# -*- coding: utf-8 -*-
"""Convolutional layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .. import backend as K
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine import Layer
from ..engine import InputSpec
from ..utils import conv_utils
from ..legacy import interfaces
# imports for backwards namespace compatibility
from .pooling import AveragePooling1D
from .pooling import AveragePooling2D
from .pooling import AveragePooling3D
from .pooling import MaxPooling1D
from .pooling import MaxPooling2D
from .pooling import MaxPooling3D
from ..legacy.layers import AtrousConvolution1D
from ..legacy.layers import AtrousConvolution2D
class _Conv(Layer):
"""Abstract nD convolution layer (private, used as implementation base).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of outputs.
If `use_bias` is True, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.
# Arguments
rank: An integer, the rank of the convolution,
e.g. "2" for 2D convolution.
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of n integers, specifying the
dimensions of the convolution window.
strides: An integer or tuple/list of n integers,
specifying the strides of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
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, ..., channels)` while `channels_first` corresponds to
inputs with shape `(batch, channels, ...)`.
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".
dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
"""
def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(_Conv, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
def build(self, 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 '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters)
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
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=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True
def call(self, inputs):
if self.rank == 1:
outputs = K.conv1d(
inputs,
self.kernel,
strides=self.strides[0],
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate[0])
if self.rank == 2:
outputs = K.conv2d(
inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.rank == 3:
outputs = K.conv3d(
inputs,
self.kernel,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
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_last':
space = input_shape[1:-1]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0],) + tuple(new_space) + (self.filters,)
if self.data_format == 'channels_first':
space = input_shape[2:]
new_space = []
for i in range(len(space)):
new_dim = conv_utils.conv_output_length(
space[i],
self.kernel_size[i],
padding=self.padding,
stride=self.strides[i],
dilation=self.dilation_rate[i])
new_space.append(new_dim)
return (input_shape[0], self.filters) + tuple(new_space)
def get_config(self):
config = {
'rank': self.rank,
'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(self.kernel_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(self.activity_regularizer),
'kernel_constraint': constraints.serialize(self.kernel_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint)
}
base_config = super(_Conv, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Conv1D(_Conv):
"""1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
with the layer input over a single spatial (or temporal) dimension
to produce a tensor of outputs.
If `use_bias` is True, a bias vector is created and added to the outputs.
Finally, if `activation` is not `None`,
it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide an `input_shape` argument
(tuple of integers or `None`, e.g.
`(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
or `(None, 128)` for variable-length sequences of 128-dimensional vectors.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of a single integer,
specifying the length of the 1D convolution window.
strides: An integer or tuple/list of a single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"`, `"causal"` or `"same"` (case-insensitive).
`"valid"` means "no padding".
`"same"` results in padding the input such that
the output has the same length as the original input.
`"causal"` results in causal (dilated) convolutions, e.g. output[t]
does not depend on input[t+1:]. Useful when modeling temporal data
where the model should not violate the temporal order.
See [WaveNet: A Generative Model for Raw Audio, section 2.1](https://arxiv.org/abs/1609.03499).
dilation_rate: an integer or tuple/list of a single integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
3D tensor with shape: `(batch_size, steps, input_dim)`
# Output shape
3D tensor with shape: `(batch_size, new_steps, filters)`
`steps` value might have changed due to padding or strides.
"""
@interfaces.legacy_conv1d_support
def __init__(self, filters,
kernel_size,
strides=1,
padding='valid',
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format='channels_last',
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=3)
def get_config(self):
config = super(Conv1D, self).get_config()
config.pop('rank')
config.pop('data_format')
return config
class Conv2D(_Conv):
"""2D convolution layer (e.g. spatial convolution over images).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers, does not include the sample axis),
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
in `data_format="channels_last"`.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
"""
@interfaces.legacy_conv2d_support
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=4)
def get_config(self):
config = super(Conv2D, self).get_config()
config.pop('rank')
return config
class Conv3D(_Conv):
"""3D convolution layer (e.g. spatial convolution over volumes).
