def set_transformer_model(self):
'''
This Function loads the base transformer model.
Args:
transformer_config_path : config path(yaml) of the transformer
transformer_weights_path : optional . if given loads the weight as well
Returns:None
'''
# load base transformer model from config
with open(self.args.transformer_config_path, 'r') as file:
config= yaml.load(file, yaml.FullLoader)
model_config = TransformerConfig(config)
input_dim = config['transformer']['input_dim']
dr= model_config.downsample_rate
hidden_size = model_config.hidden_size
output_attention= False
base_transformer_model = TransformerModel(model_config,input_dim,output_attentions=output_attention).to('cpu')
#load weights
if self.args.transformer_weights_path:
ckpt = torch.load(self.args.transformer_weights_path, map_location='cpu')
base_transformer_model.load_state_dict(ckpt['Transformer'])
self.base_transformer_model = base_transformer_model
def main():
print("Generating data...", end="")
voc_size = args.vocab_sz
inp = np.arange(2, voc_size, 2)
tgt = np.arange(3, voc_size, 2)
data_x, data_y = get_numbers(inp, tgt)
train_len = int(len(data_x) * 0.9)
train_x, val_x = data_x[:train_len], data_x[train_len:]
train_y, val_y = data_y[:train_len], data_y[train_len:]
print("Done")
print("Setting model...", end="")
model = TransformerModel(
input_sz=voc_size,
output_sz=voc_size,
d_model=args.d_model,
nhead=args.n_head,
num_encoder_layers=args.n_encoder_layers,
num_decoder_layers=args.n_decoder_layers,
dim_feedforward=args.dim_feedforward,
dropout=args.dropout,
)
if args.load_dir != ".":
model.load_state_dict(flow.load(args.load_dir))
model = to_cuda(model)
criterion = to_cuda(nn.CrossEntropyLoss())
optimizer = flow.optim.Adam(model.parameters(), lr=args.lr)
print("Done")
print("Training...")
min_loss = 100
for i in range(1, args.n_epochs + 1):
epoch_loss = train(model, criterion, optimizer, train_x, train_y)
epoch_loss_val = validation(model, criterion, val_x, val_y)
print("epoch: {} train loss: {}".format(i, epoch_loss))
print("epoch: {} val loss: {}".format(i, epoch_loss_val))
if epoch_loss < min_loss:
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
else:
shutil.rmtree(args.save_dir)
assert not os.path.exists(args.save_dir)
os.mkdir(args.save_dir)
flow.save(model.state_dict(), args.save_dir)
if i % 3 == 2:
print(test(model, test_times=10))
def main(model_name=None, hidden=64, nlayers=1):
voc_size = 10000
inp = arange(2, voc_size, 2)
tgt = arange(3, voc_size, 2)
batch_size = 128
epochs = 30
dataset = NumberLoader(inp, tgt)
train_len = int(len(dataset) * 0.9)
val_len = len(dataset) - train_len
train_set, val_set = random_split(dataset, [train_len, val_len])
train_loader = DataLoader(train_set,
batch_size=batch_size,
shuffle=True,
num_workers=1)
val_loader = DataLoader(val_set,
batch_size=batch_size,
shuffle=True,
num_workers=1)
model = TransformerModel(voc_size,
voc_size,
hidden=hidden,
nlayers=nlayers)
if model_name is not None:
model.load_state_dict(load(model_name))
model = model.cuda()
# optimizer = optim.SGD(model.parameters(), lr=0.5)
optimizer = optim.Adam(model.parameters())
# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
criterion = nn.CrossEntropyLoss()
best_loss = 100
for i in range(epochs):
epoch_loss = train(model, criterion, optimizer, train_loader)
epoch_loss_val = validation(model, criterion, val_loader)
# scheduler.step()
print("epoch: {} train loss: {}".format(i, epoch_loss))
print("epoch: {} val loss: {}".format(i, epoch_loss_val))
if epoch_loss_val < best_loss:
best_loss = epoch_loss_val
model_name = "model/model_{0:.5f}.pt".format(epoch_loss_val)
save(model.state_dict(), model_name)
return model_name
def main():
voc_size = args.vocab_sz
print("Setting model...", end="")
model = TransformerModel(
input_sz=voc_size,
output_sz=voc_size,
d_model=args.d_model,
nhead=args.n_head,
num_encoder_layers=args.n_encoder_layers,
num_decoder_layers=args.n_decoder_layers,
dim_feedforward=args.dim_feedforward,
dropout=args.dropout,
)
model.load_state_dict(flow.load(args.load_dir))
model = to_cuda(model)
print("Done")
print("Inference:")
num = args.input_start
if num % 2 != 0:
print("The input number must be an even number.")
