import argparse import logging import os import pprint import random import numpy as np import torch import torch.optim as optim from common.dataset import DatasetFactory from common.evaluation import EvaluatorFactory from common.train import TrainerFactory from utils.serialization import load_checkpoint from .model import DecAtt def get_logger(): logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) formatter = logging.Formatter('%(levelname)s - %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) return logger def evaluate_dataset(split_name, dataset_cls, model, embedding, loader, batch_size, device, keep_results=False): saved_model_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, loader, batch_size, device, keep_results=keep_results) scores, metric_names = saved_model_evaluator.get_scores() logger.info('Evaluation metrics for {}'.format(split_name)) logger.info('\t'.join([' '] + metric_names)) logger.info('\t'.join([split_name] + list(map(str, scores)))) if __name__ == '__main__': parser = argparse.ArgumentParser(description='PyTorch implementation of Multi-Perspective CNN') parser.add_argument('model_outfile', help='file to save final model') parser.add_argument('--dataset', help='dataset to use, one of [sick, msrvid, trecqa, wikiqa]', default='sick') parser.add_argument('--word-vectors-dir', help='word vectors directory', default=os.path.join(os.pardir, 'Castor-data', 'embeddings', 'GloVe')) parser.add_argument('--word-vectors-file', help='word vectors filename', default='glove.840B.300d.txt') parser.add_argument('--word-vectors-dim', type=int, default=300, help='number of dimensions of word vectors (default: 300)') parser.add_argument('--skip-training', help='will load pre-trained model', action='store_true') parser.add_argument('--device', type=int, default=0, help='GPU device, -1 for CPU (default: 0)') parser.add_argument('--wide-conv', action='store_true', default=False, help='use wide convolution instead of narrow convolution (default: false)') parser.add_argument('--sparse-features', action='store_true', default=False, help='use sparse features (default: false)') parser.add_argument('--batch-size', type=int, default=64, help='input batch size for training (default: 64)') parser.add_argument('--epochs', type=int, default=10, help='number of epochs to train (default: 10)') parser.add_argument('--optimizer', type=str, default='adam', help='optimizer to use: adam or sgd (default: adam)') parser.add_argument('--lr', type=float, default=0.001, help='learning rate (default: 0.001)') parser.add_argument('--lr-reduce-factor', type=float, default=0.3, help='learning rate reduce factor after plateau (default: 0.3)') parser.add_argument('--patience', type=float, default=2, help='learning rate patience after seeing plateau (default: 2)') parser.add_argument('--momentum', type=float, default=0, help='momentum (default: 0)') parser.add_argument('--epsilon', type=float, default=1e-8, help='Optimizer epsilon (default: 1e-8)') parser.add_argument('--log-interval', type=int, default=10, help='how many batches to wait before logging training status (default: 10)') parser.add_argument('--regularization', type=float, default=0.0001, help='Regularization for the optimizer (default: 0.0001)') parser.add_argument('--max-window-size', type=int, default=3, help='windows sizes will be [1,max_window_size] and infinity (default: 3)') parser.add_argument('--dropout', type=float, default=0.5, help='dropout probability (default: 0.1)') parser.add_argument('--maxlen', type=int, default=60, help='maximum length of text (default: 60)') parser.add_argument('--seed', type=int, default=1234, help='random seed (default: 1234)') parser.add_argument('--tensorboard', action='store_true', default=False, help='use TensorBoard to visualize training (default: false)') parser.add_argument('--run-label', type=str, help='label to describe run') parser.add_argument('--keep-results', action='store_true', help='store the output score and qrel files into disk for the test set') args = parser.parse_args() device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() and args.device >= 0 else 'cpu') random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.device != -1: torch.cuda.manual_seed(args.seed) logger = get_logger() logger.info(pprint.pformat(vars(args))) dataset_cls, embedding, train_loader, test_loader, dev_loader \ = DatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device) filter_widths = list(range(1, args.max_window_size + 1)) + [np.inf] ext_feats = dataset_cls.EXT_FEATS if args.sparse_features else 0 model = DecAtt(embedding_size=args.word_vectors_dim, device=args.device, num_units=args.word_vectors_dim, num_classes=dataset_cls.NUM_CLASSES, dropout=args.dropout, max_sentence_length=args.maxlen) model = model.to(device) embedding = embedding.to(device) optimizer = None if args.optimizer == 'adam': optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.regularization, eps=args.epsilon) elif args.optimizer == 'sgd': optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.regularization) else: raise ValueError('optimizer not recognized: it should be either adam or sgd') train_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, train_loader, args.batch_size, args.device) test_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, test_loader, args.batch_size, args.device) dev_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, dev_loader, args.batch_size, args.device) trainer_config = { 'optimizer': optimizer, 'batch_size': args.batch_size, 'log_interval': args.log_interval, 'model_outfile': args.model_outfile, 'lr_reduce_factor': args.lr_reduce_factor, 'patience': args.patience, 'tensorboard': args.tensorboard, 'run_label': args.run_label, 'logger': logger } trainer = TrainerFactory.get_trainer(args.dataset, model, embedding, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator) if not args.skip_training: total_params = 0 for param in model.parameters(): size = [s for s in param.size()] total_params += np.prod(size) logger.info('Total number of parameters: %s', total_params) trainer.train(args.epochs) _, _, state_dict, _, _ = load_checkpoint(args.model_outfile) for k, tensor in state_dict.items(): state_dict[k] = tensor.to(device) model.load_state_dict(state_dict) if dev_loader: evaluate_dataset('dev', dataset_cls, model, embedding, dev_loader, args.batch_size, args.device) evaluate_dataset('test', dataset_cls, model, embedding, test_loader, args.batch_size, args.device, args.keep_results)