This layer creates a convolution kernel that is convolved
with the layer input to produce a tensor of
outputs. If `use_bias` is True,
a bias vector is created and added to the outputs. Finally, if
`activation` is not `None`, it is applied to the outputs as well.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers, does not include the sample axis),
e.g. `input_shape=(128, 128, 128, 1)` for 128x128x128 volumes
with a single channel,
in `data_format="channels_last"`.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
depth, height and width 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 each spatial dimension.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
dilation_rate: an integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the 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:
`(samples, channels, conv_dim1, conv_dim2, conv_dim3)` if data_format='channels_first'
or 5D tensor with shape:
`(samples, conv_dim1, conv_dim2, conv_dim3, channels)` if data_format='channels_last'.
# Output shape
5D tensor with shape:
`(samples, filters, new_conv_dim1, new_conv_dim2, new_conv_dim3)` if data_format='channels_first'
or 5D tensor with shape:
`(samples, new_conv_dim1, new_conv_dim2, new_conv_dim3, filters)` if data_format='channels_last'.
`new_conv_dim1`, `new_conv_dim2` and `new_conv_dim3` values might have changed due to padding.
"""
@interfaces.legacy_conv3d_support
def __init__(self, filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv3D, self).__init__(
rank=3,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=5)
def get_config(self):
config = super(Conv3D, self).get_config()
config.pop('rank')
return config
class Conv2DTranspose(Conv2D):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers, does not include the sample axis),
e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
in `data_format="channels_last"`.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
dilation_rate: an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
4D tensor with shape:
`(batch, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(batch, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
# References
- [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
"""
@interfaces.legacy_deconv2d_support
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv2DTranspose, self).__init__(
filters,
kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=4)
def build(self, input_shape):
if len(input_shape) != 4:
raise ValueError('Inputs should have rank ' +
str(4) +
'; 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 '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
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=4, axes={channel_axis: input_dim})
self.built = True
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding)
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
outputs = K.conv2d_transpose(
inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.bias:
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):
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_length(
output_shape[h_axis], stride_h, kernel_h, self.padding)
output_shape[w_axis] = conv_utils.deconv_length(
output_shape[w_axis], stride_w, kernel_w, self.padding)
return tuple(output_shape)
def get_config(self):
config = super(Conv2DTranspose, self).get_config()
config.pop('dilation_rate')
return config
class Conv3DTranspose(Conv3D):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises
from the desire to use a transformation going in the opposite direction
of a normal convolution, i.e., from something that has the shape of the
output of some convolution to something that has the shape of its input
while maintaining a connectivity pattern that is compatible with
said convolution.
When using this layer as the first layer in a model,
provide the keyword argument `input_shape`
(tuple of integers, does not include the sample axis),
e.g. `input_shape=(128, 128, 128, 3)` for a 128x128x128 volume with 3 channels
if `data_format="channels_last"`.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of 3 integers, specifying the
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 width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch, channels, depth, 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".
dilation_rate: an integer or tuple/list of 3 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any stride value != 1.
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.
kernel_initializer: Initializer for the `kernel` weights matrix
(see [initializers](../initializers.md)).
bias_initializer: Initializer for the bias vector
(see [initializers](../initializers.md)).
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
kernel_constraint: Constraint function applied to the 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, channels, depth, 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, new_depth, new_rows, new_cols)` if data_format='channels_first'
or 5D tensor with shape:
`(batch, new_depth, new_rows, new_cols, filters)` if data_format='channels_last'.
`depth` and `rows` and `cols` values might have changed due to padding.
# References
- [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
"""
def __init__(self, filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format=None,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv3DTranspose, self).__init__(
filters,
kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=kernel_constraint,
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=5)
def build(self, input_shape):
if len(input_shape) != 5:
raise ValueError('Inputs should have rank ' +
str(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 '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
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: input_dim})
self.built = True
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
d_axis, h_axis, w_axis = 2, 3, 4
else:
d_axis, h_axis, w_axis = 1, 2, 3
depth = input_shape[d_axis]
height = input_shape[h_axis]
width = input_shape[w_axis]
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
# Infer the dynamic output shape:
out_depth = conv_utils.deconv_length(depth,
stride_d, kernel_d,
self.padding)
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding)
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_depth, out_height, out_width)
else:
output_shape = (batch_size, out_depth, out_height, out_width, self.filters)
outputs = K.conv3d_transpose(inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.bias:
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):
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
else:
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_length(output_shape[d_axis],
stride_d,
kernel_d,
self.padding)
output_shape[h_axis] = conv_utils.deconv_length(output_shape[h_axis],
stride_h,
kernel_h,
self.padding)
output_shape[w_axis] = conv_utils.deconv_length(output_shape[w_axis],
stride_w,
kernel_w,
self.padding)
return tuple(output_shape)
def get_config(self):
config = super(Conv3DTranspose, self).get_config()
config.pop('dilation_rate')
return config
class _SeparableConv(_Conv):
"""Abstract nD depthwise separable convolution layer (private).