return
if num > args.vocab_sz - MAX_LEN * 2:
print("The input sequence may be out of range.")
return
input_nums = [num + i * 2 for i in range(MAX_LEN)]
src = to_cuda(flow.tensor(input_nums)).unsqueeze(1)
pred = [0]
for i in range(MAX_LEN):
inp = to_cuda(flow.tensor(pred)).unsqueeze(1)
output = model(src, inp)
out_num = output.argmax(2)[-1].numpy()[0]
pred.append(out_num)
print("input:", input_nums)
print("pred:", pred)
vocab_to_int = vocab["vocab_to_int"]
int_to_vocab = vocab["int_to_vocab"]
ntokens = len(vocab_to_int)
emsize = 512
nhid = 512
nlayers = 4
nhead = 4
dropout = 0.2
model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers,
dropout).to(device)
model_save_path = "./models/transformer/lm-siamzone-v4-space-342.pkl"
model.load_state_dict(
torch.load(model_save_path, map_location=torch.device("cpu")))
model.eval()
print("Model initialized")
def top_k_top_p_filtering(logits,
top_k,
top_p,
temperature,
filter_value=-float("Inf")):
# Hugging Face script to apply top k and nucleus sampling
logits = logits / temperature
top_k = min(top_k, logits.size(-1)) # Safety check
if top_k > 0:
def main(args):
random_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda")
device = torch.device("cuda" if args.cuda else "cpu")
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
print('loaded dictionary')
if args.model == 'Transformer':
model = TransformerModel(
ntokens,
args.emsize,
args.nhead,
args.nhid,
args.nlayers,
args.dropout).to(device)
else:
model = RNNModel(
args.model,
ntokens,
args.emsize,
args.nhid,
args.nlayers,
args.dropout,
args.tied).to(device)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
print('loaded model')
is_transformer_model = hasattr(
model, 'model_type') and model.model_type == 'Transformer'
if not is_transformer_model:
hidden = model.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
with open(args.outf, 'w') as outf:
with torch.no_grad(): # no tracking history
for i in range(args.words):
if is_transformer_model:
output = model(input, False)
word_weights = output[-1].squeeze().div(
args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
word_tensor = torch.Tensor([[word_idx]]).long().to(device)
input = torch.cat([input, word_tensor], 0)
else:
output, hidden = model(input, hidden)
word_weights = output.squeeze().div(args.temperature).exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
input.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
outf.write(word + ('\n' if i % 20 == 19 else ' '))
if i % args.log_interval == 0:
print('| Generated {}/{} words'.format(i, args.words))
validation='wiki.valid.tokens',
test='wiki.test.tokens')
# 依据训练集构建词典
TEXT.build_vocab(train_txt)
model = TransformerModel(len(TEXT.vocab.stoi),
ninp=200,
nhead=2,
nhid=200,
nlayers=2,
dropout=0.2).to(device)
# 模型加载训练好的参数
# checkpoint = torch.load('datasets/models/best_model.pth.tar')
checkpoint = torch.load('temp/models/best_model.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
# 已知序列
history = 'it seems'
h = []
for w in history.split():
h.append([TEXT.vocab.stoi[w]])
while (True):
# 把列表转化成 tensor ，然后计算模型输出
output = model(torch.tensor(h).to(device))
# 获取概率最大的5个单词的 id
idxs = output[-1].argsort(descending=True).view(-1)[:10]
# 随机选择其中一个
r = random.randint(0, 10)
h.append([r])
def main(args):
random_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda")
device = torch.device("cuda" if args.cuda else "cpu")
corpus = data.Corpus(args.data)
ntokens = len(corpus.dictionary)
word2idx = corpus.dictionary.word2idx
idx2word = corpus.dictionary.idx2word
args.vocab_size = len(word2idx)
print('loaded dictionary')
if args.model == 'Transformer':
model = TransformerModel(
ntokens,
args.emsize,
args.nhead,
args.nhid,
args.nlayers,
args.dropout).to(device)
else:
model = RNNModel(
args.model,
ntokens,
