modelUtils2.py

import torch
import torch.nn as nn
from collections import OrderedDict
import math 




class Generator256(nn.Module):
    def __init__(self,channels_noise, channels_img, features_g):
        super(Generator256, self).__init__()

        

        self.net = nn.Sequential(

            # N x channels_noise x 1 x 1
            nn.ConvTranspose2d(
                channels_noise, features_g * 16, kernel_size=4, stride=1, padding=0
            ),
            nn.BatchNorm2d(features_g * 16),
            nn.ReLU(),
            # N x features_g*16 x 4 x 4
            nn.ConvTranspose2d(
                features_g * 16, features_g * 8, kernel_size=4, stride=2, padding=1
            ),
            nn.BatchNorm2d(features_g * 8),
            nn.ReLU(),
            nn.ConvTranspose2d(
                features_g * 8, features_g * 4, kernel_size=4, stride=2, padding=1
            ),
            nn.BatchNorm2d(features_g * 4),
            nn.ReLU(),
            nn.ConvTranspose2d(
                features_g * 4, features_g * 2, kernel_size=4, stride=2, padding=1
            ),
            nn.BatchNorm2d(features_g * 2),
            nn.ReLU(),
            nn.ConvTranspose2d(
                features_g * 2, channels_img, kernel_size=4, stride=2, padding=1
            ),
            # N x channels_img x 64 x 64
            nn.Tanh(),
        )

    def forward(self, x):
        return self.net(x)