Error when running monailabel with bundle model nodule detection #1429

monaalsh · 2023-05-10T05:59:46Z

monaalsh
May 10, 2023

I get the following error after running monailabel with bundle model nodule detection

monailabel start_server --app apps/monaibundle --studies datasets/Task06_Lung/imagesTr --conf models lung_nodule_ct_detection

below in the error I get

RuntimeError: Failed to instantiate {'target': 'RetinaNet', 'spatial_dims': 3, 'num_classes': 1, 'num_anchors': 3, 'feature_extractor': BackboneWithFPN(

[2023-05-10 08:13:15,982] [70122] [MainThread] [ERROR] (uvicorn.error:56) - Application startup failed. Exiting.

diazandr3s · 2023-05-10T10:32:12Z

diazandr3s
May 10, 2023
Maintainer

Hi @monaalsh,

Thanks for reporting this.
Would you please attach the full logs? Which MONAI Label version are you using?

Thanks!

0 replies

monaalsh · 2023-05-10T12:30:55Z

monaalsh
May 10, 2023
Author

hi @diazandr3s

Here is the log

The version of monailabel is


MONAILabel version: 0.6.0
Numpy version: 1.24.3
Pytorch version: 1.13.1+cu117
MONAILabel rev id: 40fd783a704f4d6a0baa8d34086dd336efdf1e6f

The log when running the nodule detection model is below, it doesn't happen when running other model such as the default one in the
quickstart page for Spleen segmentation.
Thank you in advance.

TypeError: __init__() got an unexpected keyword argument 'use_list_output'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/mona/.local/lib/python3.8/site-packages/monai/bundle/config_item.py", line 283, in instantiate
    return instantiate(modname, **args)
  File "/home/mona/.local/lib/python3.8/site-packages/monai/utils/module.py", line 245, in instantiate
    raise RuntimeError(f"Failed to instantiate '{path}' with kwargs: {kwargs}") from e
RuntimeError: Failed to instantiate 'monai.apps.detection.networks.retinanet_network.RetinaNet' with kwargs: {'spatial_dims': 3, 'num_classes': 1, 'num_anchors': 3, 'feature_extractor': BackboneWithFPN(
  (body): IntermediateLayerGetter(
    (conv1): Conv3d(1, 64, kernel_size=(7, 7, 7), stride=(2, 2, 1), padding=(3, 3, 3), bias=False)
    (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
    (maxpool): MaxPool3d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): ResNetBottleneck(
        (conv1): Conv3d(64, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResNetBottleneck(
        (conv1): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): ResNetBottleneck(
        (conv1): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer2): Sequential(
      (0): ResNetBottleneck(
        (conv1): Conv3d(256, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
  )
  (fpn): FeaturePyramidNetwork(
    (inner_blocks): ModuleList(
      (0): Conv3d(256, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
      (1): Conv3d(512, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
    )
    (layer_blocks): ModuleList(
      (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
      (1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
    )
    (extra_blocks): LastLevelMaxPool(
      (maxpool): MaxPool3d(kernel_size=1, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
  )
), 'size_divisible': [16, 16, 8], 'use_list_output': False}

