diff --git a/GUI/file_sync/filestate_machine0.json b/GUI/file_sync/filestate_machine0.json index 0383185..5d788aa 100644 --- a/GUI/file_sync/filestate_machine0.json +++ b/GUI/file_sync/filestate_machine0.json @@ -1,6 +1,6 @@ { "GUI.py": 1645109256.0056663, - "test.py": 1645344802.7112515, + "test.py": 1646330130.1009316, "train.py": 1643397924.974299, "components\\Generator.py": 1644689001.9005148, "components\\projected_discriminator.py": 1642348101.4661522, @@ -31,7 +31,7 @@ "utilities\\learningrate_scheduler.py": 1611123530.675422, "utilities\\logo_class.py": 1633883995.3093486, "utilities\\plot.py": 1641911100.7995758, - "utilities\\reporter.py": 1625413813.7213495, + "utilities\\reporter.py": 1646311333.3067005, "utilities\\save_heatmap.py": 1611123530.679439, "utilities\\sshupload.py": 1645168814.6421573, "utilities\\transfer_checkpoint.py": 1642397157.0163105, @@ -60,7 +60,7 @@ "face_crop.py": 1643789609.1834445, "face_crop_video.py": 1643815024.5516832, "similarity.py": 1643269705.1073737, - "train_multigpu.py": 1646101637.160833, + "train_multigpu.py": 1646329983.38444, "components\\arcface_decoder.py": 1643396144.2575414, "components\\Generator_nobias.py": 1643179001.810856, "data_tools\\data_loader_VGGFace2HQ_multigpu.py": 1644861019.9044807, @@ -105,13 +105,13 @@ "components\\Generator_ori.py": 1644689174.414655, "losses\\cos.py": 1644229583.4023254, "data_tools\\data_loader_VGGFace2HQ_multigpu1.py": 1644860106.943826, - "speed_test.py": 1645863205.1120403, + "speed_test.py": 1646304298.3483005, "components\\DeConv_Invo.py": 1644426607.1588645, "components\\Generator_reduce_up.py": 1644688655.2096283, "components\\Generator_upsample.py": 1644689723.8293872, "components\\misc\\Involution.py": 1644509321.5267963, "train_yamls\\train_Invoup.yaml": 1644689981.9794765, - "flops.py": 1646101039.8459642, + "flops.py": 1646330033.710075, "detection_test.py": 1644935512.6830947, "components\\DeConv_Depthwise.py": 1645064447.4379447, "components\\DeConv_Depthwise1.py": 1644946969.5054545, @@ -119,7 +119,7 @@ "components\\Generator_modulation_depthwise_config.py": 1645262162.9779513, "components\\Generator_modulation_up.py": 1644946498.7005584, "components\\Generator_oriae_modulation.py": 1644897798.1987727, - "components\\Generator_ori_config.py": 1644946742.3635018, + "components\\Generator_ori_config.py": 1646329319.6131227, "train_scripts\\trainer_multi_gpu1.py": 1644859528.8428593, "train_yamls\\train_Depthwise.yaml": 1644860961.099242, "train_yamls\\train_depthwise_modulation.yaml": 1645035964.9551077, @@ -142,5 +142,8 @@ "components\\misc\\Involution_ECA.py": 1645869012.4927464, "train_yamls\\train_Invobn_config.yaml": 1646101598.499709, "components\\Generator_Invobn_config2.py": 1645962618.7056074, - "components\\Generator_Invobn_config3.py": 1646100847.8995547 + "components\\Generator_Invobn_config3.py": 1646302561.1984286, + "components\\Generator_ori_modulation_config.py": 1646329636.719998, + "test_scripts\\tester_image_allstep.py": 1646312637.9363256, + "train_yamls\\train_ori_modulation_config.yaml": 1646330406.200162 } \ No newline at end of file diff --git a/components/Generator_ori_config.py b/components/Generator_ori_config.py index 69daac3..e06b002 100644 --- a/components/Generator_ori_config.py +++ b/components/Generator_ori_config.py @@ -5,7 +5,7 @@ # Created Date: Sunday January 16th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 6:09:43 pm +# Last Modified: Friday, 4th March 2022 1:41:59 am # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# @@ -106,8 +106,8 @@ class Generator(nn.Module): activation = nn.ReLU(True) - self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), - nn.Conv2d(3, in_channel, kernel_size=7, padding=0, bias=False), + self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), + nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), nn.BatchNorm2d(in_channel), activation) ### downsample self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False), @@ -153,8 +153,8 @@ class Generator(nn.Module): nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(in_channel), activation ) - self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), - nn.Conv2d(in_channel, 3, kernel_size=7, padding=0)) + self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), + nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) # self.__weights_init__() diff --git a/components/Generator_ori_modulation_config.py b/components/Generator_ori_modulation_config.py new file mode 100644 index 0000000..fc4113f --- /dev/null +++ b/components/Generator_ori_modulation_config.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: Generator.py +# Created Date: Sunday January 16th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 4th March 2022 1:47:16 am +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + +import torch +from torch import nn + +class Demodule(nn.Module): + def __init__(self, epsilon=1e-8): + """ + @notice: avoid in-place ops. + https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3 + """ + super(Demodule, self).__init__() + self.epsilon = epsilon + + def forward(self, x): + tmp = torch.mul(x, x) # or x ** 2 + tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon) + return x * tmp + +class ApplyStyle(nn.Module): + """ + @ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb + """ + def __init__(self, latent_size, channels): + super(ApplyStyle, self).__init__() + self.linear = nn.Linear(latent_size, channels * 2) + + def forward(self, x, latent): + style = self.linear(latent) # style => [batch_size, n_channels*2] + shape = [-1, 2, x.size(1), 1, 1] + style = style.view(shape) # [batch_size, 2, n_channels, ...] + #x = x * (style[:, 0] + 1.) + style[:, 1] + x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1 + return x + +class Modulation(nn.Module): + def __init__(self, latent_size, channels): + super(Modulation, self).