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chenxuanhong
2022-01-17 13:17:49 +08:00
parent bf2df5c5a6
commit 601d2ee43d
58 changed files with 2748 additions and 5696 deletions
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components/Conditional_Discriminator_Projection_big.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_Discriminator copy.py
# Created Date: Saturday April 18th 2020
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 29th June 2021 4:26:33 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from torch.nn import utils
class Discriminator(nn.Module):
def __init__(self, chn=32, k_size=3, n_class=3):
super().__init__()
# padding_size = int((k_size -1)/2)
slop = 0.2
enable_bias = True
# stage 1
self.block1 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size= k_size, stride = 2, padding=2,bias= enable_bias)),
nn.LeakyReLU(slop),
utils.spectral_norm(nn.Conv2d(in_channels = chn, out_channels = chn * 2 , kernel_size= k_size, stride = 2,padding=2, bias= enable_bias)), # 1/4
nn.LeakyReLU(slop)
)
self.aux_classfier1 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = chn * 2 , out_channels = chn , kernel_size= 5, bias=enable_bias)),
nn.LeakyReLU(slop),
nn.AdaptiveAvgPool2d(1),
)
self.embed1 = utils.spectral_norm(nn.Embedding(n_class, chn))
self.linear1= utils.spectral_norm(nn.Linear(chn, 1))
# stage 2
self.block2 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = chn * 2 , out_channels = chn * 4 , kernel_size= k_size, stride = 2, padding=2, bias= enable_bias)),# 1/8
nn.LeakyReLU(slop),
utils.spectral_norm(nn.Conv2d(in_channels = chn * 4, out_channels = chn * 8 , kernel_size= k_size, stride = 2,padding=2, bias= enable_bias)),# 1/16
nn.LeakyReLU(slop)
)
self.aux_classfier2 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = chn * 8 , out_channels = chn , kernel_size= 5, bias= enable_bias)),
nn.LeakyReLU(slop),
nn.AdaptiveAvgPool2d(1),
)
self.embed2 = utils.spectral_norm(nn.Embedding(n_class, chn))
self.linear2= utils.spectral_norm(nn.Linear(chn, 1))
# stage 3
self.block3 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = chn * 8 , out_channels = chn * 8 , kernel_size= k_size, stride = 2,padding=3, bias= enable_bias)),# 1/32
nn.LeakyReLU(slop),
utils.spectral_norm(nn.Conv2d(in_channels = chn * 8, out_channels = chn * 16 , kernel_size= k_size, stride = 2,padding=3, bias= enable_bias)),# 1/64
nn.LeakyReLU(slop)
)
self.aux_classfier3 = nn.Sequential(
utils.spectral_norm(nn.Conv2d(in_channels = chn * 16 , out_channels = chn, kernel_size= 5, bias= enable_bias)),
nn.LeakyReLU(slop),
nn.AdaptiveAvgPool2d(1),
)
self.embed3 = utils.spectral_norm(nn.Embedding(n_class, chn))
self.linear3= utils.spectral_norm(nn.Linear(chn, 1))
self.__weights_init__()
def __weights_init__(self):
print("Init weights")
for m in self.modules():
if isinstance(m,nn.Conv2d) or isinstance(m,nn.Linear):
nn.init.xavier_uniform_(m.weight)
try:
nn.init.zeros_(m.bias)
except:
print("No bias found!")
if isinstance(m, nn.Embedding):
nn.init.xavier_uniform_(m.weight)
def forward(self, input, condition):
h = self.block1(input)
prep1 = self.aux_classfier1(h)
prep1 = prep1.view(prep1.size()[0], -1)
y1 = self.embed1(condition)
y1 = torch.sum(y1 * prep1, dim=1, keepdim=True)
prep1 = self.linear1(prep1) + y1
h = self.block2(h)
prep2 = self.aux_classfier2(h)
prep2 = prep2.view(prep2.size()[0], -1)
y2 = self.embed2(condition)
y2 = torch.sum(y2 * prep2, dim=1, keepdim=True)
prep2 = self.linear2(prep2) + y2
h = self.block3(h)
prep3 = self.aux_classfier3(h)
prep3 = prep3.view(prep3.size()[0], -1)
y3 = self.embed3(condition)
y3 = torch.sum(y3 * prep3, dim=1, keepdim=True)
prep3 = self.linear3(prep3) + y3
out_prep = [prep1,prep2,prep3]
return out_prep
def get_outputs_len(self):
num = 0
for m in self.modules():
if isinstance(m,nn.Linear):
num+=1
return num
if __name__ == "__main__":
wocao = Discriminator().cuda()
from torchsummary import summary
summary(wocao, input_size=(3, 512, 512))
components/Conditional_Generator_Noskip.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_Generator_tanh.py
# Created Date: Saturday April 18th 2020
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 6th July 2021 1:16:46 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2020 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
from components.Conditional_ResBlock_ModulaConv import Conditional_ResBlock
class Generator(nn.Module):
def __init__(
self,
chn=32,
k_size=3,
res_num = 5,
class_num = 3,
**kwargs):
super().__init__()
padding_size = int((k_size -1)/2)
self.resblock_list = []
self.n_class = class_num
self.encoder1 = nn.Sequential(
# nn.InstanceNorm2d(3, affine=True),
# nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size= k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
# nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size= k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn*2, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
# nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = chn*2, out_channels = chn * 4, kernel_size= k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
# nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = chn*4 , out_channels = chn * 8, kernel_size= k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
# # nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size= k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU()
)
res_size = chn * 8
for _ in range(res_num-1):
self.resblock_list += [ResBlock(res_size,k_size),]
self.resblocks = nn.Sequential(*self.resblock_list)
self.conditional_res = Conditional_ResBlock(res_size, k_size, class_num)
self.decoder1 = nn.Sequential(
DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size= k_size),
nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
DeConv(in_channels = chn * 8, out_channels = chn *4, kernel_size= k_size),
nn.InstanceNorm2d(chn *4, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
DeConv(in_channels = chn * 4, out_channels = chn * 2 , kernel_size= k_size),
nn.InstanceNorm2d(chn*2, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
DeConv(in_channels = chn *2, out_channels = chn, kernel_size= k_size),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
# nn.ReLU(),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn, out_channels =3, kernel_size= k_size, stride=1, padding=1,bias =True)
# nn.Tanh()
)
self.__weights_init__()
def __weights_init__(self):
for layer in self.encoder1:
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, input, condition=None, get_feature = False):
feature = self.encoder1(input)
if get_feature:
return feature
out = self.conditional_res(feature, condition)
out = self.resblocks(out)
# n, _,h,w = out.size()
# attr = condition.view((n, self.n_class, 1, 1)).expand((n, self.n_class, h, w))
# out = torch.cat([out, attr], dim=1)
out = self.decoder1(out)
return out,feature
components/Conditional_ResBlock_ModulaConv.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_ResBlock_v2.py
# Created Date: Tuesday June 29th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 29th June 2021 3:59:44 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
# -*- coding:utf-8 -*-
###################################################################
### @FilePath: \ASMegaGAN\components\Conditional_ResBlock_v2.py
### @Author: Ziang Liu
### @Date: 2021-06-28 21:30:17
### @LastEditors: Ziang Liu
### @LastEditTime: 2021-06-28 21:46:24
### @Copyright (C) 2021 SJTU. All rights reserved.
###################################################################
import torch
from torch import nn
import torch.nn.functional as F
# from ops.Conditional_BN import Conditional_BN
# from components.Adain import Adain
class Conv2DMod(nn.Module):
def __init__(self, in_channels, out_channels, kernel, demod=True, stride=1, dilation=1, eps = 1e-8, **kwargs):
super().__init__()
self.filters = out_channels
self.demod = demod
self.kernel = kernel
self.stride = stride
self.dilation = dilation
self.weight = nn.Parameter(torch.randn((out_channels, in_channels, kernel, kernel)))
self.eps = eps
padding_size = int((kernel -1)/2)
self.same_padding = nn.ReplicationPad2d(padding_size)
nn.init.kaiming_normal_(self.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
def forward(self, x, y):
b, c, h, w = x.shape
w1 = y[:, None, :, None, None]
w2 = self.weight[None, :, :, :, :]
weights = w2 * (w1 + 1)
if self.demod:
d = torch.rsqrt((weights ** 2).sum(dim=(2, 3, 4), keepdim=True) + self.eps)
weights = weights * d
x = x.reshape(1, -1, h, w)
_, _, *ws = weights.shape
weights = weights.reshape(b * self.filters, *ws)
x = self.same_padding(x)
x = F.conv2d(x, weights, groups=b)
x = x.reshape(-1, self.filters, h, w)
return x
class Conditional_ResBlock(nn.Module):
def __init__(self, in_channel, k_size = 3, n_class = 2, stride=1):
super().__init__()
self.embed1 = nn.Embedding(n_class, in_channel)
self.embed2 = nn.Embedding(n_class, in_channel)
self.conv1 = Conv2DMod(in_channels = in_channel , out_channels = in_channel, kernel= k_size, stride=stride)
self.conv2 = Conv2DMod(in_channels = in_channel , out_channels = in_channel, kernel= k_size, stride=stride)
def forward(self, input, condition):
res = input
style1 = self.embed1(condition)
h = self.conv1(res, style1)
style2 = self.embed2(condition)
h = self.conv2(h, style2)
out = h + res
return out
components/DeConv.py
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import torch
from torch import nn
class DeConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2):
super().__init__()
self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
padding_size = int((kernel_size -1)/2)
# self.same_padding = nn.ReflectionPad2d(padding_size)
self.conv = nn.Conv2d(in_channels = in_channels ,padding=padding_size, out_channels = out_channels , kernel_size= kernel_size, bias= False)
self.__weights_init__()
def __weights_init__(self):
nn.init.xavier_uniform_(self.conv.weight)
def forward(self, input):
h = self.upsampling(input)
# h = self.same_padding(h)
h = self.conv(h)
return h
components/Discriminator.py
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import torch
import torch.nn as nn
class Discriminator(nn.Module):
def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, use_sigmoid=False):
super(Discriminator, self).__init__()
kw = 4
padw = 1
self.down1 = nn.Sequential(
nn.Conv2d(input_nc, 64, kernel_size=kw, stride=2, padding=padw),
norm_layer(64),
nn.LeakyReLU(0.2, True)
)
self.down2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=kw, stride=2, padding=padw),
norm_layer(128),
nn.LeakyReLU(0.2, True)
)
self.down3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=kw, stride=2, padding=padw),
norm_layer(256),
nn.LeakyReLU(0.2, True)
)
self.down4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=kw, stride=2, padding=padw),
norm_layer(512),
nn.LeakyReLU(0.2, True)
)
self.down5 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=kw, stride=2, padding=padw),
norm_layer(512),
nn.LeakyReLU(0.2, True)
)
self.conv1 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=kw, stride=1, padding=padw),
norm_layer(512),
nn.LeakyReLU(0.2, True)
)
if use_sigmoid:
self.conv2 = nn.Sequential(
nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw),
nn.Sigmoid()
)
else:
self.conv2 = nn.Sequential(
nn.Conv2d(512, 1, kernel_size=kw, stride=1, padding=padw)
)
def forward(self, input):
out = []
x = self.down1(input)
#out.append(x)
x = self.down2(x)
#out.append(x)
x = self.down3(x)
#out.append(x)
x = self.down4(x)
x = self.down5(x)
out.append(x)
x = self.conv1(x)
out.append(x)
x = self.conv2(x)
out.append(x)
return out
components/FastNST_CNN.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_Generator_gpt_LN_encoder copy.py
# Created Date: Saturday October 9th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Monday, 11th October 2021 5:22:22 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
class ImageLN(nn.Module):
def __init__(self, dim) -> None:
super().__init__()
self.layer = nn.LayerNorm(dim)
def forward(self, x):
y = self.layer(x.permute(0,2,3,1)).permute(0,3,1,2)
return y
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
class_num = kwargs["n_class"]
window_size = kwargs["window_size"]
image_size = kwargs["image_size"]
padding_size = int((k_size -1)/2)
self.resblock_list = []
embed_dim = 96
window_size = 8
num_heads = 8
mlp_ratio = 2.
