wehub-resource-sync
104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. 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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import argparse
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import os
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from functools import partial
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import numpy as np
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import paddle
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from data import convert_example, create_dataloader, read_text_pair
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from model import QuestionMatching
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from paddlenlp.data import Pad, Tuple
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from paddlenlp.datasets import load_dataset
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from paddlenlp.transformers import AutoModel, AutoTokenizer
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_file", type=str, required=True, help="The full path of input file")
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parser.add_argument("--result_file", type=str, required=True, help="The result file name")
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parser.add_argument("--params_path", type=str, required=True, help="The path to model parameters to be loaded.")
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parser.add_argument("--max_seq_length", default=256, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
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parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
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parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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args = parser.parse_args()
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# fmt: on
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def predict(model, data_loader):
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"""
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Predicts the data labels.
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Args:
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model (obj:`QuestionMatching`): A model to calculate whether the question pair is semantic similar or not.
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data_loader (obj:`List(Example)`): The processed data ids of text pair: [query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids]
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Returns:
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results(obj:`List`): cosine similarity of text pairs.
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"""
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batch_logits = []
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model.eval()
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with paddle.no_grad():
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for batch_data in data_loader:
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input_ids, token_type_ids = batch_data
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input_ids = paddle.to_tensor(input_ids)
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token_type_ids = paddle.to_tensor(token_type_ids)
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batch_logit, _ = model(input_ids=input_ids, token_type_ids=token_type_ids)
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batch_logits.append(batch_logit.numpy())
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batch_logits = np.concatenate(batch_logits, axis=0)
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return batch_logits
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if __name__ == "__main__":
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paddle.set_device(args.device)
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pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length, is_test=True)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # segment_ids
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): [data for data in fn(samples)]
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test_ds = load_dataset(read_text_pair, data_path=args.input_file, is_test=True, lazy=False)
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test_data_loader = create_dataloader(
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test_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
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)
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model = QuestionMatching(pretrained_model)
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if args.params_path and os.path.isfile(args.params_path):
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state_dict = paddle.load(args.params_path)
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model.set_dict(state_dict)
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print("Loaded parameters from %s" % args.params_path)
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else:
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raise ValueError("Please set --params_path with correct pretrained model file")
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y_probs = predict(model, test_data_loader)
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y_preds = np.argmax(y_probs, axis=1)
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with open(args.result_file, "w", encoding="utf-8") as f:
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for y_pred in y_preds:
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f.write(str(y_pred) + "\n")
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