File size: 12,074 Bytes
74c6a32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
# SRC: https://github.com/alexandrosstergiou/keras-DepthwiseConv3D
'''
This is a modification of the SeparableConv3D code in Keras,
to perform just the Depthwise Convolution (1st step) of the
Depthwise Separable Convolution layer.
'''
from __future__ import absolute_import
from tensorflow.keras import backend as K
from tensorflow.keras import initializers
from tensorflow.keras import regularizers
from tensorflow.keras import constraints
import tensorflow.keras.utils as conv_utils
from tensorflow.keras.layers import Conv3D, InputSpec
from tensorflow.python.keras.backend import _preprocess_padding, _preprocess_conv3d_input
import tensorflow as tf
class DepthwiseConv3D(Conv3D):
"""Depthwise 3D convolution.
Depth-wise part of separable convolutions consist in performing
just the first step/operation
(which acts on each input channel separately).
It does not perform the pointwise convolution (second step).
The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
# Arguments
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, width and height of the 3D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 3 integers,
specifying the strides of the convolution along the depth, width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
padding: one of `"valid"` or `"same"` (case-insensitive).
depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to `filterss_in * depth_multiplier`.
groups: The depth size of the convolution (as a variant of the original Depthwise conv)
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, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, height, width)`.
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".
activation: Activation function to use
(see [activations](../activations.md)).
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
use_bias: Boolean, whether the layer uses a bias vector.
depthwise_initializer: Initializer for the depthwise kernel matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
depthwise_regularizer: Regularizer function applied to
the depthwise kernel matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
dialation_rate: List of ints.
Defines the dilation factor for each dimension in the
input. Defaults to (1,1,1)
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
depthwise_constraint: Constraint function applied to
the depthwise kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
5D tensor with shape:
`(batch, depth, channels, rows, cols)` if data_format='channels_first'
or 5D tensor with shape:
`(batch, depth, rows, cols, channels)` if data_format='channels_last'.
# Output shape
5D tensor with shape:
`(batch, filters * depth, new_depth, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, new_depth, new_rows, new_cols, filters * depth)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
"""
#@legacy_depthwise_conv3d_support
def __init__(self,
kernel_size,
strides=(1, 1, 1),
padding='valid',
depth_multiplier=1,
groups=None,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
dilation_rate = (1, 1, 1),
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv3D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
dilation_rate=dilation_rate,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.groups = groups
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
self.dilation_rate = dilation_rate
self._padding = _preprocess_padding(self.padding)
self._strides = (1,) + self.strides + (1,)
self._data_format = "NDHWC"
self.input_dim = None
def build(self, input_shape):
if len(input_shape) < 5:
raise ValueError('Inputs to `DepthwiseConv3D` should have rank 5. '
'Received input shape:', str(input_shape))
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs to '
'`DepthwiseConv3D` '
'should be defined. Found `None`.')
self.input_dim = int(input_shape[channel_axis])
if self.groups is None:
self.groups = self.input_dim
if self.groups > self.input_dim:
raise ValueError('The number of groups cannot exceed the number of channels')
if self.input_dim % self.groups != 0:
raise ValueError('Warning! The channels dimension is not divisible by the group size chosen')
depthwise_kernel_shape = (self.kernel_size[0],
self.kernel_size[1],
self.kernel_size[2],
self.input_dim,
self.depth_multiplier)
self.depthwise_kernel = self.add_weight(
shape=depthwise_kernel_shape,
initializer=self.depthwise_initializer,
name='depthwise_kernel',
regularizer=self.depthwise_regularizer,
constraint=self.depthwise_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.groups * self.depth_multiplier,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=5, axes={channel_axis: self.input_dim})
self.built = True
def call(self, inputs, training=None):
inputs = _preprocess_conv3d_input(inputs, self.data_format)
if self.data_format == 'channels_last':
dilation = (1,) + self.dilation_rate + (1,)
else:
dilation = self.dilation_rate + (1,) + (1,)
if self._data_format == 'NCDHW':
outputs = tf.concat(
[tf.nn.conv3d(inputs[0][:, i:i+self.input_dim//self.groups, :, :, :], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=1)
else:
outputs = tf.concat(
[tf.nn.conv3d(inputs[0][:, :, :, :, i:i+self.input_dim//self.groups], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
strides=self._strides,
padding=self._padding,
dilations=dilation,
data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=-1)
if self.bias is not None:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
depth = input_shape[2]
rows = input_shape[3]
cols = input_shape[4]
out_filters = self.groups * self.depth_multiplier
elif self.data_format == 'channels_last':
depth = input_shape[1]
rows = input_shape[2]
cols = input_shape[3]
out_filters = self.groups * self.depth_multiplier
depth = conv_utils.conv_output_length(depth, self.kernel_size[0],
self.padding,
self.strides[0])
rows = conv_utils.conv_output_length(rows, self.kernel_size[1],
self.padding,
self.strides[1])
cols = conv_utils.conv_output_length(cols, self.kernel_size[2],
self.padding,
self.strides[2])
if self.data_format == 'channels_first':
return (input_shape[0], out_filters, depth, rows, cols)
elif self.data_format == 'channels_last':
return (input_shape[0], depth, rows, cols, out_filters)
def get_config(self):
config = super(DepthwiseConv3D, self).get_config()
config.pop('filters')
config.pop('kernel_initializer')
config.pop('kernel_regularizer')
config.pop('kernel_constraint')
config['depth_multiplier'] = self.depth_multiplier
config['depthwise_initializer'] = initializers.serialize(self.depthwise_initializer)
config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
return config
def __call__(self, inputs, training=True):
return self.call(inputs, training)
DepthwiseConvolution3D = DepthwiseConv3D
|