norm_layer = nn.LayerNorm
qk_scale = None
qkv_bias = True
self.patch_norm = True
self.lnnorm = norm_layer(embed_dim)
self.encoder = nn.Sequential(
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn * 2),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*2, out_channels = embed_dim, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(embed_dim),
nn.LeakyReLU(),
)
# self.encoder2 = nn.Sequential(
# nn.Conv2d(in_channels = chn*4 , out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU()
# )
self.decoder = nn.Sequential(
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
# DeConv(in_channels = chn * 8, out_channels = chn *4, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
# nn.LeakyReLU(),
DeConv(in_channels = embed_dim, out_channels = chn * 2 , kernel_size=k_size),
# nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.InstanceNorm2d(chn * 2),
nn.LeakyReLU(),
DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
nn.InstanceNorm2d(chn),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
)
# 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, input):
x2 = self.encoder(input)
out = self.decoder(x2)
return out
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/FastNST_CNN_Resblock.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_Generator_gpt_LN_encoder copy.py
# Created Date: Saturday October 9th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 7:35:08 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
class ImageLN(nn.Module):
def __init__(self, dim) -> None:
super().__init__()
self.layer = nn.LayerNorm(dim)
def forward(self, x):
y = self.layer(x.permute(0,2,3,1)).permute(0,3,1,2)
return y
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
padding_size = int((k_size -1)/2)
self.resblock_list = []
self.encoder = nn.Sequential(
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*2, out_channels = chn*4, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*4 , out_channels = chn * 4, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
nn.LeakyReLU(),
)
for _ in range(res_num):
self.resblock_list += [ResBlock(chn * 4,k_size),]
self.resblocks = nn.Sequential(*self.resblock_list)
self.decoder = nn.Sequential(
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
DeConv(in_channels = chn * 4, out_channels = chn *2, kernel_size=k_size),
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
DeConv(in_channels = chn * 2, out_channels = chn * 2 , kernel_size=k_size),
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.ReLU(),
DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
nn.ReLU(),
nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
)
# 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, input):
x2 = self.encoder(input)
x2 = self.resblocks(x2)
out = self.decoder(x2)
return out
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/FastNST_Liif.py
-144
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@@ -1,144 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: FastNST_Liif.py
# Created Date: Thursday October 14th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 2:39:09 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
from components.Liif import LIIF
class ImageLN(nn.Module):
def __init__(self, dim) -> None:
super().__init__()
self.layer = nn.LayerNorm(dim)
def forward(self, x):
y = self.layer(x.permute(0,2,3,1)).permute(0,3,1,2)
return y
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
class_num = kwargs["n_class"]
window_size = kwargs["window_size"]
image_size = kwargs["image_size"]
batch_size = kwargs["batch_size"]
# mlp_in_dim = kwargs["mlp_in_dim"]
# mlp_out_dim = kwargs["mlp_out_dim"]
mlp_hidden_list = kwargs["mlp_hidden_list"]
padding_size = int((k_size -1)/2)
self.resblock_list = []
embed_dim = 96
window_size = 8
num_heads = 8
mlp_ratio = 2.
norm_layer = nn.LayerNorm
qk_scale = None
qkv_bias = True
self.patch_norm = True
self.lnnorm = norm_layer(embed_dim)
self.encoder = nn.Sequential(
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn * 2),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*2, out_channels = chn*4, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*4 , out_channels = chn * 4, kernel_size=k_size, stride=2, padding=1,bias =False),
ImageLN(chn * 4),
nn.LeakyReLU(),
)
for _ in range(res_num):
self.resblock_list += [ResBlock(chn * 4,k_size),]
self.resblocks = nn.Sequential(*self.resblock_list)
# self.encoder2 = nn.Sequential(
# nn.Conv2d(in_channels = chn*4 , out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU()
# )
self.decoder = nn.Sequential(
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
DeConv(in_channels = chn * 4, out_channels = chn *2, kernel_size=k_size),
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
DeConv(in_channels = chn * 2, out_channels = chn, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.InstanceNorm2d(chn),
nn.LeakyReLU()
# DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
# nn.InstanceNorm2d(chn),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
)
self.upsample = LIIF(chn, 3, mlp_hidden_list)
self.upsample.gen_coord((batch_size, \
chn,image_size//2,image_size//2),(image_size,image_size))
# 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, input):
x2 = self.encoder(input)
x2 = self.resblocks(x2)
out = self.decoder(x2)
out = self.upsample(out)
return out
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/FastNST_Liif_warp.py
-150
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@@ -1,150 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: FastNST_Liif.py
# Created Date: Thursday October 14th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 4:33:51 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
from components.Liif_conv import LIIF
class ImageLN(nn.Module):
def __init__(self, dim) -> None:
super().__init__()
self.layer = nn.LayerNorm(dim)
def forward(self, x):
y = self.layer(x.permute(0,2,3,1)).permute(0,3,1,2)
return y
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
class_num = kwargs["n_class"]
window_size = kwargs["window_size"]
image_size = kwargs["image_size"]
batch_size = kwargs["batch_size"]
# mlp_in_dim = kwargs["mlp_in_dim"]
# mlp_out_dim = kwargs["mlp_out_dim"]
padding_size = int((k_size -1)/2)
self.resblock_list = []
embed_dim = 96
window_size = 8
num_heads = 8
mlp_ratio = 2.
norm_layer = nn.LayerNorm
qk_scale = None
qkv_bias = True
self.patch_norm = True
self.lnnorm = norm_layer(embed_dim)
self.encoder = nn.Sequential(
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*2, out_channels = chn*4, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn*4 , out_channels = chn * 4, kernel_size=k_size, stride=2, padding=1,bias =False),
nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
nn.LeakyReLU(),
)
for _ in range(res_num):
self.resblock_list += [ResBlock(chn * 4,k_size),]
self.resblocks = nn.Sequential(*self.resblock_list)
# self.encoder2 = nn.Sequential(
# nn.Conv2d(in_channels = chn*4 , out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
# ImageLN(chn * 8),
# nn.LeakyReLU()
# )
self.decoder = nn.Sequential(
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# nn.LeakyReLU(),
DeConv(in_channels = chn * 4, out_channels = chn *2, kernel_size=k_size),
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
# DeConv(in_channels = chn * 2, out_channels = chn, kernel_size=k_size),
# # nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
# nn.InstanceNorm2d(chn, affine=True, momentum=0),
# nn.LeakyReLU()
# DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
# nn.InstanceNorm2d(chn),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
)
self.upsample1 = LIIF(chn*2, chn)
self.upsample1.gen_coord((batch_size, \
chn,image_size//4,image_size//4),(image_size//2,image_size//2))
self.upsample2 = LIIF(chn, chn)
self.upsample2.gen_coord((batch_size, \
chn,image_size//2,image_size//2),(image_size,image_size))
self.out_conv = nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
# 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, input):
x2 = self.encoder(input)
x2 = self.resblocks(x2)
out = self.decoder(x2)
out = self.upsample1(out)
out = self.upsample2(out)
out = self.out_conv(out)
return out
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/FastNST_Liif_warpinvo.py
-146
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@@ -1,146 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: FastNST_Liif.py
# Created Date: Thursday October 14th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 8:47:28 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from torch.nn import init
from torch.nn import functional as F
from components.ResBlock import ResBlock
from components.DeConv import DeConv
from components.Liif_invo import LIIF
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
class_num = kwargs["n_class"]
window_size = kwargs["window_size"]
image_size = kwargs["image_size"]
batch_size = kwargs["batch_size"]
# mlp_in_dim = kwargs["mlp_in_dim"]
# mlp_out_dim = kwargs["mlp_out_dim"]
padding_size = int((k_size -1)/2)
self.resblock_list = []
embed_dim = 96
norm_layer = nn.LayerNorm
self.img_token = nn.Sequential(
nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn*2, out_channels = chn*4, kernel_size=k_size, stride=2, padding=1,bias =False),
# nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
# nn.LeakyReLU(),
# nn.Conv2d(in_channels = chn*4 , out_channels = chn * 4, kernel_size=k_size, stride=2, padding=1,bias =False),
# nn.InstanceNorm2d(chn * 4, affine=True, momentum=0),
# nn.LeakyReLU(),
)
image_size = image_size // 2
self.downsample1 = LIIF(chn * 2, chn * 4)
self.downsample1.gen_coord((batch_size, \
chn,image_size,image_size),(image_size//2,image_size//2))
image_size = image_size // 2
self.downsample2 = LIIF(chn * 4, chn * 4)
self.downsample2.gen_coord((batch_size, \
chn,image_size,image_size),(image_size//2,image_size//2))
for _ in range(res_num):
self.resblock_list += [ResBlock(chn * 4,k_size),]
self.resblocks = nn.Sequential(*self.resblock_list)
# self.decoder = nn.Sequential(
# # DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# # nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# # nn.LeakyReLU(),
# # DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
# # nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
# # nn.LeakyReLU(),
# DeConv(in_channels = chn * 4, out_channels = chn *2, kernel_size=k_size),
# nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
# nn.LeakyReLU(),
# # DeConv(in_channels = chn * 2, out_channels = chn, kernel_size=k_size),
# # # nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
# # nn.InstanceNorm2d(chn, affine=True, momentum=0),
# # nn.LeakyReLU()
# # DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
# # nn.InstanceNorm2d(chn),
# # nn.LeakyReLU(),
# # nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
# )
image_size = image_size // 2
self.upsample1 = LIIF(chn*4, chn * 4)
self.upsample1.gen_coord((batch_size, \
chn,image_size,image_size),(image_size*2,image_size*2))
image_size = image_size * 2
self.upsample2 = LIIF(chn*4, chn * 2)
self.upsample2.gen_coord((batch_size, \
chn,image_size,image_size),(image_size*2,image_size*2))
# image_size = image_size * 2
# self.upsample2 = LIIF(chn, chn)
# self.upsample2.gen_coord((batch_size, \
# chn,image_size,image_size),(image_size*2,image_size*2))
self.decoder = nn.Sequential(
DeConv(in_channels = chn * 2, out_channels = chn, kernel_size=k_size),
nn.InstanceNorm2d(chn, affine=True, momentum=0),
nn.LeakyReLU(),
nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
)
# self.out_conv = nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
# 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, input):
out = self.img_token(input)
out = self.downsample1(out)
out = self.downsample2(out)
out = self.resblocks(out)
out = self.upsample1(out)
out = self.upsample2(out)
out = self.decoder(out)
return out
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/Generator.py
+132 -58
View File
@@ -1,112 +1,186 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Conditional_Generator_gpt_LN_encoder copy.py
# Created Date: Saturday October 9th 2021
# File: Generator.py
# Created Date: Sunday January 16th 2022
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 26th October 2021 3:25:47 pm
# Last Modified: Sunday, 16th January 2022 11:42:14 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
# Copyright (c) 2022 Shanghai Jiao Tong University
#############################################################
import torch
from torch import nn
from ResBlock_Adain import ResBlock_Adain
from torch.nn import init
from torch.nn import functional as F
class InstanceNorm(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(InstanceNorm, self).__init__()
self.epsilon = epsilon
def forward(self, x):
x = x - torch.mean(x, (2, 3), True)
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 ResnetBlock_Adain(nn.Module):
def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
super(ResnetBlock_Adain, 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), InstanceNorm()]
self.conv1 = nn.Sequential(*conv1)
self.style1 = ApplyStyle(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)
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
self.conv2 = nn.Sequential(*conv2)
self.style2 = ApplyStyle(latent_size, dim)
def forward(self, x, dlatents_in_slice):
y = self.conv1(x)
y = self.style1(y, dlatents_in_slice)
y = self.act1(y)
y = self.conv2(y)
y = self.style2(y, dlatents_in_slice)
out = x + y
return out
from functools import partial
class Generator(nn.Module):
def __init__(
self,
**kwargs
):
super(Generator, self).__init__()
super().__init__()
input_nc = kwargs["g_conv_dim"]
output_nc = kwargs["g_kernel_size"]
latent_size = kwargs["latent_size"]
n_blocks = kwargs["resblock_num"]
norm_name = kwargs["norm_name"]
padding_type= kwargs["reflect"]
if norm_name == "bn":
norm_layer = partial(nn.BatchNorm2d, affine = True, track_running_stats=True)
elif norm_name == "in":
norm_name = nn.InstanceNorm2d
chn = kwargs["g_conv_dim"]
k_size = kwargs["g_kernel_size"]
res_num = kwargs["res_num"]
padding_size= int((k_size -1)/2)
padding_type= 'reflect'
assert (n_blocks >= 0)
activation = nn.ReLU(True)
self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, kernel_size=7, padding=0),