Separable convolutions consist in first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
# Arguments
rank: An integer, the rank of the convolution,
e.g. "2" for 2D convolution.
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
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`.
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)).
pointwise_initializer: Initializer for the pointwise 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)).
pointwise_regularizer: Regularizer function applied to
the pointwise kernel matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
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)).
pointwise_constraint: Constraint function applied to
the pointwise kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
4D tensor with shape:
`(batch, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(batch, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
"""
def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(_SeparableConv, self).__init__(
rank=rank,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.pointwise_initializer = initializers.get(pointwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.pointwise_regularizer = regularizers.get(pointwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.pointwise_constraint = constraints.get(pointwise_constraint)
def build(self, input_shape):
if len(input_shape) < self.rank + 2:
raise ValueError('Inputs to `SeparableConv' + str(self.rank) + 'D` '
'should have rank ' + str(self.rank + 2) + '. '
'Received input shape:', str(input_shape))
channel_axis = 1 if self.data_format == 'channels_first' else -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
depthwise_kernel_shape = self.kernel_size + (input_dim, self.depth_multiplier)
pointwise_kernel_shape = (1,) * self.rank + (self.depth_multiplier * input_dim, self.filters)
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)
self.pointwise_kernel = self.add_weight(
shape=pointwise_kernel_shape,
initializer=self.pointwise_initializer,
name='pointwise_kernel',
regularizer=self.pointwise_regularizer,
constraint=self.pointwise_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters,),
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=self.rank + 2,
axes={channel_axis: input_dim})
self.built = True
def call(self, inputs):
if self.rank == 1:
outputs = K.separable_conv1d(
inputs,
self.depthwise_kernel,
self.pointwise_kernel,
data_format=self.data_format,
strides=self.strides,
padding=self.padding,
dilation_rate=self.dilation_rate)
if self.rank == 2:
outputs = K.separable_conv2d(
inputs,
self.depthwise_kernel,
self.pointwise_kernel,
data_format=self.data_format,
strides=self.strides,
padding=self.padding,
dilation_rate=self.dilation_rate)
if self.bias:
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 get_config(self):
config = super(_SeparableConv, self).get_config()
config.pop('rank')
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['pointwise_initializer'] = initializers.serialize(self.pointwise_initializer)
config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
config['pointwise_regularizer'] = regularizers.serialize(self.pointwise_regularizer)
config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
config['pointwise_constraint'] = constraints.serialize(self.pointwise_constraint)
return config
class SeparableConv1D(_SeparableConv):
"""Depthwise separable 1D convolution.
Separable convolutions consist in first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of single integer,
specifying the length of the 1D convolution window.
strides: An integer or tuple/list of single integer,
specifying the stride length of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
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`.
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)).
pointwise_initializer: Initializer for the pointwise 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)).
pointwise_regularizer: Regularizer function applied to
the pointwise kernel matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
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)).
pointwise_constraint: Constraint function applied to
the pointwise kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
3D tensor with shape:
`(batch, channels, steps)` if data_format='channels_first'
or 3D tensor with shape:
`(batch, steps, channels)` if data_format='channels_last'.
# Output shape
3D tensor with shape:
`(batch, filters, new_steps)` if data_format='channels_first'
or 3D tensor with shape:
`(batch, new_steps, filters)` if data_format='channels_last'.
`new_steps` values might have changed due to padding or strides.
"""
def __init__(self, filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv1D, self).__init__(
rank=1,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
**kwargs)
class SeparableConv2D(_SeparableConv):
"""Depthwise separable 2D convolution.
