Unable to run the Code. Seems like some architectural issue. Tensor sizes seem to be mismatching.

[nximish]

Encoder selected: resnet18
Pretrained with the following strategy: imagenet
Model selected: mtbit_resnet18
Optimizer selected: adamw
Scheduler selected: step_lr
No checkpoints founded
Trainable parameters: 13198763, total parameters 13198763
Epoch: 1 - Learning rate: 0.0001
0% 0/22 [00:00<?, ?it/s]torch.Size([15, 32, 100, 100])
torch.Size([1, 32, 64, 64])
0% 0/22 [00:06<?, ?it/s]
Traceback (most recent call last):
File "/content/drive/MyDrive/3DCD/3DCD/train.py", line 203, in
out2d, out3d = net(t1, t2)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/content/drive/MyDrive/3DCD/3DCD/models/MTBIT.py", line 207, in forward
x1 = self._forward_transformer_decoder(x1, token1)
File "/content/drive/MyDrive/3DCD/3DCD/models/MTBIT.py", line 178, in _forward_transformer_decoder
x = x + self.pos_embedding_decoder
RuntimeError: The size of tensor a (100) must match the size of tensor b (64) at non-singleton dimension 3

[VMarsocci]

Hi, can you try to run it with an image size divisible by 4?