mirror of
https://github.com/wassname/Castor.git
synced 2026-09-09 11:13:20 +08:00
Victor Yang
57f53a81b5
* runnable * add util file * update readme * update final layer and add model name * update argument * update readme, delete useless args * fix comments * fix more comments
145 lines
7.4 KiB
Python
145 lines
7.4 KiB
Python
import argparse
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import logging
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import os
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import pprint
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import random
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import numpy as np
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import torch
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import torch.optim as optim
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from common.dataset import DatasetFactory
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from common.evaluation import EvaluatorFactory
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from common.train import TrainerFactory
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from utils.serialization import load_checkpoint
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from .model import StackBiLSTMMaxout
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def get_logger():
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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formatter = logging.Formatter('%(levelname)s - %(message)s')
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ch.setFormatter(formatter)
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logger.addHandler(ch)
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return logger
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def evaluate_dataset(split_name, dataset_cls, model, embedding, loader, batch_size, device, keep_results=False):
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saved_model_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, loader, batch_size, device,
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keep_results=keep_results)
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scores, metric_names = saved_model_evaluator.get_scores()
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logger.info('Evaluation metrics for {}'.format(split_name))
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logger.info('\t'.join([' '] + metric_names))
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logger.info('\t'.join([split_name] + list(map(str, scores))))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='PyTorch implementation of Multi-Perspective CNN')
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parser.add_argument('model_outfile', help='file to save final model')
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parser.add_argument('--dataset', help='dataset to use, one of [sick, msrvid, trecqa, wikiqa]', default='sick')
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parser.add_argument('--word-vectors-dir', help='word vectors directory',
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default=os.path.join(os.pardir, 'Castor-data', 'embeddings', 'GloVe'))
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parser.add_argument('--word-vectors-file', help='word vectors filename', default='glove.840B.300d.txt')
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parser.add_argument('--word-vectors-dim', type=int, default=300,
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help='number of dimensions of word vectors (default: 300)')
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parser.add_argument('--skip-training', help='will load pre-trained model', action='store_true')
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parser.add_argument('--device', type=int, default=0, help='GPU device, -1 for CPU (default: 0)')
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parser.add_argument('--wide-conv', action='store_true', default=False,
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help='use wide convolution instead of narrow convolution (default: false)')
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parser.add_argument('--sparse-features', action='store_true',
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default=False, help='use sparse features (default: false)')
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parser.add_argument('--batch-size', type=int, default=64, help='input batch size for training (default: 64)')
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parser.add_argument('--epochs', type=int, default=10, help='number of epochs to train (default: 10)')
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parser.add_argument('--optimizer', type=str, default='adam', help='optimizer to use: adam or sgd (default: adam)')
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parser.add_argument('--lr', type=float, default=0.001, help='learning rate (default: 0.001)')
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parser.add_argument('--lr-reduce-factor', type=float, default=0.3,
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help='learning rate reduce factor after plateau (default: 0.3)')
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parser.add_argument('--patience', type=float, default=2,
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help='learning rate patience after seeing plateau (default: 2)')
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parser.add_argument('--momentum', type=float, default=0, help='momentum (default: 0)')
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parser.add_argument('--epsilon', type=float, default=1e-8, help='Optimizer epsilon (default: 1e-8)')
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parser.add_argument('--log-interval', type=int, default=10,
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help='how many batches to wait before logging training status (default: 10)')
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parser.add_argument('--regularization', type=float, default=0.0001,
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help='Regularization for the optimizer (default: 0.0001)')
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parser.add_argument('--mlpD', type=int, default=1600, help='MLP dimension (default: 1600)')
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parser.add_argument('--dropout', type=float, default=0.1, help='dropout probability (default: 0.1)')
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parser.add_argument('--maxlen', type=int, default=30, help='maximum length of text (default: 60)')
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parser.add_argument('--seed', type=int, default=1234, help='random seed (default: 1234)')
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parser.add_argument('--tensorboard', action='store_true', default=False,
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help='use TensorBoard to visualize training (default: false)')
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parser.add_argument('--run-label', type=str, help='label to describe run')
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parser.add_argument('--keep-results', action='store_true',
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help='store the output score and qrel files into disk for the test set')
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args = parser.parse_args()
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device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() and args.device >= 0 else 'cpu')
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if args.device != -1:
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torch.cuda.manual_seed(args.seed)
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logger = get_logger()
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logger.info(pprint.pformat(vars(args)))
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dataset_cls, embedding, train_loader, test_loader, dev_loader \
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= DatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device)
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ext_feats = dataset_cls.EXT_FEATS if args.sparse_features else 0
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model = StackBiLSTMMaxout(d=args.word_vectors_dim, mlp_d=args.mlpD,
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num_classes=dataset_cls.NUM_CLASSES, dropout_r=args.dropout, max_l=args.maxlen)
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model = model.to(device)
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embedding = embedding.to(device)
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optimizer = None
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if args.optimizer == 'adam':
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optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.regularization, eps=args.epsilon)
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elif args.optimizer == 'sgd':
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optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.regularization)
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else:
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raise ValueError('optimizer not recognized: it should be either adam or sgd')
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train_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, train_loader, args.batch_size,
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args.device)
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test_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, test_loader, args.batch_size,
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args.device)
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dev_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, dev_loader, args.batch_size,
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args.device)
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trainer_config = {
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'optimizer': optimizer,
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'batch_size': args.batch_size,
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'log_interval': args.log_interval,
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'model_outfile': args.model_outfile,
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'lr_reduce_factor': args.lr_reduce_factor,
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'patience': args.patience,
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'tensorboard': args.tensorboard,
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'run_label': args.run_label,
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'logger': logger
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}
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trainer = TrainerFactory.get_trainer(args.dataset, model, embedding, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator)
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if not args.skip_training:
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total_params = sum(param.numel() for param in model.parameters() if param.requires_grad)
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logger.info('Total number of trainable parameters: %s', total_params)
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trainer.train(args.epochs)
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_, _, state_dict, _, _ = load_checkpoint(args.model_outfile)
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for k, tensor in state_dict.items():
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state_dict[k] = tensor.to(device)
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model.load_state_dict(state_dict)
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if dev_loader:
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evaluate_dataset('dev', dataset_cls, model, embedding, dev_loader, args.batch_size, args.device)
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evaluate_dataset('test', dataset_cls, model, embedding, test_loader, args.batch_size, args.device, args.keep_results)
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