Separable convolutions consist in first performing
a depthwise spatial convolution
(which acts on each input channel separately)
followed by a pointwise convolution which mixes together the resulting
output channels. The `depth_multiplier` argument controls how many
output channels are generated per input channel in the depthwise step.
Intuitively, separable convolutions can be understood as
a way to factorize a convolution kernel into two smaller kernels,
or as an extreme version of an Inception block.
# Arguments
filters: Integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution).
kernel_size: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: one of `"valid"` or `"same"` (case-insensitive).
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".
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`.
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)).
pointwise_initializer: Initializer for the pointwise 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)).
pointwise_regularizer: Regularizer function applied to
the pointwise kernel matrix
(see [regularizer](../regularizers.md)).
bias_regularizer: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
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)).
pointwise_constraint: Constraint function applied to
the pointwise kernel matrix
(see [constraints](../constraints.md)).
bias_constraint: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
# Input shape
4D tensor with shape:
`(batch, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(batch, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
"""
@interfaces.legacy_separable_conv2d_support
def __init__(self, filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
depth_multiplier=depth_multiplier,
activation=activation,
use_bias=use_bias,
depthwise_initializer=depthwise_initializer,
pointwise_initializer=pointwise_initializer,
bias_initializer=bias_initializer,
depthwise_regularizer=depthwise_regularizer,
pointwise_regularizer=pointwise_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
depthwise_constraint=depthwise_constraint,
pointwise_constraint=pointwise_constraint,
bias_constraint=bias_constraint,
**kwargs)
class UpSampling1D(Layer):
"""Upsampling layer for 1D inputs.
Repeats each temporal step `size` times along the time axis.
# Arguments
size: integer. Upsampling factor.
# Input shape
3D tensor with shape: `(batch, steps, features)`.
# Output shape
3D tensor with shape: `(batch, upsampled_steps, features)`.
"""
@interfaces.legacy_upsampling1d_support
def __init__(self, size=2, **kwargs):
super(UpSampling1D, self).__init__(**kwargs)
self.size = int(size)
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
size = self.size * input_shape[1] if input_shape[1] is not None else None
return (input_shape[0], size, input_shape[2])
def call(self, inputs):
output = K.repeat_elements(inputs, self.size, axis=1)
return output
def get_config(self):
config = {'size': self.size}
base_config = super(UpSampling1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class UpSampling2D(Layer):
"""Upsampling layer for 2D inputs.
Repeats the rows and columns of the data
by size[0] and size[1] respectively.
# Arguments
size: int, or tuple of 2 integers.
The upsampling factors for rows and columns.
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".
# Input shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, rows, cols)`
# Output shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, upsampled_rows, upsampled_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, upsampled_rows, upsampled_cols)`
"""
@interfaces.legacy_upsampling2d_support
def __init__(self, size=(2, 2), data_format=None, **kwargs):
super(UpSampling2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = conv_utils.normalize_tuple(size, 2, 'size')
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
return (input_shape[0],
input_shape[1],
height,
width)
elif self.data_format == 'channels_last':
height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
return (input_shape[0],
height,
width,
input_shape[3])
def call(self, inputs):
return K.resize_images(inputs, self.size[0], self.size[1],
self.data_format)
def get_config(self):
config = {'size': self.size,
'data_format': self.data_format}
base_config = super(UpSampling2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class UpSampling3D(Layer):
"""Upsampling layer for 3D inputs.
Repeats the 1st, 2nd and 3rd dimensions
of the data by size[0], size[1] and size[2] respectively.
# Arguments
size: int, or tuple of 3 integers.
The upsampling factors for dim1, dim2 and dim3.
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".