__init__() + self.linear = nn.Linear(latent_size, channels) + + def forward(self, x, latent): + style = self.linear(latent) # style => [batch_size, n_channels*2] + shape = [-1, x.size(1), 1, 1] + style = style.view(shape) # [batch_size, 2, n_channels, ...] + #x = x * (style[:, 0] + 1.) + style[:, 1] + x = x * style + return x + +class ResnetBlock_Modulation(nn.Module): + def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True),res_mode="depthwise"): + super(ResnetBlock_Modulation, self).__init__() + + p = 0 + conv1 = [] + if padding_type == 'reflect': + conv1 += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv1 += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding = p), Demodule()] + self.conv1 = nn.Sequential(*conv1) + self.style1 = Modulation(latent_size, dim) + self.act1 = activation + + p = 0 + conv2 = [] + if padding_type == 'reflect': + conv2 += [nn.ReflectionPad2d(1)] + elif padding_type == 'replicate': + conv2 += [nn.ReplicationPad2d(1)] + elif padding_type == 'zero': + p = 1 + else: + raise NotImplementedError('padding [%s] is not implemented' % padding_type) + # res_mode = "conv" + conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), Demodule()] + + self.conv2 = nn.Sequential(*conv2) + self.style2 = Modulation(latent_size, dim) + + + def forward(self, x, dlatents_in_slice): + y = self.style1(x, dlatents_in_slice) + y = self.conv1(y) + + y = self.act1(y) + y = self.style2(y, dlatents_in_slice) + y = self.conv2(y) + + out = x + y + return out + + +class Generator(nn.Module): + def __init__( + self, + **kwargs + ): + super().__init__() + + id_dim = kwargs["id_dim"] + k_size = kwargs["g_kernel_size"] + res_num = kwargs["res_num"] + in_channel = kwargs["in_channel"] + + padding_size= int((k_size -1)/2) + padding_type= 'reflect' + + activation = nn.ReLU(True) + + self.first_layer = nn.Sequential(nn.ReflectionPad2d(1), + nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False), + nn.BatchNorm2d(in_channel), activation) + ### downsample + self.down1 = nn.Sequential(nn.Conv2d(in_channel, in_channel*2, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(in_channel*2), activation) + + self.down2 = nn.Sequential(nn.Conv2d(in_channel*2, in_channel*4, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(in_channel*4), activation) + + self.down3 = nn.Sequential(nn.Conv2d(in_channel*4, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(in_channel*8), activation) + + # self.down4 = nn.Sequential(nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=2, padding=1, bias=False), + # nn.BatchNorm2d(in_channel*8), activation) + + ### resnet blocks + BN = [] + for _ in range(res_num): + BN += [ + ResnetBlock_Modulation(in_channel*8, latent_size=id_dim, + padding_type=padding_type, activation=activation)] + self.BottleNeck = nn.Sequential(*BN) + + # self.up4 = nn.Sequential( + # nn.Upsample(scale_factor=2, mode='bilinear'), + # nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False), + # nn.BatchNorm2d(in_channel*8), activation + # ) + + self.up3 = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear'), + nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(in_channel*4), activation + ) + + self.up2 = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear'), + nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(in_channel*2), activation + ) + + self.up1 = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear'), + nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False), + nn.BatchNorm2d(in_channel), activation + ) + self.last_layer = nn.Sequential(nn.ReflectionPad2d(1), + nn.Conv2d(in_channel, 3, kernel_size=3, padding=0)) + + + # self.__weights_init__() + + # def __weights_init__(self): + # for layer in self.encoder: + # if isinstance(layer,nn.Conv2d): + # nn.init.xavier_uniform_(layer.weight) + + # for layer in self.encoder2: + # if isinstance(layer,nn.Conv2d): + # nn.init.xavier_uniform_(layer.weight) + + def forward(self, img, id): + # x = input # 3*224*224 + res = self.first_layer(img) + res = self.down1(res) + res = self.down2(res) + res = self.down3(res) + # res = self.down4(res) + + for i in range(len(self.BottleNeck)): + res = self.BottleNeck[i](res, id) + + # res = self.up4(res) + res = self.up3(res) + res = self.up2(res) + res = self.up1(res) + res = self.last_layer(res) + + return res diff --git a/env/env.json b/env/env.json index abe897f..7cfd8fe 100644 --- a/env/env.json +++ b/env/env.json @@ -6,7 +6,9 @@ "dataset_paths": { "vggface2_hq": "G:/VGGFace2-HQ/VGGface2_None_norm_512_true_bygfpgan", "val_dataset_root": "", - "test_dataset_root": "" + "test_dataset_root": "", + "id_pose_source_root": "", + "id_pose_target_root": "" }, "train_config_path":"./train_yamls", "train_scripts_path":"./train_scripts", diff --git a/flops.py b/flops.py index fc1f416..b415e3c 100644 --- a/flops.py +++ b/flops.py @@ -5,7 +5,7 @@ # Created Date: Sunday February 13th 2022 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 6:15:37 pm +# Last Modified: Friday, 4th March 2022 1:53:53 am # Modified By: Chen Xuanhong # Copyright (c) 2022 Shanghai Jiao Tong University ############################################################# @@ -23,7 +23,7 @@ if __name__ == '__main__': # # script = "Generator_modulation_up" script = "Generator_Invobn_config3" - # script = "Generator_ori_config" + # script = "Generator_ori_modulation_config" # script = "Generator_ori_config" class_name = "Generator" arcface_ckpt= "arcface_ckpt/arcface_checkpoint.tar" @@ -35,7 +35,7 @@ if __name__ == '__main__': # "up_mode": "nearest", "up_mode": "bilinear", "aggregator": "eca_invo", - "res_mode": "eca_invo" + "res_mode": "conv" } diff --git a/test.py b/test.py index d2a7994..3cb00da 100644 --- a/test.py +++ b/test.py @@ -5,7 +5,7 @@ # Created Date: Saturday July 3rd 2021 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com -# Last Modified: Thursday, 3rd March 2022 9:04:25 pm +# Last Modified: Friday, 4th March 2022 5:40:11 pm # Modified By: Chen Xuanhong # Copyright (c) 2021 Shanghai Jiao Tong University ############################################################# @@ -30,11 +30,11 @@ def getParameters(): parser = argparse.ArgumentParser() # general settings - parser.add_argument('-v', '--version', type=str, default='Invobn_resinvo1', # depthwise