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"""
This file contains a code from ***MakeItTalk***
This code is originally from the project name MakeItTalk from "" at MakeItTalk repository
Link :https://github.com/yzhou359/MakeItTalk
"""
import torch
import torch.nn as nn
#import torch.nn.parallel
#from torch.autograd import Variable
import torch.nn.functional as F
#from torchvision import models
#import torch.utils.model_zoo as model_zoo
#from torch.nn import init
import os
import numpy as np
class ResUnetGenerator(nn.Module):
"""
Main Image2Image Translation Network
"""
def __init__(self, input_nc, output_nc, num_downs, ngf=64,
norm_layer=nn.BatchNorm2d, use_dropout=False):
super(ResUnetGenerator, self).__init__()
# construct unet structure
unet_block = ResUnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer,
innermost=True)
for i in range(num_downs - 5):
unet_block = ResUnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block,
norm_layer=norm_layer, use_dropout=use_dropout)
unet_block = ResUnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block,
norm_layer=norm_layer)
unet_block = ResUnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block,
norm_layer=norm_layer)
unet_block = ResUnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block,
norm_layer=norm_layer)
unet_block = ResUnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True,
norm_layer=norm_layer)
self.model = unet_block
def forward(self, input):
output = self.model(input)
return output
# Defines the submodule with skip connection.
# X -------------------identity---------------------- X
# |-- downsampling -- |submodule| -- upsampling --|
class ResUnetSkipConnectionBlock(nn.Module):
"""
Unet Layers with Residual Connection
"""
def __init__(self, outer_nc, inner_nc, input_nc=None,
submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
super(ResUnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=3,
stride=2, padding=1, bias=use_bias)
# add two resblock
res_downconv = [ResidualBlock(inner_nc, norm_layer), ResidualBlock(inner_nc, norm_layer)]
res_upconv = [ResidualBlock(outer_nc, norm_layer), ResidualBlock(outer_nc, norm_layer)]
# res_downconv = [ResidualBlock(inner_nc)]
# res_upconv = [ResidualBlock(outer_nc)]
downrelu = nn.ReLU(True)
uprelu = nn.ReLU(True)
if norm_layer != None:
downnorm = norm_layer(inner_nc)
upnorm = norm_layer(outer_nc)
if outermost:
upsample = nn.Upsample(scale_factor=2, mode='nearest')
upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias)
down = [downconv, downrelu] + res_downconv
# up = [uprelu, upsample, upconv, upnorm]
up = [upsample, upconv]
model = down + [submodule] + up
elif innermost:
upsample = nn.Upsample(scale_factor=2, mode='nearest')
upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias)
down = [downconv, downrelu] + res_downconv
if norm_layer == None:
up = [upsample, upconv, uprelu] + res_upconv
else:
up = [upsample, upconv, upnorm, uprelu] + res_upconv
model = down + up
else:
upsample = nn.Upsample(scale_factor=2, mode='nearest')
upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias)
if norm_layer == None:
down = [downconv, downrelu] + res_downconv
up = [upsample, upconv, uprelu] + res_upconv
else:
down = [downconv, downnorm, downrelu] + res_downconv
up = [upsample, upconv, upnorm, uprelu] + res_upconv
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else:
return torch.cat([x, self.model(x)], 1)
class ResidualBlock(nn.Module):
"""
Residual Connection Layers
"""
def __init__(self, in_features=64, norm_layer=nn.BatchNorm2d):
super(ResidualBlock, self).__init__()
self.relu = nn.ReLU(True)
if norm_layer == None:
# hard to converge with out batch or instance norm
self.block = nn.Sequential(
nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),
nn.ReLU(inplace=True),
nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),
)
else:
self.block = nn.Sequential(
nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),
norm_layer(in_features),
nn.ReLU(inplace=True),
nn.Conv2d(in_features, in_features, 3, 1, 1, bias=False),
norm_layer(in_features)
)
def forward(self, x):
residual = x
out = self.block(x)
out += residual
out = self.relu(out)
return out
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