def main() -> None:
parser = get_arg_parser()
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() and args.cuda else "cpu"
print('using device {}'.format(device))
print('loading vocabulary...')
if args.small:
print('using small training set')
en_vocab = load_vocab(constants.SMALL_TRAIN_EN_VOCAB_FILE)
fr_vocab = load_vocab(constants.SMALL_TRAIN_FR_VOCAB_FILE)
else:
en_vocab = load_vocab(constants.TRAIN_EN_VOCAB_FILE)
fr_vocab = load_vocab(constants.TRAIN_FR_VOCAB_FILE)
print('loaded vocabulary')
print('loading datasets...')
if args.small:
train_dataset = d.ShardedCSVDataset(
constants.WMT14_EN_FR_SMALL_TRAIN_SHARD)
else:
train_dataset = d.ShardedCSVDataset(constants.WMT14_EN_FR_TRAIN_SHARD)
valid_dataset = d.DualFileDataset(
constants.WMT14_EN_FR_VALID + ".en",
constants.WMT14_EN_FR_VALID + ".fr",
)
train_loader = d.BatchedIterator(
args.batch_size,
train_dataset,
en_vocab,
fr_vocab,
args.max_sequence_length,
)
valid_loader = d.BatchedIterator(
1,
valid_dataset,
en_vocab,
fr_vocab,
args.max_sequence_length,
)
model = build_model(parser, en_vocab, fr_vocab)
model.load_state_dict(torch.load(args.load_path))
model = model.eval()
eval_bleu(
train_loader=train_loader,
valid_loader=valid_loader,
model=model,
en_vocab=en_vocab,
fr_vocab=fr_vocab,
device=device,
multi_gpu=args.multi_gpu,
)
def make_pretrain_data(args):
vocab = load_vocab(args.vocab)
vocab_list = []
for id in range(vocab.get_piece_size()):
if not vocab.is_unknown(id):
vocab_list.append(vocab.id_to_piece(id))
line_cnt = 0
with open(args.input, "r") as in_f:
for line in in_f:
line_cnt += 1
datas = []
with open(args.input, "r") as f:
for i, line in enumerate(
tqdm(f, total=line_cnt, desc="Loading Dataset",
unit=" lines")):
data = json.loads(line)
if 0 < len(data["doc"]):
datas.append(data)
with open(args.output, "w") as out_f:
for i, data in enumerate(
tqdm(datas, desc="Make Pretrain Dataset", unit=" lines")):
instances = create_pretrain_instances(datas, i, data["doc"],
args.n_seq, args.mask_prob,
vocab_list)
for instance in instances:
out_f.write(json.dumps(instance))
out_f.write("\n")
def get_datasets(config_data):
images_root_dir = config_data['dataset']['images_root_dir']
root_train = os.path.join(images_root_dir, 'train')
root_val = os.path.join(images_root_dir, 'val')
root_test = os.path.join(images_root_dir, 'test')
train_ids_file_path = config_data['dataset']['training_ids_file_path']
val_ids_file_path = config_data['dataset']['validation_ids_file_path']
test_ids_file_path = config_data['dataset']['test_ids_file_path']
train_annotation_file = config_data['dataset'][
'training_annotation_file_path']
test_annotation_file = config_data['dataset']['test_annotation_file_path']
coco = COCO(train_annotation_file)
coco_test = COCO(test_annotation_file)
vocab_threshold = config_data['dataset']['vocabulary_threshold']
vocabulary = load_vocab(train_annotation_file, vocab_threshold)
train_data_loader = get_coco_dataloader(train_ids_file_path, root_train,
train_annotation_file, coco,
vocabulary, config_data)
val_data_loader = get_coco_dataloader(val_ids_file_path, root_val,
train_annotation_file, coco,
vocabulary, config_data)
test_data_loader = get_coco_dataloader(test_ids_file_path, root_test,
test_annotation_file, coco_test,
vocabulary, config_data)
return coco_test, vocabulary, train_data_loader, val_data_loader, test_data_loader
def process_caption(cap_dir, vocab_pkl, max_sen_len):
"""
对caption增加<start>, <end>，截断或填充至固定长度，并将结果保存在{dataset_name}_ids.pkl
:param max_sen_len:
:param vocab_pkl:
:param cap_dir: 划分后的数据集集合根目录，包括train, val, test
:return:
"""
vocab = load_vocab(vocab_pkl)
subsets = ['train', 'val', 'test']
for subset in subsets:
ann_file = cap_dir + subset + '_ids.pkl'
if os.path.exists(ann_file):
print('*' * 20, os.path.basename(ann_file), 'already exists.',
'*' * 20)
continue
# {cap_id : {caption: cap , length: len]}
data_dict = {}
path = cap_dir + subset + '.json'
coco = COCO(path)
ann_ids = list(coco.anns.keys())
for ann_id in tqdm(ann_ids):
item_new = collections.OrderedDict()
cap = coco.anns[ann_id]['caption']
cap, cap_len = fix_length(cap, vocab, max_sen_len)
item_new['caption'] = cap
item_new['length'] = cap_len
data_dict[ann_id] = item_new
print('*' * 20, 'save ann_file: ', ann_file, '*' * 20)
with open(ann_file, 'wb') as f:
pickle.dump(data_dict, f)
def make_pretrain_data(args):
""" pretrain 데이터 생성 """
if os.path.isfile(args.pretrain):
print(f"{args.pretrain} exists")
return
vocab = load_vocab(args.vocab)
vocab_list = [vocab.id_to_piece(wid) for wid in range(vocab.get_piece_size()) if not vocab.is_unknown(wid)]
docs = []
with open(args.corpus, "r") as f:
lines = f.read().splitlines()
doc = []
for line in tqdm(lines, desc=f"Loading {os.path.basename(args.corpus)}"):
line = line.strip()
if line == "":
if 0 < len(doc):
docs.append(doc)
doc = []
else:
pieces = vocab.encode_as_pieces(line)
if 0 < len(pieces):
doc.append(pieces)
if 0 < len(doc):
docs.append(doc)
with open(args.pretrain, "w") as out_f:
config = Config.load(args.config)
for i, doc in enumerate(tqdm(docs, desc=f"Making {os.path.basename(args.pretrain)}", unit=" lines")):
instances = create_pretrain_instances(docs, i, doc, config.n_enc_seq, config.mask_prob, vocab_list)
for instance in instances:
out_f.write(json.dumps(instance, ensure_ascii=False))
out_f.write("\n")
def main(args):
vocab = load_vocab()
encoder = CNNEncoder()
decoder = DecoderRNN(512,512,len(vocab))
encoder_state_dict, decoder_state_dict, optimizer, *meta = utils.load_models(args.checkpoint_file,False)
