gan_adversarial/GradPredModel.py at master · Expecto-Information/gan_adversarial · GitHub

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import torch
import torch.nn as nn

class GradPredModel(nn.Module):
    def __init__(self):
        super(GradPredModel, self).__init__()

        self.CNN = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Flatten(),
            nn.Linear(in_features=7*7*64, out_features=3*224*224),
            nn.Tanh(),
        )

    def forward(self, x):
        probas = self.CNN(x)

        # if self.training:
        #     return probabilities
        # else:

        reshaped_probas = probas.view(x.size())


        # reshaped_predictions = torch.round(reshaped_probabilities)
        return reshaped_probas.sign()


class GradDeconvModel(nn.Module):
    def __init__(self):
        super(GradDeconvModel, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),

        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(in_channels = 32, out_channels = 3, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.Sigmoid(),
        )
    def forward(self, x):
        x = self.encoder(x)
        probas = self.decoder(x)

        if self.training:
            return probas
        else:
            return torch.where(probas >= 0.5, torch.tensor(1.0), torch.tensor(-1.0))