File size: 1,127 Bytes
e4eca40 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
from torch import nn
import torch
import torch.nn.functional as F
# Generator Code
ngf = 64
num_channels = 3
class Generator(nn.Module):
def __init__(self, latent_size):
super(Generator, self).__init__()
self.latent_size = latent_size
self.conv1 = nn.ConvTranspose2d(
self.latent_size, ngf*8, 4, 1, 0, bias=False)
self.bn1 = nn.BatchNorm2d(ngf*8)
self.conv2 = nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False)
self.bn2 = nn.BatchNorm2d(ngf*4)
self.conv3 = nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False)
self.bn3 = nn.BatchNorm2d(ngf*2)
self.conv4 = nn.ConvTranspose2d(ngf*2, ngf, 4, 2, 1, bias=False)
self.bn4 = nn.BatchNorm2d(ngf)
self.conv5 = nn.ConvTranspose2d(ngf, num_channels, 4, 2, 1, bias=False)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)), inplace=True)
x = F.relu(self.bn2(self.conv2(x)), inplace=True)
x = F.relu(self.bn3(self.conv3(x)), inplace=True)
x = F.relu(self.bn4(self.conv4(x)), inplace=True)
return torch.tanh(self.conv5(x)) |