encoder.load_state_dict(encoder_state_dict)
decoder.load_state_dict(decoder_state_dict)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
inp = cv2.imread(args.image_path)
inp = transform(Image.fromarray(inp)).unsqueeze(0)
inp = utils.to_var(inp, volatile=True)
features = encoder(inp)
sampled_ids = decoder.sample(features)
sampled_ids = sampled_ids.cpu().data.numpy()[0]
sentence = utils.convert_back_to_text(sampled_ids, vocab)
print('Caption:', sentence)
def train_model(rank, world_size, args):
if 1 < args.n_gpu:
init_process_group(rank, world_size)
master = (world_size == 0 or rank % world_size == 0)
if master: wandb.init(project="transformer-evolution")
vocab = load_vocab(args.vocab)
config = cfg.Config.load(args.config)
config.n_enc_vocab, config.n_dec_vocab = len(vocab), len(vocab)
config.device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
print(config)
best_epoch, best_loss, best_score = 0, 0, 0
model = transformer.QA(config)
if os.path.isfile(args.save):
best_epoch, best_loss, best_score = model.load(args.save)
print(f"rank: {rank} load state dict from: {args.save}")
if 1 < args.n_gpu:
model.to(config.device)
model = DistributedDataParallel(model, device_ids=[rank], find_unused_parameters=True)
else:
model.to(config.device)
if master: wandb.watch(model)
criterion = torch.nn.CrossEntropyLoss()
train_loader, train_sampler = data.build_data_loader(vocab, "KorQuAD_v1.0_train.json", args, shuffle=True)
test_loader, _ = data.build_data_loader(vocab, "KorQuAD_v1.0_train.json", args, shuffle=False)
t_total = len(train_loader) * args.epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = optim.get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
offset = best_epoch
for step in trange(args.epoch, desc="Epoch"):
if train_sampler:
train_sampler.set_epoch(step)
epoch = step + offset
loss = train_epoch(config, rank, epoch, model, criterion, optimizer, scheduler, train_loader)
score = eval_epoch(config, rank, model, test_loader)
if master: wandb.log({"loss": loss, "accuracy": score})
if master and best_score < score:
best_epoch, best_loss, best_score = epoch, loss, score
if isinstance(model, DistributedDataParallel):
model.module.save(best_epoch, best_loss, best_score, args.save)
else:
model.save(best_epoch, best_loss, best_score, args.save)
print(f">>>> rank: {rank} save model to {args.save}, epoch={best_epoch}, loss={best_loss:.3f}, socre={best_score:.3f}")
if 1 < args.n_gpu:
destroy_process_group()
def main():
infer_img_path = "C:/Users/Crypto/PycharmProjects/segmented_style_transfer/data"
infer_img_name = 'two_boys.jpg'
# Read and Process Raw data
data = CaptioningData()
# Finding image files as data
data.set_all_images(cfg.images_path)
captions_dict = data.get_captions(cfg.token_file)
caption_maxlen = data.get_caption_maxlen()
vocab = load_vocab(vocab_path=cfg.data_path, vocab_name=cfg.vocab_name)
# print(vocab.word2idx)
inception_encoding = Encoder()
# Decoder model
decoder = Decoder(vocab_size=len(vocab),
embedding_size=300,
input_shape=2048,
caption_max_len=caption_maxlen)
decoder_model = decoder.get_model()
decoder_model.load_weights('model/best_weights.97-0.95.hdf5')
img_ids = data.get_val_images(cfg.val_image_files)
img_name = img_ids[19]
enc_img = inception_encoding.encode_single_img(file_path=cfg.images_path,
img_name=img_name)
# enc_img = inception_encoding.encode_single_img(file_path=infer_img_path, img_name=infer_img_name)
caption = ["<start>"]
while True:
par_caps = [vocab(i) for i in caption]
par_caps = sequence.pad_sequences([par_caps],
maxlen=40,
padding='post')
preds = decoder_model.predict(
[np.array([enc_img]), np.array(par_caps)])
word_pred = vocab.idx2word[np.argmax(preds[0])]
caption.append(word_pred)
if word_pred == "<end>" or len(caption) > 40:
break
full_img_path = os.path.join(cfg.images_path, img_name)
print(captions_dict[img_name])
print(full_img_path)
print(' '.join(caption[1:-1]))
for beam_size in [3, 5, 7]:
caption = beam_search_predictions(vocab,
enc_img,
decoder_model,
caption_maxlen,
beam_index=beam_size)
print(beam_size, caption)
def __init__(self, _hparams):
self.hparams = _hparams
set_seed(_hparams.fixed_seed)
self.train_loader = get_dataloader(_hparams.train_src_path, _hparams.train_dst_path,
_hparams.batch_size, _hparams.num_workers)
self.src_vocab, self.dst_vocab = load_vocab(_hparams.train_src_pkl, _hparams.train_dst_pkl)
self.device = torch.device(_hparams.device)
self.model = NMT(_hparams.embed_size, _hparams.hidden_size,
self.src_vocab, self.dst_vocab, self.device,
_hparams.dropout_rate).to(self.device)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=_hparams.lr)
def main():
# import ipdb; ipdb.set_trace()
config = get_args()
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(config.gpu_id)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
logger = get_logger(config)
voc = load_vocab(config)
config.n_voc = len(voc.voc)
logger.info(config)
config.embeddings = voc.embeddings
config.use_product_info = False
config.use_user_info = False
if config.use_user_info and config.use_product_info:
usrdict = UserTable('../data/' + config.dataname + '/usrlist.txt')
prddict = ProductTable('../data/' + config.dataname + '/prdlist.txt')
config.n_users = usrdict.size + 1
config.n_products = prddict.size + 1
else:
usrdict = None
prddict = None
logger.info("build model...")
with tf.device("/device:{}:{}".format(config.device_type, config.gpu_id)):
model = Model(config)
logger.info("creating session...")
