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base_networks.py
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import torch
import math
import torch.nn as nn
import torch.nn.functional as F
class DenseBlock(nn.Module):
def __init__(self, input_size, output_size, bias=True, activation='relu', norm='batch'):
super(DenseBlock, self).__init__()
self.fc = nn.Linear(input_size, output_size, bias=bias)
self.norm = norm
if self.norm =='batch':
self.bn = nn.BatchNorm1d(output_size)
elif self.norm == 'instance':
self.bn = nn.InstanceNorm1d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.fc(x))
else:
out = self.fc(x)
if self.activation is not None:
return self.act(out)
else:
return out
class ConvBlock(nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(input_size, output_size, kernel_size, stride, padding, bias=bias)
self.norm = norm
if self.norm =='batch':
self.bn = nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.bn = nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation is not None:
return self.act(out)
else:
return out
class ConvBlock3D(nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock3D, self).__init__()
self.conv = nn.Conv3d(input_size, output_size, kernel_size, stride, padding)
self.norm = norm
if self.norm =='batch':
self.bn = nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.bn = nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation is not None:
return self.act(out)
else:
return out
class DeconvBlock(nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2, padding=1, bias=True, activation='prelu', norm=None):
super(DeconvBlock, self).__init__()
self.deconv = nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding, bias=bias)
self.norm = norm
if self.norm == 'batch':
self.bn = nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.bn = nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.deconv(x))
else:
out = self.deconv(x)
if self.activation is not None:
return self.act(out)
else:
return out
class ResnetBlock(nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'):
super(ResnetBlock, self).__init__()
self.conv1 = nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding, bias=bias)
self.conv2 = nn.Conv2d(num_filter, num_filter, kernel_size, stride, padding, bias=bias)
self.norm = norm
if self.norm == 'batch':
self.bn = nn.BatchNorm2d(num_filter)
elif norm == 'instance':
self.bn = nn.InstanceNorm2d(num_filter)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
residual = x
if self.norm is not None:
out = self.bn(self.conv1(x))
else:
out = self.conv1(x)
if self.activation is not None:
out = self.act(out)
if self.norm is not None:
out = self.bn(self.conv2(out))
else:
out = self.conv2(out)
out = torch.add(out, residual)
if self.activation is not None:
out = self.act(out)
return out
class ResnetBlock3D(nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'):
super(ResnetBlock3D, self).__init__()
self.conv1 = nn.Conv3d(num_filter, num_filter, kernel_size, stride, padding)
self.conv2 = nn.Conv3d(num_filter, num_filter, kernel_size, stride, padding)
self.norm = norm
if self.norm == 'batch':
self.bn = nn.BatchNorm2d(num_filter)
elif norm == 'instance':
self.bn = nn.InstanceNorm2d(num_filter)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
residual = x
if self.norm is not None:
out = self.bn(self.conv1(x))
else:
out = self.conv1(x)
if self.activation is not None:
out = self.act(out)
if self.norm is not None:
out = self.bn(self.conv2(out))
else:
out = self.conv2(out)
out = torch.add(out, residual)
if self.activation is not None:
out = self.act(out)
return out
class UpBlock(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None):
super(UpBlock, self).__init__()
self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
h0 = self.up_conv1(x)
l0 = self.up_conv2(h0)
h1 = self.up_conv3(l0 - x)
return h1 + h0
class UpBlockPix(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, scale=4, bias=True, activation='prelu', norm=None):
super(UpBlockPix, self).__init__()
self.up_conv1 = Upsampler(scale,num_filter)
self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv3 = Upsampler(scale,num_filter)
def forward(self, x):
h0 = self.up_conv1(x)
l0 = self.up_conv2(h0)
h1 = self.up_conv3(l0 - x)
return h1 + h0
class D_UpBlock(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, bias=True, activation='prelu', norm=None):
super(D_UpBlock, self).__init__()
self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, activation, norm=None)
self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
x = self.conv(x)
h0 = self.up_conv1(x)
l0 = self.up_conv2(h0)
h1 = self.up_conv3(l0 - x)
return h1 + h0
class D_UpBlockPix(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, scale=4, bias=True, activation='prelu', norm=None):
super(D_UpBlockPix, self).__init__()
self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, activation, norm=None)
self.up_conv1 = Upsampler(scale,num_filter)
self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.up_conv3 = Upsampler(scale,num_filter)
def forward(self, x):
x = self.conv(x)
h0 = self.up_conv1(x)
l0 = self.up_conv2(h0)
h1 = self.up_conv3(l0 - x)
return h1 + h0
class DownBlock(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None):
super(DownBlock, self).__init__()
self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
l0 = self.down_conv1(x)
h0 = self.down_conv2(l0)
l1 = self.down_conv3(h0 - x)
return l1 + l0
class DownBlockPix(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, scale=4,bias=True, activation='prelu', norm=None):
super(DownBlockPix, self).__init__()
self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv2 = Upsampler(scale,num_filter)
