Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/layers/convolutional/conv3d.py: 93%
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1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Keras 3D convolution layer."""
18from keras.src import activations
19from keras.src import constraints
20from keras.src import initializers
21from keras.src import regularizers
22from keras.src.dtensor import utils
23from keras.src.layers.convolutional.base_conv import Conv
25# isort: off
26from tensorflow.python.util.tf_export import keras_export
29@keras_export("keras.layers.Conv3D", "keras.layers.Convolution3D")
30class Conv3D(Conv):
31 """3D convolution layer (e.g. spatial convolution over volumes).
33 This layer creates a convolution kernel that is convolved
34 with the layer input to produce a tensor of
35 outputs. If `use_bias` is True,
36 a bias vector is created and added to the outputs. Finally, if
37 `activation` is not `None`, it is applied to the outputs as well.
39 When using this layer as the first layer in a model,
40 provide the keyword argument `input_shape`
41 (tuple of integers or `None`, does not include the sample axis),
42 e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
43 with a single channel,
44 in `data_format="channels_last"`.
46 Examples:
48 >>> # The inputs are 28x28x28 volumes with a single channel, and the
49 >>> # batch size is 4
50 >>> input_shape =(4, 28, 28, 28, 1)
51 >>> x = tf.random.normal(input_shape)
52 >>> y = tf.keras.layers.Conv3D(
53 ... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
54 >>> print(y.shape)
55 (4, 26, 26, 26, 2)
57 >>> # With extended batch shape [4, 7], e.g. a batch of 4 videos of
58 >>> # 3D frames, with 7 frames per video.
59 >>> input_shape = (4, 7, 28, 28, 28, 1)
60 >>> x = tf.random.normal(input_shape)
61 >>> y = tf.keras.layers.Conv3D(
62 ... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
63 >>> print(y.shape)
64 (4, 7, 26, 26, 26, 2)
66 Args:
67 filters: Integer, the dimensionality of the output space (i.e. the number
68 of output filters in the convolution).
69 kernel_size: An integer or tuple/list of 3 integers, specifying the depth,
70 height and width of the 3D convolution window. Can be a single integer
71 to specify the same value for all spatial dimensions.
72 strides: An integer or tuple/list of 3 integers, specifying the strides of
73 the convolution along each spatial dimension. Can be a single integer to
74 specify the same value for all spatial dimensions. Specifying any stride
75 value != 1 is incompatible with specifying any `dilation_rate` value !=
76 1.
77 padding: one of `"valid"` or `"same"` (case-insensitive).
78 `"valid"` means no padding. `"same"` results in padding with zeros
79 evenly to the left/right or up/down of the input such that output has
80 the same height/width dimension as the input.
81 data_format: A string, one of `channels_last` (default) or
82 `channels_first`. The ordering of the dimensions in the inputs.
83 `channels_last` corresponds to inputs with shape `batch_shape +
84 (spatial_dim1, spatial_dim2, spatial_dim3, channels)` while
85 `channels_first` corresponds to inputs with shape `batch_shape +
86 (channels, spatial_dim1, spatial_dim2, spatial_dim3)`. When unspecified,
87 uses `image_data_format` value found in your Keras config file at
88 `~/.keras/keras.json` (if exists) else 'channels_last'. Note that the
89 `channels_first` format is currently not supported by TensorFlow on CPU.
90 Defaults to 'channels_last'.
91 dilation_rate: an integer or tuple/list of 3 integers, specifying the
92 dilation rate to use for dilated convolution. Can be a single integer to
93 specify the same value for all spatial dimensions. Currently, specifying
94 any `dilation_rate` value != 1 is incompatible with specifying any
95 stride value != 1.
96 groups: A positive integer specifying the number of groups in which the
97 input is split along the channel axis. Each group is convolved
98 separately with `filters / groups` filters. The output is the
99 concatenation of all the `groups` results along the channel axis. Input
100 channels and `filters` must both be divisible by `groups`.
101 activation: Activation function to use. If you don't specify anything, no
102 activation is applied (see `keras.activations`).
103 use_bias: Boolean, whether the layer uses a bias vector.
104 kernel_initializer: Initializer for the `kernel` weights matrix (see
105 `keras.initializers`). Defaults to 'glorot_uniform'.
106 bias_initializer: Initializer for the bias vector (see
107 `keras.initializers`). Defaults to 'zeros'.
108 kernel_regularizer: Regularizer function applied to the `kernel` weights
109 matrix (see `keras.regularizers`).
110 bias_regularizer: Regularizer function applied to the bias vector (see
111 `keras.regularizers`).
112 activity_regularizer: Regularizer function applied to the output of the
113 layer (its "activation") (see `keras.regularizers`).
114 kernel_constraint: Constraint function applied to the kernel matrix (see
115 `keras.constraints`).
116 bias_constraint: Constraint function applied to the bias vector (see
117 `keras.constraints`).
119 Input shape:
120 5+D tensor with shape: `batch_shape + (channels, conv_dim1, conv_dim2,
121 conv_dim3)` if data_format='channels_first'
122 or 5+D tensor with shape: `batch_shape + (conv_dim1, conv_dim2, conv_dim3,
123 channels)` if data_format='channels_last'.
125 Output shape:
126 5+D tensor with shape: `batch_shape + (filters, new_conv_dim1,
127 new_conv_dim2, new_conv_dim3)` if data_format='channels_first'
128 or 5+D tensor with shape: `batch_shape + (new_conv_dim1, new_conv_dim2,
129 new_conv_dim3, filters)` if data_format='channels_last'.
130 `new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have
131 changed due to padding.
133 Returns:
134 A tensor of rank 5+ representing
135 `activation(conv3d(inputs, kernel) + bias)`.
137 Raises:
138 ValueError: if `padding` is "causal".
139 ValueError: when both `strides > 1` and `dilation_rate > 1`.
140 """
142 @utils.allow_initializer_layout
143 def __init__(
144 self,
145 filters,
146 kernel_size,
147 strides=(1, 1, 1),
148 padding="valid",
149 data_format=None,
150 dilation_rate=(1, 1, 1),
151 groups=1,
152 activation=None,
153 use_bias=True,
154 kernel_initializer="glorot_uniform",
155 bias_initializer="zeros",
156 kernel_regularizer=None,
157 bias_regularizer=None,
158 activity_regularizer=None,
159 kernel_constraint=None,
160 bias_constraint=None,
161 **kwargs
162 ):
163 super().__init__(
164 rank=3,
165 filters=filters,
166 kernel_size=kernel_size,
167 strides=strides,
168 padding=padding,
169 data_format=data_format,
170 dilation_rate=dilation_rate,
171 groups=groups,
172 activation=activations.get(activation),
173 use_bias=use_bias,
174 kernel_initializer=initializers.get(kernel_initializer),
175 bias_initializer=initializers.get(bias_initializer),
176 kernel_regularizer=regularizers.get(kernel_regularizer),
177 bias_regularizer=regularizers.get(bias_regularizer),
178 activity_regularizer=regularizers.get(activity_regularizer),
179 kernel_constraint=constraints.get(kernel_constraint),
180 bias_constraint=constraints.get(bias_constraint),
181 **kwargs
182 )
185# Alias
187Convolution3D = Conv3D