norm_layer(64), activation)
self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64), activation)
### downsample
self.down1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
norm_layer(128), activation)
nn.BatchNorm2d(128), activation)
self.down2 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
norm_layer(256), activation)
nn.BatchNorm2d(256), activation)
self.down3 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
norm_layer(512), activation)
nn.BatchNorm2d(512), activation)
self.down4 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
norm_layer(512), activation)
nn.BatchNorm2d(512), activation)
### resnet blocks
BN = []
for i in range(n_blocks):
for i in range(res_num):
BN += [
ResBlock_Adain(512, latent_size=latent_size, padding_type=padding_type, activation=activation)]
ResnetBlock_Adain(512, latent_size=chn, padding_type=padding_type, activation=activation)]
self.BottleNeck = nn.Sequential(*BN)
if self.deep:
self.up4 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512), activation
)
self.up4 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512), activation
)
self.up3 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256), activation
)
self.up2 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128), activation
)
self.up1 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64), activation
)
self.last_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, kernel_size=7, padding=0))
self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
# 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, input, id):
x = input # 3*224*224
res = self.first_layer(x)
res = self.down1(res)
res = self.down2(res)
res = self.down4(res)
res = self.down3(res)
skip1 = self.first_layer(x)
skip2 = self.down1(skip1)
skip3 = self.down2(skip2)
skip4 = self.down3(skip3)
res = self.down4(skip4)
for i in range(len(self.BottleNeck)):
res = self.BottleNeck[i](res, id)
x = 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
if __name__ == '__main__':
upscale = 4
window_size = 8
height = 1024
width = 1024
model = Generator()
print(model)
x = self.up4(x)
x = self.up3(x)
x = self.up2(x)
x = self.up1(x)
x = self.last_layer(x)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
return x
components/Involution.py
-303
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@@ -1,303 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Involution.py
# Created Date: Tuesday July 20th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 20th July 2021 10:35:52 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
from torch.autograd import Function
import torch
from torch.nn.modules.utils import _pair
import torch.nn.functional as F
import torch.nn as nn
from mmcv.cnn import ConvModule
from collections import namedtuple
import cupy
from string import Template
Stream = namedtuple('Stream', ['ptr'])
def Dtype(t):
if isinstance(t, torch.cuda.FloatTensor):
return 'float'
elif isinstance(t, torch.cuda.DoubleTensor):
return 'double'
@cupy._util.memoize(for_each_device=True)
def load_kernel(kernel_name, code, **kwargs):
code = Template(code).substitute(**kwargs)
kernel_code = cupy.cuda.compile_with_cache(code)
return kernel_code.get_function(kernel_name)
CUDA_NUM_THREADS = 1024
kernel_loop = '''
#define CUDA_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
i < (n); \
i += blockDim.x * gridDim.x)
'''
def GET_BLOCKS(N):
return (N + CUDA_NUM_THREADS - 1) // CUDA_NUM_THREADS
_involution_kernel = kernel_loop + '''
extern "C"
__global__ void involution_forward_kernel(
const ${Dtype}* bottom_data, const ${Dtype}* weight_data, ${Dtype}* top_data) {
CUDA_KERNEL_LOOP(index, ${nthreads}) {
const int n = index / ${channels} / ${top_height} / ${top_width};
const int c = (index / ${top_height} / ${top_width}) % ${channels};
const int h = (index / ${top_width}) % ${top_height};
const int w = index % ${top_width};
const int g = c / (${channels} / ${groups});
${Dtype} value = 0;
#pragma unroll
for (int kh = 0; kh < ${kernel_h}; ++kh) {
#pragma unroll
for (int kw = 0; kw < ${kernel_w}; ++kw) {
const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h};
const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w};
if ((h_in >= 0) && (h_in < ${bottom_height})
&& (w_in >= 0) && (w_in < ${bottom_width})) {
const int offset = ((n * ${channels} + c) * ${bottom_height} + h_in)
* ${bottom_width} + w_in;
const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h)
* ${top_width} + w;
value += weight_data[offset_weight] * bottom_data[offset];
}
}
}
top_data[index] = value;
}
}
'''
_involution_kernel_backward_grad_input = kernel_loop + '''
extern "C"
__global__ void involution_backward_grad_input_kernel(
const ${Dtype}* const top_diff, const ${Dtype}* const weight_data, ${Dtype}* const bottom_diff) {
CUDA_KERNEL_LOOP(index, ${nthreads}) {
const int n = index / ${channels} / ${bottom_height} / ${bottom_width};
const int c = (index / ${bottom_height} / ${bottom_width}) % ${channels};
const int h = (index / ${bottom_width}) % ${bottom_height};
const int w = index % ${bottom_width};
const int g = c / (${channels} / ${groups});
${Dtype} value = 0;
#pragma unroll
for (int kh = 0; kh < ${kernel_h}; ++kh) {
#pragma unroll
for (int kw = 0; kw < ${kernel_w}; ++kw) {
const int h_out_s = h + ${pad_h} - kh * ${dilation_h};
const int w_out_s = w + ${pad_w} - kw * ${dilation_w};
if (((h_out_s % ${stride_h}) == 0) && ((w_out_s % ${stride_w}) == 0)) {
const int h_out = h_out_s / ${stride_h};
const int w_out = w_out_s / ${stride_w};
if ((h_out >= 0) && (h_out < ${top_height})
&& (w_out >= 0) && (w_out < ${top_width})) {
const int offset = ((n * ${channels} + c) * ${top_height} + h_out)
* ${top_width} + w_out;
const int offset_weight = ((((n * ${groups} + g) * ${kernel_h} + kh) * ${kernel_w} + kw) * ${top_height} + h_out)
* ${top_width} + w_out;
value += weight_data[offset_weight] * top_diff[offset];
}
}
}
}
bottom_diff[index] = value;
}
}
'''
_involution_kernel_backward_grad_weight = kernel_loop + '''
extern "C"
__global__ void involution_backward_grad_weight_kernel(
const ${Dtype}* const top_diff, const ${Dtype}* const bottom_data, ${Dtype}* const buffer_data) {
CUDA_KERNEL_LOOP(index, ${nthreads}) {
const int h = (index / ${top_width}) % ${top_height};
const int w = index % ${top_width};
const int kh = (index / ${kernel_w} / ${top_height} / ${top_width})
% ${kernel_h};
const int kw = (index / ${top_height} / ${top_width}) % ${kernel_w};
const int h_in = -${pad_h} + h * ${stride_h} + kh * ${dilation_h};
const int w_in = -${pad_w} + w * ${stride_w} + kw * ${dilation_w};
if ((h_in >= 0) && (h_in < ${bottom_height})
&& (w_in >= 0) && (w_in < ${bottom_width})) {
const int g = (index / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${groups};
const int n = (index / ${groups} / ${kernel_h} / ${kernel_w} / ${top_height} / ${top_width}) % ${num};
${Dtype} value = 0;
#pragma unroll
for (int c = g * (${channels} / ${groups}); c < (g + 1) * (${channels} / ${groups}); ++c) {
const int top_offset = ((n * ${channels} + c) * ${top_height} + h)
* ${top_width} + w;
const int bottom_offset = ((n * ${channels} + c) * ${bottom_height} + h_in)
* ${bottom_width} + w_in;
value += top_diff[top_offset] * bottom_data[bottom_offset];
}
buffer_data[index] = value;
} else {
buffer_data[index] = 0;
}
}
}
'''
class _involution(Function):
@staticmethod
def forward(ctx, input, weight, stride, padding, dilation):
assert input.dim() == 4 and input.is_cuda
assert weight.dim() == 6 and weight.is_cuda
batch_size, channels, height, width = input.size()
kernel_h, kernel_w = weight.size()[2:4]
output_h = int((height + 2 * padding[0] - (dilation[0] * (kernel_h - 1) + 1)) / stride[0] + 1)
output_w = int((width + 2 * padding[1] - (dilation[1] * (kernel_w - 1) + 1)) / stride[1] + 1)
output = input.new(batch_size, channels, output_h, output_w)
n = output.numel()
with torch.cuda.device_of(input):
f = load_kernel('involution_forward_kernel', _involution_kernel, Dtype=Dtype(input), nthreads=n,
num=batch_size, channels=channels, groups=weight.size()[1],
bottom_height=height, bottom_width=width,
top_height=output_h, top_width=output_w,
kernel_h=kernel_h, kernel_w=kernel_w,
stride_h=stride[0], stride_w=stride[1],
dilation_h=dilation[0], dilation_w=dilation[1],
pad_h=padding[0], pad_w=padding[1])
f(block=(CUDA_NUM_THREADS,1,1),
grid=(GET_BLOCKS(n),1,1),
args=[input.data_ptr(), weight.data_ptr(), output.data_ptr()],
stream=Stream(ptr=torch.cuda.current_stream().cuda_stream))
ctx.save_for_backward(input, weight)
ctx.stride, ctx.padding, ctx.dilation = stride, padding, dilation
return output
@staticmethod
def backward(ctx, grad_output):
assert grad_output.is_cuda and grad_output.is_contiguous()
input, weight = ctx.saved_tensors
stride, padding, dilation = ctx.stride, ctx.padding, ctx.dilation
batch_size, channels, height, width = input.size()
kernel_h, kernel_w = weight.size()[2:4]
output_h, output_w = grad_output.size()[2:]
grad_input, grad_weight = None, None
opt = dict(Dtype=Dtype(grad_output),
num=batch_size, channels=channels, groups=weight.size()[1],
bottom_height=height, bottom_width=width,
top_height=output_h, top_width=output_w,
kernel_h=kernel_h, kernel_w=kernel_w,
stride_h=stride[0], stride_w=stride[1],
dilation_h=dilation[0], dilation_w=dilation[1],
pad_h=padding[0], pad_w=padding[1])
with torch.cuda.device_of(input):
if ctx.needs_input_grad[0]:
grad_input = input.new(input.size())
n = grad_input.numel()
opt['nthreads'] = n
f = load_kernel('involution_backward_grad_input_kernel',
_involution_kernel_backward_grad_input, **opt)
f(block=(CUDA_NUM_THREADS,1,1),
grid=(GET_BLOCKS(n),1,1),
args=[grad_output.data_ptr(), weight.data_ptr(), grad_input.data_ptr()],
stream=Stream(ptr=torch.cuda.current_stream().cuda_stream))
if ctx.needs_input_grad[1]:
grad_weight = weight.new(weight.size())
n = grad_weight.numel()
opt['nthreads'] = n
f = load_kernel('involution_backward_grad_weight_kernel',
_involution_kernel_backward_grad_weight, **opt)
f(block=(CUDA_NUM_THREADS,1,1),
grid=(GET_BLOCKS(n),1,1),
args=[grad_output.data_ptr(), input.data_ptr(), grad_weight.data_ptr()],
stream=Stream(ptr=torch.cuda.current_stream().cuda_stream))
return grad_input, grad_weight, None, None, None
def _involution_cuda(input, weight, bias=None, stride=1, padding=0, dilation=1):
""" involution kernel
"""
assert input.size(0) == weight.size(0)
assert input.size(-2)//stride == weight.size(-2)
assert input.size(-1)//stride == weight.size(-1)
if input.is_cuda:
out = _involution.apply(input, weight, _pair(stride), _pair(padding), _pair(dilation))
if bias is not None:
out += bias.view(1,-1,1,1)
else:
raise NotImplementedError
return out
class involution(nn.Module):
def __init__(self,
channels,
kernel_size,
stride):
super(involution, self).__init__()
self.kernel_size = kernel_size
self.stride = stride
self.channels = channels
reduction_ratio = 4
self.group_channels = 8
self.groups = self.channels // self.group_channels
self.seblock = nn.Sequential(
nn.Conv2d(in_channels = channels, out_channels = channels // reduction_ratio, kernel_size= 1),
nn.InstanceNorm2d(channels // reduction_ratio, affine=True, momentum=0),
nn.ReLU(),
nn.Conv2d(in_channels = channels // reduction_ratio, out_channels = kernel_size**2 * self.groups, kernel_size= 1)
)
# self.conv1 = ConvModule(
# in_channels=channels,
# out_channels=channels // reduction_ratio,
# kernel_size=1,
# conv_cfg=None,
# norm_cfg=dict(type='BN'),
# act_cfg=dict(type='ReLU'))
# self.conv2 = ConvModule(
# in_channels=channels // reduction_ratio,
# out_channels=kernel_size**2 * self.groups,
# kernel_size=1,
# stride=1,
# conv_cfg=None,
# norm_cfg=None,
# act_cfg=None)
if stride > 1:
self.avgpool = nn.AvgPool2d(stride, stride)
def forward(self, x):
# weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x)))
weight = self.seblock(x)
b, c, h, w = weight.shape
weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, h, w)
out = _involution_cuda(x, weight, stride=self.stride, padding=(self.kernel_size-1)//2)
return out
components/Liif.py
-146
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@@ -1,146 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Liif.py
# Created Date: Monday October 18th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 10:27:09 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
print("i: %d, n: %d"%(i,n))
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
class MLP(nn.Module):
def __init__(self, in_dim, out_dim, hidden_list):
super().__init__()
layers = []
lastv = in_dim
for hidden in hidden_list:
layers.append(nn.Linear(lastv, hidden))
layers.append(nn.ReLU())
lastv = hidden
layers.append(nn.Linear(lastv, out_dim))
self.layers = nn.Sequential(*layers)
def forward(self, x):
shape = x.shape[:-1]
x = self.layers(x.view(-1, x.shape[-1]))
return x.view(*shape, -1)
class LIIF(nn.Module):
def __init__(self, mlp_in_dim, mlp_out_dim, mlp_hidden_list):
super().__init__()
imnet_in_dim = mlp_in_dim
imnet_in_dim *= 9
imnet_in_dim += 2 # attach coord
imnet_in_dim += 2
self.imnet = MLP(imnet_in_dim, mlp_out_dim, mlp_hidden_list).cuda()
def gen_coord(self, in_shape, output_size):
self.vx_lst = [-1, 1]
self.vy_lst = [-1, 1]
eps_shift = 1e-6
self.image_size=output_size
# field radius (global: [-1, 1])
rx = 2 / in_shape[-2] / 2
ry = 2 / in_shape[-1] / 2
coord = make_coord(output_size,flatten=False) \
.expand(in_shape[0],output_size[0],output_size[1],2) \
.view(in_shape[0],output_size[0]*output_size[1],2)
cell = torch.ones_like(coord)
cell[:, :, 0] *= 2 / coord.shape[-2]
cell[:, :, 1] *= 2 / coord.shape[-1]
feat_coord = make_coord(in_shape[-2:], flatten=False) \
.permute(2, 0, 1) \
.unsqueeze(0).expand(in_shape[0], 2, *in_shape[-2:])
areas = []
self.rel_coord = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
self.rel_cell = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
self.coord_ = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
for vx in self.vx_lst:
for vy in self.vy_lst:
self.coord_[(vx+1)//2,(vy+1)//2,:, :, :] = coord.clone()
self.coord_[(vx+1)//2,(vy+1)//2,:, :, 0] += vx * rx + eps_shift
self.coord_[(vx+1)//2,(vy+1)//2,:, :, 1] += vy * ry + eps_shift
self.coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
q_coord = F.grid_sample(
feat_coord, self.coord_[(vx+1)//2,(vy+1)//2,:, :, :].flip(-1).unsqueeze(1),
