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

keras_core/layers/__init__.py

@@ -111,6 +111,7 @@ from keras_core.layers.reshaping.reshape import Reshape
 from keras_core.layers.reshaping.up_sampling1d import UpSampling1D
 from keras_core.layers.reshaping.up_sampling2d import UpSampling2D
 from keras_core.layers.reshaping.up_sampling3d import UpSampling3D
+from keras_core.layers.reshaping.zero_padding1d import ZeroPadding1D
 from keras_core.layers.reshaping.zero_padding2d import ZeroPadding2D
 from keras_core.layers.reshaping.zero_padding3d import ZeroPadding3D
 from keras_core.layers.rnn.bidirectional import Bidirectional

keras_core/layers/reshaping/cropping1d.py

@@ -26,10 +26,12 @@ class Cropping1D(Layer):
      [[8 9]]]
 
     Args:
-        cropping: Integer or tuple of integers of length 2.
+        cropping: Int, or tuple of int (length 2), or dictionary.
-            How many units should be trimmed off at the beginning and end of
+            - If int: how many units should be trimmed off at the beginning and
-            the cropping dimension (axis 1).
+              end of the cropping dimension (axis 1).
-            If a single int is provided, the same value will be used for both.
+            - If tuple of 2 ints: how many units should be trimmed off at the
+              beginning and end of the cropping dimension
+              (`(left_crop, right_crop)`).
 
     Input shape:
         3D tensor with shape `(batch_size, axis_to_crop, features)`

keras_core/layers/reshaping/zero_padding1d.py

@@ -0,0 +1,69 @@
+from keras_core import operations as ops
+from keras_core.api_export import keras_core_export
+from keras_core.layers.input_spec import InputSpec
+from keras_core.layers.layer import Layer
+
+
+@keras_core_export("keras_core.layers.ZeroPadding1D")
+class ZeroPadding1D(Layer):
+    """Zero-padding layer for 1D input (e.g. temporal sequence).
+
+    Examples:
+
+    >>> input_shape = (2, 2, 3)
+    >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
+    >>> x
+    [[[ 0  1  2]
+      [ 3  4  5]]
+     [[ 6  7  8]
+      [ 9 10 11]]]
+    >>> y = keras_core.layers.ZeroPadding1D(padding=2)(x)
+    >>> y
+    [[[ 0  0  0]
+      [ 0  0  0]
+      [ 0  1  2]
+      [ 3  4  5]
+      [ 0  0  0]
+      [ 0  0  0]]
+     [[ 0  0  0]
+      [ 0  0  0]
+      [ 6  7  8]
+      [ 9 10 11]
+      [ 0  0  0]
+      [ 0  0  0]]]
+
+    Args:
+        padding: Int, or tuple of int (length 2), or dictionary.
+            - If int: how many zeros to add at the beginning and end of
+              the padding dimension (axis 1).
+            - If tuple of 2 ints: how many zeros to add at the beginning and the
+              end of the padding dimension (`(left_pad, right_pad)`).
+
+    Input shape:
+        3D tensor with shape `(batch_size, axis_to_pad, features)`
+
+    Output shape:
+        3D tensor with shape `(batch_size, padded_axis, features)`
+    """
+
+    def __init__(self, padding=1, name=None, dtype=None):
+        super().__init__(name=name, dtype=dtype)
+        if isinstance(padding, int):
+            padding = (padding, padding)
+        self.padding = padding
+        self.input_spec = InputSpec(ndim=3)
+
+    def compute_output_shape(self, input_shape):
+        output_shape = list(input_shape)
+        if input_shape[1] is not None:
+            input_shape[1] += self.padding[0] + self.padding[1]
+        return tuple(output_shape)
+
+    def call(self, inputs):
+        all_dims_padding = ((0, 0), self.padding, (0, 0))
+        return ops.pad(inputs, all_dims_padding)
+
+    def get_config(self):
+        config = {"padding": self.padding}
+        base_config = super().get_config()
+        return {**base_config, **config}

keras_core/layers/reshaping/zero_padding1d_test.py

@@ -0,0 +1,35 @@
+import numpy as np
+import pytest
+from absl.testing import parameterized
+
+from keras_core import backend
+from keras_core import layers
+from keras_core import testing
+
+
+class ZeroPadding1DTest(testing.TestCase, parameterized.TestCase):
+    def test_zero_padding_1d(self):
+        inputs = np.random.rand(1, 2, 3)
+        outputs = layers.ZeroPadding1D(padding=(1, 2))(inputs)
+
+        for index in [0, -1, -2]:
+            self.assertAllClose(outputs[:, index, :], 0.0)
+        self.assertAllClose(outputs[:, 1:-2, :], inputs)
+
+    @parameterized.named_parameters(("one_tuple", (2, 2)), ("one_int", 2))
+    def test_zero_padding_1d_with_same_padding(self, padding):
+        inputs = np.random.rand(1, 2, 3)
+        outputs = layers.ZeroPadding1D(padding=padding)(inputs)
+
+        for index in [0, 1, -1, -2]:
+            self.assertAllClose(outputs[:, index, :], 0.0)
+        self.assertAllClose(outputs[:, 2:-2, :], inputs)
+
+    @pytest.mark.skipif(
+        not backend.DYNAMIC_SHAPES_OK,
+        reason="Backend does not support dynamic shapes",
+    )
+    def test_zero_padding_1d_with_dynamic_spatial_dim(self):
+        input_layer = layers.Input(batch_shape=(1, None, 3))
+        padded = layers.ZeroPadding1D((1, 2))(input_layer)
+        self.assertEqual(padded.shape, (1, None, 3))

keras_core/layers/reshaping/zero_padding2d.py

@@ -67,8 +67,8 @@ class ZeroPadding2D(Layer):
           `(batch_size, channels, padded_height, padded_width)`
     """
 
-    def __init__(self, padding=(1, 1), data_format=None, **kwargs):
+    def __init__(self, padding=(1, 1), data_format=None, name=None, dtype=None):
-        super().__init__(**kwargs)
+        super().__init__(name=name, dtype=dtype)
         self.data_format = backend.standardize_data_format(data_format)
         if isinstance(padding, int):
             self.padding = ((padding, padding), (padding, padding))