Trainers: support binary, multiclass, and multilabel tasks #2219
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adamjstewart
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Aug 12, 2024
| num_classes: int = 1000, | ||
| task: str = 'multiclass', | ||
| num_classes: int | None = None, | ||
| num_labels: int | None = None, |
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The defaults here match torchmetrics. If task='multiclass', only num_classes is used. If task='multilabel', only num_labels is used. If task='binary', both are ignored. Honestly, we could have a single num_classes if we want and simply use it for both.
adamjstewart
commented
Aug 12, 2024
| @@ -266,147 +262,3 @@ def predict_step( | |||
| x = batch['image'] | |||
| y_hat: Tensor = self(x).softmax(dim=-1) | |||
| return y_hat | |||
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| class MultiLabelClassificationTask(ClassificationTask): | |||
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The right thing to do would be to deprecate this first and remove it in 0.7.0. Not sure how widely used it is. Deprecation is kind of annoying because you need to change all tests to acknowledge the warning message.
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I think we first need to add a multilabel semantic segmentation dataset to properly test this. |
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Alternatively, skip multilabel semantic segmentation and only support multilabel classification. |
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Instead of having separate trainers for binary, multiclass, and multilabel, let's create a single trainer that can handle all 3.
This applies to both Classification and Semantic Segmentation but not to our other trainers.
Closes #2205 @robmarkcole
Closes #245 @calebrob6