162 lines
5.6 KiB
Python
162 lines
5.6 KiB
Python
# 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 2D 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.UpSampling2D")
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class UpSampling2D(Layer):
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"""Upsampling layer for 2D inputs.
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Repeats the rows and columns of the data
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by `size[0]` and `size[1]` respectively.
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Examples:
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>>> input_shape = (2, 2, 1, 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 = tf.keras.layers.UpSampling2D(size=(1, 2))(x)
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>>> print(y)
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tf.Tensor(
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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]]]], shape=(2, 2, 2, 3), dtype=int64)
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Args:
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size: Int, or tuple of 2 integers.
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The upsampling factors for rows and columns.
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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, height, width, channels)` while `channels_first`
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corresponds to inputs with shape
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`(batch_size, channels, height, width)`.
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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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interpolation: A string, one of `"area"`, `"bicubic"`, `"bilinear"`,
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`"gaussian"`, `"lanczos3"`, `"lanczos5"`, `"mitchellcubic"`,
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`"nearest"`.
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Input shape:
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4D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, rows, cols, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, rows, cols)`
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Output shape:
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4D tensor with shape:
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- If `data_format` is `"channels_last"`:
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`(batch_size, upsampled_rows, upsampled_cols, channels)`
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- If `data_format` is `"channels_first"`:
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`(batch_size, channels, upsampled_rows, upsampled_cols)`
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"""
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def __init__(
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self, size=(2, 2), data_format=None, interpolation="nearest", **kwargs
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):
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super().__init__(**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, 2, "size")
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interpolations = {
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"area": tf.image.ResizeMethod.AREA,
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"bicubic": tf.image.ResizeMethod.BICUBIC,
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"bilinear": tf.image.ResizeMethod.BILINEAR,
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"gaussian": tf.image.ResizeMethod.GAUSSIAN,
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"lanczos3": tf.image.ResizeMethod.LANCZOS3,
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"lanczos5": tf.image.ResizeMethod.LANCZOS5,
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"mitchellcubic": tf.image.ResizeMethod.MITCHELLCUBIC,
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"nearest": tf.image.ResizeMethod.NEAREST_NEIGHBOR,
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}
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interploations_list = '"' + '", "'.join(interpolations.keys()) + '"'
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if interpolation not in interpolations:
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raise ValueError(
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"`interpolation` argument should be one of: "
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f'{interploations_list}. Received: "{interpolation}".'
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)
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self.interpolation = interpolation
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self.input_spec = InputSpec(ndim=4)
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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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height = (
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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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width = (
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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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return tf.TensorShape(
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[input_shape[0], input_shape[1], height, width]
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)
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else:
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height = (
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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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width = (
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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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return tf.TensorShape(
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[input_shape[0], height, width, input_shape[3]]
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)
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def call(self, inputs):
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return backend.resize_images(
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inputs,
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self.size[0],
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self.size[1],
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self.data_format,
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interpolation=self.interpolation,
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)
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def get_config(self):
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config = {
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"size": self.size,
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"data_format": self.data_format,
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"interpolation": self.interpolation,
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}
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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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