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| """Functions to support building models for StreetView text transcription."""
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| import tensorflow as tf
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| from tensorflow.contrib import slim
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| def logits_to_log_prob(logits):
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| """Computes log probabilities using numerically stable trick.
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| This uses two numerical stability tricks:
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| 1) softmax(x) = softmax(x - c) where c is a constant applied to all
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| arguments. If we set c = max(x) then the softmax is more numerically
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| stable.
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| 2) log softmax(x) is not numerically stable, but we can stabilize it
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| by using the identity log softmax(x) = x - log sum exp(x)
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| Args:
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| logits: Tensor of arbitrary shape whose last dimension contains logits.
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| Returns:
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| A tensor of the same shape as the input, but with corresponding log
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| probabilities.
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| """
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| with tf.compat.v1.variable_scope('log_probabilities'):
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| reduction_indices = len(logits.shape.as_list()) - 1
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| max_logits = tf.reduce_max(
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| input_tensor=logits, axis=reduction_indices, keepdims=True)
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| safe_logits = tf.subtract(logits, max_logits)
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| sum_exp = tf.reduce_sum(
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| input_tensor=tf.exp(safe_logits),
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| axis=reduction_indices,
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| keepdims=True)
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| log_probs = tf.subtract(safe_logits, tf.math.log(sum_exp))
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| return log_probs
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| def variables_to_restore(scope=None, strip_scope=False):
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| """Returns a list of variables to restore for the specified list of methods.
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| It is supposed that variable name starts with the method's scope (a prefix
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| returned by _method_scope function).
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| Args:
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| methods_names: a list of names of configurable methods.
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| strip_scope: if True will return variable names without method's scope.
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| If methods_names is None will return names unchanged.
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| model_scope: a scope for a whole model.
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| Returns:
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| a dictionary mapping variable names to variables for restore.
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| """
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| if scope:
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| variable_map = {}
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| method_variables = slim.get_variables_to_restore(include=[scope])
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| for var in method_variables:
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| if strip_scope:
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| var_name = var.op.name[len(scope) + 1:]
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| else:
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| var_name = var.op.name
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| variable_map[var_name] = var
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| return variable_map
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| else:
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| return {v.op.name: v for v in slim.get_variables_to_restore()}
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| def ConvertAllInputsToTensors(func):
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| """A decorator to convert all function's inputs into tensors.
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| Args:
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| func: a function to decorate.
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| Returns:
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| A decorated function.
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| """
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| def FuncWrapper(*args):
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| tensors = [tf.convert_to_tensor(value=a) for a in args]
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| return func(*tensors)
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| return FuncWrapper
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|
