# Copyright (c) 2020 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. """Sentiment Classification in Paddle Dygraph Mode. """ from __future__ import print_function import numpy as np import paddle.fluid as fluid from paddle.incubate.hapi.model import set_device, Model, CrossEntropy, Input from paddle.incubate.hapi.configure import Config from paddle.incubate.hapi.text.senta import SentaProcessor from paddle.incubate.hapi.metrics import Accuracy from models import CNN, BOW, GRU, BiGRU import json import os args = Config(yaml_file='./senta.yaml') args.build() args.Print() device = set_device("gpu" if args.use_cuda else "cpu") dev_count = fluid.core.get_cuda_device_count() if args.use_cuda else 1 def main(): if args.do_train: train() elif args.do_infer: infer() def train(): fluid.enable_dygraph(device) processor = SentaProcessor( data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed) num_labels = len(processor.get_labels()) num_train_examples = processor.get_num_examples(phase="train") max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count train_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='train', epoch=args.epoch, shuffle=False) eval_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='dev', epoch=args.epoch, shuffle=False) if args.model_type == 'cnn_net': model = CNN(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bow_net': model = BOW(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'gru_net': model = GRU(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bigru_net': model = BiGRU(args.vocab_size, args.batch_size, args.padding_size) optimizer = fluid.optimizer.Adagrad( learning_rate=args.lr, parameter_list=model.parameters()) inputs = [Input([None, None], 'int64', name='doc')] labels = [Input([None, 1], 'int64', name='label')] model.prepare( optimizer, CrossEntropy(), Accuracy(topk=(1, )), inputs, labels, device=device) model.fit(train_data=train_data_generator, eval_data=eval_data_generator, batch_size=args.batch_size, epochs=args.epoch, save_dir=args.checkpoints, eval_freq=args.eval_freq, save_freq=args.save_freq) def infer(): fluid.enable_dygraph(device) processor = SentaProcessor( data_dir=args.data_dir, vocab_path=args.vocab_path, random_seed=args.random_seed) infer_data_generator = processor.data_generator( batch_size=args.batch_size, padding_size=args.padding_size, places=device, phase='infer', epoch=1, shuffle=False) if args.model_type == 'cnn_net': model_infer = CNN(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bow_net': model_infer = BOW(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'gru_net': model_infer = GRU(args.vocab_size, args.batch_size, args.padding_size) elif args.model_type == 'bigru_net': model_infer = BiGRU(args.vocab_size, args.batch_size, args.padding_size) print('Do inferring ...... ') inputs = [Input([None, None], 'int64', name='doc')] model_infer.prepare( None, CrossEntropy(), Accuracy(topk=(1, )), inputs, device=device) model_infer.load(args.checkpoints, reset_optimizer=True) preds = model_infer.predict(test_data=infer_data_generator) preds = np.array(preds[0]).reshape((-1, 2)) if args.output_dir: with open(os.path.join(args.output_dir, 'predictions.json'), 'w') as w: for p in range(len(preds)): label = np.argmax(preds[p]) result = json.dumps({ 'index': p, 'label': label, 'probs': preds[p].tolist() }) w.write(result + '\n') print('Predictions saved at ' + os.path.join(args.output_dir, 'predictions.json')) if __name__ == '__main__': main()