args.emsize,
args.nhid,
args.nlayers,
args.dropout,
args.tied).to(device)
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
is_transformer_model = hasattr(
model, 'model_type') and model.model_type == 'Transformer'
print('loaded model')
input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
# get as starting words only most common starting word
# from data corpus(heuristics from baseline)
most_common_first_words_ids = [i[0] for i in Counter(corpus.train.tolist()).most_common()
if idx2word[i[0]][0].isupper()][:200]
# most_common_first_words = [corpus.dictionary.idx2word[i]
# for i in most_common_first_words_ids]
# private message(binary code)
bit_stream = open(args.bit_stream_path, 'r').readline()
outfile = open(args.save_path + 'generated' +
str(args.bit_num) + '_bit.txt', 'w')
bitfile = open(args.save_path + 'bitfile_' +
str(args.bit_num) + '_bit.txt', 'w')
bit_index = random.randint(0, len(word2idx))
soft = torch.nn.Softmax(0)
for uter_id, uter in tqdm.tqdm(
enumerate(range(args.utterances_to_generate))):
# with torch.no_grad(): # no tracking history
input_ = torch.LongTensor([random.choice(
most_common_first_words_ids)]).unsqueeze(0).to(device)
if not is_transformer_model:
hidden = model.init_hidden(1)
output, hidden = model(input_, hidden)
gen = np.random.choice(len(corpus.dictionary), 1,
p=np.array(soft(output.reshape(-1)).tolist()) /
sum(soft(output.reshape(-1)).tolist()))[0]
gen_res = list()
gen_res.append(idx2word[gen])
bit = ""
for word_id, word in enumerate(range(args.len_of_generation - 2)):
if is_transformer_model:
assert NotImplementedError
else:
output, hidden = model(input_, hidden)
p = output.reshape(-1)
sorted_, indices = torch.sort(p, descending=True)
words_prob = [(j, i) for i, j in
zip(sorted_[:2**int(args.bit_num)].tolist(),
indices[:2**int(args.bit_num)].tolist())]
nodes = createNodes([item[1] for item in words_prob])
root = createHuffmanTree(nodes)
codes = huffmanEncoding(nodes, root)
for i in range(2**int(args.bit_num)):
if bit_stream[bit_index:bit_index + i + 1] in codes:
code_index = codes.index(
bit_stream[bit_index:bit_index + i + 1])
gen = words_prob[code_index][0]
test_data = np.int32(gen)
gen_res.append(idx2word[gen])
if idx2word[gen] in ['\n', '', "<eos>"]:
break
bit += bit_stream[bit_index: bit_index + i + 1]
bit_index = bit_index + i + 1
break
gen_sen = ' '.join(
[word for word in gen_res if word not in ["\n", "", "<eos>"]])
outfile.write(gen_sen + "\n")
bitfile.write(bit)
# (1.0, 0.0),
# (0.0, -1.0),
# (0.0, 1.0),
# (0.0, 2.0),
# (0.0, 5.0),
# ]
a = args.alpha
b = args.beta
encoder = TransformerModel(unidirectional=False)
decoder = TransformerLMHeadModel()
logger.info(f"Start training of alpha={a} beta={b}")
states = torch.load("../TSP/TSP-best.th")
encoder.load_state_dict(states["encoder"])
decoder.load_state_dict(states["decoder"])
device = torch.device("cuda")
encoder = encoder.to(device)
decoder = decoder.to(device)
num_epochs = 10
num_gradients_accumulation = 1
num_train_optimization_steps = len(
train_dataset) * num_epochs // batch_size // num_gradients_accumulation
param_optimizer = list(encoder.named_parameters()) + list(
decoder.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
from dataset import TedDataset
from tqdm import tqdm
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load tokenizer
de_tokenizer = WordpieceTokenizer('de').load_model()
en_tokenizer = WordpieceTokenizer('en').load_model()
model = TransformerModel(d_model=512,
num_heads=8,
num_encoders=6,
num_decoders=6,
in_vocab_size=len(de_tokenizer),
out_vocab_size=len(en_tokenizer)).to(device)
model.load_state_dict(torch.load("./outputs/model-epoch10.pt"))
model.eval()
def translate(inputs):
input_len = len(inputs)
inputs = torch.tensor([
de_tokenizer.transform(input, max_length=50) for input in inputs