mode='nearest', align_corners=False)[:, :, 0, :] \
.permute(0, 2, 1)
self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, :] = coord - q_coord
self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, :] = cell.clone()
self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
area = torch.abs(self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] * self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1])
areas.append(area + 1e-9)
tot_area = torch.stack(areas).sum(dim=0)
t = areas[0]; areas[0] = areas[3]; areas[3] = t
t = areas[1]; areas[1] = areas[2]; areas[2] = t
self.area_weights = []
for item in areas:
self.area_weights.append((item / tot_area).unsqueeze(-1).cuda())
self.rel_coord = self.rel_coord.cuda()
self.rel_cell = self.rel_cell.cuda()
self.coord_ = self.coord_.cuda()
def forward(self, feat):
# B K*K*Cin H W
feat = F.unfold(feat, 3, padding=1).view(
feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3])
preds = []
for vx in [0,1]:
for vy in [0,1]:
q_feat = F.grid_sample(
feat, self.coord_[vx,vy,:,:,:].flip(-1).unsqueeze(1),
mode='nearest', align_corners=False)[:, :, 0, :] \
.permute(0, 2, 1)
inp = torch.cat([q_feat, self.rel_coord[vx,vy,:,:,:], self.rel_cell[vx,vy,:,:,:]], dim=-1)
bs, q = self.coord_[0,0,:,:,:].shape[:2]
pred = self.imnet(inp.view(bs * q, -1)).view(bs, q, -1)
# print("pred shape: ",pred.shape)
preds.append(pred)
ret = 0
for pred, area in zip(preds, self.area_weights):
ret = ret + pred * area
return ret.permute(0, 2, 1).view(-1,3,self.image_size[0],self.image_size[1])
components/Liif_conv.py
-156
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@@ -1,156 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Liif.py
# Created Date: Monday October 18th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 4:26:26 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
print("i: %d, n: %d"%(i,n))
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
class MLP(nn.Module):
def __init__(self, in_dim, out_dim, hidden_list):
super().__init__()
layers = []
lastv = in_dim
for hidden in hidden_list:
layers.append(nn.Linear(lastv, hidden))
layers.append(nn.ReLU())
lastv = hidden
layers.append(nn.Linear(lastv, out_dim))
self.layers = nn.Sequential(*layers)
def forward(self, x):
shape = x.shape[:-1]
x = self.layers(x.view(-1, x.shape[-1]))
return x.view(*shape, -1)
class LIIF(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
imnet_in_dim = in_dim
# imnet_in_dim += 2 # attach coord
# imnet_in_dim += 2
self.imnet = nn.Sequential( \
nn.Conv2d(in_channels = imnet_in_dim, out_channels = out_dim, kernel_size= 3,padding=1),
nn.InstanceNorm2d(out_dim, affine=True, momentum=0),
nn.LeakyReLU(),
# nn.Conv2d(in_channels = out_dim, out_channels = out_dim, kernel_size= 3,padding=1),
# nn.InstanceNorm2d(out_dim),
# nn.LeakyReLU(),
)
def gen_coord(self, in_shape, output_size):
self.vx_lst = [-1, 1]
self.vy_lst = [-1, 1]
eps_shift = 1e-6
self.image_size=output_size
# field radius (global: [-1, 1])
rx = 2 / in_shape[-2] / 2
ry = 2 / in_shape[-1] / 2
self.coord = make_coord(output_size,flatten=False) \
.expand(in_shape[0],output_size[0],output_size[1],2)
# cell = torch.ones_like(coord)
# cell[:, :, 0] *= 2 / coord.shape[-2]
# cell[:, :, 1] *= 2 / coord.shape[-1]
# feat_coord = make_coord(in_shape[-2:], flatten=False) \
# .permute(2, 0, 1) \
# .unsqueeze(0).expand(in_shape[0], 2, *in_shape[-2:])
# areas = []
# self.rel_coord = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# self.rel_cell = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# self.coord_ = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# for vx in self.vx_lst:
# for vy in self.vy_lst:
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, :] = coord.clone()
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, 0] += vx * rx + eps_shift
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, 1] += vy * ry + eps_shift
# self.coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
# q_coord = F.grid_sample(
# feat_coord, self.coord_[(vx+1)//2,(vy+1)//2,:, :, :].flip(-1).unsqueeze(1),
# mode='nearest', align_corners=False)[:, :, 0, :] \
# .permute(0, 2, 1)
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, :] = coord - q_coord
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, :] = cell.clone()
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
# area = torch.abs(self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] * self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1])
# areas.append(area + 1e-9)
# tot_area = torch.stack(areas).sum(dim=0)
# t = areas[0]; areas[0] = areas[3]; areas[3] = t
# t = areas[1]; areas[1] = areas[2]; areas[2] = t
# self.area_weights = []
# for item in areas:
# self.area_weights.append((item / tot_area).unsqueeze(-1).cuda())
# self.rel_coord = self.rel_coord.cuda()
# self.rel_cell = self.rel_cell.cuda()
# self.coord_ = self.coord_.cuda()
self.coord = self.coord.cuda()
def forward(self, feat):
# B K*K*Cin H W
# feat = F.unfold(feat, 3, padding=1).view(
# feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3])
# preds = []
# for vx in [0,1]:
# for vy in [0,1]:
# print("feat shape: ", feat.shape)
# print("coor shape: ", self.coord.shape)
q_feat = F.grid_sample(
feat, self.coord,
mode='bilinear', align_corners=False)
out = self.imnet(q_feat)
# inp = torch.cat([q_feat, self.rel_coord[vx,vy,:,:,:], self.rel_cell[vx,vy,:,:,:]], dim=-1)
# bs, q = self.coord_[0,0,:,:,:].shape[:2]
# pred = self.imnet(inp.view(bs * q, -1)).view(bs, q, -1)
# # print("pred shape: ",pred.shape)
# preds.append(pred)
# ret = 0
# for pred, area in zip(preds, self.area_weights):
# ret = ret + pred * area
# print("warp output shape: ",out.shape)
return out
components/Liif_invo.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: Liif.py
# Created Date: Monday October 18th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 8:25:18 pm
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
import torch
import torch.nn as nn
import torch.nn.functional as F
from components.Involution import involution
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
print("i: %d, n: %d"%(i,n))
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
class MLP(nn.Module):
def __init__(self, in_dim, out_dim, hidden_list):
super().__init__()
layers = []
lastv = in_dim
for hidden in hidden_list:
layers.append(nn.Linear(lastv, hidden))
layers.append(nn.ReLU())
lastv = hidden
layers.append(nn.Linear(lastv, out_dim))
self.layers = nn.Sequential(*layers)
def forward(self, x):
shape = x.shape[:-1]
x = self.layers(x.view(-1, x.shape[-1]))
return x.view(*shape, -1)
class LIIF(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
imnet_in_dim = in_dim
# imnet_in_dim += 2 # attach coord
# imnet_in_dim += 2
self.conv1x1 = nn.Conv2d(in_channels = imnet_in_dim, out_channels = out_dim, kernel_size= 1)
# self.same_padding = nn.ReflectionPad2d(padding_size)
# self.conv = involution(out_dim,5,1)
self.imnet = nn.Sequential( \
# nn.Conv2d(in_channels = imnet_in_dim, out_channels = out_dim, kernel_size= 3,padding=1),
involution(out_dim,5,1),
nn.InstanceNorm2d(out_dim, affine=True, momentum=0),
nn.LeakyReLU(),
# nn.Conv2d(in_channels = out_dim, out_channels = out_dim, kernel_size= 3,padding=1),
# nn.InstanceNorm2d(out_dim),
# nn.LeakyReLU(),
)
def gen_coord(self, in_shape, output_size):
self.vx_lst = [-1, 1]
self.vy_lst = [-1, 1]
eps_shift = 1e-6
self.image_size=output_size
# field radius (global: [-1, 1])
rx = 2 / in_shape[-2] / 2
ry = 2 / in_shape[-1] / 2
self.coord = make_coord(output_size,flatten=False) \
.expand(in_shape[0],output_size[0],output_size[1],2)
# cell = torch.ones_like(coord)
# cell[:, :, 0] *= 2 / coord.shape[-2]
# cell[:, :, 1] *= 2 / coord.shape[-1]
# feat_coord = make_coord(in_shape[-2:], flatten=False) \
# .permute(2, 0, 1) \
# .unsqueeze(0).expand(in_shape[0], 2, *in_shape[-2:])
# areas = []
# self.rel_coord = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# self.rel_cell = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# self.coord_ = torch.zeros((2,2,in_shape[0],output_size[0]*output_size[1],2))
# for vx in self.vx_lst:
# for vy in self.vy_lst:
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, :] = coord.clone()
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, 0] += vx * rx + eps_shift
# self.coord_[(vx+1)//2,(vy+1)//2,:, :, 1] += vy * ry + eps_shift
# self.coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
# q_coord = F.grid_sample(
# feat_coord, self.coord_[(vx+1)//2,(vy+1)//2,:, :, :].flip(-1).unsqueeze(1),
# mode='nearest', align_corners=False)[:, :, 0, :] \
# .permute(0, 2, 1)
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, :] = coord - q_coord
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
# self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, :] = cell.clone()
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 0] *= in_shape[-2]
# self.rel_cell[(vx+1)//2,(vy+1)//2,:, :, 1] *= in_shape[-1]
# area = torch.abs(self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 0] * self.rel_coord[(vx+1)//2,(vy+1)//2,:, :, 1])
# areas.append(area + 1e-9)
# tot_area = torch.stack(areas).sum(dim=0)
# t = areas[0]; areas[0] = areas[3]; areas[3] = t
# t = areas[1]; areas[1] = areas[2]; areas[2] = t
# self.area_weights = []
# for item in areas:
# self.area_weights.append((item / tot_area).unsqueeze(-1).cuda())
# self.rel_coord = self.rel_coord.cuda()
# self.rel_cell = self.rel_cell.cuda()
# self.coord_ = self.coord_.cuda()
self.coord = self.coord.cuda()
def forward(self, feat):
# B K*K*Cin H W
# feat = F.unfold(feat, 3, padding=1).view(
# feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3])
# preds = []
# for vx in [0,1]:
# for vy in [0,1]:
# print("feat shape: ", feat.shape)
# print("coor shape: ", self.coord.shape)
q_feat = self.conv1x1(feat)
q_feat = F.grid_sample(
q_feat, self.coord,
mode='bilinear', align_corners=False)
out = self.imnet(q_feat)
# inp = torch.cat([q_feat, self.rel_coord[vx,vy,:,:,:], self.rel_cell[vx,vy,:,:,:]], dim=-1)
# bs, q = self.coord_[0,0,:,:,:].shape[:2]
# pred = self.imnet(inp.view(bs * q, -1)).view(bs, q, -1)
# # print("pred shape: ",pred.shape)
# preds.append(pred)
# ret = 0
# for pred, area in zip(preds, self.area_weights):
# ret = ret + pred * area
# print("warp output shape: ",out.shape)
return out
components/ResBlock.py
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@@ -1,38 +0,0 @@
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: ResBlock.py
# Created Date: Monday July 5th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Monday, 5th July 2021 12:18:18 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
from torch import nn
class ResBlock(nn.Module):
def __init__(self, in_channel, k_size = 3, stride=1):
super().__init__()
padding_size = int((k_size -1)/2)
self.block = nn.Sequential(
nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = in_channel , out_channels = in_channel , kernel_size= k_size, stride=stride, bias= False),
nn.InstanceNorm2d(in_channel, affine=True, momentum=0),
nn.ReflectionPad2d(padding_size),
nn.Conv2d(in_channels = in_channel , out_channels = in_channel , kernel_size= k_size, stride=stride, bias= False),
nn.InstanceNorm2d(in_channel, affine=True, momentum=0)
)
self.__weights_init__()
def __weights_init__(self):
for m in self.modules():
if isinstance(m,nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
def forward(self, input):
res = input
h = self.block(input)
out = h + res
return out
components/ResBlock_Adain.py
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import torch
import torch.nn as nn
class InstanceNorm(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(InstanceNorm, self).__init__()
self.epsilon = epsilon
def forward(self, x):
x = x - torch.mean(x, (2, 3), True)
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 ResBlock_Adain(nn.Module):
def __init__(self, dim, latent_size, padding_type, activation=nn.ReLU(True)):
super(ResBlock_Adain, 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), InstanceNorm()]
self.conv1 = nn.Sequential(*conv1)
self.style1 = ApplyStyle(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)
conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
self.conv2 = nn.Sequential(*conv2)
self.style2 = ApplyStyle(latent_size, dim)
def forward(self, x, dlatents_in_slice):
y = self.conv1(x)
y = self.style1(y, dlatents_in_slice)
y = self.act1(y)
y = self.conv2(y)
y = self.style2(y, dlatents_in_slice)
out = x + y
return out
components/Transform.py
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@@ -1,14 +0,0 @@
import torch
from torch import nn
class Transform_block(nn.Module):
def __init__(self, k_size = 10):
super().__init__()
padding_size = int((k_size -1)/2)
# self.padding = nn.ReplicationPad2d(padding_size)
self.pool = nn.AvgPool2d(k_size, stride=1,padding=padding_size)
def forward(self, input_image):
# h = self.padding(input)
out = self.pool(input_image)
return out
components/network_swin.py
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@@ -1,854 +0,0 @@
# -----------------------------------------------------------------------------------
# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
# Originally Written by Ze Liu, Modified by Jingyun Liang.
# -----------------------------------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels.
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window
coords_h = torch.arange(self.window_size[0])
coords_w = torch.arange(self.window_size[1])
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
nn.init
trunc_normal_(self.relative_position_bias_table, std=.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
def extra_repr(self) -> str:
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
def flops(self, N):
# calculate flops for 1 window with token length of N
flops = 0
# qkv = self.qkv(x)
flops += N * self.dim * 3 * self.dim
# attn = (q @ k.transpose(-2, -1))
flops += self.num_heads * N * (self.dim // self.num_heads) * N
# x = (attn @ v)
flops += self.num_heads * N * N * (self.dim // self.num_heads)
# x = self.proj(x)
flops += N * self.dim * self.dim
return flops
class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resulotion.
num_heads (int): Number of attention heads.
window_size (int): Window size.