depthwise_config0 Invobn_resinvo1 + parser.add_argument('-v', '--version', type=str, default='ori_tiny', # depthwise depthwise_config0 Invobn_resinvo1 help="version name for train, test, finetune") parser.add_argument('-c', '--cuda', type=int, default=0) # >0 if it is set as -1, program will use CPU - parser.add_argument('-s', '--checkpoint_step', type=int, default=150000, + parser.add_argument('-s', '--checkpoint_step', type=int, default=80000, help="checkpoint epoch for test phase or finetune phase") parser.add_argument('--start_checkpoint_step', type=int, default=10000, help="checkpoint epoch for test phase or finetune phase") @@ -153,6 +153,7 @@ def main(): # read system environment paths env_config = readConfig('env/env.json') env_config = env_config["path"] + sys_state["env_config"] = env_config # obtain all configurations in argparse config_dic = vars(config) diff --git a/test_scripts/tester_ID_Pose.py b/test_scripts/tester_ID_Pose.py new file mode 100644 index 0000000..4be6ac6 --- /dev/null +++ b/test_scripts/tester_ID_Pose.py @@ -0,0 +1,346 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: tester_ID_Pose.py +# Created Date: Friday March 4th 2022 +# Author: Liu Naiyuan +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 4th March 2022 5:33:47 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + + +import os +import cv2 +import time +import glob +from tqdm import tqdm + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision import transforms +from torch.utils import data + +import numpy as np + +import PIL +from PIL import Image + + + + + +class TotalDataset(data.Dataset): + """Dataset class for the vggface dataset with precalulated face landmarks.""" + + def __init__(self,image_dir,content_transform, img_size=224): + self.image_dir= image_dir + self.content_transform= content_transform + self.img_size = img_size + self.dataset = [] + self.preprocess() + self.num_images = len(self.dataset) + + def preprocess(self): + """Preprocess the Face++ original frames.""" + filenames = sorted(glob.glob(os.path.join(self.image_dir, '*'), recursive=False)) + # self.total_num = len(lines) + for filename in filenames: + self.dataset.append(filename) + + print('Finished preprocessing the Face++ original frames dataset...') + + + def __getitem__(self, index): + """Return two src domain images and two dst domain images.""" + src_filename = self.dataset[index] + + split_tmp = src_filename.split('/') + + save_filename = split_tmp[-1] + + src_image1 = self.content_transform(Image.open(src_filename)) + + return src_image1, save_filename + + + def __len__(self): + """Return the number of images.""" + return len(self.dataset) + +def getLoader_sourceface(c_image_dir, + img_size=224, batch_size=16, num_workers=8): + """Build and return a data loader.""" + c_transforms = [] + + c_transforms.append(T.ToTensor()) + c_transforms.append(T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) + # c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) + + c_transforms = T.Compose(c_transforms) + + content_dataset = TotalDataset(c_image_dir, c_transforms, 224) + content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, + drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True) + return content_data_loader, len(content_dataset) + + +def getLoader_targetface(c_image_dir, + img_size=224, batch_size=16, num_workers=8): + """Build and return a data loader.""" + c_transforms = [] + + c_transforms.append(transforms.ToTensor()) + # c_transforms.append(T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])) + # c_transforms.append(T.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) + + c_transforms = transforms.Compose(c_transforms) + + content_dataset = TotalDataset(c_image_dir, c_transforms, 224) + content_data_loader = data.DataLoader(dataset=content_dataset,batch_size=batch_size, + drop_last=False,shuffle=False,num_workers=num_workers,pin_memory=True) + return content_data_loader, len(content_dataset) + +class Tester(object): + def __init__(self, config, reporter): + + self.config = config + # logger + self.reporter = reporter + + self.transformer_Arcface = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + ]) + self.imagenet_std = torch.tensor([0.229, 0.224, 0.225]).cuda().view(3,1,1) + self.imagenet_mean = torch.tensor([0.485, 0.456, 0.406]).cuda().view(3,1,1) + + + def __init_framework__(self): + ''' + This function is designed to define the framework, + and print the framework information into the log file + ''' + #===============build models================# + print("build models...") + # TODO [import models here] + model_config = self.config["model_configs"] + gscript_name = self.config["com_base"] + model_config["g_model"]["script"] + class_name = model_config["g_model"]["class_name"] + package = __import__(gscript_name, fromlist=True) + gen_class = getattr(package, class_name) + self.network = gen_class(**model_config["g_model"]["module_params"]) + + # TODO replace below lines to define the model framework + self.network = gen_class(**model_config["g_model"]["module_params"]) + self.network = self.network.eval() + # print and recorde model structure + self.reporter.writeInfo("Model structure:") + self.reporter.writeModel(self.network.__str__()) + + arcface1 = torch.load(self.arcface_ckpt, map_location=torch.device("cpu")) + self.arcface = arcface1['model'].module + self.arcface.eval() + self.arcface.requires_grad_(False) + + model_path = os.path.join(self.config["project_checkpoints"], + "step%d_%s.pth"%(self.config["checkpoint_step"], + self.config["checkpoint_names"]["generator_name"])) + self.network.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) + print('loaded trained backbone model step {}...!'