]).cuda()
outputs = torch.tensor([[2]] * input_len).cuda() #2 means sos token
for i in range(50):
prediction = model(inputs, outputs)
prediction = torch.argmax(prediction, dim=-1)[:, -1] # get final token
outputs = torch.cat((outputs, prediction.view(-1, 1)), dim=-1)
outputs = outputs.tolist()
cleanoutput = []
def main():
# from pathlib import Path
# print("File Path:", Path(__file__).absolute())
# print("Directory Path:", Path().absolute())
args = get_args()
args.n_gpu = 1
# noisy_sents_1 = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
# noisy_sents_2 = read_strings(os.path.join(args.data_dir, "train_data", "train_corpus"))
#
# noisy_sents = noisy_sents_1 + noisy_sents_2
# noise_space_ratio = []
#
# for sentence in noisy_sents:
# noise_space_ratio.append(sentence.count(' ') / len(sentence))
#
# clean_space_ratio = []
# for sentence in clean_sents:
# clean_space_ratio.append(sentence.count(' ') / len(sentence))
#
# print("noise_space_ratio: {}, clean_space_ratio: {}".format(sum(noise_space_ratio) / len(noise_space_ratio),
# sum(clean_space_ratio) / len(clean_space_ratio)))
# ##########
# ##for local
# args.num_workers=0
# args.train_batch_size = 4
# args.eval_batch_size = 4
# args.eval_interval = 10
# ##########
set_seed(args)
if args.tokenizer == 'char':
tokenizer = CharTokenizer([])
if args.tokenizer == 'kobert':
print("koBERT tokenizer")
tokenizer = KoBertTokenizer.from_pretrained('monologg/kobert')
args.vocab_size = tokenizer.vocab_size
print(args.vocab_size)
if args.load_vocab != "":
tokenizer.load(args.load_vocab)
args.vocab_size = tokenizer.__len__()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
eos_setting = args.eos_setting
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.pause:
nsml.paused(scope=locals())
if args.mode != 'test' and args.averaging != "":
sess = 't0005/rush1-3/37'
checkpoints = ["4500", "6500", "7500", "8000"]
nsml.load(checkpoint=checkpoints[0], session=sess)
args.vocab_size = tokenizer.__len__()
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
params = model.named_parameters()
new_dict_params = dict(params)
for checkpoint in checkpoints:
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
for name, param in params:
new_dict_params[name] += param / len(checkpoints)
model.load_state_dict(new_dict_params, strict=False)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.save('best')
elif args.mode == 'eval':
print("I'm in EVAL")
checkpoint = 'best'
sess = 't0005/rush1-3/507'
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = tokenizer.__len__()
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
model.eval()
#noisy_sents = open("./naver_data_clean.txt", "r", encoding='utf-8').read().splitlines()
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_corpus"))
valid_noisy = noisy_sents[:1000]
prediction = correct_beam(model,
tokenizer,
valid_noisy,
args,
eos=True,
length_limit=0.15)
for i, pred in enumerate(prediction[:1000]):
print("noisy_input: {}, pred: {}".format(valid_noisy[i], pred))
# bind_txt(prediction)
# nsml.save('prediction')
# with open('naver_data_clean_again.txt', 'w',encoding='utf-8') as f:
# for i, pred in enumerate(prediction):
# if i%500==0: print(i)
# f.write("%s\n" % pred)
## only works when char tokenizer
##TODO: kobert tokenizer, different vocabsize if it is needed
elif args.mode != 'test' and args.resubmit != "":
checkpoint = 'best'
sess = 't0005/rush1-3/' + args.resubmit
print(sess)
model = None
tokenizer = CharTokenizer([])
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = len(tokenizer)
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
bind_nsml(model, tokenizer, args, eos=eos_setting)
########## testing loaded model & tokenizer ###############
