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Merge pull request PaddlePaddle#45 from 0YuanZhang0/sequence_tagging
Sequence tagging
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright (c) 2019 PaddlePaddle 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. | ||
| """ | ||
| SequenceTagging network structure | ||
| """ | ||
|
|
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| from __future__ import division | ||
| from __future__ import print_function | ||
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||
| import io | ||
| import os | ||
| import sys | ||
| import math | ||
| import argparse | ||
| import numpy as np | ||
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| from train import SeqTagging | ||
| from utils.configure import PDConfig | ||
| from utils.check import check_gpu, check_version | ||
| from utils.metrics import chunk_count | ||
| from reader import LacDataset, create_lexnet_data_generator, create_dataloader | ||
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| work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | ||
| sys.path.append(os.path.join(work_dir, "../")) | ||
| from hapi.model import set_device, Input | ||
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| import paddle.fluid as fluid | ||
| from paddle.fluid.optimizer import AdamOptimizer | ||
| from paddle.fluid.layers.utils import flatten | ||
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| def main(args): | ||
| place = set_device(args.device) | ||
| fluid.enable_dygraph(place) if args.dynamic else None | ||
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| inputs = [Input([None, None], 'int64', name='words'), | ||
| Input([None], 'int64', name='length')] | ||
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| feed_list = None if args.dynamic else [x.forward() for x in inputs] | ||
| dataset = LacDataset(args) | ||
| eval_path = args.test_file | ||
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| chunk_evaluator = fluid.metrics.ChunkEvaluator() | ||
| chunk_evaluator.reset() | ||
|
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| eval_generator = create_lexnet_data_generator( | ||
| args, reader=dataset, file_name=eval_path, place=place, mode="test") | ||
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| eval_dataset = create_dataloader( | ||
| eval_generator, place, feed_list=feed_list) | ||
|
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| vocab_size = dataset.vocab_size | ||
| num_labels = dataset.num_labels | ||
| model = SeqTagging(args, vocab_size, num_labels) | ||
|
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| optim = AdamOptimizer( | ||
| learning_rate=args.base_learning_rate, | ||
| parameter_list=model.parameters()) | ||
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| model.mode = "test" | ||
| model.prepare(inputs=inputs) | ||
| model.load(args.init_from_checkpoint, skip_mismatch=True) | ||
|
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| for data in eval_dataset(): | ||
| if len(data) == 1: | ||
| batch_data = data[0] | ||
| targets = np.array(batch_data[2]) | ||
| else: | ||
| batch_data = data | ||
| targets = batch_data[2].numpy() | ||
| inputs_data = [batch_data[0], batch_data[1]] | ||
| crf_decode, length = model.test(inputs=inputs_data) | ||
| num_infer_chunks, num_label_chunks, num_correct_chunks = chunk_count(crf_decode, targets, length, dataset.id2label_dict) | ||
| chunk_evaluator.update(num_infer_chunks, num_label_chunks, num_correct_chunks) | ||
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| precision, recall, f1 = chunk_evaluator.eval() | ||
| print("[test] P: %.5f, R: %.5f, F1: %.5f" % (precision, recall, f1)) | ||
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| if __name__ == '__main__': | ||
| args = PDConfig(yaml_file="sequence_tagging.yaml") | ||
| args.build() | ||
| args.Print() | ||
|
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| use_gpu = True if args.device == "gpu" else False | ||
| check_gpu(use_gpu) | ||
| check_version() | ||
| main(args) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| # Copyright (c) 2019 PaddlePaddle 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. | ||
| """ | ||
| SequenceTagging network structure | ||
| """ | ||
|
|
||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import io | ||
| import os | ||
| import sys | ||
| import math | ||
| import argparse | ||
| import numpy as np | ||
|
|
||
| from train import SeqTagging | ||
| from utils.check import check_gpu, check_version | ||
| from utils.configure import PDConfig | ||
| from reader import LacDataset, create_lexnet_data_generator, create_dataloader | ||
|
|
||
| work_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | ||
| sys.path.append(os.path.join(work_dir, "../")) | ||
| from hapi.model import set_device, Input | ||
|
|
||
| import paddle.fluid as fluid | ||
| from paddle.fluid.optimizer import AdamOptimizer | ||
| from paddle.fluid.layers.utils import flatten | ||
|
|
||
|
|
||
| def main(args): | ||
| place = set_device(args.device) | ||
| fluid.enable_dygraph(place) if args.dynamic else None | ||
|
|
||
| inputs = [Input([None, None], 'int64', name='words'), | ||
| Input([None], 'int64', name='length')] | ||
|
|
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| feed_list = None if args.dynamic else [x.forward() for x in inputs] | ||
| dataset = LacDataset(args) | ||
| predict_path = args.predict_file | ||
|
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| predict_generator = create_lexnet_data_generator( | ||
| args, reader=dataset, file_name=predict_path, place=place, mode="predict") | ||
|
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| predict_dataset = create_dataloader( | ||
| predict_generator, place, feed_list=feed_list) | ||
|
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| vocab_size = dataset.vocab_size | ||
| num_labels = dataset.num_labels | ||
| model = SeqTagging(args, vocab_size, num_labels) | ||
|
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| optim = AdamOptimizer( | ||
| learning_rate=args.base_learning_rate, | ||
| parameter_list=model.parameters()) | ||
|
|
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| model.mode = "test" | ||
| model.prepare(inputs=inputs) | ||
|
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| model.load(args.init_from_checkpoint, skip_mismatch=True) | ||
|
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| f = open(args.output_file, "wb") | ||
| for data in predict_dataset(): | ||
| if len(data) == 1: | ||
| input_data = data[0] | ||
| else: | ||
| input_data = data | ||
| results, length = model.test(inputs=flatten(input_data)) | ||
| for i in range(len(results)): | ||
| word_len = length[i] | ||
| word_ids = results[i][: word_len] | ||
| tags = [dataset.id2label_dict[str(id)] for id in word_ids] | ||
| f.write("\002".join(tags) + "\n") | ||
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| if __name__ == '__main__': | ||
| args = PDConfig(yaml_file="sequence_tagging.yaml") | ||
| args.build() | ||
| args.Print() | ||
|
|
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| use_gpu = True if args.device == "gpu" else False | ||
| check_gpu(use_gpu) | ||
| check_version() | ||
| main(args) |
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