MiniImagenetCNN does not use the right hidden dimensionality

[DubiousCactus]

For MiniImageNet, the CNN baseline should use 32 convolution filters per block but the current implementation uses 64. It is even noted in the code:

class ConvBase(torch.nn.Sequential):

    # NOTE:
    #     Omniglot: hidden=64, channels=1, no max_pool
    #     MiniImagenet: hidden=32, channels=3, max_pool

    def __init__(self,
                 hidden=64,
                 channels=1,
                 max_pool=False,
                 layers=4,
                 max_pool_factor=1.0):
...

but not actually taken into consideration:

class CNN4(torch.nn.Module):
    def __init__(
        self,
        output_size,
        hidden_size=64,
        layers=4,
        channels=3,
        max_pool=True,
        embedding_size=None,
    ):
...
MiniImagenetCNN = CNN4

[seba-1511]

ProtoNet uses 64 channels, MAML uses 32. From experience, MAML also works with 64 but ProtoNet doesn't with 32, so that's why it's the default.