106 lines
3.7 KiB
Python
106 lines
3.7 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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"""Max pooling 3D layer."""
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import tensorflow.compat.v2 as tf
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from keras.layers.pooling.base_pooling3d import Pooling3D
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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.MaxPool3D", "keras.layers.MaxPooling3D")
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class MaxPooling3D(Pooling3D):
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"""Max pooling operation for 3D data (spatial or spatio-temporal).
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Downsamples the input along its spatial dimensions (depth, height, and
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width) by taking the maximum value over an input window (of size defined by
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`pool_size`) for each channel of the input. The window is shifted by
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`strides` along each dimension.
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Args:
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pool_size: Tuple of 3 integers,
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factors by which to downscale (dim1, dim2, dim3).
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`(2, 2, 2)` will halve the size of the 3D input in each dimension.
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strides: tuple of 3 integers, or None. Strides values.
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padding: One of `"valid"` or `"same"` (case-insensitive).
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`"valid"` means no padding. `"same"` results in padding evenly to
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the left/right or up/down of the input such that output has the same
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height/width dimension as the input.
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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, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
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while `channels_first` corresponds to inputs with shape
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`(batch, 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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- If `data_format='channels_last'`:
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5D tensor with shape:
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`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
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- If `data_format='channels_first'`:
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5D tensor with shape:
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`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`
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Output shape:
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- If `data_format='channels_last'`:
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5D tensor with shape:
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`(batch_size, pooled_dim1, pooled_dim2, pooled_dim3, channels)`
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- If `data_format='channels_first'`:
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5D tensor with shape:
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`(batch_size, channels, pooled_dim1, pooled_dim2, pooled_dim3)`
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Example:
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```python
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depth = 30
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height = 30
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width = 30
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input_channels = 3
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inputs = tf.keras.Input(shape=(depth, height, width, input_channels))
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layer = tf.keras.layers.MaxPooling3D(pool_size=3)
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outputs = layer(inputs) # Shape: (batch_size, 10, 10, 10, 3)
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```
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"""
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def __init__(
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self,
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pool_size=(2, 2, 2),
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strides=None,
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padding="valid",
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data_format=None,
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**kwargs
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):
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super().__init__(
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tf.nn.max_pool3d,
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pool_size=pool_size,
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strides=strides,
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padding=padding,
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data_format=data_format,
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**kwargs
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)
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# Alias
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MaxPool3D = MaxPooling3D
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