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model.py
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model.py
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from typing import Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Flatten(nn.Module):
def forward(self, inputs):
return inputs.view(inputs.size(0), -1)
class UnFlatten(nn.Module):
def forward(self, inputs, size=512):
# print(input.view(input.size(0), size, 1, 1).shape, 'fffff')
# return torch.reshape(input, (input.size(0), 32, 4, 4))
return inputs.view(inputs.size(0), 128, 4, 4)
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 128, kernel_size=5, stride=2, padding=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(128, 64, kernel_size=5, stride=2, padding=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 32, kernel_size=5, stride=2, padding=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=5, stride=2, padding=2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 4, kernel_size=5, stride=2, padding=2),
nn.LeakyReLU(0.2, inplace=True),
# nn.Flatten(),
# nn.Linear(128, 1),
nn.Sigmoid(),
)
def forward(self, img):
validity = self.model(img)
return validity
class AutoEncoder(nn.Module):
def __init__(self, image_channels=3, h_dim=2048, z_dim=128):
super(AutoEncoder, self).__init__()
self.device = 'cuda'
self.encoder = nn.Sequential(
Downscale(image_channels, 64),
Downscale(64, 128),
# Downscale(128, 128),
# ResBlock(128),
Downscale(128, 256),
# ResBlock(256),
Downscale(256, 256),
Downscale(256, 512),
# ResBlock(512),
Downscale(512, 512),
Flatten(),
)
# ([32, 2304])
self.inter_layer = nn.Sequential(
nn.Linear(h_dim, z_dim),
nn.Linear(z_dim, z_dim),
nn.Linear(z_dim, h_dim),
)
self.decoder = nn.Sequential(
UnFlatten(),
# Upscale(128, 128, kernel_size=4),
Upscale(128, 256, kernel_size=4),
# ResBlock(256),
# ResBlock(128),
# ResBlock(128),
Upscale(256, 256, kernel_size=4),
Upscale(256, 128, kernel_size=4),
# ResBlock(128),
Upscale(128, 64, kernel_size=4),
ResBlock(64),
Upscale(64, 32, kernel_size=4),
Upscale(32, 32, kernel_size=4),
nn.Conv2d(32, image_channels, kernel_size=1, stride=2),
nn.Sigmoid(),
)
self.decoder_b = nn.Sequential(
UnFlatten(),
# Upscale(128, 128, kernel_size=4),
Upscale(128, 256, kernel_size=4),
# ResBlock(256),
# ResBlock(128),
# ResBlock(128),
Upscale(256, 256, kernel_size=4),
Upscale(256, 128, kernel_size=4),
# ResBlock(128),
Upscale(128, 64, kernel_size=4),
ResBlock(64),
Upscale(64, 32, kernel_size=4),
Upscale(32, 32, kernel_size=4),
nn.Conv2d(32, image_channels, kernel_size=1, stride=2),
nn.Sigmoid(),
)
def forward(self, x, version='a'):
z = self.encoder(x)
z = self.inter_layer(z)
if version == 'a':
z = self.decoder(z)
else:
z = self.decoder_b(z)
return z
class ResBlock(nn.Module):
def __init__(self, n_ch) -> None:
super().__init__()
self.resblock_model = nn.Sequential(
nn.Conv2d(n_ch, n_ch, kernel_size=3, bias=False, padding=1),
nn.BatchNorm2d(n_ch),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Conv2d(n_ch, n_ch, kernel_size=3, bias=False, padding=1),
nn.BatchNorm2d(n_ch)
)
def forward(self, inputs):
return self.resblock_model(inputs) + inputs
class Downscale(nn.Module):
def __init__(self, in_ch, out_ch, kernel_size=3, padding=1):
super().__init__()
self.in_ch = in_ch
self.out_ch = out_ch
self.kernel_size = kernel_size
self.conv = nn.Conv2d(self.in_ch, self.out_ch, kernel_size=self.kernel_size, stride=2, padding=padding)
self.batch_norm = nn.BatchNorm2d(self.out_ch)
self.relu = nn.LeakyReLU(0.1)
self.drop = nn.Dropout2d()
def forward(self, x):
x = self.conv(x)
x = self.batch_norm(x)
x = self.relu(x)
x = self.drop(x)
return x
class Upscale(nn.Module):
def __init__(self, in_ch, out_ch, kernel_size=5, padding=2):
super().__init__()
self.conv = nn.ConvTranspose2d(in_ch, out_ch, kernel_size, stride=2, padding=1)
self.batch_norm = nn.BatchNorm2d(out_ch)
self.relu = nn.LeakyReLU(0.1)
self.drop = nn.Dropout2d()
def forward(self, x):
x = self.conv(x)
x = self.batch_norm(x)
x = self.relu(x)
x = self.drop(x)
return x