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import argparse
import logging
import time
import torch
from model import TextClassificationModel
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.data.utils import get_tokenizer, ngrams_iterator
from torchtext.datasets import DATASETS
from torchtext.prototype.transforms import load_sp_model, PRETRAINED_SP_MODEL, SentencePieceTokenizer
from torchtext.utils import download_from_url
from torchtext.vocab import build_vocab_from_iterator
r"""
This file shows the training process of the text classification model.
"""
def yield_tokens(data_iter, ngrams):
for _, text in data_iter:
yield ngrams_iterator(tokenizer(text), ngrams)
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 train(dataloader, model, optimizer, criterion, epoch):
model.train()
total_acc, total_count = 0, 0
log_interval = 500
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:
print(
"| epoch {:3d} | {:5d}/{:5d} batches "
"| accuracy {:8.3f}".format(epoch, idx, len(dataloader), total_acc / total_count)
)
total_acc, total_count = 0, 0
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(
"--use-sp-tokenizer", type=bool, default=False, help="use sentencepiece tokenizer (default=False)"
)
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
use_sp_tokenizer = args.use_sp_tokenizer
logging.basicConfig(level=getattr(logging, args.logging_level))
if use_sp_tokenizer:
sp_model_path = download_from_url(PRETRAINED_SP_MODEL["text_unigram_15000"], root=data_dir)
sp_model = load_sp_model(sp_model_path)
tokenizer = SentencePieceTokenizer(sp_model)
else:
tokenizer = get_tokenizer("basic_english")
train_iter = DATASETS[args.dataset](root=data_dir, 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_dir, split="train")
num_class = len(set([label for (label, _) in train_iter]))
criterion = torch.nn.CrossEntropyLoss().to(device)
model = TextClassificationModel(len(vocab), embed_dim, num_class).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.to("cpu"), args.save_model_path)
if args.dictionary is not None:
print("Save vocab to {}".format(args.dictionary))
torch.save(vocab, args.dictionary)
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