290 lines
8.7 KiB
Python
290 lines
8.7 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.
|
|
# =============================================================================
|
|
|
|
# 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.eager import context
|
|
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 smart_cond as smart_module
|
|
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, 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 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, true_fn=None, false_fn=None, name=None):
|
|
"""Return either `true_fn()` if predicate `pred` is true else `false_fn()`.
|
|
|
|
If `pred` is a bool or has a constant value, we return either `true_fn()`
|
|
or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.
|
|
|
|
Arguments:
|
|
pred: A scalar determining whether to return the result of `true_fn` or
|
|
`false_fn`.
|
|
true_fn: The callable to be performed if pred is true.
|
|
false_fn: 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 `true_fn` or `false_fn`.
|
|
|
|
Raises:
|
|
TypeError: If `true_fn` or `false_fn` is not callable.
|
|
"""
|
|
if isinstance(pred, variables.Variable):
|
|
return control_flow_ops.cond(
|
|
pred, true_fn=true_fn, false_fn=false_fn, name=name)
|
|
return smart_module.smart_cond(
|
|
pred, true_fn=true_fn, false_fn=false_fn, 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, or Python
|
|
interger 1 or 0.
|
|
"""
|
|
# Allow integer booleans.
|
|
if isinstance(pred, int):
|
|
if pred == 1:
|
|
pred = True
|
|
elif pred == 0:
|
|
pred = False
|
|
|
|
if isinstance(pred, variables.Variable):
|
|
return None
|
|
return smart_module.smart_constant_value(pred)
|
|
|
|
|
|
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
|
|
|
|
|
|
def get_reachable_from_inputs(inputs, targets=None):
|
|
"""Returns the set of tensors reachable from `inputs`.
|
|
|
|
Stops if all targets have been found (target is optional).
|
|
|
|
Only valid in Symbolic mode, not Eager mode.
|
|
|
|
Args:
|
|
inputs: List of tensors.
|
|
targets: List of tensors.
|
|
|
|
Returns:
|
|
A set of tensors reachable from the inputs (includes the inputs themselves).
|
|
"""
|
|
reachable = set(inputs)
|
|
if targets:
|
|
targets = set(targets)
|
|
queue = inputs[:]
|
|
|
|
while queue:
|
|
x = queue.pop()
|
|
outputs = []
|
|
try:
|
|
consumers = x.consumers()
|
|
except AttributeError:
|
|
# Case where x is a variable type
|
|
consumers = [x.op]
|
|
for z in consumers:
|
|
consumer_outputs = z.outputs
|
|
if consumer_outputs: # May be None
|
|
outputs += consumer_outputs
|
|
|
|
for y in outputs:
|
|
if y not in reachable:
|
|
reachable.add(y)
|
|
queue.insert(0, y)
|
|
|
|
if targets and targets.issubset(reachable):
|
|
return reachable
|
|
return reachable
|