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# Copyright 2018 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.
# ==============================================================================
"""Upgrader for Python scripts from 1.* TensorFlow to 2.0 TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import functools
from tensorflow.tools.compatibility import ast_edits
from tensorflow.tools.compatibility import renames_v2
class TFAPIChangeSpec(ast_edits.APIChangeSpec):
"""List of maps that describe what changed in the API."""
def __init__(self):
# Maps from a function name to a dictionary that describes how to
# map from an old argument keyword to the new argument keyword.
self.function_keyword_renames = {}
# Mapping from function to the new name of the function
self.function_renames = renames_v2.renames
# Variables that should be changed to functions.
self.change_to_function = {}
# Functions that were reordered should be changed to the new keyword args
# for safety, if positional arguments are used. If you have reversed the
# positional arguments yourself, this could do the wrong thing.
self.function_reorders = {}
# Specially handled functions.
self.function_handle = {}
for decay in ["tf.train.exponential_decay", "tf.train.piecewise_constant",
"tf.train.polynomial_decay", "tf.train.natural_exp_decay",
"tf.train.inverse_time_decay", "tf.train.cosine_decay",
"tf.train.cosine_decay_restarts",
"tf.train.linear_cosine_decay",
"tf.train.noisy_linear_cosine_decay"]:
self.function_handle[decay] = functools.partial(
self._learning_rate_decay_handler, decay_name=decay)
@staticmethod
def _learning_rate_decay_handler(file_edit_recorder, node, decay_name):
comment = ("ERROR: %s has been changed to return a callable instead of a "
"tensor when graph building, but its functionality remains "
"unchanged during eager execution (returns a callable like "
"before). The converter cannot detect and fix this reliably, so "
"you need to inspect this usage manually.\n") % decay_name
file_edit_recorder.add(
comment,
node.lineno,
node.col_offset,
decay_name,
decay_name,
error="%s requires manual check." % decay_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description="""Convert a TensorFlow Python file to 2.0
Simple usage:
tf_convert_v2.py --infile foo.py --outfile bar.py
tf_convert_v2.py --intree ~/code/old --outtree ~/code/new
""")
parser.add_argument(
"--infile",
dest="input_file",
help="If converting a single file, the name of the file "
"to convert")
parser.add_argument(
"--outfile",
dest="output_file",
help="If converting a single file, the output filename.")
parser.add_argument(
"--intree",
dest="input_tree",
help="If converting a whole tree of files, the directory "
"to read from (relative or absolute).")
parser.add_argument(
"--outtree",
dest="output_tree",
help="If converting a whole tree of files, the output "
"directory (relative or absolute).")
parser.add_argument(
"--copyotherfiles",
dest="copy_other_files",
help=("If converting a whole tree of files, whether to "
"copy the other files."),
type=bool,
default=False)
parser.add_argument(
"--reportfile",
dest="report_filename",
help=("The name of the file where the report log is "
"stored."
"(default: %(default)s)"),
default="report.txt")
args = parser.parse_args()
upgrade = ast_edits.ASTCodeUpgrader(TFAPIChangeSpec())
report_text = None
report_filename = args.report_filename
files_processed = 0
if args.input_file:
if not args.output_file:
raise ValueError(
"--outfile=<output file> argument is required when converting a "
"single file.")
files_processed, report_text, errors = upgrade.process_file(
args.input_file, args.output_file)
files_processed = 1
elif args.input_tree:
if not args.output_tree:
raise ValueError(
"--outtree=<output directory> argument is required when converting a "
"file tree.")
files_processed, report_text, errors = upgrade.process_tree(
args.input_tree, args.output_tree, args.copy_other_files)
else:
parser.print_help()
if report_text:
open(report_filename, "w").write(report_text)
print("TensorFlow 2.0 Upgrade Script")
print("-----------------------------")
print("Converted %d files\n" % files_processed)
print("Detected %d errors that require attention" % len(errors))
print("-" * 80)
print("\n".join(errors))
print("\nMake sure to read the detailed log %r\n" % report_filename)
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