedder_path = .models/blendface.pt +generator_embedder_path = .models/blendface.pt +loss_embedder_path = .models/adaface.pt gazer_path = .models/gazer.pt motion_extractor_path = .models/motion_extractor.pt face_masker_path = .models/face_masker.pt diff --git a/face_swapper/config.ini b/face_swapper/config.ini index dade0ca..36ca46d 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -11,7 +11,8 @@ num_workers = split_ratio = [training.model] -embedder_path = +generator_embedder_path = +loss_embedder_path = gazer_path = motion_extractor_path = face_masker_path = diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 026f2a8..84a16d4 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -28,14 +28,16 @@ CONFIG_PARSER.read('config.ini') class FaceSwapperTrainer(LightningModule): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() - self.config_embedder_path = config_parser.get('training.model', 'embedder_path') + self.config_generator_embedder_path = config_parser.get('training.model', 'generator_embedder_path') + self.config_loss_embedder_path = config_parser.get('training.model', 'loss_embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') self.config_face_masker_path = config_parser.get('training.model', 'face_masker_path') self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') self.config_preview_frequency = config_parser.getint('training.trainer', 'preview_frequency') - self.embedder = torch.jit.load(self.config_embedder_path, map_location = 'cpu').eval() + self.generator_embedder = torch.jit.load(self.config_generator_embedder_path, map_location = 'cpu').eval() + self.loss_embedder = torch.jit.load(self.config_loss_embedder_path, map_location = 'cpu').eval() self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() self.face_masker = torch.jit.load(self.config_face_masker_path, map_location ='cpu').eval() @@ -44,8 +46,8 @@ class FaceSwapperTrainer(LightningModule): self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) self.feature_loss = FeautureLoss(config_parser) - self.reconstruction_loss = ReconstructionLoss(config_parser, self.embedder) - self.identity_loss = IdentityLoss(config_parser, self.embedder) + self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) + self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) self.gaze_loss = GazeLoss(config_parser, self.gazer) self.mask_loss = MaskLoss(config_parser, self.face_masker) @@ -89,7 +91,7 @@ class FaceSwapperTrainer(LightningModule): do_update = (batch_index + 1) % self.config_accumulate_size == 0 generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] - source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) + source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0)) generator_target_features = self.generator.encode_features(target_tensor) generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features) generator_output_features = self.generator.encode_features(generator_output_tensor) @@ -138,9 +140,9 @@ class FaceSwapperTrainer(LightningModule): def validation_step(self, batch : Batch, batch_index : int) -> Tensor: source_tensor, target_tensor = batch - source_embedding = calc_embedding(self.embedder, source_tensor, (0, 0, 0, 0)) + source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0)) output_tensor, _ = self.forward(source_embedding, target_tensor) - output_embedding = calc_embedding(self.embedder, output_tensor, (0, 0, 0, 0)) + output_embedding = calc_embedding(self.generator_embedder, output_tensor, (0, 0, 0, 0)) validation_score = (nn.functional.cosine_similarity(source_embedding, output_embedding).mean() + 1) * 0.5 self.log('validation_score', validation_score, sync_dist = True, prog_bar = True) return validation_score From 056bacb7de32fe8a282457f994ed42c7d75b0cd1 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Thu, 10 Apr 2025 11:33:21 +0530 Subject: [PATCH 380/394] more augmentation --- face_swapper/src/dataset.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 183bb41..0a14eb4 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -88,10 +88,13 @@ class AugmentTransform: albumentations.OneOf( [ albumentations.MotionBlur(p = 0.1), - albumentations.MedianBlur(p = 0.1) - ], p = 0.3), - albumentations.RandomBrightnessContrast(p = 0.3), - albumentations.ColorJitter(p = 0.1) + albumentations.ZoomBlur(p = 0.1) + ], p = 0.2), + albumentations.RandomBrightnessContrast(p = 0.7), + albumentations.ColorJitter(p = 0.2), + albumentations.RGBShift(p = 0.7), + albumentations.Illumination(p = 0.2), + albumentations.Affine(translate_percent = (-0.03, 0.03), scale = (0.98, 1.02), rotate = (-2, 2), border_mode = 1, p = 0.7) ]) From 76fe5c351c5e7e5b1e7d7ce63dfe697c8819042c Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 13 Apr 2025 10:17:52 +0200 Subject: [PATCH 381/394] Fix typo --- face_swapper/src/models/loss.py | 2 +- face_swapper/src/training.