def main(args):
splits = ['train', 'valid'] + (['dev'] if args.test else [])
print(args)
#Load dataset
datasets = OrderedDict()
for split in splits:
datasets[split] = seq_data(data_dir=args.data_dir,
split=split,
mt=args.mt,
create_data=args.create_data,
max_src_len=args.max_src_length,
max_tgt_len=args.max_tgt_length,
min_occ=args.min_occ)
print('Data OK')
#Load model
model = Transformer(
vocab_size=datasets['train'].vocab_size,
embed_dim=args.embedding_dimension,
hidden_dim=args.hidden_dimension,
dropout=args.dropout,
N_en=args.n_layer,
N_de=args.n_layer,
max_src_len=args.max_src_length, #influence in inference stage
max_tgt_len=args.max_tgt_length,
sos_idx=datasets['train'].sos_idx,
eos_idx=datasets['train'].eos_idx,
pad_idx=datasets['train'].pad_idx,
unk_idx=datasets['train'].unk_idx)
print('Model OK')
if torch.cuda.is_available():
model = model.cuda()
device = model.device
#Training phase with validation(earlystopping)
tracker = Tracker(patience=10,
verbose=True) #record training history & es function
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
step = 0
for epoch in range(args.epochs):
for split in splits:
data_loader = DataLoader(dataset=datasets[split],
batch_size=args.n_batch,
shuffle=(split == 'train'),
num_workers=cpu_count(),
pin_memory=torch.cuda.is_available())
if split == 'train':
model.train()
else:
model.eval()
#Executing
for i, data in enumerate(data_loader):
src, srclen, tgt, tgtlen = \
data['src'], data['srclen'], data['tgt'], data['tgtlen']
#FP
logits, generations = model(src, srclen, tgt, tgtlen, split)
#FP for groundtruth
#h_pred, h_tgt = model.forward_gt(generations, tgt, tgtlen)
#LOSS(weighted)
NLL = model.loss(logits, tgt.to(device), data['tgtlen'])
#GLOBAL = model.global_loss(h_pred, h_tgt)
GLOBAL = 0
loss = (NLL + GLOBAL) / data['src'].size(0)
#BP & OPTIM
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
#RECORD & RESULT(batch)
if i % 50 == 0 or i + 1 == len(data_loader):
#NLL.data = torch.cuda.FloatTensor([NLL.data])
#KL.data = torch.cuda.FloatTensor([KL.data])
print("{} Phase - Batch {}/{}, Loss: {}, NLL: {}, G: {}".
format(split.upper(), i,
len(data_loader) - 1, loss, NLL, GLOBAL))
tracker._elbo(torch.Tensor([loss]))
if split == 'valid':
tracker.record(tgt, generations, datasets['train'].i2w,
datasets['train'].pad_idx,
datasets['train'].eos_idx,
datasets['train'].unk_idx, None)
#SAVING & RESULT(epoch)
if split == 'valid':
tracker.dumps(epoch, args.dump_file) #dump the predicted text.
else:
tracker._save_checkpoint(
epoch, args.model_file,
model.state_dict()) #save the checkpooint
print("{} Phase - Epoch {} , Mean ELBO: {}".format(
split.upper(), epoch, torch.mean(tracker.elbo)))
tracker._purge()
