File size: 3,314 Bytes
f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | # Copyright 2017 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.
# ==============================================================================
"""Functions to support building models for StreetView text transcription."""
import tensorflow as tf
from tensorflow.contrib import slim
def logits_to_log_prob(logits):
"""Computes log probabilities using numerically stable trick.
This uses two numerical stability tricks:
1) softmax(x) = softmax(x - c) where c is a constant applied to all
arguments. If we set c = max(x) then the softmax is more numerically
stable.
2) log softmax(x) is not numerically stable, but we can stabilize it
by using the identity log softmax(x) = x - log sum exp(x)
Args:
logits: Tensor of arbitrary shape whose last dimension contains logits.
Returns:
A tensor of the same shape as the input, but with corresponding log
probabilities.
"""
with tf.compat.v1.variable_scope('log_probabilities'):
reduction_indices = len(logits.shape.as_list()) - 1
max_logits = tf.reduce_max(
input_tensor=logits, axis=reduction_indices, keepdims=True)
safe_logits = tf.subtract(logits, max_logits)
sum_exp = tf.reduce_sum(
input_tensor=tf.exp(safe_logits),
axis=reduction_indices,
keepdims=True)
log_probs = tf.subtract(safe_logits, tf.math.log(sum_exp))
return log_probs
def variables_to_restore(scope=None, strip_scope=False):
"""Returns a list of variables to restore for the specified list of methods.
It is supposed that variable name starts with the method's scope (a prefix
returned by _method_scope function).
Args:
methods_names: a list of names of configurable methods.
strip_scope: if True will return variable names without method's scope.
If methods_names is None will return names unchanged.
model_scope: a scope for a whole model.
Returns:
a dictionary mapping variable names to variables for restore.
"""
if scope:
variable_map = {}
method_variables = slim.get_variables_to_restore(include=[scope])
for var in method_variables:
if strip_scope:
var_name = var.op.name[len(scope) + 1:]
else:
var_name = var.op.name
variable_map[var_name] = var
return variable_map
else:
return {v.op.name: v for v in slim.get_variables_to_restore()}
def ConvertAllInputsToTensors(func):
"""A decorator to convert all function's inputs into tensors.
Args:
func: a function to decorate.
Returns:
A decorated function.
"""
def FuncWrapper(*args):
tensors = [tf.convert_to_tensor(value=a) for a in args]
return func(*tensors)
return FuncWrapper
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