models/discriminator.py

import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np

# class Discriminator(nn.Module):
#     def __init__(self, ngpu, nc = 3, ndf = 64):
#         super(Discriminator, self).__init__()
#         self.ngpu = ngpu
#         self.main = nn.Sequential(
#             # input is (nc) x 64 x 64
#             nn.Conv2d(nc, ndf, 4, 4, 1, bias=False),
#             nn.LeakyReLU(0.2, inplace=True),
#             # state size. (ndf) x 32 x 32
#             nn.Conv2d(ndf, ndf * 2, 4, 4, 1, bias=False),
#             nn.BatchNorm2d(ndf * 2),
#             nn.LeakyReLU(0.2, inplace=True),
#             # state size. (ndf*2) x 16 x 16
#             nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
#             nn.BatchNorm2d(ndf * 4),
#             nn.LeakyReLU(0.2, inplace=True),
#             # state size. (ndf*4) x 8 x 8
#             nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
#             nn.BatchNorm2d(ndf * 8),
#             nn.LeakyReLU(0.2, inplace=True),
#             # state size. (ndf*8) x 4 x 4
#             nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
#             nn.Sigmoid()
#         )

#     def forward(self, input):
#         return self.main(input)



class Discriminator(torch.nn.Module):
    def __init__(self, channels):
        super().__init__()
        # Filters [256, 512, 1024]
        # Input_dim = channels (Cx64x64)
        # Output_dim = 1
        self.main_module = nn.Sequential(
            # Omitting batch normalization in critic because our new penalized training objective (WGAN with gradient penalty) is no longer valid
            # in this setting, since we penalize the norm of the critic's gradient with respect to each input independently and not the enitre batch.
            # There is not good & fast implementation of layer normalization --> using per instance normalization nn.InstanceNorm2d()
            # Image (Cx32x32)
            nn.Conv2d(in_channels=channels, out_channels=256, kernel_size=4, stride=2, padding=1),
            nn.InstanceNorm2d(256, affine=True),
            nn.LeakyReLU(0.2, inplace=True),

            # State (256x16x16)
            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1),
            nn.InstanceNorm2d(512, affine=True),
            nn.LeakyReLU(0.2, inplace=True),

            # State (512x8x8)
            nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=4, stride=2, padding=1),
            nn.InstanceNorm2d(1024, affine=True),
            nn.LeakyReLU(0.2, inplace=True))
            # output of main module --> State (1024x4x4)

        self.output = nn.Sequential(
            # The output of D is no longer a probability, we do not apply sigmoid at the output of D.
            nn.Conv2d(in_channels=1024, out_channels=1, kernel_size=4, stride=1, padding=0))


    def forward(self, x):
        x = self.main_module(x)
        return self.output(x)

    def feature_extraction(self, x):
        # Use discriminator for feature extraction then flatten to vector of 16384
        x = self.main_module(x)
        return x.view(-1, 1024*4*4)