.format(self.config["checkpoint_step"])) + + # train in GPU + if self.config["cuda"] >=0: + self.network = self.network.cuda() + self.arcface = self.arcface.cuda() + + + + def test(self): + + save_dir = self.config["test_samples_path"] + ckp_step = self.config["checkpoint_step"] + version = self.config["version"] + id_imgs = self.config["id_imgs"] + attr_files = self.config["attr_files"] + specified_save_path = self.config["specified_save_path"] + self.arcface_ckpt= self.config["arcface_ckpt"] + imgs_list = [] + + self.reporter.writeInfo("Version %s"%version) + + if os.path.isdir(specified_save_path): + print("Input a legal specified save path!") + save_dir = specified_save_path + + if os.path.isdir(attr_files): + print("Input a dir....") + imgs = glob.glob(os.path.join(attr_files,"**"), recursive=True) + for item in imgs: + imgs_list.append(item) + print(imgs_list) + else: + print("Input an image....") + imgs_list.append(attr_files) + id_basename = os.path.basename(id_imgs) + id_basename = os.path.splitext(os.path.basename(id_imgs))[0] + + source_loader, dataet_len = getLoader_sourceface( + self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=opt.batchSize) + target_loader, dataet_len = getLoader_targetface( + self.config["env_config"]["dataset_paths"]["id_pose_source_root"], batch_size=opt.batchSize) + + source_iter = iter(source_loader) + target_iter = iter(target_loader) + + # models + self.__init_framework__() + + id_img = cv2.imread(id_imgs) + id_img_align_crop_pil = Image.fromarray(cv2.cvtColor(id_img,cv2.COLOR_BGR2RGB)) + id_img = self.transformer_Arcface(id_img_align_crop_pil) + id_img = id_img.unsqueeze(0).cuda() + + #create latent id + id_img = F.interpolate(id_img,size=(112,112), mode='bicubic') + latend_id = self.arcface(id_img) + latend_id = F.normalize(latend_id, p=2, dim=1) + # Start time + import datetime + print("Start to test at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) + print('Start =================================== test...') + start_time = time.time() + self.network.eval() + with torch.no_grad(): + for profile_batch, filename_batch in tqdm(source_iter): + profile_batch = profile_batch.cuda() + profile_id_downsample = F.interpolate(profile_batch, (112,112), mode='bicubic') + profile_latent_id = model.netArc(profile_id_downsample) + profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1) + if init_batch ==True: + wholeid_batch = profile_latent_id.cpu() + init_batch = False + else: + wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0) + + target_source_pair_dict = np.load( + self.config["env_config"]["dataset_paths"]["pairs_dict"] ,allow_pickle=True).item() + + for target_batch, filename_batch in tqdm(target_iter): + target_index_list = [] + init_id_batch = True + + for filename_tmp in filename_batch: + source_index = int(filename_tmp.split('_')[0]) + target_index = target_source_pair_dict[source_index] + target_index_list.append(target_index) + if init_id_batch: + batch_id = wholeid_batch[target_index][None].cuda() + init_id_batch = False + else: + batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0) + img_fakes = model(None, target_batch.cuda(), batch_id, None, True) + + for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch): + filename_tmp_split = filename_tmp.split('_') + final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1] + save_path = os.path.join(simswap_eval_save_image_path,final_filename) + save_image = postprocess(img_fake.cpu().numpy().transpose(1,2,0)) + PIL.Image.fromarray(save_image).save(save_path,quality=95) + + for img in imgs_list: + print(img) + attr_img_ori= cv2.imread(img) + attr_img_align_crop_pil = Image.fromarray(cv2.cvtColor(attr_img_align_crop[0],cv2.COLOR_BGR2RGB)) + attr_img = self.transformer_Arcface(attr_img_align_crop_pil).unsqueeze(0).cuda() + + attr_img_arc = F.interpolate(attr_img,size=(112,112), mode='bicubic') + # cv2.imwrite(os.path.join("./swap_results", "id_%s.png"%(id_basename)),id_img_align_crop[0]) + attr_id = self.arcface(attr_img_arc) + attr_id = F.normalize(attr_id, p=2, dim=1) + + results = self.network(attr_img, latend_id) + + + results = results * self.imagenet_std + self.imagenet_mean + results = results.cpu().permute(0,2,3,1)[0,...] + results = results.numpy() + results = np.clip(results,0.0,1.0) + final_img = img1.astype(np.uint8) + attr_basename = os.path.splitext(os.path.basename(img))[0] + final_img = cv2.putText(final_img, 'id dis=%.4f'%results_cos_dis, (50, 50), font, 0.8, (15, 9, 255), 2) + final_img = cv2.putText(final_img, 'id--attr dis=%.4f'%cos_dis, (50, 80), font, 0.8, (15, 9, 255), 2) + save_filename = os.path.join(save_dir, + "id_%s--attr_%s_ckp_%s_v_%s.png"%(id_basename, + attr_basename,ckp_step,version)) + + cv2.imwrite(save_filename, final_img) + average_cos /= len(imgs_list) + elapsed = time.time() - start_time + elapsed = str(datetime.timedelta(seconds=elapsed)) + print("Elapsed [{}]".format(elapsed)) + print("Average cosin similarity between ID and results [{}]".format(average_cos.item())) + self.reporter.writeInfo("Average cosin similarity between ID and results [{}]".format(average_cos.item())) + + + + +if __name__ == '__main__': + opt = TestOptions().parse() + + with torch.no_grad(): + + source_loader, dataet_len = getLoader_sourceface('/home/gdp/harddisk/Data2/Faceswap/FaceForensics++_image_hififacestyle_source_Nonearcstyle', batch_size=opt.batchSize) + target_loader, dataet_len = getLoader_targetface('/home/gdp/harddisk/Data2/Faceswap/FaceForensics++_image_target_even10_pro_withmat_Nonearcstyle_256', batch_size=opt.batchSize) + + simswap_eval_save_image_path = opt.output_path + criterion = nn.L1Loss() + if not os.path.exists(simswap_eval_save_image_path): + os.makedirs(simswap_eval_save_image_path) + torch.nn.Module.dump_patches = True + model = create_model(opt) + model.eval() + + source_iter = iter(source_loader) + target_iter = iter(target_loader) + + init_batch = True + for profile_batch, filename_batch in tqdm(source_iter): + # src_batch, filename_batch = data_iter.next() + profile_batch = profile_batch.cuda() + profile_id_downsample = F.interpolate(profile_batch, (112,112)) + profile_latent_id = model.netArc(profile_id_downsample) + profile_latent_id = F.normalize(profile_latent_id, p=2, dim=1) + if init_batch ==True: + wholeid_batch = profile_latent_id.cpu() + init_batch = False + else: + wholeid_batch = torch.cat([wholeid_batch,profile_latent_id.cpu()],dim=0) + print(wholeid_batch.shape) + # np.save("simswap_wholeid_batch.npy", wholeid_batch.detach().cpu().numpy()) + + target_source_pair_dict = np.load('/home/gdp/harddisk/Data2/Faceswap/npy_file/target_source_pair.npy' ,allow_pickle=True).item() + + for target_batch, filename_batch in tqdm(target_iter): + target_index_list = [] + init_id_batch = True + + for filename_tmp in filename_batch: + source_index = int(filename_tmp.split('_')[0]) + target_index = target_source_pair_dict[source_index] + target_index_list.append(target_index) + if init_id_batch: + batch_id = wholeid_batch[target_index][None].cuda() + init_id_batch = False + else: + batch_id = torch.cat([batch_id, wholeid_batch[target_index][None].cuda()],dim = 0) + img_fakes = model(None, target_batch.cuda(), batch_id, None, True) + + for img_fake, target_index_tmp,filename_tmp in zip(img_fakes, target_index_list,filename_batch): + filename_tmp_split = filename_tmp.split('_') + final_filename = filename_tmp_split[0] + '_' +str(target_index_tmp) + '_' + filename_tmp_split[-1] + save_path = os.path.join(simswap_eval_save_image_path,final_filename) + save_image = postprocess(img_fake.cpu().numpy().transpose(1,2,0)) + PIL.Image.fromarray(save_image).save(save_path,quality=95) \ No newline at end of file diff --git a/train_multigpu.py b/train_multigpu.py index c76e517..4f33805 100644 --- a/train_multigpu.py +++ b/train_multigpu.py @@ -5,7 +5,7 @@ # Created Date: Tuesday April 28th 2020 # Author: Chen Xuanhong # Email: chenxuanhongzju@outlook.com -# Last Modified: Tuesday, 1st March 2022 10:27:16 am +# Last Modified: Friday, 4th March 2022 1:53:03 am # Modified By: Chen Xuanhong # Copyright (c) 2020 Shanghai Jiao Tong University ############################################################# @@ -31,7 +31,7 @@ def getParameters(): parser = argparse.ArgumentParser() # general settings - parser.add_argument('-v', '--version', type=str, default='Invobn_resinvo1', + parser.add_argument('-v', '--version', type=str, default='ori_tiny', help="version name for train, test, finetune") parser.add_argument('-t', '--tag', type=str, default='tiny', help="tag for current experiment") @@ -46,9 +46,9 @@ def getParameters(): # training parser.add_argument('--experiment_description', type=str, - default="尝试直接训练最小规模的网络,正往由Invo构成，Resblock用Invo+conv, 对齐batchsize 64") + default="只用conv，训练最小的模型") - parser.add_argument('--train_yaml', type=str, default="train_Invobn_config.yaml") + parser.add_argument('--train_yaml', type=str, default="train_ori_modulation_config.yaml") # system logger parser.add_argument('--logger', type=str, diff --git a/train_scripts/trainer_distillation_mgpu_withrec_importweight.py b/train_scripts/trainer_distillation_mgpu_withrec_importweight.py new file mode 100644 index 0000000..d425e9a --- /dev/null +++ b/train_scripts/trainer_distillation_mgpu_withrec_importweight.py @@ -0,0 +1,590 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: trainer_naiv512.py +# Created Date: Sunday January 9th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 4th March 2022 7:02:04 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + +import os +import time +import random +import shutil +import tempfile + +import numpy as np + +import torch +import torch.nn.functional as F + +from torch_utils import misc +from torch_utils import training_stats +from torch_utils.ops import conv2d_gradfix +from torch_utils.ops import grid_sample_gradfix + +from losses.KA import KA +from utilities.plot import plot_batch +from train_scripts.trainer_multigpu_base import TrainerBase + + +class Trainer(TrainerBase): + + def __init__(self, + config, + reporter): + super(Trainer, self).__init__(config, reporter) + + import inspect + print("Current training script -----------> %s"%inspect.getfile(inspect.currentframe())) + + def train(self): + # Launch processes. + num_gpus = len(self.config["gpus"]) + print('Launching processes...') + torch.multiprocessing.set_start_method('spawn') + with tempfile.TemporaryDirectory() as temp_dir: + torch.multiprocessing.spawn(fn=train_loop, args=(self.config, self.reporter, temp_dir), nprocs=num_gpus) + +def add_mapping_hook(network, features,mapping_layers): + mapping_hooks = [] + + def get_activation(mem, name): + def get_output_hook(module, input, output): + mem[name] = output + + return get_output_hook + + def add_hook(net, mem, mapping_layers): + for n, m in net.named_modules(): + if n in mapping_layers: + mapping_hooks.append( + m.register_forward_hook(get_activation(mem, n))) + + add_hook(network, features, mapping_layers) + + +# TODO modify this function to build your models +def init_framework(config, reporter, device, rank): + ''' + This function is designed to define the framework, + and print the framework information into the log file + ''' + #===============build models================# + print("build models...") + # TODO [import models here] + torch.cuda.set_device(rank) + torch.cuda.empty_cache() + model_config = config["model_configs"] + + if config["phase"] == "train": + gscript_name = "components." + model_config["g_model"]["script"] + file1 = os.path.join("components", model_config["g_model"]["script"]+".py") + tgtfile1 = os.path.join(config["project_scripts"], model_config["g_model"]["script"]+".py") + shutil.copyfile(file1,tgtfile1) + dscript_name = "components." + model_config["d_model"]["script"] + file1 = os.path.join("components", model_config["d_model"]["script"]+".py") + tgtfile1 = os.path.join(config["project_scripts"], model_config["d_model"]["script"]+".py") + shutil.copyfile(file1,tgtfile1) + + elif config["phase"] == "finetune": + gscript_name = config["com_base"] + model_config["g_model"]["script"] + dscript_name = config["com_base"] + model_config["d_model"]["script"] + com_base = "train_logs."+config["teacher_model"]["version"]+".scripts" + tscript_name = com_base +"."