feat(losses): add Dice loss implementation #19409
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Additional details and impacted files@@ Coverage Diff @@
## master #19409 +/- ##
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+ Coverage 75.97% 75.98% +0.01%
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Files 366 366
Lines 40742 40759 +17
Branches 7945 7946 +1
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fchollet
reviewed
Mar 30, 2024
keras/losses/losses.py
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| @keras_export("keras.losses.dice") | ||
| def dice(y_true, y_pred, smooth=1e-6): |
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You can remove the smooth argument.
keras/losses/losses.py
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| intersection = ops.sum(ops.dot(inputs, targets)) | ||
| dice = ops.divide( | ||
| 2.0 * intersection + smooth, ops.sum(y_true) + ops.sum(y_pred) + smooth |
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Instead smooth, use backend.epsilon(). Only use it for the denominator.
keras/losses/losses.py
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| Returns: | ||
| Dice loss value. | ||
| """ | ||
| y_true = ops.cast(y_true, dtype="float32") |
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There's no need to force the use of float32, you can just use ops.convert_to_tensor(y_true)
keras/losses/losses.py
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| Dice loss value. | ||
| """ | ||
| y_true = ops.cast(y_true, dtype="float32") | ||
| y_pred = ops.cast(y_pred, dtype="float32") |
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Same here.
y_pred = ops.convert_to_tensor(y_pred)
y_true = ops.cast(y_true, y_pred.dtype)
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@fchollet Thanks for reviewing 👍 |
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Shouldn't it be in keras-cv? |
fchollet
reviewed
Mar 31, 2024
keras/losses/losses.py
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| inputs = ops.reshape(y_true, [-1]) | ||
| targets = ops.reshape(y_pred, [-1]) | ||
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| intersection = ops.sum(ops.dot(inputs, targets)) |
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Easier to replace dot with * here (doesn't change numerics)
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* Refactor dtypes in codebase and add float8_* dtypes * Update comments Fix for JAX export on GPU. (keras-team#19404) Fix formatting in export_lib. (keras-team#19405) `ops/numpy.py`: Support `key` as `list` in `GetItem` (keras-team#19310) When loading a model that contains `GetItem` nodes with multidimensional indices/slices as `key`, the `key` argument is loaded from JSON as a `list`, not a `tuple` (because JSON does not have the distinction). So, treat the `key list` as equivalent to the `key tuple`. Copying is important: otherwise, the later `pop()` will remove the bound slice elements from the op itself. `saving/serialization_lib_test.py`: * Add `test_numpy_get_item_layer()`: test for consistent serialization/deserialization of a model which contains `ops.numpy.GetItem`; feat(losses): add Dice loss implementation (keras-team#19409) * feat(losses): add Dice loss implementation * removed smooth parameter and type casting * adjusted casting and dot operator Update casting Bump the github-actions group with 1 update (keras-team#19412) Bumps the github-actions group with 1 update: [github/codeql-action](https://github.com/github/codeql-action). Updates `github/codeql-action` from 3.24.6 to 3.24.9 - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](github/codeql-action@8a470fd...1b1aada) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Fix issue with shared layer deserialization Remove dead code in saving lib (keras-team#19415) Remove unused beta param for silu, use torch op directly (keras-team#19417) The beta param was only accepted on the tensorflow/torch backends and not in the `keras.ops` API, nor was it tested. I think best just to ditch, since no one could be relying on it. Fix print_fn for custom function (keras-team#19419) Add fp8 to `EinsumDense` Add test script
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Adds Dice class/function implementation to losses.