train.py

import os
import logging
import argparse
import time
from typing import Text

import torch
import sys
from torchtext.utils import download_from_url
from model import TextSentiment
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchtext.data.functional import to_map_style_dataset
#from torchtext.datasets import text_classification
from torchtext.datasets import DATASETS
from torchtext.data.utils import(
    get_tokenizer,
    ngrams_iterator,
)
from torchtext.vocab import build_vocab_from_iterator

def collate_batch(batch):
    label_list, text_list, offsets = [], [], [0]
    for (_label, _text) in batch:
        label_list.append(label_pipeline(_label))
        processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
        text_list.append(processed_text)
        offsets.append(processed_text.size(0))
    label_list = torch.tensor(label_list, dtype=torch.int64)
    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
    text_list = torch.cat(text_list)
    return label_list.to(device), text_list.to(device), offsets.to(device)

def yield_tokens(data_iter, ngrams):
    for _, text in data_iter:
        yield ngrams_iterator(tokenizer(text), ngrams)

def train(dataloader, model, optimizer, criterion, epoch):
    model.train()
    total_acc, total_count = 0, 0
    log_interval = 500
    start_time = time.time()

    for idx, (label, text, offsets) in enumerate(dataloader):
        optimizer.zero_grad()
        predited_label = model(text, offsets)
        loss = criterion(predited_label, label)
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
        optimizer.step()
        total_acc += (predited_label.argmax(1) == label).sum().item()
        total_count += label.size(0)
        if idx % log_interval == 0 and idx > 0:
            elapsed = time.time() - start_time
            print('| epoch {:3d} | {:5d}/{:5d} batches '
                  '| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
                                              total_acc / total_count))
            total_acc, total_count = 0, 0
            start_time = time.time()

def evaluate(dataloader, model):
    model.eval()
    total_acc, total_count = 0, 0

    with torch.no_grad():
        for idx, (label, text, offsets) in enumerate(dataloader):
            predited_label = model(text, offsets)
            total_acc += (predited_label.argmax(1) == label).sum().item()
            total_count += label.size(0)
    return total_acc / total_count

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Train a text classification model on text classification datasets.')
    parser.add_argument('dataset', type=str, default="AG_NEWS")
    parser.add_argument('--num-epochs', type=int, default=5,
                        help='num epochs (default=5)')
    parser.add_argument('--embed-dim', type=int, default=32,
                        help='embed dim. (default=32)')
    parser.add_argument('--batch-size', type=int, default=16,
                        help='batch size (default=16)')
    parser.add_argument('--split-ratio', type=float, default=0.95,
                        help='train/valid split ratio (default=0.95)')
    parser.add_argument('--lr', type=float, default=4.0,
                        help='learning rate (default=4.0)')
    parser.add_argument('--lr-gamma', type=float, default=0.8,
                        help='gamma value for lr (default=0.8)')
    parser.add_argument('--ngrams', type=int, default=2,
                        help='ngrams (default=2)')
    parser.add_argument('--num-workers', type=int, default=1,
                        help='num of workers (default=1)')
    parser.add_argument('--device', default='cpu',
                        help='device (default=cpu)')
    parser.add_argument('--data-dir', default='.data',
                        help='data directory (default=.data)')
    parser.add_argument('--dictionary',
                        help='path to save vocab')
    parser.add_argument('--save-model-path',
                        help='path for saving model')
    parser.add_argument('--logging-level', default='WARNING',
                        help='logging level (default=WARNING)')
    args = parser.parse_args()

    num_epochs = args.num_epochs
    embed_dim = args.embed_dim
    batch_size = args.batch_size
    lr = args.lr
    device = args.device
    data_dir = args.data_dir
    split_ratio = args.split_ratio
    ngrams = args.ngrams

    logging.basicConfig(level=getattr(logging, args.logging_level))

    if not os.path.exists(data_dir):
        print("Creating directory {}".format(data_dir))
        os.mkdir(data_dir)

    tokenizer = get_tokenizer("basic_english")
    train_iter = DATASETS[args.dataset](root='.data', split='train')
    vocab = build_vocab_from_iterator(yield_tokens(train_iter, ngrams), specials=["<unk>"])
    vocab.set_default_index(vocab["<unk>"])
    def text_pipeline(x): return vocab(list(ngrams_iterator(tokenizer(x), ngrams)))
    def label_pipeline(x): return int(x) - 1
    train_iter = DATASETS[args.dataset](root='.data', split='train')
    num_class = len(set([label for (label, _) in train_iter]))
    model = TextSentiment(len(vocab), embed_dim, num_class).to(device)

    criterion = torch.nn.CrossEntropyLoss().to(device)
    optimizer = torch.optim.SGD(model.parameters(), lr=lr)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
    train_iter, test_iter=DATASETS[args.dataset]()
    train_dataset = to_map_style_dataset(train_iter)
    test_dataset = to_map_style_dataset(test_iter)
    num_train = int(len(train_dataset) * 0.95)
    split_train_, split_valid_= random_split(train_dataset, [num_train, len(train_dataset) - num_train])
    train_dataloader=DataLoader(split_train_, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)
    valid_dataloader=DataLoader(split_valid_, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)
    test_dataloader=DataLoader(test_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)

    for epoch in range(1, num_epochs + 1):
        epoch_start_time = time.time()
        train(train_dataloader, model, optimizer, criterion, epoch)
        accu_val = evaluate(valid_dataloader, model)
        scheduler.step()
        print('-' * 59)
        print('| end of epoch {:3d} | time: {:5.2f}s | '
              'valid accuracy {:8.3f} '.format(epoch,
                                               time.time() - epoch_start_time,
                                               accu_val))
        print('-' * 59)

    print('Checking the results of test dataset.')
    accu_test = evaluate(test_dataloader, model)
    print('test accuracy {:8.3f}'.format(accu_test))

    if args.save_model_path:
        print("Saving model to {}".format(args.save_model_path))
        torch.save(model.state_dict(), args.save_model_path)
        
        ''' this shows how to script the model
        sm = torch.jit.script(model)
        sm.save("model_scipted.pt")
        '''

    if args.dictionary is not None:
        print("Save vocab to {}".format(args.dictionary))
        torch.save(vocab, args.dictionary)