+ config["teacher_model"]["model_configs"]["g_model"]["script"] + class_name = config["teacher_model"]["model_configs"]["g_model"]["class_name"] + package = __import__(tscript_name, fromlist=True) + gen_class = getattr(package, class_name) + tgen = gen_class(**config["teacher_model"]["model_configs"]["g_model"]["module_params"]) + tgen = tgen.eval() + + class_name = model_config["g_model"]["class_name"] + package = __import__(gscript_name, fromlist=True) + gen_class = getattr(package, class_name) + gen = gen_class(**model_config["g_model"]["module_params"]) + + + + # print and recorde model structure + reporter.writeInfo("Generator structure:") + reporter.writeModel(gen.__str__()) + reporter.writeInfo("Teacher structure:") + reporter.writeModel(tgen.__str__()) + + class_name = model_config["d_model"]["class_name"] + package = __import__(dscript_name, fromlist=True) + dis_class = getattr(package, class_name) + dis = dis_class(**model_config["d_model"]["module_params"]) + + + # print and recorde model structure + reporter.writeInfo("Discriminator structure:") + reporter.writeModel(dis.__str__()) + + arcface1 = torch.load(config["arcface_ckpt"], map_location=torch.device("cpu")) + arcface = arcface1['model'].module + + # train in GPU + + # if in finetune phase, load the pretrained checkpoint + if config["phase"] == "finetune": + model_path = os.path.join(config["project_checkpoints"], + "step%d_%s.pth"%(config["ckpt"], + config["checkpoint_names"]["generator_name"])) + gen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) + + model_path = os.path.join(config["project_checkpoints"], + "step%d_%s.pth"%(config["ckpt"], + config["checkpoint_names"]["discriminator_name"])) + dis.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) + + print('loaded trained backbone model step {}...!'.format(config["project_checkpoints"])) + model_path = os.path.join(config["teacher_model"]["project_checkpoints"], + "step%d_%s.pth"%(config["teacher_model"]["model_step"], + config["teacher_model"]["checkpoint_names"]["generator_name"])) + tgen.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) + print('loaded trained teacher backbone model step {}...!'.format(config["teacher_model"]["model_step"])) + tgen = tgen.to(device) + tgen.requires_grad_(False) + gen = gen.to(device) + dis = dis.to(device) + arcface= arcface.to(device) + arcface.requires_grad_(False) + arcface.eval() + + t_features = {} + s_features = {} + add_mapping_hook(tgen,t_features,config["feature_list"]) + add_mapping_hook(gen,s_features,config["feature_list"]) + + return tgen, gen, dis, arcface, t_features, s_features + +# TODO modify this function to configurate the optimizer of your pipeline +def setup_optimizers(config, reporter, gen, dis, rank): + + torch.cuda.set_device(rank) + torch.cuda.empty_cache() + g_train_opt = config['g_optim_config'] + d_train_opt = config['d_optim_config'] + + g_optim_params = [] + d_optim_params = [] + for k, v in gen.named_parameters(): + if v.requires_grad: + g_optim_params.append(v) + else: + reporter.writeInfo(f'Params {k} will not be optimized.') + print(f'Params {k} will not be optimized.') + + for k, v in dis.named_parameters(): + if v.requires_grad: + d_optim_params.append(v) + else: + reporter.writeInfo(f'Params {k} will not be optimized.') + print(f'Params {k} will not be optimized.') + + optim_type = config['optim_type'] + + if optim_type == 'Adam': + g_optimizer = torch.optim.Adam(g_optim_params,**g_train_opt) + d_optimizer = torch.optim.Adam(d_optim_params,**d_train_opt) + else: + raise NotImplementedError( + f'optimizer {optim_type} is not supperted yet.') + # self.optimizers.append(self.optimizer_g) + if config["phase"] == "finetune": + opt_path = os.path.join(config["project_checkpoints"], + "step%d_optim_%s.pth"%(config["ckpt"], + config["optimizer_names"]["generator_name"])) + g_optimizer.load_state_dict(torch.load(opt_path)) + + opt_path = os.path.join(config["project_checkpoints"], + "step%d_optim_%s.pth"%(config["ckpt"], + config["optimizer_names"]["discriminator_name"])) + d_optimizer.load_state_dict(torch.load(opt_path)) + + print('loaded trained optimizer step {}...!'.format(config["project_checkpoints"])) + return g_optimizer, d_optimizer + + +def train_loop( + rank, + config, + reporter, + temp_dir + ): + + version = config["version"] + + ckpt_dir = config["project_checkpoints"] + sample_dir = config["project_samples"] + + log_freq = config["log_step"] + model_freq = config["model_save_step"] + sample_freq = config["sample_step"] + total_step = config["total_step"] + random_seed = config["dataset_params"]["random_seed"] + + + id_w = config["id_weight"] + rec_w = config["reconstruct_weight"] + feat_w = config["feature_match_weight"] + distill_w = config["distillation_weight"] + distill_rec_w = config["teacher_reconstruction"] + distill_feat_w = config["teacher_featurematching"] + + feat_num = len(config["feature_list"]) + + num_gpus = len(config["gpus"]) + batch_gpu = config["batch_size"] // num_gpus + + init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) + if os.name == 'nt': + init_method = 'file:///' + init_file.replace('\\', '/') + torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=num_gpus) + else: + init_method = f'file://{init_file}' + torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=num_gpus) + + # Init torch_utils. + sync_device = torch.device('cuda', rank) + training_stats.init_multiprocessing(rank=rank, sync_device=sync_device) + + + + if rank == 0: + img_std = torch.Tensor([0.229, 0.224, 0.225]).view(3,1,1) + img_mean = torch.Tensor([0.485, 0.456, 0.406]).view(3,1,1) + + + # Initialize. + device = torch.device('cuda', rank) + np.random.seed(random_seed * num_gpus + rank) + torch.manual_seed(random_seed * num_gpus + rank) + torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. + torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. + conv2d_gradfix.enabled = True # Improves training speed. + grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + + # Create dataloader. + if rank == 0: + print('Loading training set...') + + dataset = config["dataset_paths"][config["dataset_name"]] + #================================================# + print("Prepare the train dataloader...") + dlModulename = config["dataloader"] + package = __import__("data_tools.data_loader_%s"%dlModulename, fromlist=True) + dataloaderClass = getattr(package, 'GetLoader') + dataloader_class= dataloaderClass + dataloader = dataloader_class(dataset, + rank, + num_gpus, + batch_gpu, + **config["dataset_params"]) + + # Construct networks. + if rank == 0: + print('Constructing networks...') + tgen, gen, dis, arcface, t_feat, s_feat = init_framework(config, reporter, device, rank) + + # Check for existing checkpoint + + # Print network summary tables. + # if rank == 0: + # attr = torch.empty([batch_gpu, 3, 512, 512], device=device) + # id = torch.empty([batch_gpu, 3, 112, 112], device=device) + # latent = misc.print_module_summary(arcface, [id]) + # img = misc.print_module_summary(gen, [attr, latent]) + # misc.print_module_summary(dis, [img, None]) + # del attr + # del id + # del latent + # del img + # torch.cuda.empty_cache() + + + # Distribute across GPUs. + if rank == 0: + print(f'Distributing across {num_gpus} GPUs...') + for module in [gen, dis, arcface, tgen]: + if module is not None and num_gpus > 1: + for param in misc.params_and_buffers(module): + torch.distributed.broadcast(param, src=0) + + # Setup training phases. + if rank == 0: + print('Setting up training phases...') + #===============build losses===================# + # TODO replace below lines to build your losses + # MSE_loss = torch.nn.MSELoss() + l1_loss = torch.nn.L1Loss() + l1_loss_import = torch.nn.L1Loss(reduce=False) + cos_loss = torch.nn.CosineSimilarity() + + g_optimizer, d_optimizer = setup_optimizers(config, reporter, gen, dis, rank) + + # Initialize logs. + if rank == 0: + print('Initializing logs...') + #==============build tensorboard=================# + if config["logger"] == "tensorboard": + import torch.utils.tensorboard as tensorboard + tensorboard_writer = tensorboard.SummaryWriter(config["project_summary"]) + logger = tensorboard_writer + + elif config["logger"] == "wandb": + import wandb + wandb.init(project="Simswap_HQ", entity="xhchen", notes="512", + tags=[config["tag"]], name=version) + + wandb.config = { + "total_step": config["total_step"], + "batch_size": config["batch_size"] + } + logger = wandb + + + random.seed(random_seed) + randindex = [i for i in range(batch_gpu)] + + # set the start point for training loop + if config["phase"] == "finetune": + start = config["ckpt"] + else: + start = 0 + if rank == 0: + import datetime + start_time = time.time() + + # Caculate the epoch number + print("Total step = %d"%total_step) + + print("Start to train at %s"%(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) + + from utilities.logo_class import logo_class + logo_class.print_start_training() + + dis.feature_network.requires_grad_(False) + + for step in range(start, total_step): + gen.train() + dis.train() + for interval in range(2): + random.shuffle(randindex) + src_image1, src_image2 = dataloader.next() + # if rank ==0: + + # elapsed = time.time() - start_time + # elapsed = str(datetime.timedelta(seconds=elapsed)) + # print("dataloader:",elapsed) + + if step%2 == 0: + img_id = src_image2 + else: + img_id = src_image2[randindex] + + img_id_112 = F.interpolate(img_id,size=(112,112), mode='bicubic') + latent_id = arcface(img_id_112) + latent_id = F.normalize(latent_id, p=2, dim=1) + + if interval == 0: + + + img_fake = gen(src_image1, latent_id) + gen_logits,_ = dis(img_fake.detach(), None) + loss_Dgen = (F.relu(torch.ones_like(gen_logits) + gen_logits)).mean() + + real_logits,_ = dis(src_image2,None) + loss_Dreal = (F.relu(torch.ones_like(real_logits) - real_logits)).mean() + + loss_D = loss_Dgen + loss_Dreal + d_optimizer.zero_grad(set_to_none=True) + loss_D.backward() + with torch.autograd.profiler.record_function('discriminator_opt'): + # params = [param for param in dis.parameters() if param.grad is not None] + # if len(params) > 0: + # flat = torch.cat([param.grad.flatten() for param in params]) + # if num_gpus > 1: + # torch.distributed.all_reduce(flat) + # flat /= num_gpus + # misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + # grads = flat.split([param.numel() for param in params]) + # for param, grad in zip(params, grads): + # param.grad = grad.reshape(param.shape) + params = [param for param in dis.parameters() if param.grad is not None] + flat = torch.cat([param.grad.flatten() for param in params]) + torch.distributed.all_reduce(flat) + flat /= num_gpus + misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + grads = flat.split([param.numel() for param in params]) + for param, grad in zip(params, grads): + param.grad = grad.reshape(param.shape) + d_optimizer.step() + # if rank ==0: + + # elapsed = time.time() - start_time + # elapsed = str(datetime.timedelta(seconds=elapsed)) + # print("Discriminator training:",elapsed) + else: + + # model.netD.requires_grad_(True) + t_fake = tgen(src_image1, latent_id) + t_id = arcface(t_fake.detach()) + t_feat = dis.get_feature(t_fake.detach()) + realism = cos_loss(t_id, latent_id) + + + + img_fake = gen(src_image1, latent_id) + + Sacts = [ + s_feat[key] for key in sorted(s_feat.keys()) + ] + Tacts = [ + t_feat[key] for key in sorted(t_feat.keys()) + ] + loss_distill = 0 + for Sact, Tact in zip(Sacts, Tacts): + loss_distill += -KA(Sact, Tact) + # G loss + loss_distill /= feat_num + gen_logits,feat = dis(img_fake, None) + + loss_Gmain = (-gen_logits).mean() + img_fake_down = F.interpolate(img_fake, size=(112,112), mode='bicubic') + latent_fake = arcface(img_fake_down) + latent_fake = F.normalize(latent_fake, p=2, dim=1) + loss_G_ID = (1 - cos_loss(latent_fake, latent_id)).mean() + real_feat = dis.get_feature(src_image1) + feat_match_loss = l1_loss(feat["3"],real_feat["3"]) + + feat_match_ts = (realism * l1_loss_import(feat["3"],t_feat)).mean() + t_rec_loss = (realism * l1_loss_import(t_fake.detach(), img_fake)).mean() + + loss_G = loss_Gmain + loss_G_ID * id_w + \ + feat_match_loss * feat_w + loss_distill * distill_w +\ + distill_feat_w * feat_match_ts + distill_rec_w * t_rec_loss + if step%2 == 0: + #G_Rec + loss_G_Rec = l1_loss(img_fake, src_image1) + loss_G += loss_G_Rec * rec_w + + g_optimizer.zero_grad(set_to_none=True) + loss_G.backward() + with torch.autograd.profiler.record_function('generator_opt'): + params = [param for param in gen.parameters() if param.grad is not None] + flat = torch.cat([param.grad.flatten() for param in params]) + torch.distributed.all_reduce(flat) + flat /= num_gpus + misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + grads = flat.split([param.numel() for param in params]) + for param, grad in zip(params, grads): + param.grad = grad.reshape(param.shape) + g_optimizer.step() + # if rank ==0: + + # elapsed = time.time() - start_time + # elapsed = str(datetime.timedelta(seconds=elapsed)) + # print("Generator training:",elapsed) + + + # Print out log info + if rank == 0 and (step + 1) % log_freq == 0: + elapsed = time.time() - start_time + elapsed = str(datetime.timedelta(seconds=elapsed)) + # print("ready to report losses") + # ID_Total= loss_G_ID + # torch.distributed.all_reduce(ID_Total) + + epochinformation="[{}], Elapsed [{}], Step [{}/{}], \ + G_ID: {:.4f}, G_loss: {:.4f}, Rec_loss: {:.4f}, Fm_loss: {:.4f}, \ + Distillaton_loss: {:.4f}, D_loss: {:.4f}, D_fake: {:.4f}, D_real: {:.4f}". \ + format(version, elapsed, step, total_step, \ + loss_G_ID.item(), loss_G.item(), loss_G_Rec.item(), feat_match_loss.item(), \ + loss_distill.item(), loss_D.item(), loss_Dgen.item(), loss_Dreal.item()) + print(epochinformation) + reporter.writeInfo(epochinformation) + + if config["logger"] == "tensorboard": + logger.add_scalar('G/G_loss', loss_G.item(), step) + logger.add_scalar('G/G_Rec', loss_G_Rec.item(), step) + logger.add_scalar('G/G_feat_match', feat_match_loss.item(), step) + logger.add_scalar('G/G_distillation', loss_distill.item(), step) + logger.add_scalar('G/G_ID', loss_G_ID.item(), step) + logger.add_scalar('D/D_loss', loss_D.item(), step) + logger.add_scalar('D/D_fake', loss_Dgen.item(), step) + logger.add_scalar('D/D_real', loss_Dreal.item(), step) + elif config["logger"] == "wandb": + logger.log({"G_Loss": loss_G.item()}, step = step) + logger.log({"G_Rec": loss_G_Rec.item()}, step = step) + logger.log({"G_feat_match": feat_match_loss.item()}, step = step) + logger.log({"G_distillation": loss_distill.item()}, step = step) + logger.log({"G_ID": loss_G_ID.item()}, step = step) + logger.log({"D_loss": loss_D.item()}, step = step) + logger.log({"D_fake": loss_Dgen.item()}, step = step) + logger.log({"D_real": loss_Dreal.item()}, step = step) + torch.cuda.empty_cache() + + if rank == 0 and ((step + 1) % sample_freq == 0 or (step+1) % model_freq==0): + gen.eval() + with torch.no_grad(): + imgs = [] + zero_img = (torch.zeros_like(src_image1[0,...])) + imgs.append(zero_img.cpu().numpy()) + save_img = ((src_image1.cpu())* img_std + img_mean).numpy() + for r in range(batch_gpu): + imgs.append(save_img[r,...]) + arcface_112 = F.interpolate(src_image2,size=(112,112), mode='bicubic') + id_vector_src1 = arcface(arcface_112) + id_vector_src1 = F.normalize(id_vector_src1, p=2, dim=1) + + for i in range(batch_gpu): + + imgs.append(save_img[i,...]) + image_infer = src_image1[i, ...].repeat(batch_gpu, 1, 1, 1) + img_fake = gen(image_infer, id_vector_src1).cpu() + + img_fake = img_fake * img_std + img_fake = img_fake + img_mean + img_fake = img_fake.numpy() + for j in range(batch_gpu): + imgs.append(img_fake[j,...]) + print("Save test data") + imgs = np.stack(imgs, axis = 0).transpose(0,2,3,1) + plot_batch(imgs, os.path.join(sample_dir, 'step_'+str(step+1)+'.jpg')) + torch.cuda.empty_cache() + + + + #===============adjust learning rate============# + # if (epoch + 1) in self.config["lr_decay_step"] and self.config["lr_decay_enable"]: + # print("Learning rate decay") + # for p in self.optimizer.param_groups: + # p['lr'] *= self.config["lr_decay"] + # print("Current learning rate is %f"%p['lr']) + + #===============save checkpoints================# + if rank == 0 and (step+1) % model_freq==0: + + torch.save(gen.state_dict(), + os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, + config["checkpoint_names"]["generator_name"]))) + torch.save(dis.state_dict(), + os.path.join(ckpt_dir, 'step{}_{}.pth'.format(step + 1, + config["checkpoint_names"]["discriminator_name"]))) + + torch.save(g_optimizer.state_dict(), + os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, + config["checkpoint_names"]["generator_name"]))) + + torch.save(d_optimizer.state_dict(), + os.path.join(ckpt_dir, 'step{}_optim_{}'.format(step + 1, + config["checkpoint_names"]["discriminator_name"]))) + print("Save step %d model checkpoint!"%(step+1)) + torch.cuda.empty_cache() + print("Rank %d process done!"%rank) + torch.distributed.barrier() \ No newline at end of file diff --git a/train_yamls/train_ori_modulation_config.yaml b/train_yamls/train_ori_modulation_config.yaml new file mode 100644 index 0000000..388ae9e --- /dev/null +++ b/train_yamls/train_ori_modulation_config.yaml @@ -0,0 +1,64 @@ +# Related scripts +train_script_name: multi_gpu + +# models' scripts +model_configs: + g_model: + script: Generator_ori_modulation_config + class_name: Generator + module_params: + id_dim: 512 + g_kernel_size: 3 + in_channel: 8 + res_num: 9 + + d_model: + script: projected_discriminator + class_name: ProjectedDiscriminator + module_params: + diffaug: False + interp224: False + backbone_kwargs: {} + +arcface_ckpt: arcface_ckpt/arcface_checkpoint.tar + +# Training information +batch_size: 32 + +# Dataset +dataloader: VGGFace2HQ_multigpu +dataset_name: vggface2_hq +dataset_params: + random_seed: 1234 + dataloader_workers: 4 + +eval_dataloader: DIV2K_hdf5 +eval_dataset_name: DF2K_H5_Eval +eval_batch_size: 2 + +# Dataset + +# Optimizer +optim_type: Adam +g_optim_config: + lr: 0.0006 + betas: [ 0, 0.99] + eps: !!float 1e-8 + +d_optim_config: + lr: 0.0006 + betas: [ 0, 0.99] + eps: !!float 1e-8 + +id_weight: 20.0 +reconstruct_weight: 10.0 +feature_match_weight: 10.0 + +# Log +log_step: 300 +model_save_step: 10000 +sample_step: 1000 +total_step: 1000000 +checkpoint_names: + generator_name: Generator + discriminator_name: Discriminator \ No newline at end of file