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 6f30ba3..ef3a889 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -49,7 +49,7 @@ class AdversarialLoss(nn.Module): return adversarial_loss, weighted_adversarial_loss -class FeautureLoss(nn.Module): +class FeatureLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 84a16d4..344fd4d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, DiscriminatorLoss, FeautureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -45,7 +45,7 @@ class FaceSwapperTrainer(LightningModule): self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) - self.feature_loss = FeautureLoss(config_parser) + self.feature_loss = FeatureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) From dc2b2dc982489638d1034e7e8293edd4a663d78e Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 11 Apr 2025 23:15:30 +0530 Subject: [PATCH 382/394] changes --- face_swapper/README.md | 1 + face_swapper/config.ini | 1 + face_swapper/src/models/loss.py | 23 ++++++++++++++++++++++- face_swapper/src/training.py | 10 ++++++++-- 4 files changed, 32 insertions(+), 3 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index 98b8375..f55308d 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -77,6 +77,7 @@ num_filters = 16 ``` [training.losses] adversarial_weight = 1.0 +cycle_weight = 1.0 feature_weight = 10.0 reconstruction_weight = 10.0 identity_weight = 20.0 diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 36ca46d..37f4627 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -37,6 +37,7 @@ num_filters = [training.losses] adversarial_weight = +cycle_weight = feature_weight = reconstruction_weight = identity_weight = diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ef3a889..13841a2 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -49,7 +49,28 @@ class AdversarialLoss(nn.Module): return adversarial_loss, weighted_adversarial_loss -class FeatureLoss(nn.Module): +class CycleLoss(nn.Module): + def __init__(self, config_parser : ConfigParser) -> None: + super().__init__() + self.config_batch_size = config_parser.getint('training.loader', 'batch_size') + self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight') + self.l1_loss = nn.L1Loss() + + def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: + temp_tensors = [] + + for target_feature, output_feature in zip(target_features, cycle_features): + temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean() + temp_tensors.append(temp_tensor) + + cycle_feature_loss = torch.stack(temp_tensors).mean() + cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor) + cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5 + weighted_feature_loss = cycle_loss * self.config_cycle_weight + return cycle_loss, weighted_feature_loss + + +class FeautureLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 344fd4d..59c47f7 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, CycleLoss, DiscriminatorLoss, FeautureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -45,7 +45,8 @@ class FaceSwapperTrainer(LightningModule): self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) - self.feature_loss = FeatureLoss(config_parser) + self.cycle_loss = CycleLoss(config_parser) + self.feature_loss = FeautureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) @@ -92,11 +93,15 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_embedding(self.generator_embedder, target_tensor, (0, 0, 0, 0)) generator_target_features = self.generator.encode_features(target_tensor) generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features) generator_output_features = self.generator.encode_features(generator_output_tensor) + cycle_output_tensor, cycle_output_mask = self.generator(target_embedding, generator_output_tensor, generator_output_features) + cycle_output_features = self.generator.encode_features(cycle_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) + cycle_loss, weighted_cycle_loss = self.cycle_loss(target_tensor, cycle_output_tensor, generator_target_features, cycle_output_features) feature_loss, weighted_feature_loss = self.feature_loss(generator_target_features, generator_output_features) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) @@ -129,6 +134,7 @@ class FaceSwapperTrainer(LightningModule): self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) self.log('adversarial_loss', adversarial_loss) + self.log('cycle_loss', cycle_loss) self.log('feature_loss', feature_loss) self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) From b215db68c30141590dc5a5e2d3ea51fbb31c8635 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 11 Apr 2025 23:18:32 +0530 Subject: [PATCH 383/394] limit discriminator training every 10 steps --- face_swapper/src/training.