def run(args):
writer = SummaryWriter()
src, tgt, _, _ = build_dataset(args)
print('Loading test data split.')
_, _, test_gen = datasets.Multi30k.splits(
exts=(build_file_extension(args.src_language),
build_file_extension(args.tgt_language)),
fields=(('src', src), ('tgt', tgt)),
filter_pred=lambda x: len(vars(x)['src']) <= args.max_seq_length and
len(vars(x)['tgt']) <= args.max_seq_length)
print('Finished loading test data split.')
src_vocab_size = len(src.vocab.itos)
tgt_vocab_size = len(tgt.vocab.itos)
_, _, test_iterator = data.Iterator.splits(
(_, _, test_gen),
sort_key=lambda x: len(x.src),
batch_sizes=(args.batch_size, args.batch_size, args.batch_size))
print('Instantiating model...')
device = args.device
model = Transformer(src_vocab_size,
tgt_vocab_size,
device,
p_dropout=args.dropout)
model = model.to(device)
model.load_state_dict(torch.load(args.model))
print('Model instantiated!')
print('Starting testing...')
test(model, test_iterator, src.vocab, tgt.vocab, args, writer)
print('Finished testing.')
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 __init__(self, src_vocab, tgt_vocab, src_vocab_size, tgt_vocab_size,
args):
self.max_seq_length = args.max_seq_length
self.device = args.device
self.src_vocab = src_vocab
self.tgt_vocab = tgt_vocab
self.beam_size = args.beam_size
model = Transformer(src_vocab_size, tgt_vocab_size, args.device)
model.load_state_dict(torch.load(args.model))
model = model.to(args.device)
self.model = model
self.model.eval()
def run(args):
writer = SummaryWriter()
src, tgt, train_iterator, val_iterator = build_dataset(args)
src_vocab_size = len(src.vocab.itos)
tgt_vocab_size = len(tgt.vocab.itos)
print('Instantiating model...')
device = args.device
model = Transformer(src_vocab_size,
tgt_vocab_size,
device,
p_dropout=args.dropout)
model = model.to(device)
if args.checkpoint is not None:
model.load_state_dict(torch.load(args.checkpoint))
else:
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
print('Model instantiated!')
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
betas=(0.9, 0.98),
eps=1e-9)
print('Starting training...')
for epoch in range(args.epochs):
acc = train(model, epoch + 1, train_iterator, optimizer, src.vocab,
tgt.vocab, args, writer)
model_file = 'models/model_' + str(epoch) + '_' + str(acc) + '.pth'
torch.save(model.state_dict(), model_file)
print('Saved model to ' + model_file)
validate(model, epoch + 1, val_iterator, src.vocab, tgt.vocab, args,
writer)
print('Finished training.')
"""
import time
import torch
from transformer.transformer import Transformer
if __name__ == '__main__':
'''
从tar中提取模型 整理成pt文件
'''
checkpoint = 'BEST_Model.tar'
print('loading {}...'.format(checkpoint))
start = time.time()
checkpoint = torch.load(checkpoint)
print('elapsed {} sec'.format(time.time() - start))
model = checkpoint['model']
print(type(model))
filename = 'reading_comprehension.pt'
print('saving {}...'.format(filename))
start = time.time()
torch.save(model.state_dict(), filename)
print('elapsed {} sec'.format(time.time() - start))
print('loading {}...'.format(filename))
start = time.time()
model = Transformer()
model.load_state_dict(torch.load(filename))
print('elapsed {} sec'.format(time.time() - start))
