# Number of dims don't match in permute in birads.py

``````x = {
"L-CC": torch.Tensor(datum_l_cc).permute(3, 1).to(device),
"L-MLO": torch.Tensor(datum_l_mlo).permute(3, 1).to(device),
"R-CC": torch.Tensor(datum_r_cc).permute(3, 1).to(device),
"R-MLO": torch.Tensor(datum_r_mlo).permute(3, 1).to(device),
}
#print(x)
transform = transforms.Compose([transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(
(.485, .456, .406), (.229, .224, .225))
])
# run prediction:
for (key,value) in x.items():
print(key)
torch_img=(x[key]).to(device)
print(torch_img.size)

``````

inference(parameters_)
File â€śbirads.pyâ€ť, line 57, in inference
â€śL-CCâ€ť: torch.Tensor(datum_l_cc).permute(3, 1).to(device),
RuntimeError: number of dims donâ€™t match in permute

Check the number of dimensions via:

``````datum_I_cc = torch.tensor(datum_l_cc)
print(datum_I_cc.shape)
``````

and make sure `permute` uses all dimensions.

Based on the values you are passing I guess the tensor might have 4 dimensions.
If you want to swap `dim3` with `dim1`, you could use `.permute(0, 3, 2, 1)`.

1 Like

Using All dimensions solves this error.