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 59c47f7..ea03267 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -121,12 +121,13 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer.zero_grad() self.untoggle_optimizer(generator_optimizer) - self.toggle_optimizer(discriminator_optimizer) - self.manual_backward(discriminator_loss) - if do_update: - discriminator_optimizer.step() - discriminator_optimizer.zero_grad() - self.untoggle_optimizer(discriminator_optimizer) + if self.global_step % 10 == 0: + self.toggle_optimizer(discriminator_optimizer) + self.manual_backward(discriminator_loss) + if do_update: + discriminator_optimizer.step() + discriminator_optimizer.zero_grad() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) From b7a6f00e8bdfa0c433282ce29f5dd3889e3a1356 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 15 Apr 2025 13:36:21 +0530 Subject: [PATCH 384/394] remove discriminator frequency --- face_swapper/src/dataset.py | 4 ++-- face_swapper/src/training.py | 15 ++++++++------- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/face_swapper/src/dataset.py b/face_swapper/src/dataset.py index 0a14eb4..db04932 100644 --- a/face_swapper/src/dataset.py +++ b/face_swapper/src/dataset.py @@ -88,13 +88,13 @@ class AugmentTransform: albumentations.OneOf( [ albumentations.MotionBlur(p = 0.1), - albumentations.ZoomBlur(p = 0.1) + albumentations.ZoomBlur(max_factor = (1.0, 1.1), p = 0.1) ], p = 0.2), albumentations.RandomBrightnessContrast(p = 0.7), albumentations.ColorJitter(p = 0.2), albumentations.RGBShift(p = 0.7), albumentations.Illumination(p = 0.2), - albumentations.Affine(translate_percent = (-0.03, 0.03), scale = (0.98, 1.02), rotate = (-2, 2), border_mode = 1, p = 0.7) + albumentations.Affine(translate_percent = (-0.03, 0.03), scale = (0.98, 1.02), rotate = (-2, 2), border_mode = 1, p = 0.3) ]) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index ea03267..1d994ae 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -116,18 +116,19 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss) + if do_update: generator_optimizer.step() generator_optimizer.zero_grad() self.untoggle_optimizer(generator_optimizer) - if self.global_step % 10 == 0: - self.toggle_optimizer(discriminator_optimizer) - self.manual_backward(discriminator_loss) - if do_update: - discriminator_optimizer.step() - discriminator_optimizer.zero_grad() - self.untoggle_optimizer(discriminator_optimizer) + self.toggle_optimizer(discriminator_optimizer) + self.manual_backward(discriminator_loss) + + if do_update: + discriminator_optimizer.step() + discriminator_optimizer.zero_grad() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) From d9fe667cedf06000507e1222b131681154cea099 Mon Sep 17 00:00:00 2001 From: henryruhs Date: Sun, 13 Apr 2025 10:17:52 +0200 Subject: [PATCH 385/394] Fix typo --- face_swapper/src/models/loss.py | 23 +---------------------- face_swapper/src/training.py | 12 ++---------- 2 files changed, 3 insertions(+), 32 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 13841a2..ef3a889 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -49,28 +49,7 @@ class AdversarialLoss(nn.Module): return adversarial_loss, weighted_adversarial_loss -class CycleLoss(nn.Module): - def __init__(self, config_parser : ConfigParser) -> None: - super().__init__() - self.config_batch_size = config_parser.getint('training.loader', 'batch_size') - self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight') - self.l1_loss = nn.L1Loss() - - def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: - temp_tensors = [] - - for target_feature, output_feature in zip(target_features, cycle_features): - temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean() - temp_tensors.append(temp_tensor) - - cycle_feature_loss = torch.stack(temp_tensors).mean() - cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor) - cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5 - weighted_feature_loss = cycle_loss * self.config_cycle_weight - return cycle_loss, weighted_feature_loss - - -class FeautureLoss(nn.Module): +class FeatureLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 1d994ae..344fd4d 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, CycleLoss, DiscriminatorLoss, FeautureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -45,8 +45,7 @@ class