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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.
# =============================================================================
# pylint: disable=unused-import,g-bad-import-order
"""Contains layer utilies for input validation and format conversion.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.ops import variables
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.util import nest
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:
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 normalize_data_format(value):
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'}:
raise ValueError('The `padding` argument must be one of "valid", "same". '
'Received: ' + str(padding))
return padding
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 smart_cond(pred, fn1, fn2, name=None):
"""Return either `fn1()` or `fn2()` based on the boolean predicate `pred`.
If `pred` is a bool or has a constant value, we return either `fn1()`
or `fn2()`, otherwise we use `tf.cond` to dynamically route to both.
Arguments:
pred: A scalar determining whether to return the result of `fn1` or `fn2`.
fn1: The callable to be performed if pred is true.
fn2: The callable to be performed if pred is false.
name: Optional name prefix when using `tf.cond`.
Returns:
Tensors returned by the call to either `fn1` or `fn2`.
Raises:
TypeError: If `fn1` or `fn2` is not callable.
"""
if not callable(fn1):
raise TypeError('`fn1` must be callable.')
if not callable(fn2):
raise TypeError('`fn2` must be callable.')
pred_value = constant_value(pred)
if pred_value is not None:
if pred_value:
return fn1()
else:
return fn2()
else:
return control_flow_ops.cond(pred, true_fn=fn1, false_fn=fn2, name=name)
def constant_value(pred):
"""Return the bool value for `pred`, or None if `pred` had a dynamic value.
Arguments:
pred: A scalar, either a Python bool or a TensorFlow boolean variable
or tensor, or the Python integer 1 or 0.
Returns:
True or False if `pred` has a constant boolean value, None otherwise.
Raises:
TypeError: If `pred` is not a Variable, Tensor or bool.
"""
# Allow integer booleans.
if pred == 0:
pred = False
elif pred == 1:
pred = True
if isinstance(pred, bool):
pred_value = pred
elif isinstance(pred, variables.Variable):
pred_value = None
elif isinstance(pred, ops.Tensor):
pred_value = tensor_util.constant_value(pred)
else:
raise TypeError('`pred` must be a Tensor, a Variable, or a Python bool.')
return pred_value
def object_list_uid(object_list):
"""Creates a single string from object ids."""
object_list = nest.flatten(object_list)
return ', '.join([str(abs(id(x))) for x in object_list])
def static_shape(x):
"""Get the static shape of a Tensor, or None if it is unavailable."""
if x is None:
return None
try:
return tuple(x.get_shape().as_list())
except ValueError:
return None
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