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=config.gpu_allocate_rate)
gpu_options.allow_growth = True
session_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
sess = tf.Session(config=session_config)
model.init_variable(sess)
trainset, devset, testset = load_dataset(config, voc, usrdict, prddict)
if config.load_model:
logger.info("restoring model...")
model.restore(sess)
if not config.test_only:
logger.info("starting training...")
model.train(sess, trainset, devset, testset)
logger.info("training done.")
logger.info("starting testing...")
test_acc, test_mae, test_rmse = model.evaluate(sess, testset)
logger.info(
"final result of testset: acc = {:.4f}, mae = {:.4f}, rmse = {:.4f}".
format(test_acc, test_mae, test_rmse))
logger.info("testing done.")
def save_dataset(dataset, vocab, output,
max_length=20):
sentences=[]
labels=[]
with open (dataset) as questions_file:
csv_reader=csv.reader(questions_file, delimiter='\t')
line_count=0
column_names=[]
for row in csv_reader:
if line_count==0:
column_names.append(row[0])
column_names.append(row[1])
line_count+=1
else:
sentences.append(row[1])
labels.append(row[2])
total_sentences=len(sentences)
vocab=load_vocab(vocab)
print('Number of sentences to be written: %d',total_sentences)
h5file = h5py.File(output, "w")
d_questions = h5file.create_dataset(
"sentences", (total_sentences, max_length), dtype='i')
d_labels = h5file.create_dataset(
"labels", (total_sentences,), dtype='i')
bar = progressbar.ProgressBar(maxval=total_sentences)
q_index = 0
for sentence,label in zip(sentences,labels):
q, length = process_text(sentence, vocab,
max_length=20)
d_questions[q_index, :length] = q
if label == 'yesno' or label == 'factoid' or label == 'list' or label == 'summary':
x=label
else:
print('error')
l = process_label(label)
d_labels[q_index] = int(l)
q_index += 1
bar.update(q_index)
h5file.close()
def make_pretrain_data(args):
vocab = load_vocab(args.vocab)
vocab_list = []
for id in range(vocab.get_piece_size()):
if not vocab.is_unknown(id):
vocab_list.append(vocab.id_to_piece(id))
line_cnt = 0
with open(args.input, "r") as in_f:
for line in in_f:
line_cnt += 1
docs = []
with open(args.input, "r") as f:
doc = []
for i, line in enumerate(
tqdm(f,
total=line_cnt,
desc=f"Loading {args.input}",
unit=" lines")):
line = line.strip()
if line == "":
if 0 < len(doc):
docs.append(doc)
doc = []
else:
pieces = vocab.encode_as_pieces(line)
if 0 < len(pieces):
doc.append(pieces)
if doc:
docs.append(doc)
for index in range(args.count):
output = args.output.format(index)
if os.path.isfile(output): continue
with open(output, "w") as out_f:
for i, doc in enumerate(
tqdm(docs, desc=f"Making {output}", unit=" lines")):
instances = create_pretrain_instances(docs, i, doc, args.n_seq,
args.mask_prob,
vocab_list)
for instance in instances:
out_f.write(json.dumps(instance))
out_f.write("\n")
def __init__(self, fea_dim, embed_dim, hid_dim, max_sen_len, vocab_pkl):
super(RNN, self).__init__()
self.fea_dim = fea_dim
self.embed_dim = embed_dim
self.hid_dim = hid_dim
self.max_sen_len = max_sen_len
self.vocab = load_vocab(vocab_pkl)
self.vocab_size = self.vocab.__len__()
self.lstm_cell = nn.LSTMCell(self.embed_dim, self.hid_dim)
self.embed = nn.Embedding(
self.vocab_size, self.embed_dim) # num_embeddings, embedding_dim
self.fc = weight_norm(nn.Linear(self.hid_dim, self.vocab_size))
self.dropout = nn.Dropout(0.5)
self.init_h = weight_norm(nn.Linear(self.fea_dim, self.hid_dim))
self.init_c = weight_norm(nn.Linear(self.fea_dim, self.hid_dim))
self.init_weight()
def get_datasets(config_data):
gen_flag = (config_data['experiment']['num_epochs'] == -1)
images_root_dir = config_data['dataset']['images_root_dir']
if not gen_flag:
root_train = os.path.join(images_root_dir, 'train')
root_val = os.path.join(images_root_dir, 'val')
root_test = os.path.join(images_root_dir, 'test')
else:
root_img = images_root_dir
if not gen_flag:
train_ids_file_path = config_data['dataset']['training_ids_file_path']
val_ids_file_path = config_data['dataset']['validation_ids_file_path']
test_ids_file_path = config_data['dataset']['test_ids_file_path']
train_annotation_file = config_data['dataset']['training_annotation_file_path']
test_annotation_file = config_data['dataset']['test_annotation_file_path']
coco = COCO(train_annotation_file)
coco_test = COCO(test_annotation_file)
vocab_threshold = config_data['dataset']['vocabulary_threshold']
vocabulary = load_vocab(train_annotation_file, vocab_threshold)
if not gen_flag:
train_data_loader = get_coco_dataloader(train_ids_file_path, root_train, train_annotation_file, coco, vocabulary,
config_data)
val_data_loader = get_coco_dataloader(val_ids_file_path, root_val, train_annotation_file, coco, vocabulary,
config_data, train=False)
test_data_loader = get_coco_dataloader(test_ids_file_path, root_test, test_annotation_file, coco_test, vocabulary,
config_data, train=False)
else:
train_data_loader, val_data_loader, test_data_loader, coco_test = 0, 0, 0, 0
return coco_test, vocabulary, train_data_loader, val_data_loader, test_data_loader
# Get dataset
# Image Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# load COCOs dataset
IMAGES_PATH = 'data/train2014'
CAPTION_FILE_PATH = 'data/annotations/captions_train2014.json'
vocab = load_vocab()
train_loader = get_coco_data_loader(path=IMAGES_PATH,
json=CAPTION_FILE_PATH,
vocab=vocab,
transform=transform,
batch_size=batch_size,
shuffle=True)
IMAGES_PATH = 'data/val2014'
CAPTION_FILE_PATH = 'data/annotations/captions_val2014.json'
val_loader = get_coco_data_loader(path=IMAGES_PATH,
json=CAPTION_FILE_PATH,
vocab=vocab,
transform=transform,
batch_size=batch_size,
def save_dataset(image_dir, questions, annotations, vocab, ans2cat, output,
im_size=224, max_q_length=20, max_a_length=4,
with_answers=False):
"""Saves the Visual Genome images and the questions in a hdf5 file.
