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78 lines
2.6 KiB
78 lines
2.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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"""Evaluation api."""
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import os
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import argparse
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import pickle
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from mindspore.common import dtype as mstype
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from mindspore import context
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from config import TransformerConfig
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from src.transformer import infer, infer_ppl
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from src.utils import Dictionary
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from src.utils import get_score
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parser = argparse.ArgumentParser(description='Evaluation MASS.')
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parser.add_argument("--config", type=str, required=True,
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help="Model config json file path.")
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parser.add_argument("--vocab", type=str, required=True,
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help="Vocabulary to use.")
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parser.add_argument("--output", type=str, required=True,
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help="Result file path.")
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parser.add_argument("--metric", type=str, default='rouge',
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help='Set eval method.')
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parser.add_argument("--platform", type=str, required=True,
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help="model working platform.")
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def get_config(config):
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config = TransformerConfig.from_json_file(config)
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config.compute_type = mstype.float32
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config.dtype = mstype.float32
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return config
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if __name__ == '__main__':
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args, _ = parser.parse_known_args()
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if args.vocab.endswith("bin"):
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vocab = Dictionary.load_from_persisted_dict(args.vocab)
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else:
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vocab = Dictionary.load_from_text([args.vocab])
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_config = get_config(args.config)
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device_id = os.getenv('DEVICE_ID', None)
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if device_id is None:
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device_id = 0
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device_id = int(device_id)
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context.set_context(
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#mode=context.GRAPH_MODE,
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mode=context.PYNATIVE_MODE,
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device_target=args.platform,
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reserve_class_name_in_scope=False,
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device_id=device_id)
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if args.metric == 'rouge':
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result = infer(_config)
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else:
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result = infer_ppl(_config)
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with open(args.output, "wb") as f:
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pickle.dump(result, f, 1)
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# get score by given metric
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score = get_score(result, vocab, metric=args.metric)
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print(score)
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