58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
# Copyright 2017 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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#
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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utility for evaluating various tasks, e.g., translation & summarization."""
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import codecs
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import os
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import re
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import subprocess
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import tensorflow as tf
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from tensorflow.core.protobuf import saver_pb2
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from tensorflow.python.lib.io import file_io
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from tensorflow.python.training import checkpoint_management as cm
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def get_all_checkpoints(output_dir):
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"""docstring."""
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ckpt = cm.get_checkpoint_state(output_dir, None)
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res = []
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if not ckpt:
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return None
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for path in ckpt.all_model_checkpoint_paths:
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# Look for either a V2 path or a V1 path, with priority for V2.
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v2_path = cm._prefix_to_checkpoint_path(path, saver_pb2.SaverDef.V2)
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v1_path = cm._prefix_to_checkpoint_path(path, saver_pb2.SaverDef.V1)
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if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
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v1_path):
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res.append(path)
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else:
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tf.logging.error("Couldn't match files for checkpoint %s", path)
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return res
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