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 VDPWIModel def evaluate_dataset(split_name, dataset_cls, model, embedding, loader, batch_size, device): saved_model_evaluator = EvaluatorFactory.get_evaluator(dataset_cls, model, embedding, loader, batch_size, device) 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 VDPWI') 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, 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('--sparse-features', action='store_true', default=False, help='use sparse features (default: false)') parser.add_argument('--batch-size', type=int, default=16, 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='rmsprop', help='optimizer to use: adam, sgd, or rmsprop (default: adam)') parser.add_argument('--lr', type=float, default=5E-4, 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=0.1, help='momentum (default: 0.1)') 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=1E-5, help='Regularization for the optimizer (default: 0.00001)') parser.add_argument('--hidden-units', type=int, default=250, help='number of hidden units in the RNN') 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') # VDPWI args parser.add_argument('--classifier', type=str, default='vdpwi', choices=['vdpwi', 'resnet']) parser.add_argument('--clip-norm', type=float, default=50) parser.add_argument('--decay', type=float, default=0.95) parser.add_argument('--res-fmaps', type=int, default=32) parser.add_argument('--res-layers', type=int, default=16) parser.add_argument('--rnn-hidden-dim', type=int, default=250) 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) # 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))) 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) model_config = { 'classifier': args.classifier, 'rnn_hidden_dim': args.rnn_hidden_dim, 'n_labels': dataset_cls.NUM_CLASSES, 'device': device, 'res_layers': args.res_layers, 'res_fmaps': args.res_fmaps } model = VDPWIModel(args.word_vectors_dim, model_config) 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) elif args.optimizer == "rmsprop": optimizer = optim.RMSprop(model.parameters(), lr=args.lr, momentum=args.momentum, alpha=args.decay, weight_decay=args.regularization) else: raise ValueError('optimizer not recognized: it should be one of adam, sgd, or rmsprop') 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, 'clip_norm': args.clip_norm } 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)