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = nn.Dropout(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
if self.shift_size > 0:
attn_mask = self.calculate_mask(self.input_resolution)
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def calculate_mask(self, x_size):
# calculate attention mask for SW-MSA
H, W = x_size
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
w_slices = (slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None))
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
def forward(self, x, x_size):
H, W = x_size
B, L, C = x.shape
# assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
else:
shifted_x = x
# partition windows
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
if self.input_resolution == x_size:
attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
else:
attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
else:
x = shifted_x
x = x.view(B, H * W, C)
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
def flops(self):
flops = 0
H, W = self.input_resolution
# norm1
flops += self.dim * H * W
# W-MSA/SW-MSA
nW = H * W / self.window_size / self.window_size
flops += nW * self.attn.flops(self.window_size * self.window_size)
# mlp
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
# norm2
flops += self.dim * H * W
return flops
class PatchMerging(nn.Module):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature.
dim (int): Number of input channels.
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self,
input_resolution,
dim, norm_layer = nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
def extra_repr(self) -> str:
return f"input_resolution={self.input_resolution}, dim={self.dim}"
def flops(self):
H, W = self.input_resolution
flops = H * W * self.dim
flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
return flops
class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList([
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer)
for i in range(depth)])
# patch merging layer
if downsample is not None:
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
else:
self.downsample = None
def forward(self, x, x_size):
for blk in self.blocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(blk, x, x_size)
else:
x = blk(x, x_size)
if self.downsample is not None:
x = self.downsample(x)
return x
def extra_repr(self) -> str:
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
def flops(self):
flops = 0
for blk in self.blocks:
flops += blk.flops()
if self.downsample is not None:
flops += self.downsample.flops()
return flops
class RSTB(nn.Module):
"""Residual Swin Transformer Block (RSTB).
Args:
dim (int): Number of input channels.
input_resolution (tuple[int]): Input resolution.
depth (int): Number of blocks.
num_heads (int): Number of attention heads.
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
img_size: Input image size.
patch_size: Patch size.
resi_connection: The convolutional block before residual connection.
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
img_size=224, patch_size=4, resi_connection='1conv'):
super(RSTB, self).__init__()
self.dim = dim
self.input_resolution = input_resolution
self.residual_group = BasicLayer(dim=dim,
input_resolution=input_resolution,
depth=depth,
num_heads=num_heads,
window_size=window_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path,
norm_layer=norm_layer,
downsample=downsample,
use_checkpoint=use_checkpoint)
if resi_connection == '1conv':
self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
elif resi_connection == '3conv':
# to save parameters and memory
self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(dim // 4, dim, 3, 1, 1))
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None)
self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
norm_layer=None)
def forward(self, x, x_size):
return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
def flops(self):
flops = 0
flops += self.residual_group.flops()
H, W = self.input_resolution
flops += H * W * self.dim * self.dim * 9
flops += self.patch_embed.flops()
flops += self.patch_unembed.flops()
return flops
class PatchEmbed(nn.Module):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
def flops(self):
flops = 0
H, W = self.img_size
if self.norm is not None:
flops += H * W * self.embed_dim
return flops
class PatchUnEmbed(nn.Module):
r""" Image to Patch Unembedding
Args:
img_size (int): Image size. Default: 224.
patch_size (int): Patch token size. Default: 4.
in_chans (int): Number of input image channels. Default: 3.
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
def forward(self, x, x_size):
B, HW, C = x.shape
x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
return x
def flops(self):
flops = 0
return flops
class Upsample(nn.Sequential):
"""Upsample module.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
def __init__(self, scale, num_feat):
m = []
if (scale & (scale - 1)) == 0: # scale = 2^n
for _ in range(int(math.log(scale, 2))):
m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(2))
elif scale == 3:
m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
m.append(nn.PixelShuffle(3))
else:
raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
super(Upsample, self).__init__(*m)
class UpsampleOneStep(nn.Sequential):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters.
Args:
scale (int): Scale factor. Supported scales: 2^n and 3.
num_feat (int): Channel number of intermediate features.
"""
def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
self.num_feat = num_feat
self.input_resolution = input_resolution
m = []
m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
m.append(nn.PixelShuffle(scale))
super(UpsampleOneStep, self).__init__(*m)
def flops(self):
H, W = self.input_resolution
flops = H * W * self.num_feat * 3 * 9
return flops
class SwinIR(nn.Module):
r""" SwinIR
A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
Args:
img_size (int | tuple(int)): Input image size. Default 64
patch_size (int | tuple(int)): Patch size. Default: 1
in_chans (int): Number of input image channels. Default: 3
embed_dim (int): Patch embedding dimension. Default: 96
depths (tuple(int)): Depth of each Swin Transformer layer.
num_heads (tuple(int)): Number of attention heads in different layers.
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
patch_norm (bool): If True, add normalization after patch embedding. Default: True
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
img_range: Image range. 1. or 255.
upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
"""
def __init__(self, img_size=64, patch_size=1, in_chans=3,
embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
**kwargs):
super(SwinIR, self).__init__()
num_in_ch = in_chans
num_out_ch = in_chans
num_feat = 64
self.img_range = img_range
if in_chans == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
else:
self.mean = torch.zeros(1, 1, 1, 1)
self.upscale = upscale
self.upsampler = upsampler
#####################################################################################################
################################### 1, shallow feature extraction ###################################
self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
#####################################################################################################
################################### 2, deep feature extraction ######################################
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = embed_dim
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# merge non-overlapping patches into image
self.patch_unembed = PatchUnEmbed(
img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None)
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
trunc_normal_(self.absolute_pos_embed, std=.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
# build Residual Swin Transformer blocks (RSTB)
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = RSTB(dim=embed_dim,
input_resolution=(patches_resolution[0],
patches_resolution[1]),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
norm_layer=norm_layer,
downsample=None,
use_checkpoint=use_checkpoint,
img_size=img_size,
patch_size=patch_size,
resi_connection=resi_connection
)
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
# build the last conv layer in deep feature extraction
if resi_connection == '1conv':
self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
elif resi_connection == '3conv':
# to save parameters and memory
self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
#####################################################################################################
################################ 3, high quality image reconstruction ################################
if self.upsampler == 'pixelshuffle':
# for classical SR
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
self.upsample = Upsample(upscale, num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR (to save parameters)
self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
(patches_resolution[0], patches_resolution[1]))
elif self.upsampler == 'nearest+conv':
# for real-world SR (less artifacts)
assert self.upscale == 4, 'only support x4 now.'
self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
nn.LeakyReLU(inplace=True))
self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
# for image denoising and JPEG compression artifact reduction
self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@torch.jit.ignore
def no_weight_decay(self):
return {'absolute_pos_embed'}
@torch.jit.ignore
def no_weight_decay_keywords(self):
return {'relative_position_bias_table'}
def forward_features(self, x):
x_size = (x.shape[2], x.shape[3])
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x, x_size)
x = self.norm(x) # B L C
x = self.patch_unembed(x, x_size)
return x
def forward(self, x):
self.mean = self.mean.type_as(x)
x = (x - self.mean) * self.img_range
if self.upsampler == 'pixelshuffle':
# for classical SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.conv_last(self.upsample(x))
elif self.upsampler == 'pixelshuffledirect':
# for lightweight SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.upsample(x)
elif self.upsampler == 'nearest+conv':
# for real-world SR
x = self.conv_first(x)
x = self.conv_after_body(self.forward_features(x)) + x
x = self.conv_before_upsample(x)
x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
x = self.conv_last(self.lrelu(self.conv_hr(x)))
else:
# for image denoising and JPEG compression artifact reduction
x_first = self.conv_first(x)
res = self.conv_after_body(self.forward_features(x_first)) + x_first
x = x + self.conv_last(res)
x = x / self.img_range + self.mean
return x
def flops(self):
flops = 0
H, W = self.patches_resolution
flops += H * W * 3 * self.embed_dim * 9
flops += self.patch_embed.flops()
for i, layer in enumerate(self.layers):
flops += layer.flops()
flops += H * W * 3 * self.embed_dim * self.embed_dim
flops += self.upsample.flops()
return flops
if __name__ == '__main__':
upscale = 4
window_size = 8
height = (1024 // upscale // window_size + 1) * window_size
width = (720 // upscale // window_size + 1) * window_size
model = SwinIR(upscale=2, img_size=(height, width),
window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
print(model)
print(height, width, model.flops() / 1e9)
x = torch.randn((1, 3, height, width))
x = model(x)
print(x.shape)
components/pg_modules/blocks.py
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@@ -0,0 +1,325 @@
import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
### single layers
def conv2d(*args, **kwargs):
return spectral_norm(nn.Conv2d(*args, **kwargs))
def convTranspose2d(*args, **kwargs):
return spectral_norm(nn.ConvTranspose2d(*args, **kwargs))
def embedding(*args, **kwargs):
return spectral_norm(nn.Embedding(*args, **kwargs))
def linear(*args, **kwargs):
return spectral_norm(nn.Linear(*args, **kwargs))
def NormLayer(c, mode='batch'):
if mode == 'group':
return nn.GroupNorm(c//2, c)
elif mode == 'batch':
return nn.BatchNorm2d(c)
### Activations
class GLU(nn.Module):
def forward(self, x):
nc = x.size(1)
assert nc % 2 == 0, 'channels dont divide 2!'
nc = int(nc/2)
return x[:, :nc] * torch.sigmoid(x[:, nc:])
class Swish(nn.Module):
def forward(self, feat):
return feat * torch.sigmoid(feat)
### Upblocks
class InitLayer(nn.Module):
def __init__(self, nz, channel, sz=4):
super().__init__()
self.init = nn.Sequential(
convTranspose2d(nz, channel*2, sz, 1, 0, bias=False),
NormLayer(channel*2),
GLU(),
)
def forward(self, noise):
noise = noise.view(noise.shape[0], -1, 1, 1)
return self.init(noise)
def UpBlockSmall(in_planes, out_planes):
block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='nearest'),
conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
NormLayer(out_planes*2), GLU())
return block
class UpBlockSmallCond(nn.Module):
def __init__(self, in_planes, out_planes, z_dim):
super().__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.up = nn.Upsample(scale_factor=2, mode='nearest')
self.conv = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
self.bn = which_bn(2*out_planes)
self.act = GLU()
def forward(self, x, c):
x = self.up(x)
x = self.conv(x)
x = self.bn(x, c)
x = self.act(x)
return x
def UpBlockBig(in_planes, out_planes):
block = nn.Sequential(
nn.Upsample(scale_factor=2, mode='nearest'),
conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False),
NoiseInjection(),
NormLayer(out_planes*2), GLU(),
conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False),
NoiseInjection(),
NormLayer(out_planes*2), GLU()
)
return block
class UpBlockBigCond(nn.Module):
def __init__(self, in_planes, out_planes, z_dim):
super().__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.up = nn.Upsample(scale_factor=2, mode='nearest')
self.conv1 = conv2d(in_planes, out_planes*2, 3, 1, 1, bias=False)
self.conv2 = conv2d(out_planes, out_planes*2, 3, 1, 1, bias=False)
which_bn = functools.partial(CCBN, which_linear=linear, input_size=z_dim)
self.bn1 = which_bn(2*out_planes)
self.bn2 = which_bn(2*out_planes)
self.act = GLU()
self.noise = NoiseInjection()
def forward(self, x, c):
# block 1
x = self.up(x)
x = self.conv1(x)
x = self.noise(x)
x = self.bn1(x, c)
x = self.act(x)
# block 2
x = self.conv2(x)
x = self.noise(x)
x = self.bn2(x, c)
x = self.act(x)
return x
class SEBlock(nn.Module):
def __init__(self, ch_in, ch_out):
super().__init__()
self.main = nn.Sequential(
nn.AdaptiveAvgPool2d(4),
conv2d(ch_in, ch_out, 4, 1, 0, bias=False),
Swish(),
conv2d(ch_out, ch_out, 1, 1, 0, bias=False),
nn.Sigmoid(),
)
def forward(self, feat_small, feat_big):
return feat_big * self.main(feat_small)
### Downblocks
class SeparableConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=False):
super(SeparableConv2d, self).__init__()
self.depthwise = conv2d(in_channels, in_channels, kernel_size=kernel_size,
groups=in_channels, bias=bias, padding=1)
self.pointwise = conv2d(in_channels, out_channels,
kernel_size=1, bias=bias)
def forward(self, x):
out = self.depthwise(x)
out = self.pointwise(out)
return out
class DownBlock(nn.Module):
def __init__(self, in_planes, out_planes, separable=False):
super().__init__()
if not separable:
self.main = nn.Sequential(
conv2d(in_planes, out_planes, 4, 2, 1),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
)
else:
self.main = nn.Sequential(
SeparableConv2d(in_planes, out_planes, 3),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
nn.AvgPool2d(2, 2),
)
def forward(self, feat):
return self.main(feat)
class DownBlockPatch(nn.Module):
def __init__(self, in_planes, out_planes, separable=False):
super().__init__()
self.main = nn.Sequential(
DownBlock(in_planes, out_planes, separable),
conv2d(out_planes, out_planes, 1, 1, 0, bias=False),
NormLayer(out_planes),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, feat):
return self.main(feat)
### CSM
class ResidualConvUnit(nn.Module):
def __init__(self, cin, activation, bn):
super().__init__()
self.conv = nn.Conv2d(cin, cin, kernel_size=3, stride=1, padding=1, bias=True)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
return self.skip_add.add(self.conv(x), x)
class FeatureFusionBlock(nn.Module):
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, lowest=False):
super().__init__()
self.deconv = deconv
self.align_corners = align_corners
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
output = xs[0]
if len(xs) == 2:
output = self.skip_add.add(output, xs[1])
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output
### Misc
class NoiseInjection(nn.Module):
def __init__(self):
super().__init__()
self.weight = nn.Parameter(torch.zeros(1), requires_grad=True)
def forward(self, feat, noise=None):
if noise is None:
batch, _, height, width = feat.shape
noise = torch.randn(batch, 1, height, width).to(feat.device)
return feat + self.weight * noise
class CCBN(nn.Module):
''' conditional batchnorm '''
def __init__(self, output_size, input_size, which_linear, eps=1e-5, momentum=0.1):
super().__init__()
self.output_size, self.input_size = output_size, input_size
# Prepare gain and bias layers
self.gain = which_linear(input_size, output_size)
self.bias = which_linear(input_size, output_size)
# epsilon to avoid dividing by 0
self.eps = eps
# Momentum
self.momentum = momentum
self.register_buffer('stored_mean', torch.zeros(output_size))
self.register_buffer('stored_var', torch.ones(output_size))
def forward(self, x, y):
# Calculate class-conditional gains and biases
gain = (1 + self.gain(y)).view(y.size(0), -1, 1, 1)
bias = self.bias(y).view(y.size(0), -1, 1, 1)
out = F.batch_norm(x, self.stored_mean, self.stored_var, None, None,
self.training, 0.1, self.eps)
return out * gain + bias
class Interpolate(nn.Module):
"""Interpolation module."""