# Input shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, dim1, dim2, dim3, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, dim1, dim2, dim3)`
# Output shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, upsampled_dim1, upsampled_dim2, upsampled_dim3, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, upsampled_dim1, upsampled_dim2, upsampled_dim3)`
"""
@interfaces.legacy_upsampling3d_support
def __init__(self, size=(2, 2, 2), data_format=None, **kwargs):
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = conv_utils.normalize_tuple(size, 3, 'size')
self.input_spec = InputSpec(ndim=5)
super(UpSampling3D, self).__init__(**kwargs)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
dim1 = self.size[0] * input_shape[2] if input_shape[2] is not None else None
dim2 = self.size[1] * input_shape[3] if input_shape[3] is not None else None
dim3 = self.size[2] * input_shape[4] if input_shape[4] is not None else None
return (input_shape[0],
input_shape[1],
dim1,
dim2,
dim3)
elif self.data_format == 'channels_last':
dim1 = self.size[0] * input_shape[1] if input_shape[1] is not None else None
dim2 = self.size[1] * input_shape[2] if input_shape[2] is not None else None
dim3 = self.size[2] * input_shape[3] if input_shape[3] is not None else None
return (input_shape[0],
dim1,
dim2,
dim3,
input_shape[4])
def call(self, inputs):
return K.resize_volumes(inputs,
self.size[0], self.size[1], self.size[2],
self.data_format)
def get_config(self):
config = {'size': self.size,
'data_format': self.data_format}
base_config = super(UpSampling3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding1D(Layer):
"""Zero-padding layer for 1D input (e.g. temporal sequence).
# Arguments
padding: int, or tuple of int (length 2), or dictionary.
- If int:
How many zeros to add at the beginning and end of
the padding dimension (axis 1).
- If tuple of int (length 2):
How many zeros to add at the beginning and at the end of
the padding dimension (`(left_pad, right_pad)`).
# Input shape
3D tensor with shape `(batch, axis_to_pad, features)`
# Output shape
3D tensor with shape `(batch, padded_axis, features)`
"""
def __init__(self, padding=1, **kwargs):
super(ZeroPadding1D, self).__init__(**kwargs)
self.padding = conv_utils.normalize_tuple(padding, 2, 'padding')
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
if input_shape[1] is not None:
length = input_shape[1] + self.padding[0] + self.padding[1]
else:
length = None
return (input_shape[0],
length,
input_shape[2])
def call(self, inputs):
return K.temporal_padding(inputs, padding=self.padding)
def get_config(self):
config = {'padding': self.padding}
base_config = super(ZeroPadding1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding2D(Layer):
"""Zero-padding layer for 2D input (e.g. picture).
This layer can add rows and columns of zeros
at the top, bottom, left and right side of an image tensor.
# Arguments
padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric padding
is applied to width and height.
- If tuple of 2 ints:
interpreted as two different
symmetric padding values for height and width:
`(symmetric_height_pad, symmetric_width_pad)`.
- If tuple of 2 tuples of 2 ints:
interpreted as
`((top_pad, bottom_pad), (left_pad, right_pad))`
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".
# Input shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, rows, cols)`
# Output shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, padded_rows, padded_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, padded_rows, padded_cols)`
"""
@interfaces.legacy_zeropadding2d_support
def __init__(self,
padding=(1, 1),
data_format=None,
**kwargs):
super(ZeroPadding2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(padding, int):
self.padding = ((padding, padding), (padding, padding))
elif hasattr(padding, '__len__'):
if len(padding) != 2:
raise ValueError('`padding` should have two elements. '
'Found: ' + str(padding))
height_padding = conv_utils.normalize_tuple(padding[0], 2,
'1st entry of padding')
width_padding = conv_utils.normalize_tuple(padding[1], 2,
'2nd entry of padding')
self.padding = (height_padding, width_padding)
else:
raise ValueError('`padding` should be either an int, '
'a tuple of 2 ints '
'(symmetric_height_pad, symmetric_width_pad), '
'or a tuple of 2 tuples of 2 ints '
'((top_pad, bottom_pad), (left_pad, right_pad)). '
'Found: ' + str(padding))
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
if input_shape[2] is not None:
rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
else:
rows = None
if input_shape[3] is not None:
cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
else:
cols = None
return (input_shape[0],
input_shape[1],
rows,
cols)
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
else:
rows = None
if input_shape[2] is not None:
cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
else:
cols = None
return (input_shape[0],
rows,
cols,
input_shape[3])
def call(self, inputs):
return K.spatial_2d_padding(inputs,
padding=self.padding,
data_format=self.data_format)
def get_config(self):
config = {'padding': self.padding,
'data_format': self.data_format}
base_config = super(ZeroPadding2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class ZeroPadding3D(Layer):
"""Zero-padding layer for 3D data (spatial or spatio-temporal).