# model.eval()
# noisy_sents = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# valid_noisy = noisy_sents[-10:]
#
# prediction = correct(model, tokenizer, valid_noisy, args, eos=True, length_limit=0.1)
#
# for pred in prediction:
# print(pred)
##################
nsml.save("best")
else:
#train_data, valid_data = None, None
if args.mode == "train" or args.mode == "pretrain" or args.mode == "semi-train":
if args.mode == "train":
# noisy_sents = open("./noisy_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# clean_sents = open("./clean_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# sents_annotation = ['None'] * len(noisy_sents)
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
if args.mode == "semi-train":
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
checkpoint = 'generated_data'
sess = 't0005/rush1-1/' + str(args.semi_dataset)
# five copy
#sess = 't0005/rush1-1/209'
# one copy
#sess = 't0005/rush1-1/224'
semi_noisy_sents, semi_clean_sents = load_generated_data(
checkpoint=checkpoint, session=sess)
semi_sents_annotation = ['None'] * len(semi_noisy_sents)
if args.mode == "pretrain":
print("PRETRAIN MODE ON!!")
noisy_sents = read_strings(
os.path.join('sejong_corpus', args.noisy_file))
clean_sents = read_strings(
os.path.join('sejong_corpus', args.clean_file))
# checkpoint = 'generated_data'
# sess = 't0005/rush1-1/113'
# noisy_sents, clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
sents_annotation = ['None'] * len(noisy_sents)
error_type_counter = Counter()
for annotation in sents_annotation:
error_type_counter += Counter(annotation.split(','))
print(error_type_counter)
# cleaning noise 버전
# pairs = [{"noisy": preprocess_sentence(noisy), "clean": clean} for noisy, clean in zip(noisy_sents, clean_sents)]
# original 버전
if args.mode == "semi-train":
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
semi_pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(
semi_noisy_sents, semi_clean_sents, semi_sents_annotation)]
train_data = pairs[:-args.num_val_data] + semi_pairs
valid_data = pairs[-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
tokenizer = CharTokenizer.from_strings(train_sents,
args.vocab_size)
bind_nsml(model, tokenizer, args, eos=eos_setting)
else:
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
train_data, valid_data = train_test_split(
pairs, test_size=args.val_ratio,
random_state=args.seed) # test: about 1000
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
# print("validation: ", valid_data)
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
# train_sents = [x['clean'] for x in train_data]
if args.load_model != "" and args.mode == "train": # Load pretrained model
print("load pretrained model")
model.load_state_dict(
torch.load(args.load_model, map_location=args.device))
if args.freeze:
model.token_embeddings.weight.requires_grad = False
model.decoder_embeddings.weight.requires_grad = False
if args.tokenizer == 'char' and args.load_vocab == "":
tokenizer = CharTokenizer.from_strings(
train_sents, args.vocab_size)
print(
f'tokenizer loaded from strings. len={len(tokenizer)}.'
)
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.tokenizer == 'char' and tokenizer is not None:
tokenizer.save('vocab.txt')
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train" or args.mode == "pretrain" or args.mode == 'semi-train':
train(model,
tokenizer,
train_data,
valid_data,
args,
eos=eos_setting)
def predict(dn, rn):
dir_name_format = "../data/{dn}-{rn}-raw"
dir_name = dir_name_format.format(dn=dn, rn=rn)
input_path = os.path.join(dir_name, "src-test.txt")
if not os.path.isfile(input_path):
print(f"File: {input_path} not exist.")
return
output_filename = f"prediction-{dn}-{rn}.txt"
output_path = os.path.join(outputDir, output_filename)
if os.path.isfile(output_path):
print(f"File {output_path} already exists.")
