Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/convolutional/conv3d_transpose.py
2023-06-19 00:49:18 +02:00

393 lines
15 KiB
Python

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras 3D transposed convolution layer (sometimes called deconvolution)."""
import tensorflow.compat.v2 as tf
from keras import activations
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.dtensor import utils
from keras.engine.input_spec import InputSpec
from keras.layers.convolutional.conv3d import Conv3D
from keras.utils import conv_utils
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export(
"keras.layers.Conv3DTranspose", "keras.layers.Convolution3DTranspose"
)
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 or `None`, 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"`.
Args:
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
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 the depth, height
and width.
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).
`"valid"` means no padding. `"same"` results in padding with zeros
evenly to the left/right or up/down of the input such that output has
the same height/width dimension as the input.
output_padding: An integer or tuple/list of 3 integers,
specifying the amount of padding along the depth, height, and
width.
Can be a single integer to specify the same value for all
spatial dimensions.
The amount of output padding along a given dimension must be
lower than the stride along that same dimension.
If set to `None` (default), the output shape is inferred.
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_size, depth, height, width, channels)` while `channels_first`
corresponds to inputs with shape
`(batch_size, 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.
If you don't specify anything, no activation is applied
(see `keras.activations`).
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix
(see `keras.initializers`). Defaults to 'glorot_uniform'.
bias_initializer: Initializer for the bias vector
(see `keras.initializers`). Defaults to 'zeros'.
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix
(see `keras.regularizers`).
bias_regularizer: Regularizer function applied to the bias vector
(see `keras.regularizers`).
activity_regularizer: Regularizer function applied to
the output of the layer (its "activation")
(see `keras.regularizers`).
kernel_constraint: Constraint function applied to the kernel matrix
(see `keras.constraints`).
bias_constraint: Constraint function applied to the bias vector
(see `keras.constraints`).
Input shape:
5D tensor with shape:
`(batch_size, channels, depth, rows, cols)` if
data_format='channels_first'
or 5D tensor with shape:
`(batch_size, depth, rows, cols, channels)` if
data_format='channels_last'.
Output shape:
5D tensor with shape:
`(batch_size, filters, new_depth, new_rows, new_cols)` if
data_format='channels_first'
or 5D tensor with shape:
`(batch_size, new_depth, new_rows, new_cols, filters)` if
data_format='channels_last'.
`depth` and `rows` and `cols` values might have changed due to padding.
If `output_padding` is specified::
```
new_depth = ((depth - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_rows = ((rows - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
new_cols = ((cols - 1) * strides[2] + kernel_size[2] - 2 * padding[2] +
output_padding[2])
```
Returns:
A tensor of rank 5 representing
`activation(conv3dtranspose(inputs, kernel) + bias)`.
Raises:
ValueError: if `padding` is "causal".
ValueError: when both `strides` > 1 and `dilation_rate` > 1.
References:
- [A guide to convolution arithmetic for deep
learning](https://arxiv.org/abs/1603.07285v1)
- [Deconvolutional
Networks](https://www.matthewzeiler.com/mattzeiler/deconvolutionalnetworks.pdf)
"""
@utils.allow_initializer_layout
def __init__(
self,
filters,
kernel_size,
strides=(1, 1, 1),
padding="valid",
output_padding=None,
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().__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs,
)
self.output_padding = output_padding
if self.output_padding is not None:
self.output_padding = conv_utils.normalize_tuple(
self.output_padding, 3, "output_padding", allow_zero=True
)
for stride, out_pad in zip(self.strides, self.output_padding):
if out_pad >= stride:
raise ValueError(
"Strides must be greater than output padding. "
f"Received strides={self.strides}, "
f"output_padding={self.output_padding}."
)
def build(self, input_shape):
input_shape = tf.TensorShape(input_shape)
if len(input_shape) != 5:
raise ValueError(
"Inputs should have rank 5. "
f"Received input_shape={input_shape}."
)
channel_axis = self._get_channel_axis()
if input_shape.dims[channel_axis].value is None:
raise ValueError(
"The channel dimension of the inputs "
"to `Conv3DTranspose` should be defined. "
f"The input_shape received is {input_shape}, "
f"where axis {channel_axis} (0-based) "
"is the channel dimension, which found to be `None`."
)
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (self.filters, input_dim)
self.input_spec = InputSpec(ndim=5, axes={channel_axis: input_dim})
self.kernel = self.add_weight(
"kernel",
shape=kernel_shape,
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint,
trainable=True,
dtype=self.dtype,
)
if self.use_bias:
self.bias = self.add_weight(
"bias",
shape=(self.filters,),
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint,
trainable=True,
dtype=self.dtype,
)
else:
self.bias = None
self.built = True
def call(self, inputs):
inputs_shape = tf.shape(inputs)
batch_size = inputs_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 = inputs_shape[d_axis]
height = inputs_shape[h_axis]
width = inputs_shape[w_axis]
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_depth = conv_utils.deconv_output_length(
depth,
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d,
)
out_height = conv_utils.deconv_output_length(
height,
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
)
out_width = conv_utils.deconv_output_length(
width,
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
)
if self.data_format == "channels_first":
output_shape = (
batch_size,
self.filters,
out_depth,
out_height,
out_width,
)
strides = (1, 1, stride_d, stride_h, stride_w)
else:
output_shape = (
batch_size,
out_depth,
out_height,
out_width,
self.filters,
)
strides = (1, stride_d, stride_h, stride_w, 1)
output_shape_tensor = tf.stack(output_shape)
outputs = tf.nn.conv3d_transpose(
inputs,
self.kernel,
output_shape_tensor,
strides,
data_format=conv_utils.convert_data_format(
self.data_format, ndim=5
),
padding=self.padding.upper(),
)
if not tf.executing_eagerly() and inputs.shape.rank:
# Infer the static output shape:
out_shape = self.compute_output_shape(inputs.shape)
outputs.set_shape(out_shape)
if self.use_bias:
outputs = tf.nn.bias_add(
outputs,
self.bias,
data_format=conv_utils.convert_data_format(
self.data_format, ndim=4
),
)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
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
if self.output_padding is None:
out_pad_d = out_pad_h = out_pad_w = None
else:
out_pad_d, out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_output_length(
output_shape[d_axis],
kernel_d,
padding=self.padding,
output_padding=out_pad_d,
stride=stride_d,
)
output_shape[h_axis] = conv_utils.deconv_output_length(
output_shape[h_axis],
kernel_h,
padding=self.padding,
output_padding=out_pad_h,
stride=stride_h,
)
output_shape[w_axis] = conv_utils.deconv_output_length(
output_shape[w_axis],
kernel_w,
padding=self.padding,
output_padding=out_pad_w,
stride=stride_w,
)
return tf.TensorShape(output_shape)
def get_config(self):
config = super().get_config()
config.pop("dilation_rate")
config["output_padding"] = self.output_padding
return config
# Alias
Convolution3DTranspose = Conv3DTranspose