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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
__all__ = [
"ResidualDenseBlock", "ResidualResidualDenseBlock", "Generator",
"DownSamplingNetwork"
]
class ResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the input image.
growths (int): The number of channels that increase in each layer of convolution.
"""
def __init__(self, channels: int, growths: int) -> None:
super(ResidualDenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1, 1), (1, 1))
self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1, 1), (1, 1))
self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1, 1), (1, 1))
self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1, 1), (1, 1))
self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1, 1), (1, 1))
self.leaky_relu = nn.LeakyReLU(0.2, True)
self.identity = nn.Identity()
def forward(self, x: Tensor) -> Tensor:
identity = x
out1 = self.leaky_relu(self.conv1(x))
out2 = self.leaky_relu(self.conv2(torch.cat([x, out1], 1)))
out3 = self.leaky_relu(self.conv3(torch.cat([x, out1, out2], 1)))
out4 = self.leaky_relu(self.conv4(torch.cat([x, out1, out2, out3], 1)))
out5 = self.identity(self.conv5(torch.cat([x, out1, out2, out3, out4], 1)))
out = out5 * 0.2 + identity
return out
class ResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the input image.
growths (int): The number of channels that increase in each layer of convolution.
"""
def __init__(self, channels: int, growths: int) -> None:
super(ResidualDenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1, 1), (1, 1))
self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1, 1), (1, 1))
self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1, 1), (1, 1))
self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1, 1), (1, 1))
self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1, 1), (1, 1))
self.leaky_relu = nn.LeakyReLU(0.2, True)
self.identity = nn.Identity()
def forward(self, x: Tensor) -> Tensor:
identity = x
out1 = self.leaky_relu(self.conv1(x))
out2 = self.leaky_relu(self.conv2(torch.cat([x, out1], 1)))
out3 = self.leaky_relu(self.conv3(torch.cat([x, out1, out2], 1)))
out4 = self.leaky_relu(self.conv4(torch.cat([x, out1, out2, out3], 1)))
out5 = self.identity(self.conv5(torch.cat([x, out1, out2, out3, out4], 1)))
out = out5 * 0.2 + identity
return out
class MiniResidualDenseBlock(nn.Module):
"""Achieves densely connected convolutional layers.
`Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993v5.pdf>` paper.
Args:
channels (int): The number of channels in the input image.
growths (int): The number of channels that increase in each layer of convolution.
"""
def __init__(self, channels: int, growths: int) -> None:
super(MiniResidualDenseBlock, self).__init__()
self.conv1 = nn.Conv2d(channels + growths * 0, growths, (3, 3), (1, 1), (1, 1))
self.conv2 = nn.Conv2d(channels + growths * 1, growths, (3, 3), (1, 1), (1, 1))
self.conv3 = nn.Conv2d(channels + growths * 2, growths, (3, 3), (1, 1), (1, 1))
self.conv4 = nn.Conv2d(channels + growths * 3, growths, (3, 3), (1, 1), (1, 1))
self.conv5 = nn.Conv2d(channels + growths * 4, channels, (3, 3), (1, 1), (1, 1))
self.leaky_relu = nn.LeakyReLU(0.2, True)
def forward(self, x: Tensor) -> Tensor:
identity = x
out1 = self.leaky_relu(self.conv1(x))
out2 = self.leaky_relu(self.conv2(torch.cat([x, out1], 1)))
out3 = self.leaky_relu(self.conv3(torch.cat([x, out1, out2], 1)))
out4 = self.leaky_relu(self.conv4(torch.cat([x, out1, out2, out3], 1)))
out5 = self.leaky_relu(self.conv5(torch.cat([x, out1, out2, out3, out4], 1)))
out = out5 * 0.2 + identity
return out
class ResidualResidualDenseBlock(nn.Module):
"""Multi-layer residual dense convolution block.
Args:
channels (int): The number of channels in the input image.
growths (int): The number of channels that increase in each layer of convolution.
"""
def __init__(self, channels: int, growths: int) -> None:
super(ResidualResidualDenseBlock, self).__init__()
self.rdb1 = ResidualDenseBlock(channels, growths)
self.rdb2 = ResidualDenseBlock(channels, growths)
self.rdb3 = ResidualDenseBlock(channels, growths)
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x
out = self.rdb1(x)
out = self.rdb2(out)
out = self.rdb3(out)
out = out * 0.2 + identity
return out
class MiniResidualResidualDenseBlock(nn.Module):
"""Multi-layer residual dense convolution block.
