Exceptions while using my own dataset

I have my own nifti dataset for labelling. I could see the below Issue while starting the monaiLabel server.

[2022-02-21 05:04:44,855] [15675] [MainThread] [INFO] (monailabel.tasks.scoring.epistemic:110) - Using /home/ec2-user/efs/apps/deepedit/model/pretrained_dynunet.pt for running Epistemic
Traceback (most recent call last):
  File "/opt/conda/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/utils/app.py", line 132, in <module>
    run_main()
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/utils/app.py", line 123, in run_main
    result = a.scoring(request)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/app.py", line 307, in scoring
    return task(copy.deepcopy(request), datastore if datastore else self.datastore())
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/tasks/scoring/epistemic.py", line 126, in __call__
    model, model_ts = self._load_model(self.model, self.network)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/tasks/scoring/epistemic.py", line 117, in _load_model
    model.load_state_dict(model_state_dict)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DynUNet:
        Unexpected key(s) in state_dict: "deep_supervision_heads.0.conv.conv.weight", "deep_supervision_heads.0.conv.conv.bias", "deep_supervision_heads.1.conv.conv.weight", "deep_supervision_heads.1.conv.conv.bias", "deep_supervision_heads.2.conv.conv.weight", "deep_supervision_heads.2.conv.conv.bias", "deep_supervision_heads.3.conv.conv.weight", "deep_supervision_heads.3.conv.conv.bias".

Facing Similar error after connecting from 3DSlicer and trying to fetch the next image. and also extra error ValueError: len(spatial_size) must be greater or equal to img spatial dimensions, got spatial_size=2 img=3.

[2022-02-21 05:18:00,001] [15313] [MainThread] [INFO] (monailabel.interfaces.utils.transform:98) - INV - Transform (ToTensord): Time: 0.0111; image: (1, 256, 256, 128)(float32); pred: torch.Size([1, 256, 256, 128])(torch.float32)
[2022-02-21 05:18:00,008] [15313] [MainThread] [ERROR] (uvicorn.error:380) - Exception in ASGI application
Traceback (most recent call last):
  File "/home/ec2-user/.local/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 377, in run_asgi
    result = await app(self.scope, self.receive, self.send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
    return await self.app(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/fastapi/applications.py", line 212, in __call__
    await super().__call__(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/applications.py", line 112, in __call__
    await self.middleware_stack(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/middleware/errors.py", line 181, in __call__
    raise exc
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/middleware/errors.py", line 159, in __call__
    await self.app(scope, receive, _send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/middleware/cors.py", line 84, in __call__
    await self.app(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/exceptions.py", line 82, in __call__
    raise exc
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/exceptions.py", line 71, in __call__
    await self.app(scope, receive, sender)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/routing.py", line 656, in __call__
    await route.handle(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/routing.py", line 259, in handle
    await self.app(scope, receive, send)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/starlette/routing.py", line 61, in app
    response = await func(request)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/fastapi/routing.py", line 226, in app
    raw_response = await run_endpoint_function(
  File "/home/ec2-user/.local/lib/python3.9/site-packages/fastapi/routing.py", line 159, in run_endpoint_function
    return await dependant.call(**values)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/endpoints/infer.py", line 177, in api_run_inference
    return run_inference(background_tasks, model, image, session_id, params, file, label, output)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/endpoints/infer.py", line 160, in run_inference
    result = instance.infer(request)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/app.py", line 231, in infer
    result_file_name, result_json = task(request)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/tasks/infer.py", line 237, in __call__
    data = self.run_invert_transforms(data, pre_transforms, self.inverse_transforms())
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/tasks/infer.py", line 288, in run_invert_transforms
    d = run_transforms(d, transforms, inverse=True, log_prefix="INV")
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/utils/transform.py", line 80, in run_transforms
    data = t.inverse(data)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monai/transforms/spatial/dictionary.py", line 589, in inverse
    d[key] = inverse_transform(d[key])
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monai/transforms/spatial/array.py", line 447, in __call__
    raise ValueError(
ValueError: len(spatial_size) must be greater or equal to img spatial dimensions, got spatial_size=2 img=3.
Traceback (most recent call last):
  File "/opt/conda/lib/python3.9/runpy.py", line 197, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.9/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/utils/app.py", line 132, in <module>
    run_main()
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/utils/app.py", line 123, in run_main
    result = a.scoring(request)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/interfaces/app.py", line 307, in scoring
    return task(copy.deepcopy(request), datastore if datastore else self.datastore())
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/tasks/scoring/epistemic.py", line 126, in __call__
    model, model_ts = self._load_model(self.model, self.network)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/monailabel/tasks/scoring/epistemic.py", line 117, in _load_model
    model.load_state_dict(model_state_dict)
  File "/home/ec2-user/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for DynUNet:
        Unexpected key(s) in state_dict: "deep_supervision_heads.0.conv.conv.weight", "deep_supervision_heads.0.conv.conv.bias", "deep_supervision_heads.1.conv.conv.weight", "deep_supervision_heads.1.conv.conv.bias", "deep_supervision_heads.2.conv.conv.weight", "deep_supervision_heads.2.conv.conv.bias", "deep_supervision_heads.3.conv.conv.weight", "deep_supervision_heads.3.conv.conv.bias".

[j-sieger]

Hi @diazandr3s ,

It is working now. Thanks for the help.

[diazandr3s]

Hi @j-sieger,

Please remove the SingleLabelSingleModalityd transform in both infer and train files.

This transform makes sure the images are single modality and labels and single class. However, this error is common when some headers are lost after converting from DICOM to NIFTI. I need to investigate more on this.

After removing those you should be able to perform inference. If not, use the ITK reader in the LoadImaged transform like this:

reader="itkreader"

For some reason, I kept thinking on the deepedit_multilabel and thought I did the same for the deepedit App. Sorry about this.

Please let me know how that goes 😃