FaceSwapperTrainer(LightningModule): self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) - self.cycle_loss = CycleLoss(config_parser) - self.feature_loss = FeautureLoss(config_parser) + self.feature_loss = FeatureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) self.motion_loss = MotionLoss(config_parser, self.motion_extractor) @@ -93,15 +92,11 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0)) - target_embedding = calc_embedding(self.generator_embedder, target_tensor, (0, 0, 0, 0)) generator_target_features = self.generator.encode_features(target_tensor) generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features) generator_output_features = self.generator.encode_features(generator_output_tensor) - cycle_output_tensor, cycle_output_mask = self.generator(target_embedding, generator_output_tensor, generator_output_features) - cycle_output_features = self.generator.encode_features(cycle_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) - cycle_loss, weighted_cycle_loss = self.cycle_loss(target_tensor, cycle_output_tensor, generator_target_features, cycle_output_features) feature_loss, weighted_feature_loss = self.feature_loss(generator_target_features, generator_output_features) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) @@ -116,7 +111,6 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss) - if do_update: generator_optimizer.step() generator_optimizer.zero_grad() @@ -124,7 +118,6 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(discriminator_optimizer) self.manual_backward(discriminator_loss) - if do_update: discriminator_optimizer.step() discriminator_optimizer.zero_grad() @@ -136,7 +129,6 @@ class FaceSwapperTrainer(LightningModule): self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) self.log('adversarial_loss', adversarial_loss) - self.log('cycle_loss', cycle_loss) self.log('feature_loss', feature_loss) self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) From f4c4066e8c37996b30a18b3e14b6ac8915df2bb9 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 11 Apr 2025 23:15:30 +0530 Subject: [PATCH 386/394] changes --- face_swapper/src/models/loss.py | 21 +++++++++++++++++++++ face_swapper/src/training.py | 8 +++++++- 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ef3a889..2087a38 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -49,6 +49,27 @@ class AdversarialLoss(nn.Module): return adversarial_loss, weighted_adversarial_loss +class CycleLoss(nn.Module): + def __init__(self, config_parser : ConfigParser) -> None: + super().__init__() + self.config_batch_size = config_parser.getint('training.loader', 'batch_size') + self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight') + self.l1_loss = nn.L1Loss() + + def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: + temp_tensors = [] + + for target_feature, output_feature in zip(target_features, cycle_features): + temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean() + temp_tensors.append(temp_tensor) + + cycle_feature_loss = torch.stack(temp_tensors).mean() + cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor) + cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5 + weighted_feature_loss = cycle_loss * self.config_cycle_weight + return cycle_loss, weighted_feature_loss + + class FeatureLoss(nn.Module): def __init__(self, config_parser : ConfigParser) -> None: super().__init__() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 344fd4d..183a46a 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, CycleLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -45,6 +45,7 @@ class FaceSwapperTrainer(LightningModule): self.discriminator = Discriminator(config_parser) self.discriminator_loss = DiscriminatorLoss() self.adversarial_loss = AdversarialLoss(config_parser) + self.cycle_loss = CycleLoss(config_parser) self.feature_loss = FeatureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) @@ -92,11 +93,15 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer, discriminator_optimizer = self.optimizers() #type:ignore[attr-defined] source_embedding = calc_embedding(self.generator_embedder, source_tensor, (0, 0, 0, 0)) + target_embedding = calc_embedding(self.generator_embedder, target_tensor, (0, 0, 0, 0)) generator_target_features = self.generator.encode_features(target_tensor) generator_output_tensor, generator_output_mask = self.generator(source_embedding, target_tensor, generator_target_features) generator_output_features = self.generator.encode_features(generator_output_tensor) + cycle_output_tensor, cycle_output_mask = self.generator(target_embedding, generator_output_tensor, generator_output_features) + cycle_output_features = self.generator.encode_features(cycle_output_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor) adversarial_loss, weighted_adversarial_loss = self.adversarial_loss(discriminator_output_tensors) + cycle_loss, weighted_cycle_loss = self.cycle_loss(target_tensor, cycle_output_tensor, generator_target_features, cycle_output_features) feature_loss, weighted_feature_loss = self.feature_loss(generator_target_features, generator_output_features) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) @@ -129,6 +134,7 @@ class FaceSwapperTrainer(LightningModule): self.log('generator_loss', generator_loss, prog_bar = True) self.log('discriminator_loss', discriminator_loss, prog_bar = True) self.log('adversarial_loss', adversarial_loss) + self.log('cycle_loss', cycle_loss) self.log('feature_loss', feature_loss) self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) From 1477850a2340ae29fcfad965e4463426e14d6fa8 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Fri, 11 Apr 2025 23:18:32 +0530 Subject: [PATCH 387/394] limit discriminator training every 10 steps --- face_swapper/src/training.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 183a46a..554a457 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -121,12 +121,13 @@ class FaceSwapperTrainer(LightningModule): generator_optimizer.zero_grad() self.untoggle_optimizer(generator_optimizer) - self.toggle_optimizer(discriminator_optimizer) - self.manual_backward(discriminator_loss) - if do_update: - discriminator_optimizer.step() - discriminator_optimizer.zero_grad() - self.untoggle_optimizer(discriminator_optimizer) + if self.global_step % 10 == 0: + self.toggle_optimizer(discriminator_optimizer) + self.manual_backward(discriminator_loss) + if do_update: + discriminator_optimizer.step() + discriminator_optimizer.zero_grad() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) From 39ce14b5903ce117874d23d0739989b5c4c60e20 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 15 Apr 2025 13:36:21 +0530 Subject: [PATCH 388/394] remove discriminator frequency --- face_swapper/src/training.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 554a457..8d1d749 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -116,18 +116,19 @@ class FaceSwapperTrainer(LightningModule): self.toggle_optimizer(generator_optimizer) self.manual_backward(generator_loss) + if do_update: generator_optimizer.step() generator_optimizer.zero_grad() self.untoggle_optimizer(generator_optimizer) - if self.global_step % 10 == 0: - self.toggle_optimizer(discriminator_optimizer) - self.manual_backward(discriminator_loss) - if do_update: - discriminator_optimizer.step() - discriminator_optimizer.zero_grad() - self.untoggle_optimizer(discriminator_optimizer) + self.toggle_optimizer(discriminator_optimizer) + self.manual_backward(discriminator_loss) + + if do_update: + discriminator_optimizer.step() + discriminator_optimizer.zero_grad() + self.untoggle_optimizer(discriminator_optimizer) if self.global_step % self.config_preview_frequency == 0: self.generate_preview(source_tensor, target_tensor, generator_output_tensor, generator_output_mask) From 4a319ec9bd0552451a326f4ff617d66e46471fa3 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 15 Apr 2025 14:05:27 +0530 Subject: [PATCH 389/394] changes --- face_swapper/src/models/loss.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 2087a38..ccbd96a 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -54,7 +54,7 @@ class CycleLoss(nn.Module): super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight') - self.l1_loss = nn.L1Loss() + self.mae_loss = nn.L1Loss() def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: temp_tensors = [] @@ -63,9 +63,9 @@ class CycleLoss(nn.Module): temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean() temp_tensors.append(temp_tensor) - cycle_feature_loss = torch.stack(temp_tensors).mean() - cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor) - cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5 + feature_loss = torch.stack(temp_tensors).mean() + mae_loss = self.mae_loss(target_tensor, cycle_tensor) + cycle_loss = (feature_loss + mae_loss) * 0.5 weighted_feature_loss = cycle_loss * self.config_cycle_weight return cycle_loss, weighted_feature_loss From 128726701b4bd5c5f0e5f7b6af0983dc6aef7e9b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 15 Apr 2025 14:10:58 +0530 Subject: [PATCH 390/394] changes --- face_swapper/src/models/loss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index ccbd96a..1cca867 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -64,8 +64,8 @@ class CycleLoss(nn.Module): temp_tensors.append(temp_tensor) feature_loss = torch.stack(temp_tensors).mean() - mae_loss = self.mae_loss(target_tensor, cycle_tensor) - cycle_loss = (feature_loss + mae_loss) * 0.5 + reconstruction_loss = self.mae_loss(target_tensor, cycle_tensor) + cycle_loss = (feature_loss + reconstruction_loss) * 0.5 weighted_feature_loss = cycle_loss * self.config_cycle_weight return cycle_loss, weighted_feature_loss From bcf5b4e5a86f6f471900543f910c7192186daeef Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Tue, 15 Apr 2025 14:14:09 +0530 Subject: [PATCH 391/394] changes --- face_swapper/src/models/loss.