# Arguments
padding: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric padding
is applied to width and height.
- If tuple of 2 ints:
interpreted as two different
symmetric padding values for height and width:
`(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)`.
- If tuple of 2 tuples of 2 ints:
interpreted as
`((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))`
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".
# Input shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad, depth)`
- If `data_format` is `"channels_first"`:
`(batch, depth, first_axis_to_pad, second_axis_to_pad, third_axis_to_pad)`
# Output shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, first_padded_axis, second_padded_axis, third_axis_to_pad, depth)`
- If `data_format` is `"channels_first"`:
`(batch, depth, first_padded_axis, second_padded_axis, third_axis_to_pad)`
"""
@interfaces.legacy_zeropadding3d_support
def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
super(ZeroPadding3D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(padding, int):
self.padding = ((padding, padding), (padding, padding), (padding, padding))
elif hasattr(padding, '__len__'):
if len(padding) != 3:
raise ValueError('`padding` should have 3 elements. '
'Found: ' + str(padding))
dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
'1st entry of padding')
dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
'2nd entry of padding')
dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
'3rd entry of padding')
self.padding = (dim1_padding, dim2_padding, dim3_padding)
else:
raise ValueError('`padding` should be either an int, '
'a tuple of 3 ints '
'(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), '
'or a tuple of 3 tuples of 2 ints '
'((left_dim1_pad, right_dim1_pad),'
' (left_dim2_pad, right_dim2_pad),'
' (left_dim3_pad, right_dim2_pad)). '
'Found: ' + str(padding))
self.input_spec = InputSpec(ndim=5)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
if input_shape[2] is not None:
dim1 = input_shape[2] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[3] is not None:
dim2 = input_shape[3] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[4] is not None:
dim3 = input_shape[4] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return (input_shape[0],
input_shape[1],
dim1,
dim2,
dim3)
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
dim1 = input_shape[1] + self.padding[0][0] + self.padding[0][1]
else:
dim1 = None
if input_shape[2] is not None:
dim2 = input_shape[2] + self.padding[1][0] + self.padding[1][1]
else:
dim2 = None
if input_shape[3] is not None:
dim3 = input_shape[3] + self.padding[2][0] + self.padding[2][1]
else:
dim3 = None
return (input_shape[0],
dim1,
dim2,
dim3,
input_shape[4])
def call(self, inputs):
return K.spatial_3d_padding(inputs,
padding=self.padding,
data_format=self.data_format)
def get_config(self):
config = {'padding': self.padding,
'data_format': self.data_format}
base_config = super(ZeroPadding3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping1D(Layer):
"""Cropping layer for 1D input (e.g. temporal sequence).
It crops along the time dimension (axis 1).
# Arguments
cropping: int or tuple of int (length 2)
How many units should be trimmed off at the beginning and end of
the cropping dimension (axis 1).
If a single int is provided,
the same value will be used for both.
# Input shape
3D tensor with shape `(batch, axis_to_crop, features)`
# Output shape
3D tensor with shape `(batch, cropped_axis, features)`
"""
def __init__(self, cropping=(1, 1), **kwargs):
super(Cropping1D, self).__init__(**kwargs)
self.cropping = conv_utils.normalize_tuple(cropping, 2, 'cropping')
self.input_spec = InputSpec(ndim=3)
def compute_output_shape(self, input_shape):
if input_shape[1] is not None:
length = input_shape[1] - self.cropping[0] - self.cropping[1]
else:
length = None
return (input_shape[0],
length,
input_shape[2])
def call(self, inputs):
if self.cropping[1] == 0:
return inputs[:, self.cropping[0]:, :]
else:
return inputs[:, self.cropping[0]: -self.cropping[1], :]
def get_config(self):
config = {'cropping': self.cropping}
base_config = super(Cropping1D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping2D(Layer):
"""Cropping layer for 2D input (e.g. picture).
It crops along spatial dimensions, i.e. width and height.
# Arguments
cropping: int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int: the same symmetric cropping
is applied to width and height.
