wehub-resource-sync
95 lines
3.7 KiB
Python
95 lines
3.7 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 paddle
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import paddle.nn.functional as F
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from data import preprocess_prediction_data
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from model import TextCNNModel
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from paddlenlp.data import JiebaTokenizer, Pad, Vocab
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--batch_size", type=int, default=1, help="Total examples' number of a batch for training.")
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parser.add_argument("--vocab_path", type=str, default="./robot_chat_word_dict.txt", help="The path to vocabulary.")
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parser.add_argument("--params_path", type=str, default='./checkpoints/final.pdparams', help="The path of model parameter to be loaded.")
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args = parser.parse_args()
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# yapf: enable
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def predict(model, data, label_map, batch_size=1, pad_token_id=0):
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"""
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Predicts the data labels.
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Args:
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model (obj:`paddle.nn.Layer`): A model to classify texts.
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data (obj:`list`): The processed data whose each element
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is a `list` object, which contains
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- word_ids(obj:`list[int]`): The list of word ids.
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label_map(obj:`dict`): The label id (key) to label str (value) map.
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batch_size(obj:`int`, defaults to 1): The number of batch.
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pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.
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Returns:
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results(obj:`dict`): All the predictions labels.
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"""
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# Separates data into some batches.
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batches = [data[idx : idx + batch_size] for idx in range(0, len(data), batch_size)]
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batchify_fn = lambda samples, fn=Pad(axis=0, pad_val=pad_token_id): [data for data in fn(samples)]
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results = []
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model.eval()
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for batch in batches:
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texts = paddle.to_tensor(batchify_fn(batch))
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logits = model(texts)
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probs = F.softmax(logits, axis=1)
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idx = paddle.argmax(probs, axis=1).numpy()
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idx = idx.tolist()
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labels = [label_map[i] for i in idx]
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results.extend(labels)
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return results
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if __name__ == "__main__":
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paddle.set_device(args.device)
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# Load vocab.
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vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
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label_map = {0: "negative", 1: "neutral", 2: "positive"}
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# Construct the network.
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vocab_size = len(vocab)
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num_classes = len(label_map)
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pad_token_id = vocab.to_indices("[PAD]")
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model = TextCNNModel(vocab_size, num_classes, padding_idx=pad_token_id, ngram_filter_sizes=(1, 2, 3))
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# Load model parameters.
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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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# Firstly pre-processing prediction data and then do predict.
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data = ["你再骂我我真的不跟你聊了", "你看看我附近有什么好吃的", "我喜欢画画也喜欢唱歌"]
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tokenizer = JiebaTokenizer(vocab)
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examples = preprocess_prediction_data(data, tokenizer, pad_token_id)
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results = predict(model, examples, label_map=label_map, batch_size=args.batch_size, pad_token_id=pad_token_id)
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for idx, text in enumerate(data):
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print("Data: {} \t Label: {}".format(text, results[idx]))
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