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models.py
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import torch
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import TensorDataset, DataLoader
class GeneratorCNN(nn.Module):
def __init__(self, input_channel, output_channel, conv_dims, deconv_dims, num_gpu):
super(GeneratorCNN, self).__init__()
self.num_gpu = num_gpu
self.layers = []
prev_dim = conv_dims[0]
self.layers.append(nn.Conv2d(input_channel, prev_dim, 4, 2, 1, bias=False))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
for out_dim in conv_dims[1:]:
self.layers.append(nn.Conv2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
prev_dim = out_dim
for out_dim in deconv_dims:
self.layers.append(nn.ConvTranspose2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.ReLU(True))
prev_dim = out_dim
self.layers.append(nn.ConvTranspose2d(prev_dim, output_channel, 4, 2, 1, bias=False))
self.layers.append(nn.Sigmoid())#nn.Tanh())
self.layer_module = nn.ModuleList(self.layers)
def main(self, x, y=None):
if not y==None:
out = torch.cat([x, y], dim=1)
else:
out = x
for layer in self.layer_module:
out = layer(out)
return out
def forward(self, x, y=None):
return self.main(x, y)
class GeneratorCNN_g(nn.Module):
def __init__(self, input_channel, output_channel, conv_dims, deconv_dims, num_gpu):
super(GeneratorCNN_g, self).__init__()
self.num_gpu = num_gpu
self.layers = []
prev_dim = conv_dims[0]
self.layers.append(nn.Conv2d(input_channel, prev_dim, 4, 2, 1, bias=False))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
for out_dim in conv_dims[1:]:
self.layers.append(nn.Conv2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
prev_dim = out_dim
for out_dim in deconv_dims:
self.layers.append(nn.ConvTranspose2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.ReLU(True))
prev_dim = out_dim
self.layers.append(nn.ConvTranspose2d(prev_dim, output_channel, 4, 2, 1, bias=False))
self.layers.append(nn.Sigmoid())#nn.Tanh())
self.layer_module = nn.ModuleList(self.layers)
def main(self, x, y):
out = torch.cat([x, y], dim=1)
for layer in self.layer_module:
out = layer(out)
return out
def forward(self, x, y):
return self.main(x, y)
class DiscriminatorCNN(nn.Module):
def __init__(self, input_channel, output_channel, hidden_dims, num_gpu):
super(DiscriminatorCNN, self).__init__()
self.num_gpu = num_gpu
self.layers = []
prev_dim = hidden_dims[0]
self.layers.append(nn.Conv2d(input_channel, prev_dim, 4, 2, 1, bias=False))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
for out_dim in hidden_dims[1:]:
self.layers.append(nn.Conv2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
prev_dim = out_dim
self.layers.append(nn.Conv2d(prev_dim, output_channel, 4, 1, 0, bias=False))
self.layers.append(nn.Sigmoid())
self.layer_module = nn.ModuleList(self.layers)
def main(self, x, y=None):
if not y==None:
out = torch.cat([x, y], dim=1)
else:
out = x
for layer in self.layer_module:
out = layer(out)
return out.view(out.size(0), -1)
def forward(self, x, y=None):
return self.main(x,y)
class DiscriminatorCNN_f(nn.Module):
def __init__(self, input_channel, output_channel, hidden_dims, num_gpu):
super(DiscriminatorCNN_f, self).__init__()
self.num_gpu = num_gpu
self.layers = []
prev_dim = hidden_dims[0]
self.layers.append(nn.Conv2d(input_channel, prev_dim, 4, 2, 1, bias=False))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
for out_dim in hidden_dims[1:]:
self.layers.append(nn.Conv2d(prev_dim, out_dim, 4, 2, 1, bias=False))
self.layers.append(nn.BatchNorm2d(out_dim))
self.layers.append(nn.LeakyReLU(0.2, inplace=True))
prev_dim = out_dim
self.layers.append(nn.Conv2d(prev_dim, output_channel, 4, 1, 0, bias=False))
self.layers.append(nn.Sigmoid())
self.layer_module = nn.ModuleList(self.layers)
def main(self, x, y):
out = torch.cat([x, y], dim=1)
for layer in self.layer_module:
out = layer(out)
return out.view(out.size(0), -1)
def forward(self, x, y):
return self.main(x,y)
class GeneratorFC(nn.Module):
def __init__(self, input_size, output_size, hidden_dims):
super(GeneratorFC, self).__init__()
self.layers = []
prev_dim = input_size
for hidden_dim in hidden_dims:
self.layers.append(nn.Linear(prev_dim, hidden_dim))
self.layers.append(nn.ReLU(True))
prev_dim = hidden_dim
self.layers.append(nn.Linear(prev_dim, output_size))
self.layer_module = nn.ModuleList(self.layers)
def forward(self, x):
out = x
for layer in self.layer_module:
out = layer(out)
return out
class DiscriminatorFC(nn.Module):
def __init__(self, input_size, output_size, hidden_dims):
super(DiscriminatorFC, self).__init__()
self.layers = []
prev_dim = input_size
for idx, hidden_dim in enumerate(hidden_dims):
self.layers.append(nn.Linear(prev_dim, hidden_dim))
self.layers.append(nn.ReLU(True))
prev_dim = hidden_dim
self.layers.append(nn.Linear(prev_dim, output_size))
self.layers.append(nn.Sigmoid())
self.layer_module = nn.ModuleList(self.layers)
def forward(self, x):
out = x
for layer in self.layer_module:
out = layer(out)
return out.view(-1, 1)