import torch.nn as nn

class DogCatClassifier(nn.Module):
    def __init__ (self):
        super().__init__()

        # 3 color (RGB) image, so tensor is of shape (B x 3 x H X W)
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding = 1), # passes conv kernel over batch and increases num channels from 3 (for RBG) to 32
            nn.ReLU(inplace = True), # relu to add nonlinearity
            nn.MaxPool2d(2), # reduces h and w of img by a factor of 2
            nn.BatchNorm2d(32) #normalizes over z distribution https://arxiv.org/abs/1502.03167
        )
        
         # tensor size is now (B x 32 x h/2 x w/2)

        self.conv2 = nn.Sequential(
            nn.Conv2d(32, 64, 3, padding = 1), # 32 channels to 64 ch with 3x3 kernel
            nn.ReLU(inplace = True),
            nn.MaxPool2d(2), # reduces # reduces h and w of img by a factor of 2
            nn.BatchNorm2d(64) #normalizes over z distribution https://arxiv.org/abs/1502.03167
        )

         # tensor size is now (B x 64 x h/4 x w/4)

        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 128, 3, padding = 1),  # 64 channels to 128 ch with 3x3 kernel
            nn.ReLU(inplace = True),
            nn.MaxPool2d(2),  # reduces # reduces h and w of img by a factor of 2
            nn.BatchNorm2d(128)  # normalizes over z distribution https://arxiv.org/abs/1502.03167
        )
         
        # tensor size is now (B x 128 x h/8 x w/8)

        self.fc1 = nn.Linear(128 * 4 * 4 , 512)# 2048, lowkey had to calculator it lol
        self.dropout = 0.5 # tunable, removes half of the values and replaces them with 0s
        self.fc2 = nn.Linear(512, 1) # 512 ch to 1 ch output
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = x.view(x.size(0), -1)
        # reformats for use in linear layer
        x = self.fc1(x)
        x = nn.functional.relu(x) # relu to add nonlinearity
        x = self.fc2(x)
        x = nn.functional.sigmoid(x) # 1 / 1 + e ^(-x)
        return x