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 1cca867..82642bb 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -54,7 +54,7 @@ class CycleLoss(nn.Module): super().__init__() self.config_batch_size = config_parser.getint('training.loader', 'batch_size') self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight') - self.mae_loss = nn.L1Loss() + self.l1_loss = nn.L1Loss() def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]: temp_tensors = [] @@ -64,7 +64,7 @@ class CycleLoss(nn.Module): temp_tensors.append(temp_tensor) feature_loss = torch.stack(temp_tensors).mean() - reconstruction_loss = self.mae_loss(target_tensor, cycle_tensor) + reconstruction_loss = self.l1_loss(target_tensor, cycle_tensor) cycle_loss = (feature_loss + reconstruction_loss) * 0.5 weighted_feature_loss = cycle_loss * self.config_cycle_weight return cycle_loss, weighted_feature_loss From 982a94b53584b26e9a41d1487daacc7ab10c075b Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 21 Apr 2025 15:37:13 +0530 Subject: [PATCH 392/394] add 1024 --- face_swapper/src/networks/aad.py | 11 +++++++++++ face_swapper/src/networks/unet.py | 18 ++++++++++++++++++ face_swapper/tests/test_networks.py | 2 +- 3 files changed, 30 insertions(+), 1 deletion(-) diff --git a/face_swapper/src/networks/aad.py b/face_swapper/src/networks/aad.py index 880065e..10c1231 100644 --- a/face_swapper/src/networks/aad.py +++ b/face_swapper/src/networks/aad.py @@ -47,6 +47,17 @@ class AAD(nn.Module): AdaptiveFeatureModulation(1024, 512, 512, self.config_source_channels, self.config_num_blocks) ]) + if self.config_output_size == 1024: + layers.extend( + [ + AdaptiveFeatureModulation(4096, 4096, 4096, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(4096, 4096, 8192, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(4096, 4096, 4096, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(4096, 2048, 2048, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(2048, 1024, 1024, self.config_source_channels, self.config_num_blocks), + AdaptiveFeatureModulation(1024, 512, 512, self.config_source_channels, self.config_num_blocks) + ]) + layers.extend( [ AdaptiveFeatureModulation(512, 256, 256, self.config_source_channels, self.config_num_blocks), diff --git a/face_swapper/src/networks/unet.py b/face_swapper/src/networks/unet.py index c12f3da..5d63561 100644 --- a/face_swapper/src/networks/unet.py +++ b/face_swapper/src/networks/unet.py @@ -45,6 +45,15 @@ class UNet(nn.Module): DownSample(2048, 2048) ]) + if self.config_output_size == 1024: + down_samples.extend( + [ + DownSample(512, 1024), + DownSample(1024, 2048), + DownSample(2048, 4096), + DownSample(4096, 4096) + ]) + return down_samples def create_up_samples(self) -> nn.ModuleList: @@ -71,6 +80,15 @@ class UNet(nn.Module): UpSample(2048, 512) ]) + if self.config_output_size == 1024: + up_samples.extend( + [ + UpSample(4096, 4096), + UpSample(8192, 2048), + UpSample(4096, 1024), + UpSample(2048, 512) + ]) + up_samples.extend( [ UpSample(1024, 256), diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index 654a172..ddd1397 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -8,7 +8,7 @@ from face_swapper.src.networks.masknet import MaskNet from face_swapper.src.networks.unet import UNet -@pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) +@pytest.mark.parametrize('output_size', [ 128, 256, 512, 1024 ]) def test_aad_with_unet(output_size : int) -> None: config_parser = ConfigParser() config_parser.read_dict( From d990ce457585bb05e4ed75806e345e14b08afc19 Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Mon, 21 Apr 2025 15:41:22 +0530 Subject: [PATCH 393/394] remove 1024 from test --- face_swapper/tests/test_networks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/face_swapper/tests/test_networks.py b/face_swapper/tests/test_networks.py index ddd1397..654a172 100644 --- a/face_swapper/tests/test_networks.py +++ b/face_swapper/tests/test_networks.py @@ -8,7 +8,7 @@ from face_swapper.src.networks.masknet import MaskNet from face_swapper.src.networks.unet