Merge branch 'master' of github.com:keras-team/keras

Author: fchollet

.github/workflows/actions.yml

@@ -54,7 +54,7 @@ jobs:
       - name: Test applications with pytest
         if: ${{ steps.filter.outputs.applications == 'true' }}
         run: |
-          pytest keras/src/applications --cov=keras/src/applications
+          pytest keras/src/applications --cov=keras/src/applications --cov-config=pyproject.toml
           coverage xml --include='keras/src/applications/*' -o apps-coverage.xml
       - name: Codecov keras.applications
         if: ${{ steps.filter.outputs.applications == 'true' }}
@@ -86,19 +86,16 @@ jobs:
           python integration_tests/torch_custom_fit_test.py
       - name: Test with pytest
         run: |
-          pytest keras --ignore keras/src/applications
-      # - name: Test with pytest
-      #   run: |
-      #     pytest keras --ignore keras/src/applications --cov=keras
-      #     coverage xml --omit='keras/src/applications/*,keras/api' -o core-coverage.xml
-      # - name: Codecov keras
-      #   uses: codecov/codecov-action@v4
-      #   with:
-      #     env_vars: PYTHON,KERAS_HOME
-      #     flags: keras,keras-${{ matrix.backend }}
-      #     files: core-coverage.xml
-      #     token: ${{ secrets.CODECOV_TOKEN }}
-      #     fail_ci_if_error: false
+          pytest keras --ignore keras/src/applications --cov=keras --cov-config=pyproject.toml
+          coverage xml --omit='keras/src/applications/*,keras/api' -o core-coverage.xml
+      - name: Codecov keras
+        uses: codecov/codecov-action@v4
+        with:
+          env_vars: PYTHON,KERAS_HOME
+          flags: keras,keras-${{ matrix.backend }}
+          files: core-coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
+          fail_ci_if_error: false
 
   format:
     name: Check the code format

.github/workflows/nightly.yml

@@ -55,7 +55,7 @@ jobs:
           pytest integration_tests/torch_workflow_test.py
       - name: Test with pytest
         run: |
-          pytest keras --ignore keras/src/applications --cov=keras
+          pytest keras --ignore keras/src/applications --cov=keras --cov-config=pyproject.toml
 
   format:
     name: Check the code format

.kokoro/github/ubuntu/gpu/build.sh

@@ -36,7 +36,8 @@ then
    # TODO: keras/layers/merging/merging_test.py::MergingLayersTest::test_sparse_dot_2d Fatal Python error: Aborted
    pytest keras --ignore keras/src/applications \
                --ignore keras/src/layers/merging/merging_test.py \
-               --cov=keras
+               --cov=keras \
+               --cov-config=pyproject.toml
 fi
 
 if [ "$KERAS_BACKEND" == "jax" ]
@@ -56,9 +57,10 @@ then
                --ignore keras/src/trainers/data_adapters/py_dataset_adapter_test.py \
                --ignore keras/src/backend/jax/distribution_lib_test.py \
                --ignore keras/src/distribution/distribution_lib_test.py \
-               --cov=keras
+               --cov=keras \
+               --cov-config=pyproject.toml
 
-   pytest keras/src/distribution/distribution_lib_test.py --cov=keras
+   pytest keras/src/distribution/distribution_lib_test.py --cov=keras --cov-config=pyproject.toml
 fi
 
 if [ "$KERAS_BACKEND" == "torch" ]
@@ -71,5 +73,7 @@ then
    python3 -c 'import torch;assert torch.cuda.is_available()'
 
    pytest keras --ignore keras/src/applications \
-               --cov=keras
+               --cov=keras \
+               --cov-config=pyproject.toml
+
 fi

keras/api/_tf_keras/keras/activations/__init__.py

@@ -7,6 +7,7 @@
 from keras.src.activations import deserialize
 from keras.src.activations import get
 from keras.src.activations import serialize
+from keras.src.activations.activations import celu
 from keras.src.activations.activations import elu
 from keras.src.activations.activations import exponential
 from keras.src.activations.activations import gelu

keras/api/_tf_keras/keras/layers/__init__.py

@@ -145,6 +145,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.center_crop import (
     CenterCrop,
 )
+from keras.src.layers.preprocessing.image_preprocessing.max_num_bounding_box import (
+    MaxNumBoundingBoxes,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )

keras/api/_tf_keras/keras/ops/__init__.py

@@ -62,6 +62,7 @@
 from keras.src.ops.nn import batch_normalization
 from keras.src.ops.nn import binary_crossentropy
 from keras.src.ops.nn import categorical_crossentropy
+from keras.src.ops.nn import celu
 from keras.src.ops.nn import conv
 from keras.src.ops.nn import conv_transpose
 from keras.src.ops.nn import ctc_decode

keras/api/_tf_keras/keras/ops/nn/__init__.py

@@ -8,6 +8,7 @@
 from keras.src.ops.nn import batch_normalization
 from keras.src.ops.nn import binary_crossentropy
 from keras.src.ops.nn import categorical_crossentropy
+from keras.src.ops.nn import celu
 from keras.src.ops.nn import conv
 from keras.src.ops.nn import conv_transpose
 from keras.src.ops.nn import ctc_decode

keras/api/_tf_keras/keras/utils/__init__.py

@@ -4,6 +4,7 @@
 since your modifications would be overwritten.
 """
 
