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import torch
import torch.nn as nn
import torch.nn.parallel
#from torchvision import models
#from options.train_options import TrainOptions
import os
#opt = TrainOptions().parse()
class ResidualBlock(nn.Module):
def __init__(self, in_features=64, norm_layer=nn.BatchNorm2d):
super(ResidualBlock, self).__init__()
self.relu = nn.ReLU(True)
if norm_layer == None:
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
class ResUnetGenerator(nn.Module):
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):
return self.model(input)
# Defines the submodule with skip connection.
# X -------------------identity---------------------- X
# |-- downsampling -- |submodule| -- upsampling --|
class ResUnetSkipConnectionBlock(nn.Module):
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)]
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 = [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 Vgg19(nn.Module):
def __init__(self, requires_grad=False):
super(Vgg19, self).__init__()
vgg_pretrained_features = models.vgg19(pretrained=True).features
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
self.slice5 = nn.Sequential()
for x in range(2):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(2, 7):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(7, 12):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 21):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(21, 30):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h_relu1 = self.slice1(X)
h_relu2 = self.slice2(h_relu1)
h_relu3 = self.slice3(h_relu2)
h_relu4 = self.slice4(h_relu3)
h_relu5 = self.slice5(h_relu4)
out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
return out
class VGGLoss(nn.Module):
def __init__(self, layids = None):
super(VGGLoss, self).__init__()
self.vgg = Vgg19()
self.vgg.cuda()
self.criterion = nn.L1Loss()
self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]
self.layids = layids
def forward(self, x, y):
x_vgg, y_vgg = self.vgg(x), self.vgg(y)
loss = 0
if self.layids is None:
self.layids = list(range(len(x_vgg)))
for i in self.layids:
loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
return loss
def save_checkpoint(model, save_path):
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
torch.save(model.state_dict(), save_path)
def load_checkpoint_parallel(model, checkpoint_path):
if not os.path.exists(checkpoint_path):
print('No checkpoint!')
return
checkpoint = torch.load(checkpoint_path, map_location='cuda:{}'.format(opt.local_rank))
checkpoint_new = model.state_dict()
for param in checkpoint_new:
checkpoint_new[param] = checkpoint[param]
model.load_state_dict(checkpoint_new)
def load_checkpoint_part_parallel(model, checkpoint_path):
if not os.path.exists(checkpoint_path):
print('No checkpoint!')
return
checkpoint = torch.load(checkpoint_path,map_location='cuda:{}'.format(opt.local_rank))
checkpoint_new = model.state_dict()
for param in checkpoint_new:
if 'cond_' not in param and 'aflow_net.netRefine' not in param or 'aflow_net.cond_style' in param:
checkpoint_new[param] = checkpoint[param]
model.load_state_dict(checkpoint_new)
def load_checkpoint(model, checkpoint_path):
if not os.path.exists(checkpoint_path):
print('No checkpoint!')
return
checkpoint = torch.load(checkpoint_path)
checkpoint_new = model.state_dict()
for param in checkpoint_new:
checkpoint_new[param] = checkpoint[param]
model.load_state_dict(checkpoint_new)
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