Args:
image_dir: Directory with all the images.
questions: Location of the questions.
annotations: Location of all the annotations.
vocab: Location of the vocab file.
ans2cat: Mapping from answers to category.
output: Location of the hdf5 file to save to.
im_size: Size of image.
max_q_length: Maximum length of the questions.
max_a_length: Maximum length of the answers.
with_answers: Whether to also save the answers.
"""
# Load the data.
vocab = load_vocab(vocab)
with open(annotations) as f:
annos = json.load(f)
with open(questions) as f:
questions = json.load(f)
with open(ans2cat) as f:
ans2cat = json.load(f)
# Get the mappings from qid to answers.
qid2ans, image_ids = create_answer_mapping(annos, ans2cat)
total_questions = len(qid2ans.keys())
total_images = len(image_ids)
print "Number of images to be written: %d" % total_images
print "Number of QAs to be written: %d" % total_questions
h5file = h5py.File(output, "w")
d_questions = h5file.create_dataset(
"questions", (total_questions, max_q_length), dtype='i')
d_indices = h5file.create_dataset(
"image_indices", (total_questions,), dtype='i')
d_images = h5file.create_dataset(
"images", (total_images, im_size, im_size, 3), dtype='f')
d_answers = h5file.create_dataset(
"answers", (total_questions, max_a_length), dtype='i')
d_answer_types = h5file.create_dataset(
"answer_types", (total_questions,), dtype='i')
# Create the transforms we want to apply to every image.
transform = transforms.Compose([
transforms.Resize((im_size, im_size))])
# Iterate and save all the questions and images.
bar = progressbar.ProgressBar(maxval=total_questions)
i_index = 0
q_index = 0
done_img2idx = {}
for entry in questions['questions']:
image_id = entry['image_id']
question_id = entry['question_id']
if image_id not in image_ids:
continue
if question_id not in qid2ans:
continue
if image_id not in done_img2idx:
try:
path = "%d.jpg" % (image_id)
image = Image.open(os.path.join(image_dir, path)).convert('RGB')
except IOError:
path = "%012d.jpg" % (image_id)
image = Image.open(os.path.join(image_dir, path)).convert('RGB')
image = transform(image)
d_images[i_index, :, :, :] = np.array(image)
done_img2idx[image_id] = i_index
i_index += 1
q, length = process_text(entry['question'], vocab,
max_length=max_q_length)
d_questions[q_index, :length] = q
answer = qid2ans[question_id]
a, length = process_text(answer, vocab,
max_length=max_a_length)
d_answers[q_index, :length] = a
d_answer_types[q_index] = ans2cat[answer]
d_indices[q_index] = done_img2idx[image_id]
q_index += 1
bar.update(q_index)
h5file.close()
print "Number of images written: %d" % i_index
print "Number of QAs written: %d" % q_index
def train_model(rank, world_size, args):
print('dd22')
if 1 < args.n_gpu:
init_process_group(rank, world_size)
master = (world_size == 0 or rank % world_size == 0)
vocab = load_vocab(args.vocab)
config = cfg.Config.load(args.config)
config.n_enc_vocab = len(vocab)
# GPU 사용 여부를 확인합니다.
config.device = torch.device(
f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
print(config)
best_epoch, best_loss = 0, 0
"""학습 실행"""
# BERTPretrain을 생성합니다.
model = bert.BERTPretrain(config)
# 기존에 학습된 pretrain 값이 있다면 이를 로드 합니다.
if os.path.isfile(args.save):
best_epoch, best_loss = model.bert.load(args.save)
print(
f"rank: {rank} load pretrain from: {args.save}, epoch={best_epoch}, loss={best_loss}"
)
best_epoch += 1
# BERTPretrain이 GPU 또는 CPU를 지원하도록 합니다.
if 1 < args.n_gpu:
model.to(config.device)
model = DistributedDataParallel(model,
device_ids=[rank],
find_unused_parameters=True)
else:
model.to(config.device)
# MLM loss(criterion_lm) 및 NLP loss(criterion_cls) 함수를 선언 합니다.
criterion_lm = torch.nn.CrossEntropyLoss(ignore_index=-1, reduction='mean')
criterion_cls = torch.nn.CrossEntropyLoss()
train_loader = data.build_pretrain_loader(vocab,
args,
epoch=best_epoch,
shuffle=True)
t_total = len(train_loader) * args.epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
# optimizer를 선언 합니다.
optimizer = optim.AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = optim.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
offset = best_epoch
losses = []
for step in trange(args.epoch, desc="Epoch"):
print('step offset')
print(step)
print(offset)