import UNet -@pytest.mark.parametrize('output_size', [ 128, 256, 512, 1024 ]) +@pytest.mark.parametrize('output_size', [ 128, 256, 512 ]) def test_aad_with_unet(output_size : int) -> None: config_parser = ConfigParser() config_parser.read_dict( From d44ac98e38c771f615e98a08607e8d779f61d48c Mon Sep 17 00:00:00 2001 From: harisreedhar Date: Wed, 23 Apr 2025 22:00:42 +0530 Subject: [PATCH 394/394] changes --- face_swapper/README.md | 3 --- face_swapper/config.ini | 3 --- face_swapper/src/models/loss.py | 44 +-------------------------------- face_swapper/src/training.py | 10 ++------ face_swapper/src/types.py | 1 - 5 files changed, 3 insertions(+), 58 deletions(-) diff --git a/face_swapper/README.md b/face_swapper/README.md index f55308d..a6c65a4 100644 --- a/face_swapper/README.md +++ b/face_swapper/README.md @@ -46,7 +46,6 @@ split_ratio = 0.9995 generator_embedder_path = .models/blendface.pt loss_embedder_path = .models/adaface.pt gazer_path = .models/gazer.pt -motion_extractor_path = .models/motion_extractor.pt face_masker_path = .models/face_masker.pt ``` @@ -82,8 +81,6 @@ feature_weight = 10.0 reconstruction_weight = 10.0 identity_weight = 20.0 gaze_weight = 0.05 -pose_weight = 0.05 -expression_weight = 0.05 mask_weight = 5.0 ``` diff --git a/face_swapper/config.ini b/face_swapper/config.ini index 37f4627..fd94c01 100644 --- a/face_swapper/config.ini +++ b/face_swapper/config.ini @@ -14,7 +14,6 @@ split_ratio = generator_embedder_path = loss_embedder_path = gazer_path = -motion_extractor_path = face_masker_path = [training.model.generator] @@ -42,8 +41,6 @@ feature_weight = reconstruction_weight = identity_weight = gaze_weight = -pose_weight = -expression_weight = mask_weight = [training.trainer] diff --git a/face_swapper/src/models/loss.py b/face_swapper/src/models/loss.py index 82642bb..dbe8e10 100644 --- a/face_swapper/src/models/loss.py +++ b/face_swapper/src/models/loss.py @@ -7,7 +7,7 @@ from torch import Tensor, nn from torchvision import transforms from ..helper import calc_embedding -from ..types import EmbedderModule, FaceMaskerModule, Feature, GazerModule, Loss, Mask, MotionExtractorModule +from ..types import EmbedderModule, FaceMaskerModule, Feature, GazerModule, Loss, Mask class DiscriminatorLoss(nn.Module): @@ -126,48 +126,6 @@ class IdentityLoss(nn.Module): return identity_loss, weighted_identity_loss -class MotionLoss(nn.Module): - def __init__(self, config_parser : ConfigParser, motion_extractor : MotionExtractorModule): - super().__init__() - self.config_pose_weight = config_parser.getfloat('training.losses', 'pose_weight') - self.config_expression_weight = config_parser.getfloat('training.losses', 'expression_weight') - self.motion_extractor = motion_extractor - self.mse_loss = nn.MSELoss() - - def forward(self, target_tensor : Tensor, output_tensor : Tensor) -> Tuple[Loss, Loss, Loss, Loss]: - target_poses, target_expression = self.detect_motions(target_tensor) - output_poses, output_expression = self.detect_motions(output_tensor) - pose_loss, weighted_pose_loss = self.calc_pose_loss(target_poses, output_poses) - expression_loss, weighted_expression_loss = self.calc_expression_loss(target_expression, output_expression) - return pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss - - def calc_pose_loss(self, target_poses : Tuple[Tensor, ...], output_poses : Tuple[Tensor, ...]) -> Tuple[Loss, Loss]: - temp_tensors = [] - - for target_pose, output_pose in zip(target_poses, output_poses): - temp_tensor = self.mse_loss(target_pose, output_pose) - temp_tensors.append(temp_tensor) - - pose_loss = torch.stack(temp_tensors).mean() - weighted_pose_loss = pose_loss * self.config_pose_weight - return pose_loss, weighted_pose_loss - - def calc_expression_loss(self, target_expression : Tensor, output_expression : Tensor) -> Tuple[Loss, Loss]: - expression_loss = (1 - torch.cosine_similarity(target_expression, output_expression)).mean() - weighted_expression_loss = expression_loss * self.config_expression_weight - return expression_loss, weighted_expression_loss - - def detect_motions(self, input_tensor : Tensor) -> Tuple[Tuple[Tensor, ...], Tensor]: - input_tensor = (input_tensor + 1) * 0.5 - - with torch.no_grad(): - pitch, yaw, roll, translation, expression, scale, motion_points = self.motion_extractor(input_tensor) - - rotation = torch.cat([ pitch, yaw, roll ], dim = 1) - pose = translation, scale, rotation, motion_points - return pose, expression - - class GazeLoss(nn.Module): def __init__(self, config_parser : ConfigParser, gazer : GazerModule) -> None: super().