- If tuple of 2 ints:
interpreted as two different
symmetric cropping values for height and width:
`(symmetric_height_crop, symmetric_width_crop)`.
- If tuple of 2 tuples of 2 ints:
interpreted as
`((top_crop, bottom_crop), (left_crop, right_crop))`
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".
# Input shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, rows, cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, rows, cols)`
# Output shape
4D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, cropped_rows, cropped_cols, channels)`
- If `data_format` is `"channels_first"`:
`(batch, channels, cropped_rows, cropped_cols)`
# Examples
```python
# Crop the input 2D images or feature maps
model = Sequential()
model.add(Cropping2D(cropping=((2, 2), (4, 4)),
input_shape=(28, 28, 3)))
# now model.output_shape == (None, 24, 20, 3)
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Cropping2D(cropping=((2, 2), (2, 2))))
# now model.output_shape == (None, 20, 16. 64)
```
"""
@interfaces.legacy_cropping2d_support
def __init__(self, cropping=((0, 0), (0, 0)),
data_format=None, **kwargs):
super(Cropping2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(cropping, int):
self.cropping = ((cropping, cropping), (cropping, cropping))
elif hasattr(cropping, '__len__'):
if len(cropping) != 2:
raise ValueError('`cropping` should have two elements. '
'Found: ' + str(cropping))
height_cropping = conv_utils.normalize_tuple(
cropping[0], 2,
'1st entry of cropping')
width_cropping = conv_utils.normalize_tuple(
cropping[1], 2,
'2nd entry of cropping')
self.cropping = (height_cropping, width_cropping)
else:
raise ValueError('`cropping` should be either an int, '
'a tuple of 2 ints '
'(symmetric_height_crop, symmetric_width_crop), '
'or a tuple of 2 tuples of 2 ints '
'((top_crop, bottom_crop), (left_crop, right_crop)). '
'Found: ' + str(cropping))
self.input_spec = InputSpec(ndim=4)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
return (input_shape[0],
input_shape[1],
input_shape[2] - self.cropping[0][0] - self.cropping[0][1] if input_shape[2] else None,
input_shape[3] - self.cropping[1][0] - self.cropping[1][1] if input_shape[3] else None)
elif self.data_format == 'channels_last':
return (input_shape[0],
input_shape[1] - self.cropping[0][0] - self.cropping[0][1] if input_shape[1] else None,
input_shape[2] - self.cropping[1][0] - self.cropping[1][1] if input_shape[2] else None,
input_shape[3])
def call(self, inputs):
if self.data_format == 'channels_first':
if self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]:]
elif self.cropping[0][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]: -self.cropping[1][1]]
elif self.cropping[1][1] == 0:
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:]
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1]]
elif self.data_format == 'channels_last':
if self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]:,
:]
elif self.cropping[0][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]: -self.cropping[1][1],
:]
elif self.cropping[1][1] == 0:
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:,
:]
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1],
:]
def get_config(self):
config = {'cropping': self.cropping,
'data_format': self.data_format}
base_config = super(Cropping2D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class Cropping3D(Layer):
"""Cropping layer for 3D data (e.g. spatial or spatio-temporal).
# Arguments
cropping: int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
- If int: the same symmetric cropping
is applied to depth, height, and width.
- If tuple of 3 ints:
interpreted as two different
symmetric cropping values for depth, height, and width:
`(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
- If tuple of 3 tuples of 2 ints:
interpreted as
`((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_crop))`
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".