def __init__(self, size, mode='bilinear', align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.size = size
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x,
size=self.size,
mode=self.mode,
align_corners=self.align_corners,
)
return x
components/pg_modules/diffaug.py
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# Differentiable Augmentation for Data-Efficient GAN Training
# Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han
# https://arxiv.org/pdf/2006.10738
import torch
import torch.nn.functional as F
def DiffAugment(x, policy='', channels_first=True):
if policy:
if not channels_first:
x = x.permute(0, 3, 1, 2)
for p in policy.split(','):
for f in AUGMENT_FNS[p]:
x = f(x)
if not channels_first:
x = x.permute(0, 2, 3, 1)
x = x.contiguous()
return x
def rand_brightness(x):
x = x + (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) - 0.5)
return x
def rand_saturation(x):
x_mean = x.mean(dim=1, keepdim=True)
x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) * 2) + x_mean
return x
def rand_contrast(x):
x_mean = x.mean(dim=[1, 2, 3], keepdim=True)
x = (x - x_mean) * (torch.rand(x.size(0), 1, 1, 1, dtype=x.dtype, device=x.device) + 0.5) + x_mean
return x
def rand_translation(x, ratio=0.125):
shift_x, shift_y = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
translation_x = torch.randint(-shift_x, shift_x + 1, size=[x.size(0), 1, 1], device=x.device)
translation_y = torch.randint(-shift_y, shift_y + 1, size=[x.size(0), 1, 1], device=x.device)
grid_batch, grid_x, grid_y = torch.meshgrid(
torch.arange(x.size(0), dtype=torch.long, device=x.device),
torch.arange(x.size(2), dtype=torch.long, device=x.device),
torch.arange(x.size(3), dtype=torch.long, device=x.device),
)
grid_x = torch.clamp(grid_x + translation_x + 1, 0, x.size(2) + 1)
grid_y = torch.clamp(grid_y + translation_y + 1, 0, x.size(3) + 1)
x_pad = F.pad(x, [1, 1, 1, 1, 0, 0, 0, 0])
x = x_pad.permute(0, 2, 3, 1).contiguous()[grid_batch, grid_x, grid_y].permute(0, 3, 1, 2)
return x
def rand_cutout(x, ratio=0.2):
cutout_size = int(x.size(2) * ratio + 0.5), int(x.size(3) * ratio + 0.5)
offset_x = torch.randint(0, x.size(2) + (1 - cutout_size[0] % 2), size=[x.size(0), 1, 1], device=x.device)
offset_y = torch.randint(0, x.size(3) + (1 - cutout_size[1] % 2), size=[x.size(0), 1, 1], device=x.device)
grid_batch, grid_x, grid_y = torch.meshgrid(
torch.arange(x.size(0), dtype=torch.long, device=x.device),
torch.arange(cutout_size[0], dtype=torch.long, device=x.device),
torch.arange(cutout_size[1], dtype=torch.long, device=x.device),
)
grid_x = torch.clamp(grid_x + offset_x - cutout_size[0] // 2, min=0, max=x.size(2) - 1)
grid_y = torch.clamp(grid_y + offset_y - cutout_size[1] // 2, min=0, max=x.size(3) - 1)
mask = torch.ones(x.size(0), x.size(2), x.size(3), dtype=x.dtype, device=x.device)
mask[grid_batch, grid_x, grid_y] = 0
x = x * mask.unsqueeze(1)
return x
AUGMENT_FNS = {
'color': [rand_brightness, rand_saturation, rand_contrast],
'translation': [rand_translation],
'cutout': [rand_cutout],
}
components/pg_modules/discriminator.py
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from functools import partial
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from components.pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
from components.pg_modules.projector import F_RandomProj
from components.pg_modules.diffaug import DiffAugment
class SingleDisc(nn.Module):
def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
super().__init__()
channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
256: 32, 512: 16, 1024: 8}
# interpolate for start sz that are not powers of two
if start_sz not in channel_dict.keys():
sizes = np.array(list(channel_dict.keys()))
start_sz = sizes[np.argmin(abs(sizes - start_sz))]
self.start_sz = start_sz
# if given ndf, allocate all layers with the same ndf
if ndf is None:
nfc = channel_dict
else:
nfc = {k: ndf for k, v in channel_dict.items()}
# for feature map discriminators with nfc not in channel_dict
# this is the case for the pretrained backbone (midas.pretrained)
if nc is not None and head is None:
nfc[start_sz] = nc
layers = []
# Head if the initial input is the full modality
if head:
layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True)]
# Down Blocks
DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
while start_sz > end_sz:
layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
start_sz = start_sz // 2
layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
self.main = nn.Sequential(*layers)
def forward(self, x, c):
return self.main(x)
class SingleDiscCond(nn.Module):
def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128):
super().__init__()
self.cmap_dim = cmap_dim
# midas channels
channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
256: 32, 512: 16, 1024: 8}
# interpolate for start sz that are not powers of two
if start_sz not in channel_dict.keys():
sizes = np.array(list(channel_dict.keys()))
start_sz = sizes[np.argmin(abs(sizes - start_sz))]
self.start_sz = start_sz
# if given ndf, allocate all layers with the same ndf
if ndf is None:
nfc = channel_dict
else:
nfc = {k: ndf for k, v in channel_dict.items()}
# for feature map discriminators with nfc not in channel_dict
# this is the case for the pretrained backbone (midas.pretrained)
if nc is not None and head is None:
nfc[start_sz] = nc
layers = []
# Head if the initial input is the full modality
if head:
layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True)]
# Down Blocks
DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
while start_sz > end_sz:
layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
start_sz = start_sz // 2
self.main = nn.Sequential(*layers)
# additions for conditioning on class information
self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
self.embed_proj = nn.Sequential(
nn.Linear(self.embed.embedding_dim, self.cmap_dim),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, x, c):
h = self.main(x)
out = self.cls(h)
# conditioning via projection
cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1)
out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
return out
class MultiScaleD(nn.Module):
def __init__(
self,
channels,
resolutions,
num_discs=1,
proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
cond=0,
separable=False,
patch=False,
**kwargs,
):
super().__init__()
assert num_discs in [1, 2, 3, 4]
# the first disc is on the lowest level of the backbone
self.disc_in_channels = channels[:num_discs]
self.disc_in_res = resolutions[:num_discs]
Disc = SingleDiscCond if cond else SingleDisc
mini_discs = []
for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
start_sz = res if not patch else 16
mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)],
self.mini_discs = nn.ModuleDict(mini_discs)
def forward(self, features, c):
all_logits = []
for k, disc in self.mini_discs.items():
all_logits.append(disc(features[k], c).view(features[k].size(0), -1))
all_logits = torch.cat(all_logits, dim=1)
return all_logits
class ProjectedDiscriminator(torch.nn.Module):
def __init__(
self,
diffaug=True,
interp224=True,
backbone_kwargs={},
**kwargs
):
super().__init__()
self.diffaug = diffaug
self.interp224 = interp224
self.feature_network = F_RandomProj(**backbone_kwargs)
self.discriminator = MultiScaleD(
channels=self.feature_network.CHANNELS,
resolutions=self.feature_network.RESOLUTIONS,
**backbone_kwargs,
)
def train(self, mode=True):
self.feature_network = self.feature_network.train(False)
self.discriminator = self.discriminator.train(mode)
return self
def eval(self):
return self.train(False)
def forward(self, x, c):
if self.diffaug:
x = DiffAugment(x, policy='color,translation,cutout')
if self.interp224:
x = F.interpolate(x, 224, mode='bilinear', align_corners=False)
features = self.feature_network(x)
logits = self.discriminator(features, c)
return logits
components/pg_modules/networks_fastgan.py
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# original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py
#
# modified by Axel Sauer for "Projected GANs Converge Faster"
#
import torch.nn as nn
from pg_modules.blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d)
def normalize_second_moment(x, dim=1, eps=1e-8):
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
class DummyMapping(nn.Module):
def __init__(self):
super().__init__()
def forward(self, z, c, **kwargs):
return z.unsqueeze(1) # to fit the StyleGAN API
class FastganSynthesis(nn.Module):
def __init__(self, ngf=128, z_dim=256, nc=3, img_resolution=256, lite=False):
super().__init__()
self.img_resolution = img_resolution
self.z_dim = z_dim
# channel multiplier
nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5,
512:0.25, 1024:0.125}
nfc = {}
for k, v in nfc_multi.items():
nfc[k] = int(v*ngf)
# layers
self.init = InitLayer(z_dim, channel=nfc[2], sz=4)
UpBlock = UpBlockSmall if lite else UpBlockBig
self.feat_8 = UpBlock(nfc[4], nfc[8])
self.feat_16 = UpBlock(nfc[8], nfc[16])
self.feat_32 = UpBlock(nfc[16], nfc[32])
self.feat_64 = UpBlock(nfc[32], nfc[64])
self.feat_128 = UpBlock(nfc[64], nfc[128])
self.feat_256 = UpBlock(nfc[128], nfc[256])
self.se_64 = SEBlock(nfc[4], nfc[64])
self.se_128 = SEBlock(nfc[8], nfc[128])
self.se_256 = SEBlock(nfc[16], nfc[256])
self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True)
if img_resolution > 256:
self.feat_512 = UpBlock(nfc[256], nfc[512])
self.se_512 = SEBlock(nfc[32], nfc[512])
if img_resolution > 512:
self.feat_1024 = UpBlock(nfc[512], nfc[1024])
def forward(self, input, c, **kwargs):
# map noise to hypersphere as in "Progressive Growing of GANS"
input = normalize_second_moment(input[:, 0])
feat_4 = self.init(input)
feat_8 = self.feat_8(feat_4)
feat_16 = self.feat_16(feat_8)
feat_32 = self.feat_32(feat_16)
feat_64 = self.se_64(feat_4, self.feat_64(feat_32))
feat_128 = self.se_128(feat_8, self.feat_128(feat_64))
if self.img_resolution >= 128:
feat_last = feat_128
if self.img_resolution >= 256:
feat_last = self.se_256(feat_16, self.feat_256(feat_last))
if self.img_resolution >= 512:
feat_last = self.se_512(feat_32, self.feat_512(feat_last))
if self.img_resolution >= 1024:
feat_last = self.feat_1024(feat_last)
return self.to_big(feat_last)
class FastganSynthesisCond(nn.Module):
def __init__(self, ngf=64, z_dim=256, nc=3, img_resolution=256, num_classes=1000, lite=False):
super().__init__()
self.z_dim = z_dim
nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5,
512:0.25, 1024:0.125, 2048:0.125}
nfc = {}
for k, v in nfc_multi.items():
nfc[k] = int(v*ngf)
self.img_resolution = img_resolution
self.init = InitLayer(z_dim, channel=nfc[2], sz=4)
UpBlock = UpBlockSmallCond if lite else UpBlockBigCond
self.feat_8 = UpBlock(nfc[4], nfc[8], z_dim)
self.feat_16 = UpBlock(nfc[8], nfc[16], z_dim)
self.feat_32 = UpBlock(nfc[16], nfc[32], z_dim)
self.feat_64 = UpBlock(nfc[32], nfc[64], z_dim)
self.feat_128 = UpBlock(nfc[64], nfc[128], z_dim)
self.feat_256 = UpBlock(nfc[128], nfc[256], z_dim)
self.se_64 = SEBlock(nfc[4], nfc[64])
self.se_128 = SEBlock(nfc[8], nfc[128])
self.se_256 = SEBlock(nfc[16], nfc[256])
self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True)
if img_resolution > 256:
self.feat_512 = UpBlock(nfc[256], nfc[512])
self.se_512 = SEBlock(nfc[32], nfc[512])
if img_resolution > 512:
self.feat_1024 = UpBlock(nfc[512], nfc[1024])
self.embed = nn.Embedding(num_classes, z_dim)
def forward(self, input, c, update_emas=False):
c = self.embed(c.argmax(1))
# map noise to hypersphere as in "Progressive Growing of GANS"
input = normalize_second_moment(input[:, 0])
feat_4 = self.init(input)
feat_8 = self.feat_8(feat_4, c)
feat_16 = self.feat_16(feat_8, c)
feat_32 = self.feat_32(feat_16, c)
feat_64 = self.se_64(feat_4, self.feat_64(feat_32, c))
feat_128 = self.se_128(feat_8, self.feat_128(feat_64, c))
if self.img_resolution >= 128:
feat_last = feat_128
if self.img_resolution >= 256:
feat_last = self.se_256(feat_16, self.feat_256(feat_last, c))
if self.img_resolution >= 512:
feat_last = self.se_512(feat_32, self.feat_512(feat_last, c))
if self.img_resolution >= 1024:
feat_last = self.feat_1024(feat_last, c)
return self.to_big(feat_last)
class Generator(nn.Module):
def __init__(
self,
z_dim=256,
c_dim=0,
w_dim=0,
img_resolution=256,
img_channels=3,
ngf=128,
cond=0,
mapping_kwargs={},
synthesis_kwargs={}
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_channels = img_channels
# Mapping and Synthesis Networks
self.mapping = DummyMapping() # to fit the StyleGAN API
Synthesis = FastganSynthesisCond if cond else FastganSynthesis
self.synthesis = Synthesis(ngf=ngf, z_dim=z_dim, nc=img_channels, img_resolution=img_resolution, **synthesis_kwargs)
def forward(self, z, c, **kwargs):
w = self.mapping(z, c)
img = self.synthesis(w, c)
return img
components/pg_modules/networks_stylegan2.py
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# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
# modified by Axel Sauer for "Projected GANs Converge Faster"
#
import numpy as np
import torch
from torch_utils import misc
from torch_utils import persistence
from torch_utils.ops import conv2d_resample
from torch_utils.ops import upfirdn2d
from torch_utils.ops import bias_act
from torch_utils.ops import fma
@misc.profiled_function
def normalize_2nd_moment(x, dim=1, eps=1e-8):
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
@misc.profiled_function
def modulated_conv2d(
x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
styles, # Modulation coefficients of shape [batch_size, in_channels].