self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
l0 = self.down_conv1(x)
h0 = self.down_conv2(l0)
l1 = self.down_conv3(h0 - x)
return l1 + l0
class D_DownBlock(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, bias=True, activation='prelu', norm=None):
super(D_DownBlock, self).__init__()
self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, bias, activation, norm=None)
self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
x = self.conv(x)
l0 = self.down_conv1(x)
h0 = self.down_conv2(l0)
l1 = self.down_conv3(h0 - x)
return l1 + l0
class D_DownBlockPix(nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, num_stages=1, scale=4, bias=True, activation='prelu', norm=None):
super(D_DownBlockPix, self).__init__()
self.conv = ConvBlock(num_filter*num_stages, num_filter, 1, 1, 0, bias, activation, norm=None)
self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
self.down_conv2 = Upsampler(scale,num_filter)
self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, bias, activation, norm=None)
def forward(self, x):
x = self.conv(x)
l0 = self.down_conv1(x)
h0 = self.down_conv2(l0)
l1 = self.down_conv3(h0 - x)
return l1 + l0
class PSBlock(nn.Module):
def __init__(self, input_size, output_size, scale_factor, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'):
super(PSBlock, self).__init__()
self.conv = nn.Conv2d(input_size, output_size * scale_factor**2, kernel_size, stride, padding, bias=bias)
self.ps = nn.PixelShuffle(scale_factor)
self.norm = norm
if self.norm == 'batch':
self.bn = nn.BatchNorm2d(output_size)
elif norm == 'instance':
self.bn = nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.ps(self.conv(x)))
else:
out = self.ps(self.conv(x))
if self.activation is not None:
out = self.act(out)
return out
class Upsampler(nn.Module):
def __init__(self, scale, n_feat, bn=False, act='prelu', bias=True):
super(Upsampler, self).__init__()
modules = []
for _ in range(int(math.log(scale, 2))):
modules.append(ConvBlock(n_feat, 4 * n_feat, 3, 1, 1, bias, activation=None, norm=None))
modules.append(nn.PixelShuffle(2))
if bn: modules.append(nn.BatchNorm2d(n_feat))
#modules.append(nn.PReLU())
self.up = nn.Sequential(*modules)
self.activation = act
if self.activation == 'relu':
self.act = nn.ReLU(True)
elif self.activation == 'prelu':
self.act = nn.PReLU()
elif self.activation == 'lrelu':
self.act = nn.LeakyReLU(0.1, True)
elif self.activation == 'tanh':
self.act = nn.Tanh()
elif self.activation == 'sigmoid':
self.act = nn.Sigmoid()
def forward(self, x):
out = self.up(x)
if self.activation is not None:
out = self.act(out)
return out
class Upsample2xBlock(nn.Module):
def __init__(self, input_size, output_size, bias=True, upsample='deconv', activation='relu', norm='batch'):
super(Upsample2xBlock, self).__init__()
scale_factor = 2
# 1. Deconvolution (Transposed convolution)
if upsample == 'deconv':
self.upsample = DeconvBlock(input_size, output_size,
kernel_size=4, stride=2, padding=1,
bias=bias, activation=activation, norm=norm)
# 2. Sub-pixel convolution (Pixel shuffler)
elif upsample == 'ps':
self.upsample = PSBlock(input_size, output_size, scale_factor=scale_factor,
bias=bias, activation=activation, norm=norm)
# 3. Resize and Convolution
elif upsample == 'rnc':
self.upsample = nn.Sequential(
nn.Upsample(scale_factor=scale_factor, mode='nearest'),
ConvBlock(input_size, output_size,
kernel_size=3, stride=1, padding=1,
bias=bias, activation=activation, norm=norm)
)
def forward(self, x):
out = self.upsample(x)
return out
class PyramidModule(nn.Module):
def __init__(self, num_inchannels,activation='prelu'):
super(PyramidModule, self).__init__()
self.l1_1 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l1_2 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l1_3 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l1_4 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l1_5 = ResnetBlock(num_inchannels, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l2_1 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l2_2 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l2_3 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l2_4 = ResnetBlock(num_inchannels*2, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l3_1 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l3_2 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.l3_3 = ResnetBlock(num_inchannels*4, kernel_size=3, stride=1, padding=1, bias=True, activation=activation, norm=None)
self.down1 = ConvBlock(num_inchannels, num_inchannels*2, 4, 2, 1, bias=True, activation=activation, norm=None)
self.down2 = ConvBlock(num_inchannels*2, num_inchannels*4, 4, 2, 1, bias=True, activation=activation, norm=None)
self.up1 = DeconvBlock(num_inchannels*2, num_inchannels, 4, 2, 1, bias=True, activation=activation, norm=None)
self.up2 = DeconvBlock(num_inchannels*4, num_inchannels*2, 4, 2, 1, bias=True, activation=activation, norm=None)
self.final = ConvBlock(num_inchannels, num_inchannels, 3, 1, 1, bias=True, activation=activation, norm=None)
def forward(self, x):
out1_1 = self.l1_1(x)
out2_1 = self.l2_1(self.down1(out1_1))
out3_1 = self.l3_1(self.down2(out2_1))
out1_2 = self.l1_2(out1_1 + self.up1(out2_1))
out2_2 = self.l2_2(out2_1 + self.down1(out1_2) + self.up2(out3_1))
out3_2 = self.l3_2(out3_1 + self.down2(out2_2))
out1_3 = self.l1_3(out1_2 + self.up1(out2_2))
out2_3 = self.l2_3(out2_2 + self.down1(out1_3) + self.up2(out3_2))
out3_3 = self.l3_3(out3_2 + self.down2(out2_3))
out1_4 = self.l1_4(out1_3 + self.up1(out2_3))
out2_4 = self.l2_4(out2_3 + self.down1(out1_4) + self.up2(out3_3))
out1_5 = self.l1_5(out1_4 + self.up1(out2_4))
final = self.final(out1_5)
return final