Add UpSampling3D layer and its test. (#179)
* Commit a new file up_sampling3d.py * Complete UpSampling3D and its test. * Addresses comments. * Last comment
This commit is contained in:
Rick Chao
Francois Chollet
parent
04436b0da6
commit
9b0184f644
@ -103,6 +103,7 @@ from keras_core.layers.reshaping.permute import Permute
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from keras_core.layers.reshaping.repeat_vector import RepeatVector
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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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from keras_core.layers.reshaping.up_sampling3d import UpSampling3D
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from keras_core.layers.reshaping.zero_padding3d import ZeroPadding3D
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from keras_core.layers.rnn.bidirectional import Bidirectional
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from keras_core.layers.rnn.conv_lstm1d import ConvLSTM1D
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136
keras_core/layers/reshaping/up_sampling3d.py
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136
keras_core/layers/reshaping/up_sampling3d.py
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@ -0,0 +1,136 @@
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from keras_core import backend
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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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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from keras_core.utils import argument_validation
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@keras_core_export("keras_core.layers.UpSampling3D")
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class UpSampling3D(Layer):
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"""Upsampling layer for 3D inputs.
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Repeats the 1st, 2nd and 3rd dimensions
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of the data by `size[0]`, `size[1]` and `size[2]` respectively.
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Examples:
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>>> input_shape = (2, 1, 2, 1, 3)
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>>> x = np.ones(input_shape)
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>>> y = keras_core.layers.UpSampling3D(size=(2, 2, 2))(x)
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>>> y.shape
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(2, 2, 4, 2, 3)
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Args:
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size: Int, or tuple of 3 integers.
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The upsampling factors for dim1, dim2 and dim3.
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data_format: A string,
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one of `"channels_last"` (default) or `"channels_first"`.
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The ordering of the dimensions in the inputs.
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`"channels_last"` corresponds to inputs with shape
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`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
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while `"channels_first"` corresponds to inputs with shape
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`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
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When unspecified, uses
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`image_data_format` value found in your Keras config file at
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`~/.keras/keras.json` (if exists) else `"channels_last"`.
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Defaults to `"channels_last"`.
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Input shape:
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5D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, dim1, dim2, dim3, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, dim1, dim2, dim3)`
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Output shape:
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5D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, upsampled_dim1, upsampled_dim2, upsampled_dim3,
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channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, upsampled_dim1, upsampled_dim2,
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upsampled_dim3)`
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"""
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def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
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super().__init__(**kwargs)
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self.data_format = backend.config.standardize_data_format(data_format)
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self.size = argument_validation.standardize_tuple(size, 3, "size")
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self.input_spec = InputSpec(ndim=5)
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def compute_output_shape(self, input_shape):
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if self.data_format == "channels_first":
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dim1 = (
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self.size[0] * input_shape[2]
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if input_shape[2] is not None
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else None
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)
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dim2 = (
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self.size[1] * input_shape[3]
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if input_shape[3] is not None
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else None
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)
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dim3 = (
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self.size[2] * input_shape[4]
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if input_shape[4] is not None
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else None
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)
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return (input_shape[0], input_shape[1], dim1, dim2, dim3)
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else:
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dim1 = (
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self.size[0] * input_shape[1]
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if input_shape[1] is not None
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else None
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)
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dim2 = (
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self.size[1] * input_shape[2]
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if input_shape[2] is not None
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else None
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)
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dim3 = (
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self.size[2] * input_shape[3]
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if input_shape[3] is not None
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else None
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)
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return (input_shape[0], dim1, dim2, dim3, input_shape[4])
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def call(self, inputs):
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return self._resize_volumes(
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inputs, self.size[0], self.size[1], self.size[2], self.data_format
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)
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def get_config(self):
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config = {"size": self.size, "data_format": self.data_format}
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base_config = super().get_config()
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return {**base_config, **config}
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def _resize_volumes(
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self, x, depth_factor, height_factor, width_factor, data_format
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):
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"""Resizes the volume contained in a 5D tensor.
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Args:
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x: Tensor or variable to resize.
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depth_factor: Positive integer.
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height_factor: Positive integer.
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width_factor: Positive integer.
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data_format: One of `"channels_first"`, `"channels_last"`.
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Returns:
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A tensor.
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Raises:
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ValueError: if `data_format` is neither
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`channels_last` or `channels_first`.