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/mona/.local/lib/python3.8/site-packages/starlette/routing.py", line 635, in lifespan
    async with self.lifespan_context(app):
  File "/home/mona/.local/lib/python3.8/site-packages/starlette/routing.py", line 530, in __aenter__
    await self._router.startup()
  File "/home/mona/.local/lib/python3.8/site-packages/starlette/routing.py", line 612, in startup
    await handler()
  File "/home/mona/.local/lib/python3.8/site-packages/monailabel/app.py", line 106, in startup_event
    instance = app_instance()
  File "/home/mona/.local/lib/python3.8/site-packages/monailabel/interfaces/utils/app.py", line 51, in app_instance
    app = c(app_dir=app_dir, studies=studies, conf=conf)
  File "/home/mona/Documents/Lung_cancer/apps/monaibundle/main.py", line 47, in __init__
    super().__init__(
  File "/home/mona/.local/lib/python3.8/site-packages/monailabel/interfaces/app.py", line 101, in __init__
    self._infers = self.init_infers()
  File "/home/mona/Documents/Lung_cancer/apps/monaibundle/main.py", line 62, in init_infers
    i = BundleInferTask(b, self.conf)
  File "/home/mona/.local/lib/python3.8/site-packages/monailabel/tasks/infer/bundle.py", line 108, in __init__
    network = self.bundle_config.get_parsed_content(self.const.key_network_def(), instantiate=True)
  File "/home/mona/.local/lib/python3.8/site-packages/monai/bundle/config_parser.py", line 271, in get_parsed_content
    return self.ref_resolver.get_resolved_content(id=id, **kwargs)
  File "/home/mona/.local/lib/python3.8/site-packages/monai/bundle/reference_resolver.py", line 185, in get_resolved_content
    return self._resolve_one_item(id=id, **kwargs)
  File "/home/mona/.local/lib/python3.8/site-packages/monai/bundle/reference_resolver.py", line 163, in _resolve_one_item
    self.resolved_content[id] = item.instantiate() if kwargs.get("instantiate", True) else item
  File "/home/mona/.local/lib/python3.8/site-packages/monai/bundle/config_item.py", line 285, in instantiate
    raise RuntimeError(f"Failed to instantiate {self}.") from e
RuntimeError: Failed to instantiate {'_target_': 'RetinaNet', 'spatial_dims': 3, 'num_classes': 1, 'num_anchors': 3, 'feature_extractor': BackboneWithFPN(
  (body): IntermediateLayerGetter(
    (conv1): Conv3d(1, 64, kernel_size=(7, 7, 7), stride=(2, 2, 1), padding=(3, 3, 3), bias=False)
    (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
    (maxpool): MaxPool3d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): ResNetBottleneck(
        (conv1): Conv3d(64, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
          (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResNetBottleneck(
        (conv1): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): ResNetBottleneck(
        (conv1): Conv3d(256, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(64, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
    (layer2): Sequential(
      (0): ResNetBottleneck(
        (conv1): Conv3d(256, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (downsample): Sequential(
          (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2))
          (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (2): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
      (3): ResNetBottleneck(
        (conv1): Conv3d(512, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)
        (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (conv3): Conv3d(128, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
        (bn3): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
      )
    )
  )
  (fpn): FeaturePyramidNetwork(
    (inner_blocks): ModuleList(
      (0): Conv3d(256, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
      (1): Conv3d(512, 256, kernel_size=(1, 1, 1), stride=(1, 1, 1))
    )
    (layer_blocks): ModuleList(
      (0): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
      (1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
    )
    (extra_blocks): LastLevelMaxPool(
      (maxpool): MaxPool3d(kernel_size=1, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
  )
), 'size_divisible': [16, 16, 8], 'use_list_output': False}.

[2023-05-10 15:27:55,304] [353705] [MainThread] [ERROR] (uvicorn.error:56) - Application startup failed. Exiting.

0 replies

junxiant · 2023-05-12T02:08:34Z

junxiant
May 12, 2023

Hello,

I faced the same issue earlier, you have to update your monailabel to the rc version

pip install monailabel==0.7.0rc6

worked for me

0 replies

diazandr3s · 2023-05-13T09:51:06Z

diazandr3s
May 13, 2023
Maintainer

Thanks, @junxiant.

@monaalsh could you please update the MONAI Label and let us know if that works for you?

Thanks, both!

0 replies

diazandr3s · 2023-05-13T09:51:39Z

diazandr3s
May 13, 2023
Maintainer

#1430 (comment)

0 replies

wrecheis · 2024-01-09T09:22:03Z

wrecheis
Jan 9, 2024

Since I am pretty new in monailabel I may have missed something essential in this case. I testet pancreas dint CT detection, that worked perfectly smooth (with own data) with 3D Slicer. But the bundle nodule detection throws the following error (output of monai server debug mode):
2024-01-09 09:29:42,911] [117108] [MainThread] [ERROR] (uvicorn.error:434) - Exception in ASGI application
Traceback (most recent call last):
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/utils/module.py", line 261, in instantiate
return component(**kwargs)
TypeError: init() got an unexpected keyword argument 'meta_key_postfix'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/config_item.py", line 293, in instantiate
return instantiate(modname, mode, **args)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/utils/module.py", line 271, in instantiate
raise RuntimeError(
RuntimeError: Failed to instantiate component 'monai.transforms.utility.dictionary.EnsureChannelFirstd' with kwargs: {'keys': 'image', 'meta_key_postfix': 'meta_dict'}
set 'mode=debug' to enter the debugging mode.
The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 429, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in call
return await self.app(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/fastapi/applications.py", line 276, in call
await super().call(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/applications.py", line 122, in call
await self.middleware_stack(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/middleware/errors.py", line 184, in call
raise exc
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/middleware/errors.py", line 162, in call
await self.app(scope, receive, _send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/middleware/cors.py", line 84, in call
await self.app(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 79, in call
raise exc
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/middleware/exceptions.py", line 68, in call
await self.app(scope, receive, sender)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py", line 21, in call
raise e
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in call
await self.app(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/routing.py", line 718, in call
await route.handle(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/routing.py", line 276, in handle
await self.app(scope, receive, send)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/starlette/routing.py", line 66, in app
response = await func(request)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/fastapi/routing.py", line 237, in app
raw_response = await run_endpoint_function(
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
return await dependant.call(**values)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monailabel/endpoints/infer.py", line 206, in api_run_inference
return run_inference(background_tasks, model, image, session_id, params, file, label, output)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monailabel/endpoints/infer.py", line 172, in run_inference
result = instance.infer(request)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monailabel/interfaces/app.py", line 307, in infer
result_file_name, result_json = task(request)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monailabel/tasks/infer/basic_infer.py", line 297, in call
pre_transforms = self.pre_transforms(data)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monailabel/tasks/infer/bundle.py", line 201, in pre_transforms
c = self.bundle_config.get_parsed_content(k, instantiate=True)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/config_parser.py", line 290, in get_parsed_content
return self.ref_resolver.get_resolved_content(id=id, **kwargs)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/reference_resolver.py", line 193, in get_resolved_content
return self._resolve_one_item(id=id, **kwargs)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/reference_resolver.py", line 163, in _resolve_one_item
self._resolve_one_item(id=d, waiting_list=waiting_list, **kwargs)
File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/reference_resolver.py", line 171, in _resolve_one_item
self.resolved_content[id] = item.instantiate() if kwargs.get("instantiate", True) else item

