mirror of
https://github.com/wassname/Castor.git
synced 2026-09-09 11:13:20 +08:00
126 lines
6.9 KiB
Python
126 lines
6.9 KiB
Python
import argparse
|
|
import logging
|
|
import os
|
|
import pprint
|
|
import random
|
|
|
|
import numpy as np
|
|
import sys
|
|
import torch
|
|
import torch.optim as optim
|
|
|
|
from mp_cnn.dataset import MPCNNDatasetFactory
|
|
from mp_cnn.evaluation import MPCNNEvaluatorFactory
|
|
from mp_cnn.model import MPCNN
|
|
from mp_cnn.train import MPCNNTrainerFactory
|
|
|
|
|
|
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, twitter]', default='sick')
|
|
parser.add_argument('--word-vectors-dir', help='word vectors directory', default=os.path.join(os.pardir, os.pardir, 'data', 'GloVe'))
|
|
parser.add_argument('--word-vectors-file', help='word vectors filename', default='glove.840B.300d.txt')
|
|
parser.add_argument('--train_dirs', nargs='+', help='training directory names for twitter dataset')
|
|
parser.add_argument('--test_dirs', nargs='+', help='testing directory names for twitter dataset')
|
|
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('--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='Adam 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: 300)')
|
|
parser.add_argument('--holistic-filters', type=int, default=300, help='number of holistic filters (default: 300)')
|
|
parser.add_argument('--per-dim-filters', type=int, default=20, help='number of per-dimension filters (default: 20)')
|
|
parser.add_argument('--hidden-units', type=int, default=150, help='number of hidden units in each of the two hidden layers (default: 150)')
|
|
parser.add_argument('--dropout', type=float, default=0.5, help='dropout probability (default: 0.5)')
|
|
parser.add_argument('--seed', type=int, default=1, help='random seed (default: 1)')
|
|
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')
|
|
args = parser.parse_args()
|
|
|
|
random.seed(args.seed)
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
if args.device != -1:
|
|
torch.cuda.manual_seed(args.seed)
|
|
|
|
# logging setup
|
|
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)
|
|
|
|
logger.info(pprint.pformat(vars(args)))
|
|
|
|
if args.dataset == 'twitter':
|
|
if not args.train_dirs or not args.test_dirs:
|
|
print('For twitter dataset --train_dirs and --test_dirs must be specified')
|
|
sys.exit(1)
|
|
dataset_cls, embedding, train_loader, test_loader, dev_loader \
|
|
= MPCNNDatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device, train_dirs=args.train_dirs, test_dirs=args.test_dirs)
|
|
else:
|
|
dataset_cls, embedding, train_loader, test_loader, dev_loader \
|
|
= MPCNNDatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device)
|
|
|
|
import ipdb; ipdb.set_trace()
|
|
|
|
filter_widths = list(range(1, args.max_window_size + 1)) + [np.inf]
|
|
model = MPCNN(embedding, args.holistic_filters, args.per_dim_filters, filter_widths,
|
|
args.hidden_units, dataset_cls.NUM_CLASSES, args.dropout, args.sparse_features)
|
|
|
|
if args.device != -1:
|
|
with torch.cuda.device(args.device):
|
|
model.cuda()
|
|
|
|
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 = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, train_loader, args.batch_size, args.device)
|
|
test_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
|
|
dev_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, 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 = MPCNNTrainerFactory.get_trainer(args.dataset, model, 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)
|
|
|
|
model = torch.load(args.model_outfile)
|
|
saved_model_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
|
|
scores, metric_names = saved_model_evaluator.get_scores()
|
|
logger.info('Evaluation metrics for test')
|
|
logger.info('\t'.join([' '] + metric_names))
|
|
logger.info('\t'.join(['test'] + list(map(str, scores))))
|