+from keras.api.utils import bounding_boxes
 from keras.api.utils import legacy
 from keras.src.backend.common.global_state import clear_session
 from keras.src.backend.common.keras_tensor import is_keras_tensor

keras/api/_tf_keras/keras/utils/bounding_boxes/__init__.py

@@ -0,0 +1,21 @@
+"""DO NOT EDIT.
+
+This file was autogenerated. Do not edit it by hand,
+since your modifications would be overwritten.
+"""
+
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    affine_transform,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    clip_to_image_size,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    convert_format,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    crop,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    pad,
+)

keras/api/activations/__init__.py

@@ -7,6 +7,7 @@
 from keras.src.activations import deserialize
 from keras.src.activations import get
 from keras.src.activations import serialize
+from keras.src.activations.activations import celu
 from keras.src.activations.activations import elu
 from keras.src.activations.activations import exponential
 from keras.src.activations.activations import gelu

keras/api/layers/__init__.py

@@ -145,6 +145,9 @@
 from keras.src.layers.preprocessing.image_preprocessing.center_crop import (
     CenterCrop,
 )
+from keras.src.layers.preprocessing.image_preprocessing.max_num_bounding_box import (
+    MaxNumBoundingBoxes,
+)
 from keras.src.layers.preprocessing.image_preprocessing.random_brightness import (
     RandomBrightness,
 )

keras/api/ops/__init__.py

@@ -62,6 +62,7 @@
 from keras.src.ops.nn import batch_normalization
 from keras.src.ops.nn import binary_crossentropy
 from keras.src.ops.nn import categorical_crossentropy
+from keras.src.ops.nn import celu
 from keras.src.ops.nn import conv
 from keras.src.ops.nn import conv_transpose
 from keras.src.ops.nn import ctc_decode

keras/api/ops/nn/__init__.py

@@ -8,6 +8,7 @@
 from keras.src.ops.nn import batch_normalization
 from keras.src.ops.nn import binary_crossentropy
 from keras.src.ops.nn import categorical_crossentropy
+from keras.src.ops.nn import celu
 from keras.src.ops.nn import conv
 from keras.src.ops.nn import conv_transpose
 from keras.src.ops.nn import ctc_decode

keras/api/utils/__init__.py

@@ -4,6 +4,7 @@
 since your modifications would be overwritten.
 """
 
+from keras.api.utils import bounding_boxes
 from keras.api.utils import legacy
 from keras.src.backend.common.global_state import clear_session
 from keras.src.backend.common.keras_tensor import is_keras_tensor

keras/api/utils/bounding_boxes/__init__.py

@@ -0,0 +1,21 @@
+"""DO NOT EDIT.
+
+This file was autogenerated. Do not edit it by hand,
+since your modifications would be overwritten.
+"""
+
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    affine_transform,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    clip_to_image_size,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    convert_format,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    crop,
+)
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
+    pad,
+)

keras/src/activations/__init__.py

@@ -1,5 +1,6 @@
 import types
 
+from keras.src.activations.activations import celu
 from keras.src.activations.activations import elu
 from keras.src.activations.activations import exponential
 from keras.src.activations.activations import gelu
@@ -27,6 +28,7 @@
     leaky_relu,
     relu6,
     softmax,
+    celu,
     elu,
     selu,
     softplus,

keras/src/activations/activations.py

@@ -302,6 +302,27 @@ def gelu(x, approximate=False):
     return ops.gelu(x, approximate=approximate)
 
 
+@keras_export("keras.activations.celu")
+def celu(x, alpha=1.0):
+    """Continuously Differentiable Exponential Linear Unit.
+
+    The CeLU activation function is defined as:
+
+    `celu(x) = alpha * (exp(x / alpha) - 1) for x < 0`,`celu(x) = x for x >= 0`.
+
+    where `alpha` is a scaling parameter that controls the activation's shape.
+
+    Args:
+        x: Input tensor.
+        alpha: The α value for the CeLU formulation. Defaults to `1.0`.
+
+    Reference:
+
+    - [Barron, J. T., 2017](https://arxiv.org/abs/1704.07483)
+    """
+    return ops.celu(x, alpha=alpha)
+
+
 @keras_export("keras.activations.tanh")
 def tanh(x):
     """Hyperbolic tangent activation function.

keras/src/activations/activations_test.py

@@ -582,6 +582,22 @@ def gelu(x, approximate=False):
         expected = gelu(x, True)
         self.assertAllClose(result, expected, rtol=1e-05)
 
