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# 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.
# ==============================================================================
"""Utilities used by convolution layers.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range # pylint: disable=redefined-builtin
from tensorflow.python.keras import backend
def convert_data_format(data_format, ndim):
if data_format == 'channels_last':
if ndim == 3:
return 'NWC'
elif ndim == 4:
return 'NHWC'
elif ndim == 5:
return 'NDHWC'
else:
raise ValueError('Input rank not supported:', ndim)
elif data_format == 'channels_first':
if ndim == 3:
return 'NCW'
elif ndim == 4:
return 'NCHW'
elif ndim == 5:
return 'NCDHW'
else:
raise ValueError('Input rank not supported:', ndim)
else:
raise ValueError('Invalid data_format:', data_format)
def normalize_tuple(value, n, name):
"""Transforms a single integer or iterable of integers into an integer tuple.
Arguments:
value: The value to validate and convert. Could an int, or any iterable
of ints.
n: The size of the tuple to be returned.
name: The name of the argument being validated, e.g. "strides" or
"kernel_size". This is only used to format error messages.
Returns:
A tuple of n integers.
Raises:
ValueError: If something else than an int/long or iterable thereof was
passed.
"""
if isinstance(value, int):
return (value,) * n
else:
try:
value_tuple = tuple(value)
except TypeError:
raise ValueError('The `' + name + '` argument must be a tuple of ' +
str(n) + ' integers. Received: ' + str(value))
if len(value_tuple) != n:
raise ValueError('The `' + name + '` argument must be a tuple of ' +
str(n) + ' integers. Received: ' + str(value))
for single_value in value_tuple:
try:
int(single_value)
except (ValueError, TypeError):
raise ValueError('The `' + name + '` argument must be a tuple of ' +
str(n) + ' integers. Received: ' + str(value) + ' '
'including element ' + str(single_value) + ' of type' +
' ' + str(type(single_value)))
return value_tuple
def conv_output_length(input_length, filter_size, padding, stride, dilation=1):
"""Determines output length of a convolution given input length.
Arguments:
input_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
dilation: dilation rate, integer.
Returns:
The output length (integer).
"""
if input_length is None:
return None
assert padding in {'same', 'valid', 'full'}
dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
if padding == 'same':
output_length = input_length
elif padding == 'valid':
output_length = input_length - dilated_filter_size + 1
elif padding == 'full':
output_length = input_length + dilated_filter_size - 1
return (output_length + stride - 1) // stride
def conv_input_length(output_length, filter_size, padding, stride):
"""Determines input length of a convolution given output length.
Arguments:
output_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
Returns:
The input length (integer).
"""
if output_length is None:
return None
assert padding in {'same', 'valid', 'full'}
if padding == 'same':
pad = filter_size // 2
elif padding == 'valid':
pad = 0
elif padding == 'full':
pad = filter_size - 1
return (output_length - 1) * stride - 2 * pad + filter_size
def deconv_output_length(input_length, filter_size, padding, stride):
"""Determines output length of a transposed convolution given input length.
Arguments:
input_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
Returns:
The output length (integer).
"""
if input_length is None:
return None
input_length *= stride
if padding == 'valid':
input_length += max(filter_size - stride, 0)
elif padding == 'full':
input_length -= (stride + filter_size - 2)
return input_length
def normalize_data_format(value):
if value is None:
value = backend.image_data_format()
data_format = value.lower()
if data_format not in {'channels_first', 'channels_last'}:
raise ValueError('The `data_format` argument must be one of '
'"channels_first", "channels_last". Received: ' +
str(value))
return data_format
def normalize_padding(value):
padding = value.lower()
if padding not in {'valid', 'same', 'causal'}:
raise ValueError('The `padding` argument must be one of '
'"valid", "same" (or "causal", only for `Conv1D). '
'Received: ' + str(padding))
return padding
def convert_kernel(kernel):
"""Converts a Numpy kernel matrix from Theano format to TensorFlow format.
Also works reciprocally, since the transformation is its own inverse.
Arguments:
kernel: Numpy array (3D, 4D or 5D).
Returns:
The converted kernel.
Raises:
ValueError: in case of invalid kernel shape or invalid data_format.
"""
kernel = np.asarray(kernel)
if not 3 <= kernel.ndim <= 5:
raise ValueError('Invalid kernel shape:', kernel.shape)
slices = [slice(None, None, -1) for _ in range(kernel.ndim)]
no_flip = (slice(None, None), slice(None, None))
slices[-2:] = no_flip
return np.copy(kernel[slices])
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