epoch = step + offset
# 각 epoch 마다 새로 train_loader를 생성 합니다.
# step이 0인 경우는 위에서 생성했기 때문에 생성하지 않습니다.
if 0 < step:
del train_loader
train_loader = data.build_pretrain_loader(vocab,
args,
epoch=epoch,
shuffle=True)
# 각 epoch 마다 학습을 합니다.
loss = train_epoch(config, rank, epoch, model, criterion_lm,
criterion_cls, optimizer, scheduler, train_loader)
losses.append(loss)
if master:
best_epoch, best_loss = epoch, loss
if isinstance(model, DistributedDataParallel):
model.module.bert.save(best_epoch, best_loss, args.save)
else:
model.bert.save(best_epoch, best_loss, args.save)
print(
f">>>> rank: {rank} save model to {args.save}, epoch={best_epoch}, loss={best_loss:.3f}"
)
print(f">>>> rank: {rank} losses: {losses}")
if 1 < args.n_gpu:
destroy_process_group()
def train_model(rank, world_size, args):
""" 모델 학습 """
if 1 < args.n_gpu:
init_process_group(rank, world_size)
master = (world_size == 0 or rank % world_size == 0)
if master and args.wandb:
wandb.init(project=args.project)
vocab = load_vocab(args.vocab)
config = Config.load(args.config)
config.n_enc_vocab = len(vocab)
config.device = f"cuda:{rank}" if torch.cuda.is_available() else "cpu"
print(config)
best_epoch, best_loss = 0, 0
train_model = ALBERTPretrain(config)
if os.path.isfile(args.pretrain_save):
try:
best_epoch, best_loss = train_model.albert.load(args.pretrain_save)
print(
f"load pretrain from: {os.path.basename(args.pretrain_save)}, epoch={best_epoch}, loss={best_loss:.4f}"
)
except:
print(f'load {os.path.basename(args.pretrain_save)} failed.')
if 1 < args.n_gpu:
train_model.to(config.device)
# noinspection PyArgumentList
train_model = DistributedDataParallel(train_model,
device_ids=[rank],
find_unused_parameters=True)
else:
train_model.to(config.device)
if master and args.wandb:
wandb.watch(train_model)
criterion_lm = torch.nn.CrossEntropyLoss(ignore_index=-1, reduction='mean')
criterion_cls = torch.nn.CrossEntropyLoss()
train_loader: DataLoader = data.build_pretrain_loader(vocab,
args,
shuffle=True)
t_total = len(train_loader) * args.epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in train_model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.weight_decay
}, {
'params': [
p for n, p in train_model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = optim.AdamW(optimizer_grouped_parameters,
lr=config.learning_rate,
eps=config.adam_epsilon)
scheduler = optim.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=config.warmup_steps,
num_training_steps=t_total)
start_epoch = best_epoch + 1
losses = []
with trange(args.epoch, desc="Epoch", position=0) as pbar:
pbar.set_postfix_str(
f"best epoch: {best_epoch}, loss: {best_loss:.4f}")
for step in pbar:
epoch = step + start_epoch
loss = train_epoch(config, rank, train_model, criterion_lm,
criterion_cls, optimizer, scheduler,
train_loader)
losses.append(loss)
if master and args.wandb:
wandb.log({"loss": loss})
if master:
best_epoch, best_loss = epoch, loss
if isinstance(train_model, DistributedDataParallel):
train_model.module.albert.save(best_epoch, best_loss,
args.pretrain_save)
else:
train_model.albert.save(best_epoch, best_loss,
args.pretrain_save)
pbar.set_postfix_str(
f"best epoch: {best_epoch}, loss: {best_loss:.4f}")
if 1 < args.n_gpu:
destroy_process_group()
parser = argparse.ArgumentParser()
parser.add_argument(
"--mode",
default="prepare",
type=str,
required=False,
help="동작모드 입니다. download: 학습할 데이터 다운로드, prepare: 학습할 데이터셋 생성")
args = parser.parse_args()
if not os.path.exists("data"):
os.makedirs("data")
if args.mode == "download":
download_data(args)
elif args.mode == "prepare":
vocab = load_vocab("kowiki.model")
args.corpus = "data/kowiki.txt"
if not os.path.isfile(args.corpus):
build_corpus("data/kowiki.csv", args.corpus)
if not os.path.isfile("data/kowiki.json"):
prepare_pretrain(args, vocab, "data/kowiki.json")
if not os.path.isfile("data/ratings_train.json"):
prepare_train(args, vocab, "data/ratings_train.txt",
"data/ratings_train.json")
if not os.path.isfile("data/ratings_test.json"):
prepare_train(args, vocab, "data/ratings_test.txt",
"data/ratings_test.json")
else:
print(
f"지원하지 않는 모드 입니다. {args.mode}\n- downlaod: 학습할 데이터 다운로드\n- preapre: 학습할 데이터셋 생성"
)
def __init__(self, _hparams):
self.hparams = _hparams
self.src_vocab, self.dst_vocab = load_vocab(_hparams.train_src_pkl,
_hparams.train_dst_pkl)
self.device = torch.device(_hparams.device)
def test() -> None:
en_vocab = load_vocab(TRAIN_EN_VOCAB_FILE)
fr_vocab = load_vocab(TRAIN_FR_VOCAB_FILE)
print("English Vocab Size: {} French Vocab Size: {}".format(
len(en_vocab), len(fr_vocab)))
def train_model(rank, world_size, args):
""" 모델 학습 """
master = (world_size == 0 or rank % world_size == 0)
if master and args.wandb:
wandb.init(project=args.project, resume=args.name, tags=args.tags)
if 1 < args.n_gpu:
init_process_group(rank, world_size)
vocab = load_vocab(args.vocab)
config = Config.load(args.config)
config.n_enc_vocab, config.n_dec_vocab = len(vocab), len(vocab)
config.device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
print(config)
best_epoch, best_loss, best_score = 0, 0, 0
model: MovieClassification = transformer.MovieClassification(config)
