121 lines
4.5 KiB
Python
121 lines
4.5 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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"""Private base class for pooling 2D layers."""
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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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class Pooling2D(Layer):
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"""Pooling layer for arbitrary pooling functions, for 2D data (e.g. images).
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This class only exists for code reuse. It will never be an exposed API.
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Args:
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pool_function: The pooling function to apply, e.g. `tf.nn.max_pool2d`.
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pool_size: An integer or tuple/list of 2 integers:
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(pool_height, pool_width)
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specifying the size of the pooling window.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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strides: An integer or tuple/list of 2 integers,
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specifying the strides of the pooling operation.
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Can be a single integer to specify the same value for
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all spatial dimensions.
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padding: A string. The padding method, either 'valid' or 'same'.
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Case-insensitive.
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data_format: A string, one of `channels_last` (default) or
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`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, height, width, channels)` while `channels_first` corresponds to
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inputs with shape `(batch, channels, height, width)`.
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name: A string, the name of the layer.
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"""
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def __init__(
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self,
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pool_function,
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pool_size,
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strides,
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padding="valid",
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data_format=None,
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name=None,
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**kwargs
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):
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super().__init__(name=name, **kwargs)
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if data_format is None:
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data_format = backend.image_data_format()
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if strides is None:
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strides = pool_size
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self.pool_function = pool_function
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self.pool_size = conv_utils.normalize_tuple(pool_size, 2, "pool_size")
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self.strides = conv_utils.normalize_tuple(
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strides, 2, "strides", allow_zero=True
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)
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self.padding = conv_utils.normalize_padding(padding)
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self.data_format = conv_utils.normalize_data_format(data_format)
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self.input_spec = InputSpec(ndim=4)
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def call(self, inputs):
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if self.data_format == "channels_last":
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pool_shape = (1,) + self.pool_size + (1,)
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strides = (1,) + self.strides + (1,)
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else:
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pool_shape = (1, 1) + self.pool_size
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strides = (1, 1) + self.strides
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outputs = self.pool_function(
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inputs,
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ksize=pool_shape,
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strides=strides,
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padding=self.padding.upper(),
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data_format=conv_utils.convert_data_format(self.data_format, 4),
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)
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return outputs
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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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rows = input_shape[2]
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cols = input_shape[3]
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else:
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rows = input_shape[1]
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cols = input_shape[2]
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rows = conv_utils.conv_output_length(
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rows, self.pool_size[0], self.padding, self.strides[0]
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)
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cols = conv_utils.conv_output_length(
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cols, self.pool_size[1], self.padding, self.strides[1]
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)
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if self.data_format == "channels_first":
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return tf.TensorShape([input_shape[0], input_shape[1], rows, cols])
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else:
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return tf.TensorShape([input_shape[0], rows, cols, input_shape[3]])
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def get_config(self):
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config = {
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"pool_size": self.pool_size,
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"padding": self.padding,
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"strides": self.strides,
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"data_format": self.data_format,
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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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