/
net_microAST.py
324 lines (259 loc) · 12.5 KB
/
net_microAST.py
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import torch.nn as nn
from function import adaptive_instance_normalization as featMod
from function import calc_mean_std
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, groupnum):
super(ConvLayer, self).__init__()
# Padding Layer
padding_size = kernel_size // 2
self.reflection_pad = nn.ReflectionPad2d(padding_size)
# Convolution Layer
self.conv_layer = nn.Conv2d(in_channels, out_channels, kernel_size, stride, groups=groupnum)
def forward(self, x):
x = self.reflection_pad(x)
x = self.conv_layer(x)
return x
class ResidualLayer(nn.Module):
def __init__(self, channels=128, kernel_size=3, groupnum=1):
super(ResidualLayer, self).__init__()
self.conv1 = ConvLayer(channels, channels, kernel_size, stride=1, groupnum=groupnum)
self.relu = nn.ReLU(inplace=True)
self.conv2 = ConvLayer(channels, channels, kernel_size, stride=1, groupnum=groupnum)
def forward(self, x, weight=None, bias=None, filterMod=False):
if filterMod:
x1 = self.conv1(x)
x2 = weight * x1 + bias * x
x3 = self.relu(x2)
x4 = self.conv2(x3)
x5 = weight * x4 + bias * x3
return x + x5
else:
return x + self.conv2(self.relu(self.conv1(x)))
# Control the number of channels
slim_factor = 1
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.enc1 = nn.Sequential(
ConvLayer(3, int(16*slim_factor), kernel_size=9, stride=1, groupnum=1),
nn.ReLU(inplace=True),
ConvLayer(int(16*slim_factor), int(32*slim_factor), kernel_size=3, stride=2, groupnum=int(16*slim_factor)),
nn.ReLU(inplace=True),
ConvLayer(int(32*slim_factor), int(32*slim_factor), kernel_size=1, stride=1, groupnum=1),
nn.ReLU(inplace=True),
ConvLayer(int(32*slim_factor), int(64*slim_factor), kernel_size=3, stride=2, groupnum=int(32*slim_factor)),
nn.ReLU(inplace=True),
ConvLayer(int(64*slim_factor), int(64*slim_factor), kernel_size=1, stride=1, groupnum=1),
nn.ReLU(inplace=True),
ResidualLayer(int(64*slim_factor), kernel_size=3),
)
self.enc2 = nn.Sequential(
ResidualLayer(int(64*slim_factor), kernel_size=3)
)
def forward(self, x):
x1 = self.enc1(x)
x2 = self.enc2(x1)
out = [x1, x2]
return out
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.dec1 = ResidualLayer(int(64*slim_factor), kernel_size=3)
self.dec2 = ResidualLayer(int(64*slim_factor), kernel_size=3)
self.dec3 = nn.Sequential(
nn.Upsample(scale_factor=2, mode='bilinear'),
ConvLayer(int(64*slim_factor), int(32*slim_factor), kernel_size=3, stride=1, groupnum=int(32*slim_factor)),
nn.ReLU(inplace=True),
ConvLayer(int(32*slim_factor), int(32*slim_factor), kernel_size=1, stride=1, groupnum=1),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2, mode='bilinear'),
ConvLayer(int(32*slim_factor), int(16*slim_factor), kernel_size=3, stride=1, groupnum=int(16*slim_factor)),
nn.ReLU(inplace=True),
ConvLayer(int(16*slim_factor), int(16*slim_factor), kernel_size=1, stride=1, groupnum=1),
nn.ReLU(inplace=True),
ConvLayer(int(16*slim_factor), 3, kernel_size=9, stride=1, groupnum=1)
)
def forward(self, x, s, w, b, alpha):
x1 = featMod(x[1], s[1])
x1 = alpha * x1 + (1-alpha) * x[1]
x2 = self.dec1(x1, w[1], b[1], filterMod=True)
x3 = featMod(x2, s[0])
x3 = alpha * x3 + (1-alpha) * x2
x4 = self.dec2(x3, w[0], b[0], filterMod=True)
out = self.dec3(x4)
return out
class Modulator(nn.Module):
def __init__(self):
super(Modulator, self).__init__()
self.weight1 = nn.Sequential(
ConvLayer(int(64*slim_factor), int(64*slim_factor), kernel_size=3, stride=1, groupnum=int(64*slim_factor)),
nn.AdaptiveAvgPool2d((1,1))
)
self.bias1 = nn.Sequential(
ConvLayer(int(64*slim_factor), int(64*slim_factor), kernel_size=3, stride=1, groupnum=int(64*slim_factor)),
nn.AdaptiveAvgPool2d((1,1))
)
self.weight2 = nn.Sequential(
ConvLayer(int(64*slim_factor), int(64*slim_factor), kernel_size=3, stride=1, groupnum=int(64*slim_factor)),
nn.AdaptiveAvgPool2d((1,1))
)
self.bias2 = nn.Sequential(
ConvLayer(int(64*slim_factor), int(64*slim_factor), kernel_size=3, stride=1, groupnum=int(64*slim_factor)),
nn.AdaptiveAvgPool2d((1,1))
)
def forward(self, x):
w1 = self.weight1(x[0])
b1 = self.bias1(x[0])
w2 = self.weight2(x[1])
b2 = self.bias2(x[1])
return [w1,w2], [b1,b2]
vgg = nn.Sequential(
nn.Conv2d(3, 3, (1, 1)),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(3, 64, (3, 3)),
nn.ReLU(), # relu1-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(), # relu1-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 128, (3, 3)),
nn.ReLU(), # relu2-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(), # relu2-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 256, (3, 3)),