if args.resume and os.path.isfile(args.save):
best_epoch, best_loss, best_score = model.load(args.save)
print(f"rank: {rank}, last epoch: {best_epoch} load state dict from: {os.path.basename(args.save)}")
model.to(config.device)
if master and args.wandb:
wandb.watch(model)
if 1 < args.n_gpu:
model = DistributedDataParallel(model, device_ids=[rank], find_unused_parameters=True)
criterion = torch.nn.CrossEntropyLoss()
train_loader, train_sampler = data.build_data_loader(rank, vocab, os.path.abspath(os.path.join(os.getcwd(), args.data_dir, "ratings_train.json")), args, shuffle=True)
test_loader, test_sampler = data.build_data_loader(rank, vocab, os.path.abspath(os.path.join(os.getcwd(), args.data_dir, "ratings_test.json")), args, shuffle=False)
t_total = len(train_loader) * args.epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = optimization.AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = optimization.get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total, last_epoch=best_epoch)
print(f'total_memory: {torch.cuda.get_device_properties(rank).total_memory / (1024 * 1024):.3f} MB')
with tqdm(initial=best_epoch + 1, total=args.epoch, position=0) as pbar:
for epoch in range(best_epoch + 1, args.epoch + 1):
if train_sampler:
train_sampler.set_epoch(epoch)
train_loss = train_epoch(args, config, rank, epoch, model, criterion, optimizer, scheduler, train_loader)
test_loss, test_accuracy = eval_epoch(config, rank, model, test_loader, test_sampler)
if master and args.wandb:
wandb.config.update(args)
wandb.log(row={"train loss": train_loss, "accuracy": test_accuracy}, step=epoch)
if master:
if best_score < test_accuracy:
best_epoch, best_loss, best_score = epoch, train_loss, test_accuracy
pbar.set_description(f'Best (score={best_score:.3f}, epoch={best_epoch})')
if isinstance(model, DistributedDataParallel):
model.module.save(best_epoch, best_loss, best_score, args.save)
else:
model.save(best_epoch, best_loss, best_score, args.save)
else:
if best_epoch + 5 < epoch: # early stop
break
pbar.update()
break
print(f'total_memory: {torch.cuda.get_device_properties(rank).total_memory / (1024 * 1024):.3f} MB')
if master and args.wandb:
wandb.save(args.name)
if 1 < args.n_gpu:
destroy_process_group()
parser.add_argument("--optimize_batch_size", type=int, default=256)
parser.add_argument("--generate_size", type=int, default=8196)
parser.add_argument("--generate_batch_size", type=int, default=256)
parser.add_argument("--learning_rate", type=float, default=1e-3)
parser.add_argument("--kl_div_coef", type=float, default=0.0)
parser.add_argument("--disable_neptune", action="store_true")
args = parser.parse_args()
#args.disable_neptune = True
device = torch.device(0)
random.seed(0)
vocab = load_vocab(name=args.vocab_name)
obj_func = load_score_func(name=args.objective)
model = RecurrentNetwork(vocab_size=len(vocab),
hidden_size=args.hidden_size,
num_layers=args.num_layers)
model.load_state_dict(torch.load(args.pretrained_model_path))
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
scheme = HillClimbScheme()
if not args.disable_neptune:
neptune.init(project_qualified_name="sungsoo.ahn/real-mol-opt")
experiment = neptune.create_experiment(name="hill_climb",
params=vars(args))
result_dir = f"./result/tmp/{experiment.id}"
else:
import utils, pickle, vocab
# glove_path = '/home/ouzj01/zhangyc/project/glove/glove.840B.300d.txt'
glove_path = 'data/glove/glove_ori.6B.50d.txt'
# save_path = 'data/glove/glove_multiwoz.840B.300d.txt'
# save_path = 'data/glove/glove_multiwoz.6B.50d.txt'
save_path = 'data/glove/glove_kvret.6B.50d.txt'
vocab = vocab.Vocab(1100)
vocab.load_vocab('data/kvret/vocab')
# vocab.load_vocab('data/MultiWOZ/processed/vocab')
vec_array = []
with open(glove_path, 'r', encoding='UTF-8') as ef:
with open(save_path, 'w') as f:
for line in ef.readlines():
line_sep = line.strip().split(' ')
word, vec = line_sep[0], line_sep[1:]
if vocab.has_word(word):
f.write(line)
ef.close()
def main() -> None:
parser = get_arg_parser()
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() and args.cuda else "cpu"
print('using device {}'.format(device))
print('loading vocabulary...')
if args.small:
print('using small training set')
en_vocab = load_vocab(constants.SMALL_TRAIN_EN_VOCAB_FILE)
fr_vocab = load_vocab(constants.SMALL_TRAIN_FR_VOCAB_FILE)
else:
en_vocab = load_vocab(constants.TRAIN_EN_VOCAB_FILE)
fr_vocab = load_vocab(constants.TRAIN_FR_VOCAB_FILE)
print('loaded vocabulary')
print('loading datasets...')
if args.small:
train_dataset = d.ShardedCSVDataset(
constants.WMT14_EN_FR_SMALL_TRAIN_SHARD)
else:
train_dataset = d.ShardedCSVDataset(constants.WMT14_EN_FR_TRAIN_SHARD)
# valid_dataset = d.DualFileDataset(
# constants.WMT14_EN_FR_VALID + ".en",
# constants.WMT14_EN_FR_VALID + ".fr",
# )
train_loader = d.BatchedIterator(
args.batch_size,
train_dataset,
en_vocab,
fr_vocab,
args.max_sequence_length,
)
# valid_loader = d.BatchedIterator(
# 1,
# valid_dataset,
# en_vocab,
# fr_vocab,
# args.max_sequence_length,
# )
model = build_model(parser, en_vocab, fr_vocab)
print('using model...')
