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| #!/usr/bin/env python | ||
| # coding: utf-8 | ||
| """ | ||
| Tencent is pleased to support the open source community by making NeuralClassifier available. | ||
| Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved. | ||
| Licensed under the MIT License (the "License"); you may not use this file except in compliance | ||
| with the License. You may obtain a copy of the License at | ||
| http://opensource.org/licenses/MIT | ||
| 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 thespecific language governing permissions and limitations under | ||
| the License. | ||
| """ | ||
|
|
||
| import codecs | ||
| import math | ||
| import numpy as np | ||
| import os | ||
| import sys | ||
| import json | ||
|
|
||
| import torch | ||
| from torch.utils.data import DataLoader | ||
|
|
||
| from config import Config | ||
| from dataset.classification_dataset import ClassificationDataset | ||
| from dataset.collator import ClassificationCollator | ||
| from dataset.collator import ClassificationType | ||
| from dataset.collator import FastTextCollator | ||
| from model.classification.drnn import DRNN | ||
| from model.classification.fasttext import FastText | ||
| from model.classification.textcnn import TextCNN | ||
| from model.classification.textvdcnn import TextVDCNN | ||
| from model.classification.textrnn import TextRNN | ||
| from model.classification.textrcnn import TextRCNN | ||
| from model.classification.transformer import Transformer | ||
| from model.classification.dpcnn import DPCNN | ||
| from model.classification.attentive_convolution import AttentiveConvNet | ||
| from model.classification.region_embedding import RegionEmbedding | ||
| from model.model_util import get_optimizer, get_hierar_relations | ||
|
|
||
| ClassificationDataset, ClassificationCollator, FastTextCollator,FastText, TextCNN, TextRNN, TextRCNN, DRNN, TextVDCNN, Transformer, DPCNN, AttentiveConvNet, RegionEmbedding | ||
|
|
||
| class Predictor(object): | ||
| def __init__(self, config): | ||
| self.config = config | ||
| self.model_name = config.model_name | ||
| self.use_cuda = config.device.startswith("cuda") | ||
| self.dataset_name = "ClassificationDataset" | ||
| self.collate_name = "FastTextCollator" if self.model_name == "FastText" \ | ||
| else "ClassificationCollator" | ||
| self.dataset = globals()[self.dataset_name](config, [], mode="infer") | ||
| self.collate_fn = globals()[self.collate_name](config, len(self.dataset.label_map)) | ||
| self.model = Predictor._get_classification_model(self.model_name, self.dataset, config) | ||
| Predictor._load_checkpoint(config.eval.model_dir, self.model, self.use_cuda) | ||
| self.model.eval() | ||
|
|
||
| @staticmethod | ||
| def _get_classification_model(model_name, dataset, conf): | ||
| model = globals()[model_name](dataset, conf) | ||
| model = model.cuda(conf.device) if conf.device.startswith("cuda") else model | ||
| return model | ||
|
|
||
| @staticmethod | ||
| def _load_checkpoint(file_name, model, use_cuda): | ||
| if use_cuda: | ||
| checkpoint = torch.load(file_name) | ||
| else: | ||
| checkpoint = torch.load(file_name, map_location=lambda storage, loc: storage) | ||
| model.load_state_dict(checkpoint["state_dict"]) | ||
|
|
||
| def predict(self, texts): | ||
| """ | ||
| input texts should be json objects | ||
| """ | ||
| with torch.no_grad(): | ||
| batch_texts = [self.dataset._get_vocab_id_list(json.loads(text)) for text in texts] | ||
| batch_texts = self.collate_fn(batch_texts) | ||
| logits = self.model(batch_texts) | ||
| if self.config.task_info.label_type != ClassificationType.MULTI_LABEL: | ||
| probs = torch.softmax(logits, dim=1) | ||
| else: | ||
| probs = torch.sigmoid(logits) | ||
| probs = probs.cpu().tolist() | ||
| return np.array(probs) | ||
|
|
||
| if __name__ == "__main__": | ||
| config = Config(config_file=sys.argv[1]) | ||
| predictor = Predictor(config) | ||
| batch_size = config.eval.batch_size | ||
| input_texts = [] | ||
| predict_probs = [] | ||
| is_multi = config.task_info.label_type == ClassificationType.MULTI_LABEL | ||
| for line in codecs.open(sys.argv[2], "r", predictor.dataset.CHARSET): | ||
| input_texts.append(line.strip("\n")) | ||
| epoches = math.ceil(int(len(input_texts)/batch_size)) | ||
| for i in range(epoches): | ||
| batch_texts = input_texts[i*batch_size:(i+1)*batch_size] | ||
| predict_prob = predictor.predict(batch_texts) | ||
| for j in predict_prob: | ||
| predict_probs.append(j) | ||
| with codecs.open("predict.txt", "w", predictor.dataset.CHARSET) as of: | ||
| for predict_prob in predict_probs: | ||
| if not is_multi: | ||
| predict_label_ids = [predict_prob.argmax()] | ||
| else: | ||
| predict_label_ids = [] | ||
| predict_label_idx = np.argsort(-predict_prob) | ||
| for j in range(0, config.eval.top_k): | ||
| if predict_prob[predict_label_idx[j]] > config.eval.threshold: | ||
| predict_label_ids.append(predict_label_idx[j]) | ||
| predict_label_name = [predictor.dataset.id_to_label_map[predict_label_id] \ | ||
| for predict_label_id in predict_label_ids] | ||
| of.write(";".join(predict_label_name) + "\n") |