return
# 作用：将src进行index
preprocess = IndexedInputTargetTranslationDataset.preprocess(source_dictionary)
# 作用：将输出逆index为句子
postprocess = lambda x: ''.join(
[token for token in target_dictionary.tokenize_indexes(x) if token != END_TOKEN and token != START_TOKEN and token != PAD_TOKEN])
device = torch.device(f'cuda:{args.device}' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
print('Building model...')
model = TransformerModel(source_dictionary.vocabulary_size, target_dictionary.vocabulary_size,
config['d_model'],
config['nhead'],
config['nhid'],
config['nlayers'])
model.eval()
checkpoint_filepath = checkpoint_path
checkpoint = torch.load(checkpoint_filepath, map_location='cpu')
model.load_state_dict(checkpoint)
translator = Translator(
model=model,
beam_size=args.beam_size,
max_seq_len=args.max_seq_len,
trg_bos_idx=target_dictionary.token_to_index(START_TOKEN),
trg_eos_idx=target_dictionary.token_to_index(END_TOKEN)
).to(device)
from utils.pipe import PAD_INDEX
def pad_src(batch):
sources_lengths = [len(sources) for sources in batch]
sources_max_length = max(sources_lengths)
sources_padded = [sources + [PAD_INDEX] * (sources_max_length - len(sources)) for sources in batch]
sources_tensor = torch.tensor(sources_padded)
return sources_tensor
def process(seq):
seq = seq.strip()
def is_proof(name):
return name.count("balance") > 0 or name.count("one") > 0
if is_proof(data_name) and not is_proof(dn):
seq += ",$,1"
global is_proof_process
if is_proof_process:
print("processing")
is_proof_process = False
return seq
batch_size = args.bs
print(f"Output to {output_path}:")
with open(output_path, 'w', encoding='utf-8') as outFile:
with open(input_path, 'r', encoding='utf-8') as inFile:
seqs = []
for seq in tqdm(inFile):
seq = process(seq)
src_seq = preprocess(seq)
seqs.append(src_seq)
if len(seqs) >= batch_size:
pred_seq = translator.translate_sentence(pad_src(seqs).to(device))
pred_line = [postprocess(pred) for pred in pred_seq]
# print(pred_line)
outFile.writelines([p.strip() + '\n' for p in pred_line])
seqs.clear()
# endif
# endfor
if seqs: # last batch
pred_seq = translator.translate_sentence(pad_src(seqs).to(device))
pred_line = [postprocess(pred).replace(START_TOKEN, '').replace(END_TOKEN, '') for pred in pred_seq]
# print(pred_line)
outFile.writelines([p.strip() + '\n' for p in pred_line])
seqs.clear()
# endwith
# endwith
print(f'[Info] {input_path} Finished.')
if epoch_loss_val < best_loss:
best_loss = epoch_loss_val
model_name = "model/model_{0:.5f}.pt".format(epoch_loss_val)
save(model.state_dict(), model_name)
return model_name
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
'A PyTorch Transformer Language Model for Predicting Odd Numbers')
parser.add_argument('--test_model',
type=str,
help='the model file to load')
parser.add_argument('--train_model',
type=str,
help='the model file to load')
args = parser.parse_args()
hidden = 128
nlayers = 2
if args.test_model is None:
if args.train_model is not None:
model_name = main(args.train_model, hidden=hidden, nlayers=nlayers)
else:
model_name = main(hidden=hidden, nlayers=nlayers)
else:
model_name = args.test_model
model = TransformerModel(10000, 10000, hidden=hidden, nlayers=nlayers)
model.load_state_dict(load(model_name))
test(model, test_times=10)
def main():
### settings
args = set_args()
save_path = args.save_path
if not os.path.isdir(save_path):
os.makedirs(save_path)
logger.info(args)
### prepare for data
train_dataset = COCOMultiLabel(args,
train=True,
image_path=args.image_path)
test_dataset = COCOMultiLabel(args,
train=False,
image_path=args.image_path)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=True,
shuffle=True,
