# AlexNet-inspired discriminator adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py

class Discriminator(torch.nn.Module):

    def __init__(self):

        super(Discriminator, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(64, 192, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2),
        )
        
        #classify image class out of 10 possibles CIFAR10 classes
        self.clf = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 2 * 2, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, nl),    #nl = number of classes
            nn.Softmax()
        )
        
        #real or fake
        self.discr = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 2 * 2, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 1),
        )

    def forward(self, input):
        
        features = self.features(input)
        valid = self.discr(features.contiguous().view(features.shape[0], -1))
        clf = self.clf(features.contiguous().view(features.shape[0], -1))
        return valid, clf
