BinaryPairwiseMeasures.__init__() got an unexpected keyword argument 'axis'
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Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 52/100
- Issue type
- Bug
- Clarity
- Clearly specified
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Read monai/metrics/wrapper.py at MetricsReloadedBinary._compute_tensor and compare its BinaryPairwiseMeasures call with the installed MetricsReloaded API. Re-run the supplied MONAI example; done means both metric calls and aggregate(reduction="none") execute without the unexpected axis error.
Written by the indexing model from the issue text.
Description
When I ran the example in MONAI, I encountered this error.
MONAI version 1.3.2
MetricsReloaded version 0.1.0
The example code is as follows:
import torch
from monai.metrics import MetricsReloadedBinary
metric_name = "Cohens Kappa"
metric = MetricsReloadedBinary(metric_name=metric_name)
y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]])
y = torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])
print(metric(y_pred, y))
y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 0.0]]]])
y = torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])
print(metric(y_pred, y))
print(metric.aggregate(reduction="none"))
metric.reset()
The errors are as follows:
{
"name": "TypeError",
"message": "BinaryPairwiseMeasures.__init__() got an unexpected keyword argument 'axis'",
"stack": "---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File /home/bowen/git/prostate/practice.py:13
11 y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 1.0]]]])
12 y = torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]])
---> 13 print(metric(y_pred, y))
15 # second iteration
16 # shape [batch=1, channel=1, 2, 2]
17 y_pred = torch.tensor([[[[1.0, 0.0], [0.0, 0.0]]]])
File ~/.conda/envs/pytorch/lib/python3.11/site-packages/monai/metrics/metric.py:347, in CumulativeIterationMetric.__call__(self, y_pred, y, **kwargs)
327 def __call__(
328 self, y_pred: TensorOrList, y: TensorOrList | None = None, **kwargs: Any
329 ) -> torch.Tensor | Sequence[torch.Tensor | Sequence[torch.Tensor]]:
330 \"\"\"
331 Execute basic computation for model prediction and ground truth.
332 It can support both `list of channel-first Tensor` and `batch-first Tensor`.
(...)
345 a `batch-first` tensor (BC[HWD]) or a list of `batch-first` tensors.
346 \"\"\"
--> 347 ret = super().__call__(y_pred=y_pred, y=y, **kwargs)
348 if isinstance(ret, (tuple, list)):
349 self.extend(*ret)
File ~/.conda/envs/pytorch/lib/python3.11/site-packages/monai/metrics/metric.py:80, in IterationMetric.__call__(self, y_pred, y, **kwargs)
78 if isinstance(y_pred, torch.Tensor):
79 y_ = y.detach() if isinstance(y, torch.Tensor) else None
---> 80 return self._compute_tensor(y_pred.detach(), y_, **kwargs)
81 raise ValueError(\"y_pred or y must be a list/tuple of `channel-first` Tensors or a `batch-first` Tensor.\")
File ~/.conda/envs/pytorch/lib/python3.11/site-packages/monai/metrics/wrapper.py:169, in MetricsReloadedBinary._compute_tensor(self, y_pred, y)
166 y = convert_to_numpy(y)
168 # Create binary pairwise metric object
--> 169 bpm = BinaryPairwiseMeasures(y_pred, y, axis=tuple(range(2, dims)), smooth_dr=1e-5)
171 # Is requested metric available?
172 if self.metric_name not in bpm.metrics:
TypeError: BinaryPairwiseMeasures.__init__() got an unexpected keyword argument 'axis'"
}
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