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train.py
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import torch
import torch.optim as optim
import torch.nn as nn
from torchtext.data import Field, TabularDataset, BucketIterator, LabelField
from model import RNN, BiLSTM
from utils import get_embedding_dim, binary_accuracy, train, evaluate, epoch_time, generate_bigrams
import random
import time
import spacy
spacy.load("en_core_web_sm")
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', type = str, default = 'rnn',
help = "Available models are: 'rnn', 'cnn', 'bilstm', 'fasttext', and 'distilbert'\nDefault is 'rnn'")
parser.add_argument('--train_data_path', type = str, default = "./data/train_clean.csv",
help = "Path to the training data")
parser.add_argument('--test_data_path', type = str, default = "./data/dev_clean.csv",
help = "Path to the test data")
parser.add_argument('--seed', type = int, default = 1234)
parser.add_argument('--vectors', type = str, default = 'fasttext.simple.300d',
help = """
Pretrained vectors:
Visit
https://github.com/pytorch/text/blob/9ce7986ddeb5b47d9767a5299954195a1a5f9043/torchtext/vocab.py#L146
for more
""")
parser.add_argument('--max_vocab_size', type = int, default = 750)
parser.add_argument('--batch_size', type = int, default = 32)
parser.add_argument('--bidirectional', type = bool, default = True)
parser.add_argument('--dropout', type = float, default = 0.5)
parser.add_argument('--hidden_dim', type = int, default = 64)
parser.add_argument('--output_dim', type = int, default = 1)
parser.add_argument('--n_layers', type = int, default = 2)
parser.add_argument('--lr', type = float, default = 1e-3)
parser.add_argument('--n_epochs', type = int, default = 5)
parser.add_argument('--n_filters', type = int, default = 100)
parser.add_argument('--filter_sizes', type = list, default = [3,4,5])
args = parser.parse_args()
torch.manual_seed(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
########## BILSTM ##########
if args.model == "bilstm":
print('\nBiLSTM')
TEXT = Field(tokenize = 'spacy')
LABEL = LabelField(dtype = torch.float)
data_fields = [("text", TEXT), ("label", LABEL)]
train_data = TabularDataset(args.train_data_path,
format = 'csv',
fields = data_fields,
skip_header = True,
csv_reader_params = {'delimiter': ","})
test_data = TabularDataset(args.test_data_path,
format = 'csv',
fields = data_fields,
skip_header = True,
csv_reader_params = {'delimiter': ","})
train_data, val_data = train_data.split(split_ratio = 0.8, random_state = random.seed(args.seed))
TEXT.build_vocab(train_data,
max_size = args.max_vocab_size,
vectors = args.vectors,
unk_init = torch.Tensor.normal_)
LABEL.build_vocab(train_data)
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, val_data, test_data),
batch_size = args.batch_size,
sort_key = lambda x: len(x.text),
device = device
)
input_dim = len(TEXT.vocab)
embedding_dim = get_embedding_dim(args.vectors)
pad_idx = TEXT.vocab.stoi[TEXT.pad_token]
unk_idx = TEXT.vocab.stoi[TEXT.unk_token]
model = BiLSTM(input_dim,
embedding_dim,
args.hidden_dim,
args.output_dim,
args.n_layers,
args.bidirectional,
args.dropout,
pad_idx
)
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
model.embedding.weight.data[unk_idx] = torch.zeros(embedding_dim)
model.embedding.weight.data[pad_idx] = torch.zeros(embedding_dim)
optimizer = optim.Adam(model.parameters(), lr = args.lr)
criterion = nn.BCEWithLogitsLoss()
model.to(device)
criterion.to(device)
best_valid_loss = float('inf')
print("\nTraining...")
print("===========")
for epoch in range(1, args.n_epochs+1):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), './checkpoints/{}-model.pt'.format(args.model))
print(f'[Epoch: {epoch:02}] | Epoch Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')
model.load_state_dict(torch.load('./checkpoints/{}-model.pt'.format(args.model)))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print('\nEvaluating...')
print("=============")
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%') # Test Loss: 0.139, Test Acc: 95.27%
########## VANILLA RNN ##########
else:
print('\nVanilla RNN')
TEXT = Field(tokenize = 'spacy')
LABEL = LabelField(dtype = torch.float)
data_fields = [("text", TEXT), ("label", LABEL)]
train_data = TabularDataset(args.train_data_path,
format = 'csv',
fields = data_fields,
skip_header = True,
csv_reader_params = {'delimiter': ","})
test_data = TabularDataset(args.test_data_path,
format = 'csv',
fields = data_fields,
skip_header = True,
csv_reader_params = {'delimiter': ","})
train_data, val_data = train_data.split(split_ratio = 0.8, random_state = random.seed(args.seed))
TEXT.build_vocab(train_data,
max_size = args.max_vocab_size,
vectors = args.vectors)
LABEL.build_vocab(train_data)
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train_data, val_data, test_data),
batch_size = args.batch_size,
sort_key = lambda x: len(x.text),
device = device
)
input_dim = len(TEXT.vocab)
embedding_dim = get_embedding_dim(args.vectors)
model = RNN(input_dim,
embedding_dim,
args.hidden_dim,
args.output_dim
)
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)
optimizer = optim.Adam(model.parameters(), lr = args.lr)
criterion = nn.BCEWithLogitsLoss()
model.to(device)
criterion.to(device)
best_valid_loss = float('inf')
print("\nTraining...")
print("===========")
for epoch in range(1, args.n_epochs+1):
start_time = time.time()
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), './checkpoints/{}-model.pt'.format(args.model))
print(f'[Epoch: {epoch:02}] | Epoch Time: {epoch_mins}m {epoch_secs}s')
print(f'\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')
print(f'\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')
model.load_state_dict(torch.load('./checkpoints/{}-model.pt'.format(args.model)))
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print('\nEvaluating...')
print("=============")
print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%') # Test Loss: 0.138, Test Acc: 95.05%
if __name__ == "__main__":
main()