__init__() diff --git a/face_swapper/src/training.py b/face_swapper/src/training.py index 8d1d749..67662b4 100644 --- a/face_swapper/src/training.py +++ b/face_swapper/src/training.py @@ -16,7 +16,7 @@ from .dataset import DynamicDataset from .helper import calc_embedding, overlay_mask from .models.discriminator import Discriminator from .models.generator import Generator -from .models.loss import AdversarialLoss, CycleLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, MotionLoss, ReconstructionLoss +from .models.loss import AdversarialLoss, CycleLoss, DiscriminatorLoss, FeatureLoss, GazeLoss, IdentityLoss, MaskLoss, ReconstructionLoss from .types import Batch, Embedding, Mask, OptimizerSet warnings.filterwarnings('ignore', category = UserWarning, module = 'torch') @@ -31,7 +31,6 @@ class FaceSwapperTrainer(LightningModule): self.config_generator_embedder_path = config_parser.get('training.model', 'generator_embedder_path') self.config_loss_embedder_path = config_parser.get('training.model', 'loss_embedder_path') self.config_gazer_path = config_parser.get('training.model', 'gazer_path') - self.config_motion_extractor_path = config_parser.get('training.model', 'motion_extractor_path') self.config_face_masker_path = config_parser.get('training.model', 'face_masker_path') self.config_accumulate_size = config_parser.getfloat('training.trainer', 'accumulate_size') self.config_learning_rate = config_parser.getfloat('training.trainer', 'learning_rate') @@ -39,7 +38,6 @@ class FaceSwapperTrainer(LightningModule): self.generator_embedder = torch.jit.load(self.config_generator_embedder_path, map_location = 'cpu').eval() self.loss_embedder = torch.jit.load(self.config_loss_embedder_path, map_location = 'cpu').eval() self.gazer = torch.jit.load(self.config_gazer_path, map_location = 'cpu').eval() - self.motion_extractor = torch.jit.load(self.config_motion_extractor_path, map_location = 'cpu').eval() self.face_masker = torch.jit.load(self.config_face_masker_path, map_location ='cpu').eval() self.generator = Generator(config_parser) self.discriminator = Discriminator(config_parser) @@ -49,7 +47,6 @@ class FaceSwapperTrainer(LightningModule): self.feature_loss = FeatureLoss(config_parser) self.reconstruction_loss = ReconstructionLoss(config_parser, self.loss_embedder) self.identity_loss = IdentityLoss(config_parser, self.loss_embedder) - self.motion_loss = MotionLoss(config_parser, self.motion_extractor) self.gaze_loss = GazeLoss(config_parser, self.gazer) self.mask_loss = MaskLoss(config_parser, self.face_masker) self.automatic_optimization = False @@ -105,10 +102,9 @@ class FaceSwapperTrainer(LightningModule): feature_loss, weighted_feature_loss = self.feature_loss(generator_target_features, generator_output_features) reconstruction_loss, weighted_reconstruction_loss = self.reconstruction_loss(source_tensor, target_tensor, generator_output_tensor) identity_loss, weighted_identity_loss = self.identity_loss(generator_output_tensor, source_tensor) - pose_loss, weighted_pose_loss, expression_loss, weighted_expression_loss = self.motion_loss(target_tensor, generator_output_tensor) gaze_loss, weighted_gaze_loss = self.gaze_loss(target_tensor, generator_output_tensor) mask_loss, weighted_mask_loss = self.mask_loss(target_tensor, generator_output_mask) - generator_loss = weighted_adversarial_loss + weighted_feature_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_pose_loss + weighted_gaze_loss + weighted_expression_loss + weighted_mask_loss + generator_loss = weighted_adversarial_loss + weighted_cycle_loss + weighted_feature_loss + weighted_reconstruction_loss + weighted_identity_loss + weighted_gaze_loss + weighted_mask_loss discriminator_source_tensors = self.discriminator(source_tensor) discriminator_output_tensors = self.discriminator(generator_output_tensor.detach()) @@ -140,8 +136,6 @@ class FaceSwapperTrainer(LightningModule): self.log('feature_loss', feature_loss) self.log('reconstruction_loss', reconstruction_loss) self.log('identity_loss', identity_loss) - self.log('pose_loss', pose_loss) - self.log('expression_loss', expression_loss) self.log('gaze_loss', gaze_loss) self.log('mask_loss', mask_loss) return generator_loss diff --git a/face_swapper/src/types.py b/face_swapper/src/types.py index 342dc6f..d840897 100644 --- a/face_swapper/src/types.py +++ b/face_swapper/src/types.py @@ -16,7 +16,6 @@ Padding : TypeAlias = Tuple[int, int, int, int] GeneratorModule : TypeAlias = Module EmbedderModule : TypeAlias = Module GazerModule : TypeAlias = Module -MotionExtractorModule : TypeAlias = Module FaceMaskerModule : TypeAlias = Module OptimizerSet : TypeAlias = Any

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