131 lines
4.6 KiB
Python
131 lines
4.6 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras upsampling layer for 3D inputs."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.utils import conv_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.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 = tf.constant(1, shape=input_shape)
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>>> y = tf.keras.layers.UpSampling3D(size=2)(x)
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>>> print(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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It defaults to the `image_data_format` value found in your
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Keras config file at `~/.keras/keras.json`.
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If you never set it, then it will be "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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self.data_format = conv_utils.normalize_data_format(data_format)
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self.size = conv_utils.normalize_tuple(size, 3, "size")
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self.input_spec = InputSpec(ndim=5)
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super().__init__(**kwargs)
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def compute_output_shape(self, input_shape):
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input_shape = tf.TensorShape(input_shape).as_list()
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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 tf.TensorShape(
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[input_shape[0], input_shape[1], dim1, dim2, dim3]
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)
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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 tf.TensorShape(
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[input_shape[0], dim1, dim2, dim3, input_shape[4]]
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)
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def call(self, inputs):
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return backend.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 dict(list(base_config.items()) + list(config.items()))
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