Initial commit of UpSampling1D layer. (#94)
* Initial commit of UpSampling1D layer * Add __init__.py to fix the test problem. * The test passes. * Fix docs. * Fix testing test. * Add a TODO. * Add more tests, and fix indents.
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@ -68,3 +68,4 @@ from keras_core.layers.regularization.spatial_dropout import SpatialDropout2D
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from keras_core.layers.regularization.spatial_dropout import SpatialDropout3D
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from keras_core.layers.reshaping.flatten import Flatten
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from keras_core.layers.reshaping.reshape import Reshape
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from keras_core.layers.reshaping.up_sampling1d import UpSampling1D
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keras_core/layers/reshaping/up_sampling1d.py
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keras_core/layers/reshaping/up_sampling1d.py
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@ -0,0 +1,59 @@
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from keras_core import operations as ops
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from keras_core.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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class UpSampling1D(Layer):
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"""Upsampling layer for 1D inputs.
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Repeats each temporal step `size` times along the time axis.
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Examples:
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>>> input_shape = (2, 2, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> print(x)
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[[[ 0 1 2]
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[ 3 4 5]]
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[[ 6 7 8]
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[ 9 10 11]]]
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>>> y = keras_core.layers.UpSampling1D(size=2)(x)
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>>> print(y)
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[[[ 0. 1. 2.]
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[ 0. 1. 2.]
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[ 3. 4. 5.]
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[ 3. 4. 5.]]
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[[ 6. 7. 8.]
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[ 6. 7. 8.]
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[ 9. 10. 11.]
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[ 9. 10. 11.]]]
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Args:
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size: Integer. Upsampling factor.
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Input shape:
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3D tensor with shape: `(batch_size, steps, features)`.
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Output shape:
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3D tensor with shape: `(batch_size, upsampled_steps, features)`.
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"""
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def __init__(self, size=2, **kwargs):
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super().__init__(**kwargs)
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self.size = int(size)
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self.input_spec = InputSpec(ndim=3)
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def compute_output_shape(self, input_shape):
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size = (
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self.size * input_shape[1] if input_shape[1] is not None else None
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)
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return [input_shape[0], size, input_shape[2]]
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def call(self, inputs):
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return ops.repeat(x=inputs, repeats=self.size, axis=1)
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def get_config(self):
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config = {"size": self.size}
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base_config = super().get_config()
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return {**base_config, **config}
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keras_core/layers/reshaping/up_sampling1d_test.py
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keras_core/layers/reshaping/up_sampling1d_test.py
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import numpy as np
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import pytest
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from keras_core import backend
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from keras_core import layers
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from keras_core import testing
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from keras_core.backend.common.keras_tensor import KerasTensor
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class UpSamplingTest(testing.TestCase):
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def test_upsampling_1d(self):
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self.run_layer_test(
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layers.UpSampling1D,
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init_kwargs={"size": 2},
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input_shape=(3, 5, 4),
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expected_output_shape=(3, 10, 4),
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expected_output_dtype="float32",
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expected_num_trainable_weights=0,
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expected_num_non_trainable_weights=0,
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expected_num_seed_generators=0,
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expected_num_losses=0,
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supports_masking=False,
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)
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def test_upsampling_1d_correctness(self):
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np.testing.assert_array_equal(
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layers.UpSampling1D(size=2)(np.arange(12).reshape((2, 2, 3))),
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np.array(
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[
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[
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[0.0, 1.0, 2.0],
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[0.0, 1.0, 2.0],
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[3.0, 4.0, 5.0],
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[3.0, 4.0, 5.0],
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],
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[
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[6.0, 7.0, 8.0],
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[6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0],
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[9.0, 10.0, 11.0],
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],
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]
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),
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)
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def test_upsampling_1d_correctness_with_ones(self):
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np.testing.assert_array_equal(
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layers.UpSampling1D(size=3)(np.ones((2, 1, 5))), np.ones((2, 3, 5))
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)
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@pytest.mark.skipif(
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not backend.DYNAMIC_SHAPES_OK,
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reason="Backend does not support dynamic shapes",
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)
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def test_upsampling_1d_with_dynamic_shape(self):
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x = KerasTensor([None, 2, 3])
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self.assertEqual(layers.UpSampling1D(size=2)(x).shape, (None, 4, 3))
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self.assertEqual(layers.UpSampling1D(size=4)(x).shape, (None, 8, 3))
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y = KerasTensor([2, None, 3])
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self.assertEqual(layers.UpSampling1D(size=2)(y).shape, (2, None, 3))
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self.assertEqual(layers.UpSampling1D(size=4)(y).shape, (2, None, 3))
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z = KerasTensor([2, 3, None])
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self.assertEqual(layers.UpSampling1D(size=2)(z).shape, (2, 6, None))
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self.assertEqual(layers.UpSampling1D(size=4)(z).shape, (2, 12, None))
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