File "/home/wolfgang/anaconda3/envs/monai/lib/python3.9/site-packages/monai/bundle/config_item.py", line 295, in instantiate
raise RuntimeError(f"Failed to instantiate {self}") from e
RuntimeError: Failed to instantiate ConfigComponent:
{'target': 'EnsureChannelFirstd',
'keys': 'image',
'meta_key_postfix': 'meta_dict'}

My setup is:
monai 1.3.0
monailabel 0.8.1

Many thanks for any suggestion!

1 reply

diazandr3s Jan 9, 2024
Maintainer

Hi @wrecheis,

Can you please share the command you used to start the server? What bundle specifically are you referring to?

wrecheis · 2024-01-10T07:29:03Z

wrecheis
Jan 10, 2024

Hi @diazandr3s ,
my command I used is the following:
monailabel start_server --app ./monaibundle --studies /home/wolfgang/daten/ct-lunge/lunge-CA/ --conf models lung_nodule_ct_detection

Changing --conf models to pancreas_ct_dints_segmentation works perfectly smooth
Again many thanks

0 replies

zhanghaorui · 2024-02-04T14:52:09Z

zhanghaorui
Feb 4, 2024

@monaalsh Dear, I'm having the same problem as you. Have you solved it?

monailabel -v
Using PYTHONPATH=/Users/zhanghaorui:

MONAILabel version: 0.8.1
Numpy version: 1.25.2
Pytorch version: 2.0.1
MONAILabel rev id: 5ef153f

0 replies

wrecheis · 2024-02-08T15:42:53Z

wrecheis
Feb 8, 2024

Hi, sorry for my late answer: In my case I had to delete the line "meta_key_postfix": "meta_dict" in the inference.json file (approx line number 100 in my editor) of the lung_nodule_detection module. After this everything runs smooth. I really wonder why this error happens or were there any changes after the release in monai model zoo? Maybe an update is necessary? Thanks anyway for the great work!

1 reply

diazandr3s Feb 11, 2024
Maintainer

Hi @wrecheis,

Thanks for the update. It's good to hear it is working on your end. Sorry, I'm just catching up on these issues.
Do you remember exactly which line was that? I can't see that on the latest bundle version: https://github.com/Project-MONAI/model-zoo/blob/dev/models/lung_nodule_ct_detection/configs/inference.json

Let us know,

wrecheis · 2024-02-12T08:25:21Z

wrecheis
Feb 12, 2024

Hi, I downloaded the stuff from https://monai.io/model-zoo. I did not check for updated versions on github - my fault. On you github version the line is already deleted. Anyway many thanks

0 replies

Error when running monailabel with bundle model nodule detection #1429

Uh oh!

Uh oh!

monaalsh May 10, 2023

Replies: 10 comments · 2 replies

Uh oh!

diazandr3s May 10, 2023 Maintainer

Uh oh!

monaalsh May 10, 2023 Author

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junxiant May 12, 2023

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diazandr3s May 13, 2023 Maintainer

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diazandr3s May 13, 2023 Maintainer

Uh oh!

wrecheis Jan 9, 2024

Uh oh!

diazandr3s Jan 9, 2024 Maintainer

Uh oh!

wrecheis Jan 10, 2024

Uh oh!

zhanghaorui Feb 4, 2024

Uh oh!

wrecheis Feb 8, 2024

Uh oh!

diazandr3s Feb 11, 2024 Maintainer

Uh oh!

wrecheis Feb 12, 2024

monaalsh
May 10, 2023

Replies: 10 comments 2 replies

diazandr3s
May 10, 2023
Maintainer

monaalsh
May 10, 2023
Author

junxiant
May 12, 2023

diazandr3s
May 13, 2023
Maintainer

diazandr3s
May 13, 2023
Maintainer

wrecheis
Jan 9, 2024

diazandr3s Jan 9, 2024
Maintainer

wrecheis
Jan 10, 2024

zhanghaorui
Feb 4, 2024

wrecheis
Feb 8, 2024

diazandr3s Feb 11, 2024
Maintainer

wrecheis
Feb 12, 2024