noise = None, # Optional noise tensor to add to the output activations.
up = 1, # Integer upsampling factor.
down = 1, # Integer downsampling factor.
padding = 0, # Padding with respect to the upsampled image.
resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
demodulate = True, # Apply weight demodulation?
flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation?
):
batch_size = x.shape[0]
out_channels, in_channels, kh, kw = weight.shape
misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
# Pre-normalize inputs to avoid FP16 overflow.
if x.dtype == torch.float16 and demodulate:
weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk
styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
# Calculate per-sample weights and demodulation coefficients.
w = None
dcoefs = None
if demodulate or fused_modconv:
w = weight.unsqueeze(0) # [NOIkk]
w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
if demodulate:
dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
if demodulate and fused_modconv:
w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
# Execute by scaling the activations before and after the convolution.
if not fused_modconv:
x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
if demodulate and noise is not None:
x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
elif demodulate:
x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
elif noise is not None:
x = x.add_(noise.to(x.dtype))
return x
# Execute as one fused op using grouped convolution.
with misc.suppress_tracer_warnings(): # this value will be treated as a constant
batch_size = int(batch_size)
misc.assert_shape(x, [batch_size, in_channels, None, None])
x = x.reshape(1, -1, *x.shape[2:])
w = w.reshape(-1, in_channels, kh, kw)
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
x = x.reshape(batch_size, -1, *x.shape[2:])
if noise is not None:
x = x.add_(noise)
return x
@persistence.persistent_class
class FullyConnectedLayer(torch.nn.Module):
def __init__(self,
in_features, # Number of input features.
out_features, # Number of output features.
bias = True, # Apply additive bias before the activation function?
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
lr_multiplier = 1, # Learning rate multiplier.
bias_init = 0, # Initial value for the additive bias.
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.activation = activation
self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier)
self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
self.weight_gain = lr_multiplier / np.sqrt(in_features)
self.bias_gain = lr_multiplier
def forward(self, x):
w = self.weight.to(x.dtype) * self.weight_gain
b = self.bias
if b is not None:
b = b.to(x.dtype)
if self.bias_gain != 1:
b = b * self.bias_gain
if self.activation == 'linear' and b is not None:
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = bias_act.bias_act(x, b, act=self.activation)
return x
def extra_repr(self):
return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
@persistence.persistent_class
class Conv2dLayer(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels.
out_channels, # Number of output channels.
kernel_size, # Width and height of the convolution kernel.
bias = True, # Apply additive bias before the activation function?
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
up = 1, # Integer upsampling factor.
down = 1, # Integer downsampling factor.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = None, # Clamp the output to +-X, None = disable clamping.
channels_last = False, # Expect the input to have memory_format=channels_last?
trainable = True, # Update the weights of this layer during training?
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.activation = activation
self.up = up
self.down = down
self.conv_clamp = conv_clamp
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.padding = kernel_size // 2
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
self.act_gain = bias_act.activation_funcs[activation].def_gain
memory_format = torch.channels_last if channels_last else torch.contiguous_format
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)
bias = torch.zeros([out_channels]) if bias else None
if trainable:
self.weight = torch.nn.Parameter(weight)
self.bias = torch.nn.Parameter(bias) if bias is not None else None
else:
self.register_buffer('weight', weight)
if bias is not None:
self.register_buffer('bias', bias)
else:
self.bias = None
def forward(self, x, gain=1):
w = self.weight * self.weight_gain
b = self.bias.to(x.dtype) if self.bias is not None else None
flip_weight = (self.up == 1) # slightly faster
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
return x
def extra_repr(self):
return ' '.join([
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
f'up={self.up}, down={self.down}'])
@persistence.persistent_class
class MappingNetwork(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality, 0 = no latent.
c_dim, # Conditioning label (C) dimensionality, 0 = no label.
w_dim, # Intermediate latent (W) dimensionality.
num_ws, # Number of intermediate latents to output, None = do not broadcast.
num_layers = 8, # Number of mapping layers.
embed_features = None, # Label embedding dimensionality, None = same as w_dim.
layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim.
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.num_ws = num_ws
self.num_layers = num_layers
self.w_avg_beta = w_avg_beta
if embed_features is None:
embed_features = w_dim
if c_dim == 0:
embed_features = 0
if layer_features is None:
layer_features = w_dim
features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
if c_dim > 0:
self.embed = FullyConnectedLayer(c_dim, embed_features)
for idx in range(num_layers):
in_features = features_list[idx]
out_features = features_list[idx + 1]
layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
setattr(self, f'fc{idx}', layer)
if num_ws is not None and w_avg_beta is not None:
self.register_buffer('w_avg', torch.zeros([w_dim]))
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
# Embed, normalize, and concat inputs.
x = None
with torch.autograd.profiler.record_function('input'):
if self.z_dim > 0:
misc.assert_shape(z, [None, self.z_dim])
x = normalize_2nd_moment(z.to(torch.float32))
if self.c_dim > 0:
misc.assert_shape(c, [None, self.c_dim])
y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
x = torch.cat([x, y], dim=1) if x is not None else y
# Main layers.
for idx in range(self.num_layers):
layer = getattr(self, f'fc{idx}')
x = layer(x)
# Update moving average of W.
if update_emas and self.w_avg_beta is not None:
with torch.autograd.profiler.record_function('update_w_avg'):
self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
# Broadcast.
if self.num_ws is not None:
with torch.autograd.profiler.record_function('broadcast'):
x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
# Apply truncation.
if truncation_psi != 1:
with torch.autograd.profiler.record_function('truncate'):
assert self.w_avg_beta is not None
if self.num_ws is None or truncation_cutoff is None:
x = self.w_avg.lerp(x, truncation_psi)
else:
x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
return x
def extra_repr(self):
return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
@persistence.persistent_class
class SynthesisLayer(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels.
out_channels, # Number of output channels.
w_dim, # Intermediate latent (W) dimensionality.
resolution, # Resolution of this layer.
kernel_size = 3, # Convolution kernel size.
up = 1, # Integer upsampling factor.
use_noise = True, # Enable noise input?
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
channels_last = False, # Use channels_last format for the weights?
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.w_dim = w_dim
self.resolution = resolution
self.up = up
self.use_noise = use_noise
self.activation = activation
self.conv_clamp = conv_clamp
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.padding = kernel_size // 2
self.act_gain = bias_act.activation_funcs[activation].def_gain
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
memory_format = torch.channels_last if channels_last else torch.contiguous_format
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
if use_noise:
self.register_buffer('noise_const', torch.randn([resolution, resolution]))
self.noise_strength = torch.nn.Parameter(torch.zeros([]))
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
assert noise_mode in ['random', 'const', 'none']
in_resolution = self.resolution // self.up
misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.use_noise and noise_mode == 'random':
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
if self.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
act_gain = self.act_gain * gain
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
return x
def extra_repr(self):
return ' '.join([
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
@persistence.persistent_class
class ToRGBLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.w_dim = w_dim
self.conv_clamp = conv_clamp
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
memory_format = torch.channels_last if channels_last else torch.contiguous_format
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
def forward(self, x, w, fused_modconv=True):
styles = self.affine(w) * self.weight_gain
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
return x
def extra_repr(self):
return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
@persistence.persistent_class
class SynthesisBlock(torch.nn.Module):
def __init__(self,
in_channels, # Number of input channels, 0 = first block.
out_channels, # Number of output channels.
w_dim, # Intermediate latent (W) dimensionality.
resolution, # Resolution of this block.
img_channels, # Number of output color channels.
is_last, # Is this the last block?
architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'.
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
use_fp16 = False, # Use FP16 for this block?
fp16_channels_last = False, # Use channels-last memory format with FP16?
fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
**layer_kwargs, # Arguments for SynthesisLayer.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.in_channels = in_channels
self.w_dim = w_dim
self.resolution = resolution
self.img_channels = img_channels
self.is_last = is_last
self.architecture = architecture
self.use_fp16 = use_fp16
self.channels_last = (use_fp16 and fp16_channels_last)
self.fused_modconv_default = fused_modconv_default
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
self.num_conv = 0
self.num_torgb = 0
if in_channels == 0:
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
if in_channels != 0:
self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
self.num_conv += 1
self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
self.num_conv += 1
if is_last or architecture == 'skip':
self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
conv_clamp=conv_clamp, channels_last=self.channels_last)
self.num_torgb += 1
if in_channels != 0 and architecture == 'resnet':
self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
resample_filter=resample_filter, channels_last=self.channels_last)
def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
_ = update_emas # unused
misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
w_iter = iter(ws.unbind(dim=1))
if ws.device.type != 'cuda':
force_fp32 = True
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
if fused_modconv is None:
fused_modconv = self.fused_modconv_default
if fused_modconv == 'inference_only':
fused_modconv = (not self.training)
# Input.
if self.in_channels == 0:
x = self.const.to(dtype=dtype, memory_format=memory_format)
x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
else:
misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
x = x.to(dtype=dtype, memory_format=memory_format)
# Main layers.
if self.in_channels == 0:
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
elif self.architecture == 'resnet':
y = self.skip(x, gain=np.sqrt(0.5))
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
x = y.add_(x)
else:
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
# ToRGB.
if img is not None:
misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
img = upfirdn2d.upsample2d(img, self.resample_filter)
if self.is_last or self.architecture == 'skip':
y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
img = img.add_(y) if img is not None else y
assert x.dtype == dtype
assert img is None or img.dtype == torch.float32
return x, img
def extra_repr(self):
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
@persistence.persistent_class
class SynthesisNetwork(torch.nn.Module):
def __init__(self,
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output image resolution.
img_channels, # Number of color channels.
channel_base = 32768, # Overall multiplier for the number of channels.
channel_max = 512, # Maximum number of channels in any layer.
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
**block_kwargs, # Arguments for SynthesisBlock.
):
assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
super().__init__()
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_resolution_log2 = int(np.log2(img_resolution))
self.img_channels = img_channels
self.num_fp16_res = num_fp16_res
self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)]
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
self.num_ws = 0
for res in self.block_resolutions:
in_channels = channels_dict[res // 2] if res > 4 else 0
out_channels = channels_dict[res]
use_fp16 = (res >= fp16_resolution)
is_last = (res == self.img_resolution)
block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
self.num_ws += block.num_conv
if is_last:
self.num_ws += block.num_torgb
setattr(self, f'b{res}', block)
def forward(self, ws, c=None, **block_kwargs):
block_ws = []
with torch.autograd.profiler.record_function('split_ws'):
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
ws = ws.to(torch.float32)
w_idx = 0
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
w_idx += block.num_conv
x = img = None
for res, cur_ws in zip(self.block_resolutions, block_ws):
block = getattr(self, f'b{res}')
x, img = block(x, img, cur_ws, **block_kwargs)
return img
def extra_repr(self):
return ' '.join([
f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
f'num_fp16_res={self.num_fp16_res:d}'])
@persistence.persistent_class
class Generator(torch.nn.Module):
def __init__(self,
z_dim, # Input latent (Z) dimensionality.
c_dim, # Conditioning label (C) dimensionality.
w_dim, # Intermediate latent (W) dimensionality.
img_resolution, # Output resolution.
img_channels, # Number of output color channels.
mapping_kwargs = {}, # Arguments for MappingNetwork.
