Hi everyone, long time TF user here hoping to switch over to PyTorch. I’m trying to mimic a CNN I wrote with Keras and am running into quite a bit of trouble.
First off, I am trying to feed in pre-made numpy arrays (ran into a host of problems trying to make my own dataset class with the arrays as is, so I figured it would be better to just feed them in more directly) using:
my_dataset = TensorDataset(x_train, y_train)
training_loader = DataLoader(my_dataset)

The numpy arrays are of the shape (# of samples, # of channels, height, width) - does simply feeding in batch_size=256 as an argument to DataLoader take care of the whole batch size problem for me? I have been unsure about this.
I am unsure how to calculate the input shapes for each layer in my CNN - I’ve read some threads on here about it but they all solve the problem directly and don’t actually explain HOW to do this. I am getting a size mismatch runtime error at my first fully connected layer, which in my case appears after a max pooling layer. How can I go about calculating this?
For reference I will include the error message and my code:
RuntimeError: size mismatch, m1: [1 x 2302720], m2: [768 x 120] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:290

class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 2, 1)
        self.conv2 = nn.Conv2d(32, 32, 2, 1)
        
        self.conv3 = nn.Conv2d(32, 64, 3, 1)
        self.conv4 = nn.Conv2d(64, 64, 3, 1)
        
        self.fc1 = nn.Linear(896, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 1)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = F.max_pool2d(x, 2, 2)
        x = torch.flatten(x)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.sigmoid(self.fc3(x))        
        return x

Thank you for any help!!