print(model)
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
if not os.path.exists(os.path.join(args.save_dir, args.model_name)):
os.makedirs(os.path.join(args.save_dir, args.model_name))
# model.load_state_dict(torch.load('delete/model_1543183590.2138884/unk_problem.pt'))
train(
train_loader=train_loader,
valid_loader=None, # valid_loader,
model=model,
epochs=args.num_epochs,
learning_rate=args.learning_rate,
weight_decay=args.weight_decay,
log_dir=args.log_dir,
save_dir=args.save_dir,
en_vocab=en_vocab,
fr_vocab=fr_vocab,
device=device,
multi_gpu=args.multi_gpu,
save_step=args.save_step,
model_name=args.model_name,
optimizer=args.optimizer,
)
def main(args):
# hyperparameters
batch_size = args.batch_size
num_workers = 1
# Image Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# load COCOs dataset
IMAGES_PATH = 'data/train2014'
CAPTION_FILE_PATH = 'data/annotations/captions_train2014.json'
vocab = load_vocab()
train_loader = get_coco_data_loader(path=IMAGES_PATH,
json=CAPTION_FILE_PATH,
vocab=vocab,
transform=transform,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
IMAGES_PATH = 'data/val2014'
CAPTION_FILE_PATH = 'data/annotations/captions_val2014.json'
val_loader = get_coco_data_loader(path=IMAGES_PATH,
json=CAPTION_FILE_PATH,
vocab=vocab,
transform=transform,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
losses_val = []
losses_train = []
# Build the models
ngpu = 1
initial_step = initial_epoch = 0
embed_size = args.embed_size
num_hiddens = args.num_hidden
learning_rate = 5e-4
num_epochs = 2
log_step = args.log_step
save_step = 500
checkpoint_dir = args.checkpoint_dir
encoder = CNNEncoder()
decoder = DecoderRNN(embed_size, num_hiddens, len(vocab))
# Loss
criterion = nn.CrossEntropyLoss()
if args.checkpoint_file:
encoder_state_dict, decoder_state_dict, optimizer, *meta = utils.load_models(
args.checkpoint_file, args.sample)
initial_step, initial_epoch, losses_train, losses_val = meta
encoder.load_state_dict(encoder_state_dict)
decoder.load_state_dict(decoder_state_dict)
else:
params = list(decoder.parameters()) + list(
encoder.batchnorm.parameters())
optimizer = torch.optim.Adam(params, lr=learning_rate)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
if args.sample:
return utils.sample(encoder, decoder, vocab, val_loader)
# Train the Models
total_step = len(train_loader)
try:
for epoch in range(initial_epoch, num_epochs):
for step, (images, captions,
lengths) in enumerate(train_loader, start=initial_step):
# Set mini-batch dataset
images = utils.to_var(images, volatile=True)
captions = utils.to_var(captions)
targets = pack_padded_sequence(captions,
lengths,
batch_first=True)[0]
# Forward, Backward and Optimize
decoder.zero_grad()
encoder.zero_grad()
if ngpu > 1:
# run on multiple GPU
features = nn.parallel.data_parallel(
encoder, images, range(ngpu))
outputs, alphas = nn.parallel.data_parallel(
decoder, features, range(ngpu))
else:
# run on single GPU
features = encoder(images)
outputs, alphas = decoder(features, captions, lengths)
train_loss = criterion(outputs, targets.cpu())
train_loss += ((1. - alphas.sum(dim=1))**2).mean()
losses_train.append(train_loss.data)
train_loss.backward()
optimizer.step()
print('Epoch: {} - Step: {} - Train Loss: {}'.format(
epoch, step, losses_train[-1]))
# Run validation set and predict
if step % log_step == 404:
encoder.batchnorm.eval()
# run validation set
batch_loss_val = []
for val_step, (images, captions,
lengths) in enumerate(val_loader):
images = utils.to_var(images, volatile=True)
captions = utils.to_var(captions, volatile=True)
targets = pack_padded_sequence(captions,
lengths,
batch_first=True)[0]
features = encoder(images)
outputs, alphas = decoder(features, captions, lengths)
val_loss = criterion(outputs, targets.cpu())
val_loss += ((1. - alphas.sum(dim=1))**2).mean()
batch_loss_val.append(val_loss.data)
if val_step % 50 == 0:
print('Epoch: {} - Step: {} - Mini Eval Loss: {}'.