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"""
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if data_format == "channels_first":
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output = ops.repeat(x, depth_factor, axis=2)
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output = ops.repeat(output, height_factor, axis=3)
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output = ops.repeat(output, width_factor, axis=4)
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return output
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elif data_format == "channels_last":
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output = ops.repeat(x, depth_factor, axis=1)
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output = ops.repeat(output, height_factor, axis=2)
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output = ops.repeat(output, width_factor, axis=3)
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return output
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else:
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raise ValueError("Invalid data_format: " + str(data_format))
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125
keras_core/layers/reshaping/up_sampling3d_test.py
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125
keras_core/layers/reshaping/up_sampling3d_test.py
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import numpy as np
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from absl.testing import parameterized
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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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class UpSampling3dTest(testing.TestCase, parameterized.TestCase):
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@parameterized.product(
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data_format=["channels_first", "channels_last"],
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length_dim1=[2, 3],
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length_dim2=[2],
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length_dim3=[3],
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)
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def test_upsampling_3d(
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self, data_format, length_dim1, length_dim2, length_dim3
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):
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num_samples = 2
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stack_size = 2
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input_len_dim1 = 10
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input_len_dim2 = 11
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input_len_dim3 = 12
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if data_format == "channels_first":
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inputs = np.random.rand(
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num_samples,
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stack_size,
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input_len_dim1,
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input_len_dim2,
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input_len_dim3,
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)
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else:
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inputs = np.random.rand(
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num_samples,
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input_len_dim1,
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input_len_dim2,
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input_len_dim3,
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stack_size,
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)
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# basic test
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if data_format == "channels_first":
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expected_output_shape = (2, 2, 20, 22, 24)
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else:
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expected_output_shape = (2, 20, 22, 24, 2)
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self.run_layer_test(
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layers.UpSampling3D,
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init_kwargs={"size": (2, 2, 2), "data_format": data_format},
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input_shape=inputs.shape,
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expected_output_shape=expected_output_shape,
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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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layer = layers.UpSampling3D(
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size=(length_dim1, length_dim2, length_dim3),
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data_format=data_format,
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)
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layer.build(inputs.shape)
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np_output = layer(inputs=backend.Variable(inputs))
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if data_format == "channels_first":
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assert np_output.shape[2] == length_dim1 * input_len_dim1
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assert np_output.shape[3] == length_dim2 * input_len_dim2
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assert np_output.shape[4] == length_dim3 * input_len_dim3
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else: # tf
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assert np_output.shape[1] == length_dim1 * input_len_dim1
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assert np_output.shape[2] == length_dim2 * input_len_dim2
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assert np_output.shape[3] == length_dim3 * input_len_dim3
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# compare with numpy
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if data_format == "channels_first":
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expected_out = np.repeat(inputs, length_dim1, axis=2)
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expected_out = np.repeat(expected_out, length_dim2, axis=3)
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expected_out = np.repeat(expected_out, length_dim3, axis=4)
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else: # tf
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expected_out = np.repeat(inputs, length_dim1, axis=1)
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expected_out = np.repeat(expected_out, length_dim2, axis=2)
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expected_out = np.repeat(expected_out, length_dim3, axis=3)
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np.testing.assert_allclose(np_output, expected_out)
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def test_upsampling_3d_correctness(self):
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input_shape = (2, 1, 2, 1, 3)
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x = np.arange(np.prod(input_shape)).reshape(input_shape)
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np.testing.assert_array_equal(
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layers.UpSampling3D(size=(2, 2, 2))(x),
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np.array(
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[
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[
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[
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[[0.0, 1.0, 2.0], [0.0, 1.0, 2.0]],
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[[0.0, 1.0, 2.0], [0.0, 1.0, 2.0]],
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[[3.0, 4.0, 5.0], [3.0, 4.0, 5.0]],
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[[3.0, 4.0, 5.0], [3.0, 4.0, 5.0]],
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],
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[
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[[0.0, 1.0, 2.0], [0.0, 1.0, 2.0]],
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[[0.0, 1.0, 2.0], [0.0, 1.0, 2.0]],
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[[3.0, 4.0, 5.0], [3.0, 4.0, 5.0]],
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[[3.0, 4.0, 5.0], [3.0, 4.0, 5.0]],
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],
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],
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[
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[
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[[6.0, 7.0, 8.0], [6.0, 7.0, 8.0]],
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[[6.0, 7.0, 8.0], [6.0, 7.0, 8.0]],
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[[9.0, 10.0, 11.0], [9.0, 10.0, 11.0]],
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[[9.0, 10.0, 11.0], [9.0, 10.0, 11.0]],
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],
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[
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[[6.0, 7.0, 8.0], [6.0, 7.0, 8.0]],
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[[6.0, 7.0, 8.0], [6.0, 7.0, 8.0]],
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[[9.0, 10.0, 11.0], [9.0, 10.0, 11.0]],
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[[9.0, 10.0, 11.0], [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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)
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