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wgan.py
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from torch import nn
from torch.autograd import grad
import torch
import pdb
class MyConvo2d(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init = True, stride = 1, bias = True):
super(MyConvo2d, self).__init__()
self.he_init = he_init
self.padding = int((kernel_size - 1)/2)
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1, padding=self.padding, bias = bias)
def forward(self, input):
output = self.conv(input)
return output
class ConvMeanPool(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init = True):
super(ConvMeanPool, self).__init__()
self.he_init = he_init
self.conv = MyConvo2d(input_dim, output_dim, kernel_size, he_init = self.he_init)
def forward(self, input):
output = self.conv(input)
output = (output[:,:,::2,::2] + output[:,:,1::2,::2] + output[:,:,::2,1::2] + output[:,:,1::2,1::2]) / 4
return output
class MeanPoolConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init = True):
super(MeanPoolConv, self).__init__()
self.he_init = he_init
self.conv = MyConvo2d(input_dim, output_dim, kernel_size, he_init = self.he_init)
def forward(self, input):
output = input
output = (output[:,:,::2,::2] + output[:,:,1::2,::2] + output[:,:,::2,1::2] + output[:,:,1::2,1::2]) / 4
output = self.conv(output)
return output
class DepthToSpace(nn.Module):
def __init__(self, block_size):
super(DepthToSpace, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size*block_size
def forward(self, input):
output = input.permute(0, 2, 3, 1)
(batch_size, input_height, input_width, input_depth) = output.size()
output_depth = int(input_depth / self.block_size_sq)
output_width = int(input_width * self.block_size)
output_height = int(input_height * self.block_size)
t_1 = output.reshape(batch_size, input_height, input_width, self.block_size_sq, output_depth)
spl = t_1.split(self.block_size, 3)
stacks = [t_t.reshape(batch_size,input_height,output_width,output_depth) for t_t in spl]
output = torch.stack(stacks,0).transpose(0,1).permute(0,2,1,3,4).reshape(batch_size,output_height,output_width,output_depth)
output = output.permute(0, 3, 1, 2)
return output
class UpSampleConv(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, he_init = True, bias=True):
super(UpSampleConv, self).__init__()
self.he_init = he_init
self.conv = MyConvo2d(input_dim, output_dim, kernel_size, he_init = self.he_init, bias=bias)
self.depth_to_space = DepthToSpace(2)
def forward(self, input):
output = input
output = torch.cat((output, output, output, output), 1)
output = self.depth_to_space(output)
output = self.conv(output)
return output
class ResidualBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, resample=None, hw=64):
super(ResidualBlock, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.kernel_size = kernel_size
self.resample = resample
self.bn1 = None
self.bn2 = None
self.relu1 = nn.ReLU()
self.relu2 = nn.ReLU()
if resample == 'down':
self.bn1 = nn.LayerNorm([input_dim, hw, hw])
self.bn2 = nn.LayerNorm([input_dim, hw, hw])
elif resample == 'up':
self.bn1 = nn.BatchNorm2d(input_dim)
self.bn2 = nn.BatchNorm2d(output_dim)
elif resample == None:
#TODO: ????
self.bn1 = nn.BatchNorm2d(output_dim)
self.bn2 = nn.LayerNorm([input_dim, hw, hw])
else:
raise Exception('invalid resample value')
if resample == 'down':
self.conv_shortcut = MeanPoolConv(input_dim, output_dim, kernel_size = 1, he_init = False)
self.conv_1 = MyConvo2d(input_dim, input_dim, kernel_size = kernel_size, bias = False)
self.conv_2 = ConvMeanPool(input_dim, output_dim, kernel_size = kernel_size)
elif resample == 'up':
self.conv_shortcut = UpSampleConv(input_dim, output_dim, kernel_size = 1, he_init = False)
self.conv_1 = UpSampleConv(input_dim, output_dim, kernel_size = kernel_size, bias = False)
self.conv_2 = MyConvo2d(output_dim, output_dim, kernel_size = kernel_size)
elif resample == None:
self.conv_shortcut = MyConvo2d(input_dim, output_dim, kernel_size = 1, he_init = False)
self.conv_1 = MyConvo2d(input_dim, input_dim, kernel_size = kernel_size, bias = False)
self.conv_2 = MyConvo2d(input_dim, output_dim, kernel_size = kernel_size)
else:
raise Exception('invalid resample value')
def forward(self, input):
if self.input_dim == self.output_dim and self.resample == None:
shortcut = input
else:
shortcut = self.conv_shortcut(input)
output = input
output = self.bn1(output)
output = self.relu1(output)
output = self.conv_1(output)
output = self.bn2(output)
output = self.relu2(output)
output = self.conv_2(output)
return shortcut + output
class GoodGenerator(nn.Module):
def __init__(self, dim=64, output_dim=3*64*64):
super(GoodGenerator, self).__init__()
self.dim = dim
self.ssize = self.dim // 16
self.ln1 = nn.Linear(128, self.ssize*self.ssize*8*self.dim)
self.rb1 = ResidualBlock(8*self.dim, 8*self.dim, 3, resample = 'up')
self.rb2 = ResidualBlock(8*self.dim, 4*self.dim, 3, resample = 'up')
self.rb3 = ResidualBlock(4*self.dim, 2*self.dim, 3, resample = 'up')
self.rb4 = ResidualBlock(2*self.dim, 1*self.dim, 3, resample = 'up')
self.bn = nn.BatchNorm2d(self.dim)
self.conv1 = MyConvo2d(1*self.dim, 3, 3)
self.relu = nn.ReLU()
self.tanh = nn.Tanh()
def forward(self, input):
output = self.ln1(input.contiguous())
output = output.view(-1, 8*self.dim, self.ssize, self.ssize)
output = self.rb1(output)
output = self.rb2(output)
output = self.rb3(output)
output = self.rb4(output)
output = self.bn(output)
output = self.relu(output)
output = self.conv1(output)
output = self.tanh(output)
output = output.view(-1, 3 * self.dim * self.dim)
return output
class GoodDiscriminator(nn.Module):
def __init__(self, dim=64):
super(GoodDiscriminator, self).__init__()
self.dim = dim
self.ssize = self.dim // 16
self.conv1 = MyConvo2d(3, self.dim, 3, he_init = False)
self.rb1 = ResidualBlock(self.dim, 2*self.dim, 3, resample = 'down', hw=self.dim)
self.rb2 = ResidualBlock(2*self.dim, 4*self.dim, 3, resample = 'down', hw=int(self.dim/2))
self.rb3 = ResidualBlock(4*self.dim, 8*self.dim, 3, resample = 'down', hw=int(self.dim/4))
self.rb4 = ResidualBlock(8*self.dim, 8*self.dim, 3, resample = 'down', hw=int(self.dim/8))
self.ln1 = nn.Linear(self.ssize*self.ssize*8*self.dim, 1)
def forward(self, input):
output = input.contiguous()
output = output.view(-1, 3, self.dim, self.dim)
output = self.conv1(output)
output = self.rb1(output)
output = self.rb2(output)
output = self.rb3(output)
output = self.rb4(output)
output = output.view(-1, self.ssize*self.ssize*8*self.dim)
output = self.ln1(output)
output = output.view(-1)
return output