🔨 Rework for Probabilistic Regression & Pixel Regression #125
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Contributor
alafage
commented
Nov 28, 2024
•
alafage
commented
- Rework Distribution Layers to be classifiers returning distribution parameters in a dictionary.
- Update RegressionRoutine
- Update PixelRegressionRoutine
- Update Tutorials
- Update API Reference
- Fix DistributionNLL (loss / metric)
- Update BTS baseline
- Add tutorial for simple probabilistic regression
- Update existing tests
- Write tests
Codecov ReportAll modified and coverable lines are covered by tests ✅
✅ All tests successful. No failed tests found. Additional details and impacted files@@ Coverage Diff @@
## dev #125 +/- ##
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Coverage 99.21% 99.22%
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Files 142 142
Lines 6792 6835 +43
Branches 849 857 +8
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+ Hits 6739 6782 +43
Misses 24 24
Partials 29 29
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
format and check using ruff
o-laurent
reviewed
Dec 17, 2024
o-laurent
approved these changes
Dec 18, 2024
| ) | ||
| self.min_scale = min_scale | ||
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| def forward(self, x: Tensor) -> dict[str, Tensor]: |
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| Args: | ||
| x (Tensor): The input of the model. | ||
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| Returns: |
| loss (nn.Module): Loss function to optimize the :attr:`model`. | ||
| dist_family (str, optional): The distribution family to use for | ||
| probabilistic pixel regression. Defaults to ``None``. |
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If None, pointwise regression
| @@ -76,7 +79,9 @@ def __init__( | |||
| ) | |||
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| self.model = model | |||
| self.probabilistic = probabilistic | |||
| self.dist_family = dist_family | |||
| self.dist_estimate = dist_estimate | |||
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Why is self.dist_estimate here and not in pixel-wise regression?
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That's a mistake, thanks!
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Thank you for the great review! This last commit should address all your worries. Feel free to merge it into dev anytime! |
o-laurent
approved these changes
Dec 18, 2024
| Depending of the :attr:`dist_layer` of the backbone, the output can | ||
| be a distribution or a single tensor. | ||
| Depending of the :attr:`dist_family` of the backbone, the output can | ||
| be a dictionnary of distribution parameters or a single tensor. |
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