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| """Contains common flags and functions."""
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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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| import locale
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| import os
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| from absl import logging
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| import numpy as np
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| import tensorflow as tf
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| def get_seq_middle(seq_length):
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| """Returns relative index for the middle frame in sequence."""
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| half_offset = int((seq_length - 1) / 2)
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| return seq_length - 1 - half_offset
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| def info(obj):
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| """Return info on shape and dtype of a numpy array or TensorFlow tensor."""
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| if obj is None:
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| return 'None.'
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| elif isinstance(obj, list):
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| if obj:
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| return 'List of %d... %s' % (len(obj), info(obj[0]))
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| else:
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| return 'Empty list.'
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| elif isinstance(obj, tuple):
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| if obj:
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| return 'Tuple of %d... %s' % (len(obj), info(obj[0]))
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| else:
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| return 'Empty tuple.'
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| else:
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| if is_a_numpy_array(obj):
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| return 'Array with shape: %s, dtype: %s' % (obj.shape, obj.dtype)
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| else:
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| return str(obj)
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| def is_a_numpy_array(obj):
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| """Returns true if obj is a numpy array."""
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| return type(obj).__module__ == np.__name__
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| def count_parameters(also_print=True):
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| """Cound the number of parameters in the model.
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| Args:
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| also_print: Boolean. If True also print the numbers.
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| Returns:
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| The total number of parameters.
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| """
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| total = 0
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| if also_print:
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| logging.info('Model Parameters:')
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| for v in get_vars_to_restore():
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| shape = v.get_shape()
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| if also_print:
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| logging.info('%s %s: %s', v.op.name, shape,
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| format_number(shape.num_elements()))
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| total += shape.num_elements()
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| if also_print:
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| logging.info('Total: %s', format_number(total))
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| return total
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|
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| def get_vars_to_restore(ckpt=None):
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| """Returns list of variables that should be saved/restored.
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| Args:
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| ckpt: Path to existing checkpoint. If present, returns only the subset of
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| variables that exist in given checkpoint.
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| Returns:
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| List of all variables that need to be saved/restored.
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| """
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| model_vars = tf.trainable_variables()
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|
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| bn_vars = [v for v in tf.global_variables()
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| if 'moving_mean' in v.op.name or 'moving_variance' in v.op.name]
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| model_vars.extend(bn_vars)
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| model_vars = sorted(model_vars, key=lambda x: x.op.name)
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| if ckpt is not None:
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| ckpt_var_names = tf.contrib.framework.list_variables(ckpt)
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| ckpt_var_names = [name for (name, unused_shape) in ckpt_var_names]
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| for v in model_vars:
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| if v.op.name not in ckpt_var_names:
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| logging.warn('Missing var %s in checkpoint: %s', v.op.name,
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| os.path.basename(ckpt))
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| model_vars = [v for v in model_vars if v.op.name in ckpt_var_names]
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| return model_vars
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|
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| def format_number(n):
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| """Formats number with thousands commas."""
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| locale.setlocale(locale.LC_ALL, 'en_US')
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| return locale.format('%d', n, grouping=True)
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|
| def read_text_lines(filepath):
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| with open(filepath, 'r') as f:
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| lines = f.readlines()
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| lines = [l.rstrip() for l in lines]
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| return lines
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|