+    def test_celu(self):
+        def celu(x, alpha=1.0):
+            return np.maximum(x, 0.0) + alpha * np.expm1(
+                np.minimum(x, 0.0) / alpha
+            )
+
+        x = np.random.random((2, 5))
+        result = activations.celu(x[np.newaxis, :])[0]
+        expected = celu(x)
+        self.assertAllClose(result, expected, rtol=1e-05)
+
+        x = np.random.random((2, 5))
+        result = activations.celu(x[np.newaxis, :], alpha=0.5)[0]
+        expected = celu(x, True)
+        self.assertAllClose(result, expected, rtol=1e-05)
+
     def test_elu(self):
         x = np.random.random((2, 5))
         result = activations.elu(x[np.newaxis, :])[0]

keras/src/backend/jax/nn.py

@@ -85,6 +85,11 @@ def gelu(x, approximate=True):
     return jnn.gelu(x, approximate)
 
 
+def celu(x, alpha=1.0):
+    x = convert_to_tensor(x)
+    return jnn.celu(x, alpha=alpha)
+
+
 def softmax(x, axis=-1):
     x = convert_to_tensor(x)
     return jnn.softmax(x, axis=axis)

keras/src/backend/jax/trainer.py

@@ -381,6 +381,7 @@ def fit(
         )
 
         self._symbolic_build(iterator=epoch_iterator)
+        epoch_iterator.reset()
 
         # Container that configures and calls callbacks.
         if not isinstance(callbacks, callbacks_module.CallbackList):
@@ -405,7 +406,7 @@ def fit(
             callbacks.on_epoch_begin(epoch)
 
             self._jax_state_synced = True
-            for step, data in epoch_iterator.enumerate_epoch():
+            for step, data in epoch_iterator:
                 # Callbacks
                 callbacks.on_train_batch_begin(step)
 
@@ -539,6 +540,7 @@ def evaluate(
             )
 
         self._symbolic_build(iterator=epoch_iterator)
+        epoch_iterator.reset()
 
         # Container that configures and calls callbacks.
         if not isinstance(callbacks, callbacks_module.CallbackList):
@@ -560,7 +562,7 @@ def evaluate(
         self.reset_metrics()
 
         self._jax_state_synced = True
-        for step, data in epoch_iterator.enumerate_epoch():
+        for step, data in epoch_iterator:
             callbacks.on_test_batch_begin(step)
 
             if self._jax_state_synced:
@@ -625,7 +627,7 @@ def predict(
 
         if not all(layer.built for layer in self._flatten_layers()):
             # Build the model on one batch of data.
-            for _, data in epoch_iterator.enumerate_epoch():
+            for _, data in epoch_iterator:
                 # Build model
                 x, _, _ = data_adapter_utils.unpack_x_y_sample_weight(data[0])
                 with backend.StatelessScope():
@@ -667,7 +669,7 @@ def append_to_outputs(batch_outputs, outputs):
         self._jax_state_synced = True
         outputs = None
         non_trainable_variables = None
-        for step, x in epoch_iterator.enumerate_epoch():
+        for step, x in epoch_iterator:
             callbacks.on_predict_batch_begin(step)
             if self._jax_state_synced:
                 # The state may have been synced by a callback.

keras/src/backend/numpy/nn.py

@@ -113,6 +113,14 @@ def gelu(x, approximate=True):
         )
 
 
+def celu(x, alpha=1.0):
+    x = convert_to_tensor(x)
+    alpha = np.array(alpha, x.dtype)
+    return np.maximum(x, np.array(0.0, dtype=x.dtype)) + alpha * np.expm1(
+        np.minimum(x, np.array(0.0, dtype=x.dtype)) / alpha
+    )
+
+
 def softmax(x, axis=None):
     exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
     return exp_x / np.sum(exp_x, axis=axis, keepdims=True)

keras/src/backend/numpy/trainer.py

@@ -212,7 +212,7 @@ def append_to_outputs(batch_outputs, outputs):
         self.stop_predicting = False
         callbacks.on_predict_begin()
         outputs = None
-        for step, data in epoch_iterator.enumerate_epoch():
+        for step, data in epoch_iterator:
             callbacks.on_predict_batch_begin(step)
             batch_outputs = self.predict_function(data)
             outputs = append_to_outputs(batch_outputs, outputs)
@@ -256,7 +256,7 @@ def evaluate(
 
         if not all(layer.built for layer in self._flatten_layers()):
             # Build the model on one batch of data.
-            for _, data in epoch_iterator.enumerate_epoch():
+            for _, data in epoch_iterator:
                 data_batch = data[0]
                 self._symbolic_build(data_batch)
                 break
@@ -278,7 +278,7 @@ def evaluate(
         callbacks.on_test_begin()
         logs = {}
         self.reset_metrics()
-        for step, data in epoch_iterator.enumerate_epoch():
+        for step, data in epoch_iterator:
             callbacks.on_test_batch_begin(step)
             logs = self.test_function(data)
             callbacks.on_test_batch_end(step, logs)

keras/src/backend/tensorflow/distribute_test.py

@@ -103,7 +103,7 @@ def test_epoch_iterator(self):
             distribute_strategy=strategy,
         )
         steps_seen = []
-        for step, data_iterator in epoch_iterator.enumerate_epoch():
+        for step, data_iterator in epoch_iterator:
             steps_seen.append(step)
             batch = next(data_iterator)
             self.assertEqual(len(batch), 3)