**synthesis_kwargs, # Arguments for SynthesisNetwork.
):
super().__init__()
self.z_dim = z_dim
self.c_dim = c_dim
self.w_dim = w_dim
self.img_resolution = img_resolution
self.img_channels = img_channels
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
self.num_ws = self.synthesis.num_ws
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
return img
components/pg_modules/projector.py
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import torch
import torch.nn as nn
import timm
from components.pg_modules.blocks import FeatureFusionBlock
def _make_scratch_ccm(scratch, in_channels, cout, expand=False):
# shapes
out_channels = [cout, cout*2, cout*4, cout*8] if expand else [cout]*4
scratch.layer0_ccm = nn.Conv2d(in_channels[0], out_channels[0], kernel_size=1, stride=1, padding=0, bias=True)
scratch.layer1_ccm = nn.Conv2d(in_channels[1], out_channels[1], kernel_size=1, stride=1, padding=0, bias=True)
scratch.layer2_ccm = nn.Conv2d(in_channels[2], out_channels[2], kernel_size=1, stride=1, padding=0, bias=True)
scratch.layer3_ccm = nn.Conv2d(in_channels[3], out_channels[3], kernel_size=1, stride=1, padding=0, bias=True)
scratch.CHANNELS = out_channels
return scratch
def _make_scratch_csm(scratch, in_channels, cout, expand):
scratch.layer3_csm = FeatureFusionBlock(in_channels[3], nn.ReLU(False), expand=expand, lowest=True)
scratch.layer2_csm = FeatureFusionBlock(in_channels[2], nn.ReLU(False), expand=expand)
scratch.layer1_csm = FeatureFusionBlock(in_channels[1], nn.ReLU(False), expand=expand)
scratch.layer0_csm = FeatureFusionBlock(in_channels[0], nn.ReLU(False))
# last refinenet does not expand to save channels in higher dimensions
scratch.CHANNELS = [cout, cout, cout*2, cout*4] if expand else [cout]*4
return scratch
def _make_efficientnet(model):
pretrained = nn.Module()
pretrained.layer0 = nn.Sequential(model.conv_stem, model.bn1, model.act1, *model.blocks[0:2])
pretrained.layer1 = nn.Sequential(*model.blocks[2:3])
pretrained.layer2 = nn.Sequential(*model.blocks[3:5])
pretrained.layer3 = nn.Sequential(*model.blocks[5:9])
return pretrained
def calc_channels(pretrained, inp_res=224):
channels = []
tmp = torch.zeros(1, 3, inp_res, inp_res)
# forward pass
tmp = pretrained.layer0(tmp)
channels.append(tmp.shape[1])
tmp = pretrained.layer1(tmp)
channels.append(tmp.shape[1])
tmp = pretrained.layer2(tmp)
channels.append(tmp.shape[1])
tmp = pretrained.layer3(tmp)
channels.append(tmp.shape[1])
return channels
def _make_projector(im_res, cout, proj_type, expand=False):
assert proj_type in [0, 1, 2], "Invalid projection type"
### Build pretrained feature network
model = timm.create_model('tf_efficientnet_lite0', pretrained=True)
pretrained = _make_efficientnet(model)
# determine resolution of feature maps, this is later used to calculate the number
# of down blocks in the discriminators. Interestingly, the best results are achieved
# by fixing this to 256, ie., we use the same number of down blocks per discriminator
# independent of the dataset resolution
im_res = 256
pretrained.RESOLUTIONS = [im_res//4, im_res//8, im_res//16, im_res//32]
pretrained.CHANNELS = calc_channels(pretrained)
if proj_type == 0: return pretrained, None
### Build CCM
scratch = nn.Module()
scratch = _make_scratch_ccm(scratch, in_channels=pretrained.CHANNELS, cout=cout, expand=expand)
pretrained.CHANNELS = scratch.CHANNELS
if proj_type == 1: return pretrained, scratch
### build CSM
scratch = _make_scratch_csm(scratch, in_channels=scratch.CHANNELS, cout=cout, expand=expand)
# CSM upsamples x2 so the feature map resolution doubles
pretrained.RESOLUTIONS = [res*2 for res in pretrained.RESOLUTIONS]
pretrained.CHANNELS = scratch.CHANNELS
return pretrained, scratch
class F_RandomProj(nn.Module):
def __init__(
self,
im_res=256,
cout=64,
expand=True,
proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
**kwargs,
):
super().__init__()
self.proj_type = proj_type
self.cout = cout
self.expand = expand
# build pretrained feature network and random decoder (scratch)
self.pretrained, self.scratch = _make_projector(im_res=im_res, cout=self.cout, proj_type=self.proj_type, expand=self.expand)
self.CHANNELS = self.pretrained.CHANNELS
self.RESOLUTIONS = self.pretrained.RESOLUTIONS
def forward(self, x, get_features=False):
# predict feature maps
out0 = self.pretrained.layer0(x)
out1 = self.pretrained.layer1(out0)
out2 = self.pretrained.layer2(out1)
out3 = self.pretrained.layer3(out2)
# start enumerating at the lowest layer (this is where we put the first discriminator)
backbone_features = {
'0': out0,
'1': out1,
'2': out2,
'3': out3,
}
if get_features:
return backbone_features
if self.proj_type == 0: return backbone_features
out0_channel_mixed = self.scratch.layer0_ccm(backbone_features['0'])
out1_channel_mixed = self.scratch.layer1_ccm(backbone_features['1'])
out2_channel_mixed = self.scratch.layer2_ccm(backbone_features['2'])
out3_channel_mixed = self.scratch.layer3_ccm(backbone_features['3'])
out = {
'0': out0_channel_mixed,
'1': out1_channel_mixed,
'2': out2_channel_mixed,
'3': out3_channel_mixed,
}
if self.proj_type == 1: return out
# from bottom to top
out3_scale_mixed = self.scratch.layer3_csm(out3_channel_mixed)
out2_scale_mixed = self.scratch.layer2_csm(out3_scale_mixed, out2_channel_mixed)
out1_scale_mixed = self.scratch.layer1_csm(out2_scale_mixed, out1_channel_mixed)
out0_scale_mixed = self.scratch.layer0_csm(out1_scale_mixed, out0_channel_mixed)
out = {
'0': out0_scale_mixed,
'1': out1_scale_mixed,
'2': out2_scale_mixed,
'3': out3_scale_mixed,
}
return out, backbone_features
components/projected_discriminator.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from components.pg_modules.blocks import DownBlock, DownBlockPatch, conv2d
from components.pg_modules.projector import F_RandomProj
# from components.pg_modules.diffaug import DiffAugment
class SingleDisc(nn.Module):
def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False):
super().__init__()
channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
256: 32, 512: 16, 1024: 8}
# interpolate for start sz that are not powers of two
if start_sz not in channel_dict.keys():
sizes = np.array(list(channel_dict.keys()))
start_sz = sizes[np.argmin(abs(sizes - start_sz))]
self.start_sz = start_sz
# if given ndf, allocate all layers with the same ndf
if ndf is None:
nfc = channel_dict
else:
nfc = {k: ndf for k, v in channel_dict.items()}
# for feature map discriminators with nfc not in channel_dict
# this is the case for the pretrained backbone (midas.pretrained)
if nc is not None and head is None:
nfc[start_sz] = nc
layers = []
# Head if the initial input is the full modality
if head:
layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True)]
# Down Blocks
DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
while start_sz > end_sz:
layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
start_sz = start_sz // 2
layers.append(conv2d(nfc[end_sz], 1, 4, 1, 0, bias=False))
self.main = nn.Sequential(*layers)
def forward(self, x, c):
return self.main(x)
class SingleDiscCond(nn.Module):
def __init__(self, nc=None, ndf=None, start_sz=256, end_sz=8, head=None, separable=False, patch=False, c_dim=1000, cmap_dim=64, embedding_dim=128):
super().__init__()
self.cmap_dim = cmap_dim
# midas channels
channel_dict = {4: 512, 8: 512, 16: 256, 32: 128, 64: 64, 128: 64,
256: 32, 512: 16, 1024: 8}
# interpolate for start sz that are not powers of two
if start_sz not in channel_dict.keys():
sizes = np.array(list(channel_dict.keys()))
start_sz = sizes[np.argmin(abs(sizes - start_sz))]
self.start_sz = start_sz
# if given ndf, allocate all layers with the same ndf
if ndf is None:
nfc = channel_dict
else:
nfc = {k: ndf for k, v in channel_dict.items()}
# for feature map discriminators with nfc not in channel_dict
# this is the case for the pretrained backbone (midas.pretrained)
if nc is not None and head is None:
nfc[start_sz] = nc
layers = []
# Head if the initial input is the full modality
if head:
layers += [conv2d(nc, nfc[256], 3, 1, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True)]
# Down Blocks
DB = partial(DownBlockPatch, separable=separable) if patch else partial(DownBlock, separable=separable)
while start_sz > end_sz:
layers.append(DB(nfc[start_sz], nfc[start_sz//2]))
start_sz = start_sz // 2
self.main = nn.Sequential(*layers)
# additions for conditioning on class information
self.cls = conv2d(nfc[end_sz], self.cmap_dim, 4, 1, 0, bias=False)
self.embed = nn.Embedding(num_embeddings=c_dim, embedding_dim=embedding_dim)
self.embed_proj = nn.Sequential(
nn.Linear(self.embed.embedding_dim, self.cmap_dim),
nn.LeakyReLU(0.2, inplace=True),
)
def forward(self, x, c):
h = self.main(x)
out = self.cls(h)
# conditioning via projection
cmap = self.embed_proj(self.embed(c.argmax(1))).unsqueeze(-1).unsqueeze(-1)
out = (out * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
return out
class MultiScaleD(nn.Module):
def __init__(
self,
channels,
resolutions,
num_discs=4,
proj_type=2, # 0 = no projection, 1 = cross channel mixing, 2 = cross scale mixing
cond=0,
separable=False,
patch=False,
**kwargs,
):
super().__init__()
assert num_discs in [1, 2, 3, 4]
# the first disc is on the lowest level of the backbone
self.disc_in_channels = channels[:num_discs]
self.disc_in_res = resolutions[:num_discs]
Disc = SingleDiscCond if cond else SingleDisc
mini_discs = []
for i, (cin, res) in enumerate(zip(self.disc_in_channels, self.disc_in_res)):
start_sz = res if not patch else 16
mini_discs += [str(i), Disc(nc=cin, start_sz=start_sz, end_sz=8, separable=separable, patch=patch)],
self.mini_discs = nn.ModuleDict(mini_discs)
def forward(self, features, c):
all_logits = []
for k, disc in self.mini_discs.items():
res = disc(features[k], c).view(features[k].size(0), -1)
all_logits.append(res)
all_logits = torch.cat(all_logits, dim=1)
return all_logits
class ProjectedDiscriminator(torch.nn.Module):
def __init__(
self,
**kwargs
):
super().__init__()
self.diffaug = kwargs["diffaug"]
self.interp224 = kwargs["interp224"]
backbone_kwargs = kwargs["backbone_kwargs"]
self.interp224 = False
self.feature_network = F_RandomProj(**backbone_kwargs)
self.discriminator = MultiScaleD(
channels=self.feature_network.CHANNELS,
resolutions=self.feature_network.RESOLUTIONS,
**backbone_kwargs,
)
def train(self, mode=True):
self.feature_network = self.feature_network.train(False)
self.discriminator = self.discriminator.train(mode)
return self
def eval(self):
return self.train(False)
def get_feature(self, x):
features = self.feature_network(x, get_features=True)
return features
def forward(self, x, c):
# if self.diffaug:
# x = DiffAugment(x, policy='color,translation,cutout')
# if self.interp224:
# x = F.interpolate(x, 224, mode='bilinear', align_corners=False)
features,backbone_features = self.feature_network(x)
logits = self.discriminator(features, c)
return logits,backbone_features
components/warp_invo.py
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#!/usr/bin/env python3
# -*- coding:utf-8 -*-
#############################################################
# File: warp_invo.py
# Created Date: Tuesday October 19th 2021
# Author: Chen Xuanhong
# Email: chenxuanhongzju@outlook.com
# Last Modified: Tuesday, 19th October 2021 11:27:13 am
# Modified By: Chen Xuanhong
# Copyright (c) 2021 Shanghai Jiao Tong University
#############################################################
from torch import nn
from components.Involution import involution
class DeConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size = 3, upsampl_scale = 2, padding="reflect"):
super().__init__()
self.upsampling = nn.UpsamplingNearest2d(scale_factor=upsampl_scale)
padding_size = int((kernel_size -1)/2)
self.conv1x1 = nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= 1)
# self.same_padding = nn.ReflectionPad2d(padding_size)
if padding.lower() == "reflect":
self.conv = involution(out_channels,5,1)
# self.conv = nn.Sequential(
# nn.ReflectionPad2d(padding_size),
# nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size= kernel_size, bias= False))
# for layer in self.conv:
# if isinstance(layer,nn.Conv2d):
# nn.init.xavier_uniform_(layer.weight)
elif padding.lower() == "zero":
self.conv = involution(out_channels,5,1)
# nn.init.xavier_uniform_(self.conv.weight)
# self.__weights_init__()
# def __weights_init__(self):
# nn.init.xavier_uniform_(self.conv.weight)
def forward(self, input):
h = self.conv1x1(input)
h = self.upsampling(h)
h = self.conv(h)
return h