# Input shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)`
- If `data_format` is `"channels_first"`:
`(batch, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)`
# Output shape
5D tensor with shape:
- If `data_format` is `"channels_last"`:
`(batch, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)`
- If `data_format` is `"channels_first"`:
`(batch, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)`
"""
@interfaces.legacy_cropping3d_support
def __init__(self, cropping=((1, 1), (1, 1), (1, 1)),
data_format=None, **kwargs):
super(Cropping3D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
if isinstance(cropping, int):
self.cropping = ((cropping, cropping),
(cropping, cropping),
(cropping, cropping))
elif hasattr(cropping, '__len__'):
if len(cropping) != 3:
raise ValueError('`cropping` should have 3 elements. '
'Found: ' + str(cropping))
dim1_cropping = conv_utils.normalize_tuple(cropping[0], 2,
'1st entry of cropping')
dim2_cropping = conv_utils.normalize_tuple(cropping[1], 2,
'2nd entry of cropping')
dim3_cropping = conv_utils.normalize_tuple(cropping[2], 2,
'3rd entry of cropping')
self.cropping = (dim1_cropping, dim2_cropping, dim3_cropping)
else:
raise ValueError('`cropping` should be either an int, '
'a tuple of 3 ints '
'(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop), '
'or a tuple of 3 tuples of 2 ints '
'((left_dim1_crop, right_dim1_crop),'
' (left_dim2_crop, right_dim2_crop),'
' (left_dim3_crop, right_dim2_crop)). '
'Found: ' + str(cropping))
self.input_spec = InputSpec(ndim=5)
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
if input_shape[2] is not None:
dim1 = input_shape[2] - self.cropping[0][0] - self.cropping[0][1]
else:
dim1 = None
if input_shape[3] is not None:
dim2 = input_shape[3] - self.cropping[1][0] - self.cropping[1][1]
else:
dim2 = None
if input_shape[4] is not None:
dim3 = input_shape[4] - self.cropping[2][0] - self.cropping[2][1]
else:
dim3 = None
return (input_shape[0],
input_shape[1],
dim1,
dim2,
dim3)
elif self.data_format == 'channels_last':
if input_shape[1] is not None:
dim1 = input_shape[1] - self.cropping[0][0] - self.cropping[0][1]
else:
dim1 = None
if input_shape[2] is not None:
dim2 = input_shape[2] - self.cropping[1][0] - self.cropping[1][1]
else:
dim2 = None
if input_shape[3] is not None:
dim3 = input_shape[3] - self.cropping[2][0] - self.cropping[2][1]
else:
dim3 = None
return (input_shape[0],
dim1,
dim2,
dim3,
input_shape[4])
def call(self, inputs):
if self.data_format == 'channels_first':
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]:,
self.cropping[2][0]:]
elif self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]:,
self.cropping[2][0]: -self.cropping[2][1]]
elif self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:,
self.cropping[2][0]:]
elif self.cropping[0][1] == self.cropping[2][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]:]
elif self.cropping[0][1] == 0:
return inputs[:,
:,
self.cropping[0][0]:,
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]: -self.cropping[2][1]]
elif self.cropping[1][1] == 0:
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:,
self.cropping[2][0]: -self.cropping[2][1]]
elif self.cropping[2][1] == 0:
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]:]
return inputs[:,
:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]: -self.cropping[2][1]]
elif self.data_format == 'channels_last':
if self.cropping[0][1] == self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]:,
self.cropping[2][0]:,
:]
elif self.cropping[0][1] == self.cropping[1][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]:,
self.cropping[2][0]: -self.cropping[2][1],
:]
elif self.cropping[1][1] == self.cropping[2][1] == 0:
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:,
self.cropping[2][0]:,
:]
elif self.cropping[0][1] == self.cropping[2][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]:-self.cropping[1][1],
self.cropping[2][0]:,
:]
elif self.cropping[0][1] == 0:
return inputs[:,
self.cropping[0][0]:,
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]: -self.cropping[2][1],
:]
elif self.cropping[1][1] == 0:
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]:,
self.cropping[2][0]: -self.cropping[2][1],
:]
elif self.cropping[2][1] == 0:
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]:,
:]
return inputs[:,
self.cropping[0][0]: -self.cropping[0][1],
self.cropping[1][0]: -self.cropping[1][1],
self.cropping[2][0]: -self.cropping[2][1],
:]
def get_config(self):
config = {'cropping': self.cropping,
'data_format': self.data_format}
base_config = super(Cropping3D, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
# Aliases
Convolution1D = Conv1D
Convolution2D = Conv2D
Convolution3D = Conv3D
SeparableConvolution1D = SeparableConv1D
SeparableConvolution2D = SeparableConv2D
Convolution2DTranspose = Conv2DTranspose
Deconvolution2D = Deconv2D = Conv2DTranspose
Deconvolution3D = Deconv3D = Conv3DTranspose
# Legacy aliases
AtrousConv1D = AtrousConvolution1D
AtrousConv2D = AtrousConvolution2D