Comments (10)
@bhadreshpsavani please feel free to open a PR with what you got, then we can better help out :]
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Hi @SkafteNicki and @Borda,
Can I take this?
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sure, open a draft PR and we can help you there 🐰
to run tests locally, you can use make test
which is running following command under the hood
https://github.com/PyTorchLightning/metrics/blob/fb0ee3ff0509fdb13bd07b6aac3e20c642bb5683/Makefile#L20
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Hi! thanks for your contribution!, great first issue!
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@bhadreshpsavani please feel free to do so :]
Ping me either here or on our slack channel if you need any help.
from torchmetrics.
@bhadreshpsavani for the metrics that ware not differentiable, please also wrap the forward
and compute
functions with torch.no_grad
to reduce the memory footprint.
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@bhadreshpsavani how is it going, have you started on adding these tests?
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Hi @Borda,
I have made changes for two metrics. I was not very confident about the changes. Since I don't know how to run that test cases and check the changes. I am not very sure I did it the right way.
Shall I create a PR so someone can review it and suggest?
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I have made a pull request but a lot of tests are failing,
I try to test the changes using the below commands make test
, python -m pytest torchmetrics tests -v --cov=torchmetrics
before making changes but it was giving an error.
(metrics-env) D:\metrics>make test
FIND: Parameter format not correct
# clean all temp runs
process_begin: CreateProcess(NULL, # clean all temp runs, ...) failed.
make (e=2): The system cannot find the file specified.
make: *** [Makefile:7: clean] Error 2
and
(metrics-env) D:\metrics>python -m pytest torchmetrics tests -v --cov=torchmetrics
ERROR: usage: __main__.py [options] [file_or_dir] [file_or_dir] [...]
__main__.py: error: unrecognized arguments: --cov=torchmetrics
inifile: D:\metrics\setup.cfg
rootdir: D:\metrics
from torchmetrics.
Hi @SkafteNicki,
Is it possible that for few metrics can't have this property?
I was trying to implement this for cohen_kappa
and ``avarage_precision` i wrote code but few test cases I wrote were failing. I could not figure out how to fix it.
Let me create PR for that so that you can review the changes.
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Related Issues (20)
- The default value of `compute_with_cache` should be `True` . HOT 2
- Bug in ERGAS HOT 2
- Ordinal classification metrics HOT 1
- Binary Classification Expected Calibration Error HOT 2
- `intersection_over_union` error HOT 2
- Clarify that nan is supported in zero_division HOT 4
- Add `_filter_kwargs` in `ClasswiseWrapper` metric wrapper HOT 3
- MetricTracker use higher_is_better as default for maximize HOT 2
- Unpredictable class order when `panoptic_quality(..., return_per_class=True)`
- torchmetrics Accuracy HOT 8
- BootStrapper.reset() does not reset properly
- segmentation.MeanIoU is wrong HOT 3
- DataLoader worker is killed in Docker HOT 4
- MeanAveragePrecision - bug in `max_detection_thresholds` HOT 3
- Documentation of ERGAS HOT 1
- Support for DLM (AIM) metric HOT 1
- Error for index tensor verification in GeneralizedDice if target is zeros HOT 1
- Incorrect caching (`m._compute`) of metrics inside a `MetricCollection` if `compute_groups` are used and `.compute` is called twice HOT 1
- Classification Report, maybe with the option to select even more metrics HOT 1
- `MeanAveragePrecision.tm_to_coco` example fails with `IndexError: list index out of range` HOT 1
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