uark-aicv / 3dconvcaps Goto Github PK

Hi!
According to the paper, patch_size is 323232, but bash file shows 646464, is the parameter in bash file the optimal value?

Hi!

I am trying to run train.py, but I receive this error:

Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py", line 3505, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "", line 41, in
parser = Trainer.add_argparse_args(parser)
^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: type object 'Trainer' has no attribute 'add_argparse_args'

Apparently, the pytorch lightning's Trainer does not have the method add_argparse_args in this line:

parser = Trainer.add_argparse_args(parser)

Would you please help and tell me how I can get around this error? Thank you so much :)

Best,
Arman

when computing MarginLoss, I found that the third dimension is not match at all. The raw_logits shape is torch.Size([8, 256, 64]), while the labels(after interpolate and reshape) shape is torch.Size([8, 256, 8])

and there is the error: RuntimeError: The size of tensor a (8) must match the size of tensor b (64) at non-singleton dimension 2

what's more, when choosing heart as the datasaet, the input_channels should change to 1 rather than 1,is that right?

Hi,

I have a question related to the _build_encoder function within module/mod_upcaps.py 👍

This block of code defines 6 capsule layers after the primary capsule layer:

    self.encoder_output_dim = [16, 16, 8, 8, 8, self.out_channels]
    self.encoder_output_atoms = [8, 8, 16, 16, 32, 64]

    for i in range(len(self.encoder_output_dim)):
        if i == 0:
            input_dim = self.primary_caps.output_dim
            input_atoms = self.primary_caps.output_atoms
        else:
            input_dim = self.encoder_output_dim[i - 1]
            input_atoms = self.encoder_output_atoms[i - 1]

        stride = 2 if i % 2 == 0 else 1

        self.encoder_conv_caps.append(
            ConvSlimCapsule3D(
                kernel_size=self.encoder_kernel_size,
                input_dim=input_dim,
                output_dim=self.encoder_output_dim[i],
                input_atoms=input_atoms,
                output_atoms=self.encoder_output_atoms[i],
                stride=stride,
                padding=1,
                dilation=1,
                num_routing=3,
                share_weight=self.share_weight,
            )
        )

I wanted to double check if it is correct, since in your paper you only have 2 capsule layers.

Thank you so much in advance :)
Arman