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Listening to the pixels - AAN
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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
# Code from Hang Zhao (@hangzhaomit) | |
class Unet(nn.Module): | |
def __init__(self, fc_dim=64, num_downs=5, ngf=64, use_dropout=False): | |
super(Unet, self).__init__() | |
# construct unet structure | |
unet_block = UnetBlock( | |
ngf * 8, ngf * 8, input_nc=None, | |
submodule=None, innermost=True) | |
for i in range(num_downs - 5): | |
unet_block = UnetBlock( | |
ngf * 8, ngf * 8, input_nc=None, | |
submodule=unet_block, use_dropout=use_dropout) | |
unet_block = UnetBlock( | |
ngf * 4, ngf * 8, input_nc=None, | |
submodule=unet_block) | |
unet_block = UnetBlock( | |
ngf * 2, ngf * 4, input_nc=None, | |
submodule=unet_block) | |
unet_block = UnetBlock( | |
ngf, ngf * 2, input_nc=None, | |
submodule=unet_block) | |
unet_block = UnetBlock( | |
fc_dim, ngf, input_nc=1, | |
submodule=unet_block, outermost=True) | |
self.bn0 = nn.BatchNorm2d(1) | |
self.unet_block = unet_block | |
def forward(self, x): | |
x = self.bn0(x) | |
x = self.unet_block(x) | |
return x | |
# Defines the submodule with skip connection. | |
# X -------------------identity---------------------- X | |
# |-- downsampling -- |submodule| -- upsampling --| | |
class UnetBlock(nn.Module): | |
def __init__(self, outer_nc, inner_input_nc, input_nc=None, | |
submodule=None, outermost=False, innermost=False, | |
use_dropout=False, inner_output_nc=None, noskip=False): | |
super(UnetBlock, self).__init__() | |
self.outermost = outermost | |
self.noskip = noskip | |
use_bias = False | |
if input_nc is None: | |
input_nc = outer_nc | |
if innermost: | |
inner_output_nc = inner_input_nc | |
elif inner_output_nc is None: | |
inner_output_nc = 2 * inner_input_nc | |
downrelu = nn.LeakyReLU(0.2, True) | |
downnorm = nn.BatchNorm2d(inner_input_nc) | |
uprelu = nn.ReLU(True) | |
upnorm = nn.BatchNorm2d(outer_nc) | |
upsample = nn.Upsample( | |
scale_factor=2, mode='bilinear', align_corners=True) | |
if outermost: | |
downconv = nn.Conv2d( | |
input_nc, inner_input_nc, kernel_size=4, | |
stride=2, padding=1, bias=use_bias) | |
upconv = nn.Conv2d( | |
inner_output_nc, outer_nc, kernel_size=3, padding=1) | |
down = [downconv] | |
up = [uprelu, upsample, upconv] | |
model = down + [submodule] + up | |
elif innermost: | |
downconv = nn.Conv2d( | |
input_nc, inner_input_nc, kernel_size=4, | |
stride=2, padding=1, bias=use_bias) | |
upconv = nn.Conv2d( | |
inner_output_nc, outer_nc, kernel_size=3, | |
padding=1, bias=use_bias) | |
down = [downrelu, downconv] | |
up = [uprelu, upsample, upconv, upnorm] | |
model = down + up | |
else: | |
downconv = nn.Conv2d( | |
input_nc, inner_input_nc, kernel_size=4, | |
stride=2, padding=1, bias=use_bias) | |
upconv = nn.Conv2d( | |
inner_output_nc, outer_nc, kernel_size=3, | |
padding=1, bias=use_bias) | |
down = [downrelu, downconv, downnorm] | |
up = [uprelu, upsample, upconv, upnorm] | |
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 or self.noskip: | |
return self.model(x) | |
else: | |
return torch.cat([x, self.model(x)], 1) |
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