nn.ReLU(), # relu3-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 512, (3, 3)),
nn.ReLU(), # relu4-1, this is the last layer used
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU() # relu5-4
)
class Net(nn.Module):
def __init__(self, vgg, content_encoder, style_encoder, modulator, decoder):
super(Net, self).__init__()
vgg_enc_layers = list(vgg.children())
self.vgg_enc_1 = nn.Sequential(*vgg_enc_layers[:4]) # input -> relu1_1
self.vgg_enc_2 = nn.Sequential(*vgg_enc_layers[4:11]) # relu1_1 -> relu2_1
self.vgg_enc_3 = nn.Sequential(*vgg_enc_layers[11:18]) # relu2_1 -> relu3_1
self.vgg_enc_4 = nn.Sequential(*vgg_enc_layers[18:31]) # relu3_1 -> relu4_1
self.content_encoder = content_encoder
self.style_encoder = style_encoder
self.modulator = modulator
self.decoder = decoder
self.mse_loss = nn.MSELoss()
# fix the encoder
for name in ['vgg_enc_1', 'vgg_enc_2', 'vgg_enc_3', 'vgg_enc_4']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_vgg_intermediate(self, input):
results = [input]
for i in range(4):
func = getattr(self, 'vgg_enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
# extract relu4_1 from input image
def encode_vgg_content(self, input):
for i in range(4):
input = getattr(self, 'vgg_enc_{:d}'.format(i + 1))(input)
return input
def calc_content_loss(self, input, target):
assert (input.size() == target.size())
return self.mse_loss(input, target)
def calc_style_loss(self, input, target):
assert (input.size() == target.size())
input_mean, input_std = calc_mean_std(input)
target_mean, target_std = calc_mean_std(target)
return self.mse_loss(input_mean, target_mean) + \
self.mse_loss(input_std, target_std)
def forward(self, content, style, alpha=1.0):
assert 0 <= alpha <= 1
# extract style modulation signals
style_feats = self.style_encoder(style)
filter_weights, filter_biases = self.modulator(style_feats)
# extract content features
content_feats = self.content_encoder(content)
# generate results
res = self.decoder(content_feats, style_feats, filter_weights, filter_biases, alpha)
# vgg content and style loss
res_feats_vgg = self.encode_with_vgg_intermediate(res)
style_feats_vgg = self.encode_with_vgg_intermediate(style)
content_feats_vgg = self.encode_vgg_content(content)
loss_c = self.calc_content_loss(res_feats_vgg[-1], content_feats_vgg)
loss_s = self.calc_style_loss(res_feats_vgg[0], style_feats_vgg[0])
for i in range(1, 4):
loss_s = loss_s + self.calc_style_loss(res_feats_vgg[i], style_feats_vgg[i])
res_style_feats = self.style_encoder(res)
res_filter_weights, res_filter_biases = self.modulator(res_style_feats)
# style signal contrastive loss
loss_contrastive = 0.
for i in range(int(style.size(0))):
pos_loss = 0.
neg_loss = 0.
for j in range(int(style.size(0))):
if j==i:
FeatMod_loss = self.calc_style_loss(res_style_feats[0][i].unsqueeze(0), style_feats[0][j].unsqueeze(0)) + \
self.calc_style_loss(res_style_feats[1][i].unsqueeze(0), style_feats[1][j].unsqueeze(0))
FilterMod_loss = self.calc_content_loss(res_filter_weights[0][i], filter_weights[0][j]) + \
self.calc_content_loss(res_filter_weights[1][i], filter_weights[1][j]) + \
self.calc_content_loss(res_filter_biases[0][i], filter_biases[0][j]) + \
self.calc_content_loss(res_filter_biases[1][i], filter_biases[1][j])
pos_loss = FeatMod_loss + FilterMod_loss
else:
FeatMod_loss = self.calc_style_loss(res_style_feats[0][i].unsqueeze(0), res_style_feats[0][j].unsqueeze(0)) + \
self.calc_style_loss(res_style_feats[1][i].unsqueeze(0), style_feats[1][j].unsqueeze(0))
FilterMod_loss = self.calc_content_loss(res_filter_weights[0][i], filter_weights[0][j]) + \
self.calc_content_loss(res_filter_weights[1][i], filter_weights[1][j]) + \
self.calc_content_loss(res_filter_biases[0][i], filter_biases[0][j]) + \
self.calc_content_loss(res_filter_biases[1][i], filter_biases[1][j])
neg_loss = neg_loss + FeatMod_loss + FilterMod_loss
loss_contrastive = loss_contrastive + pos_loss/neg_loss
return res, loss_c, loss_s, loss_contrastive
class TestNet(nn.Module):
def __init__(self, content_encoder, style_encoder, modulator, decoder):
super(TestNet, self).__init__()
self.content_encoder = content_encoder
self.style_encoder = style_encoder
self.modulator = modulator
self.decoder = decoder
def forward(self, content, style, alpha=1.0):
assert 0 <= alpha <= 1
style_feats = self.style_encoder(style)
filter_weights, filter_biases = self.modulator(style_feats)
content_feats = self.content_encoder(content)
res = self.decoder(content_feats, style_feats, filter_weights, filter_biases, alpha)
return res