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models.py
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models.py
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import torch
from torch import nn
class ConvLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(ConvLayer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu(self.conv(x))
class DenseLayer(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size):
super(DenseLayer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return torch.cat([x, self.relu(self.conv(x))], 1)
class DenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers):
super(DenseBlock, self).__init__()
self.block = [ConvLayer(in_channels, growth_rate, kernel_size=3)]
for i in range(num_layers - 1):
self.block.append(DenseLayer(growth_rate * (i + 1), growth_rate, kernel_size=3))
self.block = nn.Sequential(*self.block)
def forward(self, x):
return torch.cat([x, self.block(x)], 1)
class SRDenseNet(nn.Module):
def __init__(self, num_channels=1, growth_rate=16, num_blocks=8, num_layers=8):
super(SRDenseNet, self).__init__()
# low level features
self.conv = ConvLayer(num_channels, growth_rate * num_layers, 3)
# high level features
self.dense_blocks = []
for i in range(num_blocks):
self.dense_blocks.append(DenseBlock(growth_rate * num_layers * (i + 1), growth_rate, num_layers))
self.dense_blocks = nn.Sequential(*self.dense_blocks)
# bottleneck layer
self.bottleneck = nn.Sequential(
nn.Conv2d(growth_rate * num_layers + growth_rate * num_layers * num_blocks, 256, kernel_size=1),
nn.ReLU(inplace=True)
)
# deconvolution layers
self.deconv = nn.Sequential(
nn.ConvTranspose2d(256, 256, kernel_size=3, stride=2, padding=3 // 2, output_padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(256, 256, kernel_size=3, stride=2, padding=3 // 2, output_padding=1),
nn.ReLU(inplace=True)
)
# reconstruction layer
self.reconstruction = nn.Conv2d(256, num_channels, kernel_size=3, padding=3 // 2)
self._initialize_weights()
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')
if m.bias is not None:
nn.init.zeros_(m.bias.data)
def forward(self, x):
x = self.conv(x)
x = self.dense_blocks(x)
x = self.bottleneck(x)
x = self.deconv(x)
x = self.reconstruction(x)
return x