drop_last=True,
collate_fn=my_collate)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=True,
shuffle=False,
drop_last=False,
collate_fn=my_collate)
## prepare for models
encoder = CNN_Encoder().cuda()
decoder = TransformerModel(args).cuda()
## set different parameter for training or only evaluation'
if args.use_eval:
weights_dic = torch.load(args.use_model)
encoder.load_state_dict(
convert_weights(weights_dic['encoder_state_dict']))
decoder.load_state_dict(
convert_weights(weights_dic['decoder_state_dict']))
else:
encoder.load_state_dict(
convert_weights(torch.load(args.encoder_weights)))
encoder_optimizer = torch.optim.Adam(encoder.parameters(),
lr=args.encoder_lr)
decoder_optimizer = torch.optim.Adam(decoder.parameters(),
lr=args.decoder_lr)
## whether using dataparallel'
if torch.cuda.device_count() > 1:
encoder = nn.DataParallel(encoder)
decoder = nn.DataParallel(decoder)
## set hinge loss function'
loss_hinge = torch.nn.HingeEmbeddingLoss(margin=args.C,
size_average=None,
reduce=None,
reduction='mean')
## if only evaluation, return"
if args.use_eval:
f1 = test(args, encoder, decoder, test_loader, args.threshold, 1)
return
## training stage
highest_f1 = 0
epochs_without_improve = 0
for epoch in range(args.epochs):
## train and test
train(args, encoder, decoder, train_loader, encoder_optimizer,
decoder_optimizer, epoch, loss_hinge)
f1 = test(args, encoder, decoder, test_loader, args.threshold, epoch)
### save parameter
save_dict = {
'encoder_state_dict': encoder.state_dict(),
'decoder_state_dict': decoder.state_dict(),
'epoch': epoch,
'f1': f1,
'decoder_optimizer_state_dict': decoder_optimizer.state_dict(),
'encoder_optimizer_state_dict': encoder_optimizer.state_dict(),
'epochs_without_improve': epochs_without_improve
}
### save models'
torch.save(save_dict,
args.save_path + "/checkpoint_" + timestr + '.pt.tar')
if f1 > highest_f1:
torch.save(
save_dict,
args.save_path + "/BEST_checkpoint_" + timestr + '.pt.tar')
logger.info("Now the highest f1 is {}, it was {}".format(
100 * f1, 100 * highest_f1))
highest_f1 = f1
epochs_without_improve = 0
else:
epochs_without_improve += 1
if epochs_without_improve == 3:
adjust_learning_rate(decoder_optimizer, args.coeff)
adjust_learning_rate(encoder_optimizer, args.coeff)
epochs_without_imp = 0
parser.add_argument('--pretrain_emb_path', type=str, default=hp.pretrain_emb_path)
parser.add_argument('--pretrain_cnn_path', type=str, default=hp.pretrain_cnn_path)
parser.add_argument('--pretrain_model_path', type=str, default=hp.pretrain_model_path)
args = parser.parse_args()
for k, v in vars(args).items():
setattr(hp, k, v)
args = parser.parse_args()
pretrain_emb = align_word_embedding(hp.word_dict_pickle_path, hp.pretrain_emb_path, hp.ntoken,
hp.nhid) if hp.load_pretrain_emb else None
pretrain_cnn = torch.load(hp.pretrain_cnn_path) if hp.load_pretrain_cnn else None
model = TransformerModel(hp.ntoken, hp.ninp, hp.nhead, hp.nhid, hp.nlayers, hp.batch_size, dropout=0.2,
pretrain_cnn=pretrain_cnn, pretrain_emb=pretrain_emb, freeze_cnn=hp.freeze_cnn).to(device)
if hp.load_pretrain_model:
model.load_state_dict(torch.load(hp.pretrain_model_path))
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=hp.lr, weight_decay=1e-6)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, hp.scheduler_decay)
if hp.label_smoothing:
criterion = LabelSmoothingLoss(hp.ntoken, smoothing=0.1)
else:
criterion = nn.CrossEntropyLoss(ignore_index=hp.ntoken - 1)
now_time = str(time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time())))
log_dir = 'models/{name}'.format(name=hp.name)
writer = SummaryWriter(log_dir=log_dir)
log_path = os.path.join(log_dir, 'train.log')
