import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
# from torch import nn
from tqdm import tqdm
from config import device, print_freq, vocab_size, sos_id, eos_id
from data_gen import AiShellDataset, pad_collate
from transformer.decoder import Decoder
from transformer.encoder import Encoder
from transformer.loss import cal_performance
from transformer.optimizer import TransformerOptimizer
from transformer.transformer import Transformer
from utils import parse_args, save_checkpoint, AverageMeter, get_logger
def train_net(args):
torch.manual_seed(7)
np.random.seed(7)
checkpoint = args.checkpoint
start_epoch = 0
best_loss = float('inf')
writer = SummaryWriter()
epochs_since_improvement = 0
# Initialize / load checkpoint
if checkpoint is None:
# model
encoder = Encoder(args.d_input * args.LFR_m, args.n_layers_enc, args.n_head,
args.d_k, args.d_v, args.d_model, args.d_inner,
dropout=args.dropout, pe_maxlen=args.pe_maxlen)
decoder = Decoder(sos_id, eos_id, vocab_size,
args.d_word_vec, args.n_layers_dec, args.n_head,
args.d_k, args.d_v, args.d_model, args.d_inner,
dropout=args.dropout,
tgt_emb_prj_weight_sharing=args.tgt_emb_prj_weight_sharing,
pe_maxlen=args.pe_maxlen)
model = Transformer(encoder, decoder)
# print(model)
# model = nn.DataParallel(model)
# optimizer
optimizer = TransformerOptimizer(
torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.98), eps=1e-09))
else:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint['epoch'] + 1
epochs_since_improvement = checkpoint['epochs_since_improvement']
model = checkpoint['model']
optimizer = checkpoint['optimizer']
logger = get_logger()
# Move to GPU, if available
model = model.to(device)
# Custom dataloaders
train_dataset = AiShellDataset(args, 'train')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, collate_fn=pad_collate,
pin_memory=True, shuffle=True, num_workers=args.num_workers)
valid_dataset = AiShellDataset(args, 'dev')
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.batch_size, collate_fn=pad_collate,
pin_memory=True, shuffle=False, num_workers=args.num_workers)
# Epochs
for epoch in range(start_epoch, args.epochs):
# One epoch's training
train_loss = train(train_loader=train_loader,
model=model,
optimizer=optimizer,
epoch=epoch,
logger=logger)
writer.add_scalar('model/train_loss', train_loss, epoch)
lr = optimizer.lr
print('\nLearning rate: {}'.format(lr))
writer.add_scalar('model/learning_rate', lr, epoch)
step_num = optimizer.step_num
print('Step num: {}\n'.format(step_num))
# One epoch's validation
valid_loss = valid(valid_loader=valid_loader,
model=model,
logger=logger)
writer.add_scalar('model/valid_loss', valid_loss, epoch)
# Check if there was an improvement
is_best = valid_loss < best_loss
best_loss = min(valid_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
else:
epochs_since_improvement = 0
# Save checkpoint
save_checkpoint(epoch, epochs_since_improvement, model, optimizer, best_loss, is_best)
def train(train_loader, model, optimizer, epoch, logger):
model.train() # train mode (dropout and batchnorm is used)
losses = AverageMeter()
# Batches
for i, (data) in enumerate(train_loader):
# Move to GPU, if available
padded_input, padded_target, input_lengths = data
padded_input = padded_input.to(device)
padded_target = padded_target.to(device)
input_lengths = input_lengths.to(device)
# Forward prop.
pred, gold = model(padded_input, input_lengths, padded_target)
loss, n_correct = cal_performance(pred, gold, smoothing=args.label_smoothing)
# Back prop.
optimizer.zero_grad()
loss.backward()
# Update weights
optimizer.step()
# Keep track of metrics
losses.update(loss.item())
# Print status
if i % print_freq == 0:
logger.info('Epoch: [{0}][{1}/{2}]\t'
'Loss {loss.val:.5f} ({loss.avg:.5f})'.format(epoch, i, len(train_loader), loss=losses))
return losses.avg
def valid(valid_loader, model, logger):
model.eval()
losses = AverageMeter()
# Batches
for data in tqdm(valid_loader):
# Move to GPU, if available
padded_input, padded_target, input_lengths = data
padded_input = padded_input.to(device)
padded_target = padded_target.to(device)
input_lengths = input_lengths.to(device)
with torch.no_grad():
# Forward prop.
pred, gold = model(padded_input, input_lengths, padded_target)
loss, n_correct = cal_performance(pred, gold, smoothing=args.label_smoothing)
# Keep track of metrics
losses.update(loss.item())
# Print status
logger.info('\nValidation Loss {loss.val:.5f} ({loss.avg:.5f})\n'.format(loss=losses))
return losses.avg
def main():
global args
args = parse_args()
train_net(args)
if __name__ == '__main__':
main()
