def do_predict():
train_iterator, valid_iterator, test_iterator, SRC, TGT = prepare_data_multi30k()
src_pad_idx = SRC.vocab.stoi[SRC.pad_token]
tgt_pad_idx = TGT.vocab.stoi[TGT.pad_token]
src_vocab_size = len(SRC.vocab)
tgt_vocab_size = len(TGT.vocab)
model = Transformer(n_src_vocab=src_vocab_size,
n_trg_vocab=tgt_vocab_size,
src_pad_idx=src_pad_idx,
trg_pad_idx=tgt_pad_idx,
d_word_vec=256,
d_model=256,
d_inner=512,
n_layer=3,
n_head=8,
dropout=0.1,
n_position=200)
model.cuda()
model_dir = "./checkpoint/transformer"
model_path = os.path.join(model_dir, "model_9.pt")
state_dict = torch.load(model_path)
model.load_state_dict(state_dict)
model.eval()
pre_sents = []
gth_sents = []
for idx, batch in enumerate(test_iterator):
if idx % 10 == 0: print("[TIME] --- time: {} --- [TIME]".format(time.ctime(time.time())))
# src_seq: [seq_len, batch_size]
# tgt_seq: [seq_len, batch_size]
src_seq, src_len = batch.src
tgt_seq, tgt_len = batch.trg
batch_size = src_seq.size(0)
pre_tokens = []
with torch.no_grad():
for idx in range(batch_size):
tokens = translate_tokens(src_seq[idx], SRC, TGT, model, max_len=32)
pre_tokens.append(tokens)
# tgt: [batch_size, seq_len]
gth_tokens = tgt_seq.cpu().detach().numpy().tolist()
for tokens, gth_ids in zip(pre_tokens, gth_tokens):
gth = [TGT.vocab.itos[idx] for idx in gth_ids]
pre_sents.append(" ".join(tokens))
gth_sents.append(" ".join(gth))
pre_path = os.path.join(model_dir, "pre.json")
gth_path = os.path.join(model_dir, "gth.json")
with open(pre_path, "w", encoding="utf-8") as writer:
json.dump(pre_sents, writer, ensure_ascii=False, indent=4)
with open(gth_path, "w", encoding="utf-8") as writer:
json.dump(gth_sents, writer, ensure_ascii=False, indent=4)
def do_train():
train_iterator, valid_iterator, test_iterator, SRC, TGT = prepare_data_multi30k()
src_pad_idx = SRC.vocab.stoi[SRC.pad_token]
tgt_pad_idx = TGT.vocab.stoi[TGT.pad_token]
src_vocab_size = len(SRC.vocab)
tgt_vocab_size = len(TGT.vocab)
model = Transformer(n_src_vocab=src_vocab_size,
n_trg_vocab=tgt_vocab_size,
src_pad_idx=src_pad_idx,
trg_pad_idx=tgt_pad_idx,
d_word_vec=256,
d_model=256,
d_inner=512,
n_layer=3,
n_head=8,
dropout=0.1,
n_position=200)
model.cuda()
optimizer = Adam(model.parameters(), lr=5e-4)
num_epoch = 10
results = []
model_dir = os.path.join("./checkpoint/transformer")
for epoch in range(num_epoch):
train_loss, train_accuracy = train_epoch(model, optimizer, train_iterator, tgt_pad_idx, smoothing=False)
eval_loss, eval_accuracy = eval_epoch(model, valid_iterator, tgt_pad_idx, smoothing=False)
os.makedirs(model_dir, exist_ok=True)
model_path = os.path.join(model_dir, f"model_{epoch}.pt")
torch.save(model.state_dict(), model_path)
results.append({"epoch": epoch, "train_loss": train_loss, "eval_loss": eval_loss})
print("[TIME] --- {} --- [TIME]".format(time.ctime(time.time())))
print("epoch: {}, train_loss: {}, eval_loss: {}".format(epoch, train_loss, eval_loss))
print("epoch: {}, train_accuracy: {}, eval_accuracy: {}".format(epoch, train_accuracy, eval_accuracy))
result_path = os.path.join(model_dir, "result.json")
with open(result_path, "w", encoding="utf-8") as writer:
json.dump(results, writer, ensure_ascii=False, indent=4)
