TypeError: SequentialWithIdx.forward() missing 1 required positional argument: 'indices'" #7475
Description
Describe the bug
Quicknat not work as expected.
To Reproduce
from monai.networks.nets import Quicknat
import torch
model = Quicknat(
num_classes=1,
num_channels=1,
num_filters=64,
se_block=None
).to("cuda:0")
data = torch.rand((1, 1, 32, 32)).to("cuda:0")
out = model(data)
Error Log
{
"name": "TypeError",
"message": "SequentialWithIdx.forward() missing 1 required positional argument: 'indices'",
"stack": "---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[2], line 14
5 model = Quicknat(
6 num_classes=1,
7 num_channels=1,
(...)
10 se_block=None
11 ).to(\"cuda:0\")
12 data = torch.rand((1, 1, 32, 32)).to(\"cuda:0\")
---> 14 out = model(data)
17 # export
18 # convert_to_onnx(model, dataloader, filename='quicknat.onnx', verify=True, use_ort=True)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File /workspace/MONAI/monai/networks/nets/quicknat.py:438, in Quicknat.forward(self, input)
437 def forward(self, input: torch.Tensor) -> torch.Tensor:
--> 438 input, _ = self.model(input, None)
439 return input
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File /workspace/MONAI/monai/networks/nets/quicknat.py:62, in SequentialWithIdx.forward(self, input, indices)
60 def forward(self, input, indices):
61 for module in self:
---> 62 input, indices = module(input, indices)
63 return input, indices
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
File /workspace/MONAI/monai/networks/nets/quicknat.py:46, in SkipConnectionWithIdx.forward(self, input, indices)
45 def forward(self, input, indices):
---> 46 return super().forward(input), indices
File /workspace/MONAI/monai/networks/layers/simplelayers.py:129, in SkipConnection.forward(self, x)
128 def forward(self, x: torch.Tensor) -> torch.Tensor:
--> 129 y = self.submodule(x)
131 if self.mode == \"cat\":
132 return torch.cat([x, y], dim=self.dim)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1511, in Module._wrapped_call_impl(self, *args, **kwargs)
1509 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1510 else:
-> 1511 return self._call_impl(*args, **kwargs)
File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1520, in Module._call_impl(self, *args, **kwargs)
1515 # If we don't have any hooks, we want to skip the rest of the logic in
1516 # this function, and just call forward.
1517 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1518 or _global_backward_pre_hooks or _global_backward_hooks
1519 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1520 return forward_call(*args, **kwargs)
1522 try:
1523 result = None
TypeError: SequentialWithIdx.forward() missing 1 required positional argument: 'indices'"
}
Quicknat is introduced by this PR.
Looks like the unit tests are always not tested since squeeze_and_excitation is not a required dependency.
Line 39 in ff43028