Args:
channels (int): The number of channels in the input image.
growths (int): The number of channels that increase in each layer of convolution.
"""
def __init__(self, channels: int, growths: int) -> None:
super(MiniResidualResidualDenseBlock, self).__init__()
self.M_rdb1 = MiniResidualDenseBlock(channels, growths)
self.M_rdb2 = MiniResidualDenseBlock(channels, growths)
self.M_rdb3 = MiniResidualDenseBlock(channels, growths)
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x
out = self.M_rdb1(x)
out = self.M_rdb2(out)
out = self.M_rdb3(out)
out = out * 0.2 + identity
return out
class Generator(nn.Module):
def __init__(self) -> None:
super(Generator, self).__init__()
#RLNet
self.RLNetconv_block1 = nn.Conv2d(1, 64, (3, 3), (1, 1), (1, 1))
RLNettrunk = []
for _ in range(4):
RLNettrunk += [ResidualResidualDenseBlock(64, 32)]
self.RLNettrunk = nn.Sequential(*RLNettrunk)
self.RLNetconv_block2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
self.RLNetconv_block3 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True)
)
self.RLNetconv_block4 = nn.Sequential(
nn.Conv2d(64, 1, (3, 3), (1, 1), (1, 1)),
nn.Tanh()
)
#############################################################################
# Generator
self.conv_block1 = nn.Conv2d(1, 64, (3, 3), (1, 1), (1, 1))
trunk = []
for _ in range(16):
trunk += [ResidualResidualDenseBlock(64, 32)]
self.trunk = nn.Sequential(*trunk)
# After the feature extraction network, reconnect a layer of convolutional blocks.
self.conv_block2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
# Upsampling convolutional layer.
self.upsampling = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True)
)
# Reconnect a layer of convolution block after upsampling.
self.conv_block3 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True)
)
self.conv_block4 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
#nn.Sigmoid()
)
self.conv_block0_branch0 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 128, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 128, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 64, (3, 3), (1, 1), (1, 1)),
nn.Tanh()
)
self.conv_block0_branch1 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 128, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 128, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(128, 64, (3, 3), (1, 1), (1, 1)),
nn.Tanh()
)
self.conv_block1_branch0 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 1, (3, 3), (1, 1), (1, 1)),
#nn.LeakyReLU(0.2, True),
#nn.Conv2d(32, 1, (3, 3), (1, 1), (1, 1)),
nn.Sigmoid()
)
self.conv_block1_branch1 = nn.Sequential(
nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1)),
nn.LeakyReLU(0.2, True),
nn.Conv2d(64, 1, (3, 3), (1, 1), (1, 1)),
nn.Sigmoid())
def _forward_impl(self, x: Tensor) -> Tensor:
#RLNet
out1 = self.RLNetconv_block1(x)
out = self.RLNettrunk(out1)
out2 = self.RLNetconv_block2(out)
out = out1 + out2
out = self.RLNetconv_block3(out)
out = self.RLNetconv_block4(out)
rlNet_out = out + x
#Generator
out1 = self.conv_block1(rlNet_out)
out = self.trunk(out1)
out2 = self.conv_block2(out)
out = out1 + out2
out = self.upsampling(F.interpolate(out, scale_factor=2, mode="bicubic"))
out = self.upsampling(F.interpolate(out, scale_factor=2, mode="bicubic"))
out = self.conv_block3(out)
#
out = self.conv_block4(out)
#demResidual = out[:, 1:2, :, :]
#grayResidual = out[:, 0:1, :, :]
# out = self.trunkRGB(out_4)
#
# out_dem = out[:, 3:4, :, :] * 0.2 + demResidual # DEM images extracted
# out_rgb = out[:, 0:3, :, :] * 0.2 + rgbResidual # RGB images extracted
#ra0
#out_rgb= rgbResidual + self.conv_block0_branch0(rgbResidual)
out_dem = out + self.conv_block0_branch1(out) #out+ tanh()
out_gray = out + self.conv_block0_branch0(out) #out+ tanh()
out_gray = self.conv_block1_branch0(out_gray) #sigmoid()
out_dem = self.conv_block1_branch1(out_dem) #sigmoid()
return out_gray, out_dem, rlNet_out
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _initialize_weights(self) -> None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
m.weight.data *= 0.1
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
m.weight.data *= 0.1
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