75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
![]() |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
||
|
#
|
||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
# you may not use this file except in compliance with the License.
|
||
|
# You may obtain a copy of the License at
|
||
|
#
|
||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
#
|
||
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
# See the License for the specific language governing permissions and
|
||
|
# limitations under the License.
|
||
|
# ==============================================================================
|
||
|
"""Global max pooling 3D layer."""
|
||
|
|
||
|
|
||
|
from keras import backend
|
||
|
from keras.layers.pooling.base_global_pooling3d import GlobalPooling3D
|
||
|
|
||
|
# isort: off
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
@keras_export("keras.layers.GlobalMaxPool3D", "keras.layers.GlobalMaxPooling3D")
|
||
|
class GlobalMaxPooling3D(GlobalPooling3D):
|
||
|
"""Global Max pooling operation for 3D data.
|
||
|
|
||
|
Args:
|
||
|
data_format: A string,
|
||
|
one of `channels_last` (default) or `channels_first`.
|
||
|
The ordering of the dimensions in the inputs.
|
||
|
`channels_last` corresponds to inputs with shape
|
||
|
`(batch, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
while `channels_first` corresponds to inputs with shape
|
||
|
`(batch, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
|
||
|
It defaults to the `image_data_format` value found in your
|
||
|
Keras config file at `~/.keras/keras.json`.
|
||
|
If you never set it, then it will be "channels_last".
|
||
|
keepdims: A boolean, whether to keep the spatial dimensions or not.
|
||
|
If `keepdims` is `False` (default), the rank of the tensor is reduced
|
||
|
for spatial dimensions.
|
||
|
If `keepdims` is `True`, the spatial dimensions are retained with
|
||
|
length 1.
|
||
|
The behavior is the same as for `tf.reduce_max` or `np.max`.
|
||
|
|
||
|
Input shape:
|
||
|
- If `data_format='channels_last'`:
|
||
|
5D tensor with shape:
|
||
|
`(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
|
||
|
- If `data_format='channels_first'`:
|
||
|
5D tensor with shape:
|
||
|
`(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`
|
||
|
|
||
|
Output shape:
|
||
|
- If `keepdims`=False:
|
||
|
2D tensor with shape `(batch_size, channels)`.
|
||
|
- If `keepdims`=True:
|
||
|
- If `data_format='channels_last'`:
|
||
|
5D tensor with shape `(batch_size, 1, 1, 1, channels)`
|
||
|
- If `data_format='channels_first'`:
|
||
|
5D tensor with shape `(batch_size, channels, 1, 1, 1)`
|
||
|
"""
|
||
|
|
||
|
def call(self, inputs):
|
||
|
if self.data_format == "channels_last":
|
||
|
return backend.max(inputs, axis=[1, 2, 3], keepdims=self.keepdims)
|
||
|
else:
|
||
|
return backend.max(inputs, axis=[2, 3, 4], keepdims=self.keepdims)
|
||
|
|
||
|
|
||
|
# Alias
|
||
|
|
||
|
GlobalMaxPool3D = GlobalMaxPooling3D
|