import torch


class ConvNet(torch.nn.Module):
    class Block(torch.nn.Module):
        def __init__(self, n_input, n_output, stride=1):
            super().__init__()
            self.net = torch.nn.Sequential(
                torch.nn.Conv2d(n_input, n_output, kernel_size=3, padding=1, stride=stride),
                torch.nn.BatchNorm2d(n_output),
                torch.nn.ReLU(),
                torch.nn.Conv2d(n_output, n_output, kernel_size=3, padding=1),
                torch.nn.BatchNorm2d(n_output),
                torch.nn.ReLU()
            )
            self.downsample = None
            if stride != 1 or n_input != n_output:
                self.downsample = torch.nn.Sequential(torch.nn.Conv2d(n_input, n_output, 1, stride=stride),
                                                      torch.nn.BatchNorm2d(n_output))

        def forward(self, x):
            identity = x
            if self.downsample is not None:
                identity = self.downsample(x)
            return self.net(x)+identity

    def __init__(self, layers=[32, 64, 128], n_input_channels=3):
        super().__init__()
        L = [torch.nn.Conv2d(n_input_channels, 32, kernel_size=7, padding=3, stride=2),
             torch.nn.BatchNorm2d(32),
             torch.nn.ReLU()#,
#             torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
             ]
        c = 32
        for l in layers:
            L.append(self.Block(c, l, stride=2))
            c = l
        self.network = torch.nn.Sequential(*L)
        self.classifier = torch.nn.Linear(c, 1)

    def forward(self, x):
        # Compute the features
        z = self.network(x)
        # Global average pooling
        z = z.mean(dim=[2, 3])
        # Classify
        return self.classifier(z)[:, 0]

import torch


class ConvNet(torch.nn.Module):
    class Block(torch.nn.Module):
        def __init__(self, n_input, n_output, stride=1):
            super().__init__()
            self.net = torch.nn.Sequential(
                torch.nn.Conv2d(n_input, n_output, kernel_size=3, padding=1, stride=stride),
                torch.nn.BatchNorm2d(n_output),
                torch.nn.ReLU(),
                torch.nn.Conv2d(n_output, n_output, kernel_size=3, padding=1),
                torch.nn.BatchNorm2d(n_output),
                torch.nn.ReLU()
            )
            self.downsample = None
            if stride != 1 or n_input != n_output:
                self.downsample = torch.nn.Sequential(torch.nn.Conv2d(n_input, n_output, 1, stride=stride),
                                                      torch.nn.BatchNorm2d(n_output))

        def forward(self, x):
            identity = x
            if self.downsample is not None:
                identity = self.downsample(x)
            return self.net(x)+identity

    def __init__(self, layers=[32, 64, 128], n_input_channels=3):
        super().__init__()
        L = [torch.nn.Conv2d(n_input_channels, 32, kernel_size=7, padding=3, stride=2),
             torch.nn.BatchNorm2d(32),
             torch.nn.ReLU()#,
#             torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
             ]
        c = 32
        for l in layers:
            L.append(self.Block(c, l, stride=2))
            c = l
        self.network = torch.nn.Sequential(*L)
        self.classifier = torch.nn.Linear(c, 1)

    def forward(self, x):
        # Compute the features
        z = self.network(x)
        # Global average pooling
        z = z.mean(dim=[2, 3])
        # Classify
        return self.classifier(z)[:, 0]

#, #             torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # Compute the features # Global average pooling # Classify