mirror of
https://github.com/wassname/Castor.git
synced 2026-09-28 13:42:00 +08:00
Michael Tu
09b3a790a2
* Add SICK torchtext Dataset * SICK dataset - torchtext postprocess into class probs * Update model, driver, trainer, evaluator for SICK for torchtext * MP-CNN: Fix bugs that prevent SICK from running on gpu 0 * MP-CNN: make SICK dataset w/ torchtext GPU-agnostic * MP-CNN: support sparse features / idf overlap with torchtext * Add MSRVID dataset with torchtext and update MP-CNN code to use it * MP-CNN: Make torchtext deterministic by setting python random seed * SICK and MSRVID datasets - add pair id for debug and build test vocab * MP-CNN: Update readme to address potential module not found error * MP-CNN: address review comments, can run on cpu
110 lines
6.1 KiB
Python
110 lines
6.1 KiB
Python
import argparse
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import os
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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 dataset import MPCNNDatasetFactory
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from evaluation import MPCNNEvaluatorFactory
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from model import MPCNN
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from train import MPCNNTrainerFactory
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# logging setup
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import logging
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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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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]', default='sick')
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parser.add_argument('--word-vectors-dir', help='word vectors directory', default=os.path.join(os.pardir, os.pardir, 'data', '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('--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('--sparse-features', action='store_true', 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, help='learning rate reduce factor after plateau (default: 0.3)')
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parser.add_argument('--patience', type=float, default=2, 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='Adam epsilon (default: 1e-8)')
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parser.add_argument('--log-interval', type=int, default=10, 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, help='Regularization for the optimizer (default: 0.0001)')
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parser.add_argument('--max-window-size', type=int, default=3, help='windows sizes will be [1,max_window_size] and infinity (default: 300)')
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parser.add_argument('--holistic-filters', type=int, default=300, help='number of holistic filters (default: 300)')
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parser.add_argument('--per-dim-filters', type=int, default=20, help='number of per-dimension filters (default: 20)')
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parser.add_argument('--hidden-units', type=int, default=150, help='number of hidden units in each of the two hidden layers (default: 150)')
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parser.add_argument('--dropout', type=float, default=0.5, help='dropout probability (default: 0.5)')
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parser.add_argument('--seed', type=int, default=1, help='random seed (default: 1)')
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parser.add_argument('--tensorboard', action='store_true', default=False, 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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args = parser.parse_args()
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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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dataset_cls, embedding, train_loader, test_loader, dev_loader \
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= MPCNNDatasetFactory.get_dataset(args.dataset, args.word_vectors_dir, args.word_vectors_file, args.batch_size, args.device)
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filter_widths = list(range(1, args.max_window_size + 1)) + [np.inf]
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model = MPCNN(embedding, args.holistic_filters, args.per_dim_filters, filter_widths,
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args.hidden_units, dataset_cls.NUM_CLASSES, args.dropout, args.sparse_features)
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if args.device != -1:
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with torch.cuda.device(args.device):
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model.cuda()
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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 = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, train_loader, args.batch_size, args.device)
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test_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
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dev_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, dev_loader, args.batch_size, 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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}
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trainer = MPCNNTrainerFactory.get_trainer(args.dataset, model, 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 = 0
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for param in model.parameters():
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size = [s for s in param.size()]
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total_params += np.prod(size)
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logger.info('Total number of parameters: %s', total_params)
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trainer.train(args.epochs)
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model = torch.load(args.model_outfile)
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saved_model_evaluator = MPCNNEvaluatorFactory.get_evaluator(dataset_cls, model, test_loader, args.batch_size, args.device)
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scores, metric_names = saved_model_evaluator.get_scores()
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logger.info('Evaluation metrics for test')
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logger.info('\t'.join([' '] + metric_names))
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logger.info('\t'.join(['test'] + list(map(str, scores))))
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