I don’t understand why my transformations lead to my model failing to run (RuntimeError: mat1 and mat2 shapes cannot be multiplied (4x44944 and 400x120)). Generally, I stick to the documentation of training a classifier (Training a Classifier — PyTorch Tutorials 1.13.0+cu117 documentation) only that I transformed my data differently.
transform = transforms.Compose([transforms.RandomRotation(30), transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
Looking at the shape of my batch tensor I don’t see why the model as indicated in the documentation now doesn’t run anymore. Do these transformation change something that is relevant to the model?
class Net(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.pool = nn.MaxPool2d(2, 2) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = torch.flatten(x, 1) # flatten all dimensions except batch x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
In general, I know how to fix this error, but I don’t understand what makes my tensor so big all of a sudden. Since nothing is changed compared to the documentation it must have something to do with my previous transforms. Can someone explain, what happened?
I would be happy about any help!