format(epoch, val_step, val_loss))
sampled_ids = decoder.sample(features)
sampled_ids = sampled_ids.cpu().data.numpy()[0]
sentence = utils.convert_back_to_text(
sampled_ids, vocab)
print('Sample:', sentence)
true_ids = captions.cpu().data.numpy()[0]
sentence = utils.convert_back_to_text(
true_ids, vocab)
print('Target:', sentence)
losses_val.append(np.mean(batch_loss_val))
# predict
print('Epoch: {} - Step: {} - Eval Loss: {}'.format(
epoch, step, losses_val[-1]))
encoder.batchnorm.train()
# Save the models
if (step + 1) % save_step == 0:
utils.save_models(encoder, decoder, optimizer, step, epoch,
losses_train, losses_val, checkpoint_dir)
utils.dump_losses(
losses_train, losses_val,
os.path.join(checkpoint_dir, 'losses.pkl'))
except KeyboardInterrupt:
pass
finally:
# Do final save
utils.save_models(encoder, decoder, optimizer, step, epoch,
losses_train, losses_val, checkpoint_dir)
utils.dump_losses(losses_train, losses_val,
os.path.join(checkpoint_dir, 'losses.pkl'))
def main():
construct_vocab = False
encode_images = False
train = True
# Read and Process Raw data
data = CaptioningData()
# Finding image files as data
data.set_all_images(cfg.images_path)
captions_dict = data.get_captions(cfg.token_file)
caption_maxlen = data.get_caption_maxlen()
# Construct vocabulary
if construct_vocab:
# get all caption to construct Vocab
all_captions = data.get_all_captions()
vocab = build_vocab(vocab_path=cfg.data_path,
vocab_name=cfg.vocab_name,
captions=all_captions,
threshold=2)
else:
vocab = load_vocab(vocab_path=cfg.data_path, vocab_name=cfg.vocab_name)
# print(vocab.word2idx)
inception_encoding = Encoder()
# train data
if train:
train_images = data.get_train_images(cfg.train_image_files)
train_pairs = [
ImgCaptionPair(img_id, captions_dict[img_id])
for img_id in train_images
]
# Image Encoding
if encode_images:
train_img_encoding = inception_encoding.encode_images(
file_path=cfg.images_path,
image_list=train_images,
encoding_file=cfg.train_img_encoding_file)
else:
train_img_encoding = inception_encoding.load_image_encoding(
encoding_file=cfg.train_img_encoding_file)
train_data_generator = data_generator(vocab,
train_pairs,
train_img_encoding,
batch_size=1800,
max_len=caption_maxlen)
# next(g)
# Decoder model
decoder = Decoder(vocab_size=len(vocab),
embedding_size=300,
input_shape=2048,
caption_max_len=caption_maxlen)
decoder_model = decoder.get_model()
decoder_model.load_weights('best_weights.97-0.95.hdf5')
if train:
decoder_model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
ckpt = ModelCheckpoint('weights.{epoch:02d}-{loss:.2f}.hdf5',
monitor='loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=30)
best_ckpt = ModelCheckpoint('best_weights.{epoch:02d}-{loss:.2f}.hdf5',
monitor='loss',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
decoder_model.fit_generator(train_data_generator,
steps_per_epoch=30,
epochs=100,
callbacks=[ckpt, best_ckpt])
decoder_model.save('decoder_model.h5')
img_ids = data.get_val_images(cfg.val_image_files)
img_name = img_ids[9]
enc_img = inception_encoding.encode_single_img(file_path=cfg.images_path,
img_name=img_name)
caption = ["<start>"]
while True:
par_caps = [vocab(i) for i in caption]
par_caps = sequence.pad_sequences([par_caps],
maxlen=40,
padding='post')
preds = decoder_model.predict(
[np.array([enc_img]), np.array(par_caps)])
word_pred = vocab.idx2word[np.argmax(preds[0])]
caption.append(word_pred)
if word_pred == "<end>" or len(caption) > 40:
break
full_img_path = os.path.join(cfg.images_path, img_name)
print(captions_dict[img_name])
print(full_img_path)
print(' '.join(caption[1:-1]))
def train_model(rank, world_size, args):
if 1 < args.n_gpu:
init_process_group(rank, world_size)
master = (world_size == 0 or rank % world_size == 0)
vocab = load_vocab(args.vocab)
config = cfg.Config.load(args.config)
config.n_enc_vocab, config.n_dec_vocab = len(vocab), len(vocab)
config.device = torch.device(
f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
print(config)
best_epoch, best_loss = 0, 0
model = albert.ALBERTPretrain(config)
if os.path.isfile(args.save):
model.albert.load(args.save)
print(f"rank: {rank} load pretrain from: {args.save}")
if 1 < args.n_gpu:
model.to(config.device)
model = DistributedDataParallel(model,
device_ids=[rank],
find_unused_parameters=True)
else:
model.to(config.device)
criterion_lm = torch.nn.CrossEntropyLoss(ignore_index=-1, reduction='mean')
criterion_cls = torch.nn.CrossEntropyLoss()
train_loader, train_sampler = data.build_pretrain_loader(vocab,
args,
shuffle=True)
t_total = len(train_loader) * args.epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = optim.AdamW(optimizer_grouped_parameters,
lr=config.learning_rate,
eps=config.adam_epsilon)
scheduler = optim.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=config.warmup_steps,
num_training_steps=t_total)
offset = best_epoch
for step in trange(args.epoch, desc="Epoch"):
if train_sampler:
train_sampler.set_epoch(step)
epoch = step + offset
loss = train_epoch(config, rank, epoch, model, criterion_lm,
criterion_cls, optimizer, scheduler, train_loader)
if master:
best_epoch, best_loss = epoch, loss
if isinstance(model, DistributedDataParallel):
model.module.albert.save(best_epoch, best_loss, args.save)
else:
model.albert.save(best_epoch, best_loss, args.save)
print(
f">>>> rank: {rank} save model to {args.save}, epoch={best_epoch}, loss={best_loss:.3f}"
)
if 1 < args.n_gpu:
destroy_process_group()
def build_data_loader(vocab, infile, args, shuffle=True):
""" 데이터 로더 """
dataset = MovieDataSet(vocab, infile)
if 1 < args.n_gpu and shuffle:
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch, sampler=sampler, collate_fn=movie_collate_fn)
else:
sampler = None
loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch, sampler=sampler, shuffle=shuffle, collate_fn=movie_collate_fn)
return loader, sampler
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--input", default="../data/kowiki.txt", type=str, required=False,
help="input text file")
parser.add_argument("--output", default="../data/kowiki_gpt.json", type=str, required=False,
help="output json file")
parser.add_argument("--n_seq", default=256, type=int, required=False,
help="sequence length")
args = parser.parse_args()
if not os.path.isfile(args.output):
vocab = load_vocab("../kowiki.model")
make_pretrain_data(args, vocab)
else:
print(f"{args.output} exists")
def main(args):
# hyperparameters
batch_size = args.batch_size
num_workers = 2
# Image Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
vocab = load_vocab()
loader = get_basic_loader(dir_path=os.path.join(args.image_path),
transform=transform,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
# Build the models
embed_size = args.embed_size
num_hiddens = args.num_hidden
checkpoint_path = 'checkpoints'
encoder = CNN(embed_size)
decoder = RNN(embed_size,
num_hiddens,
len(vocab),
1,
rec_unit=args.rec_unit)
encoder_state_dict, decoder_state_dict, optimizer, *meta = utils.load_models(
args.checkpoint_file)
encoder.load_state_dict(encoder_state_dict)
decoder.load_state_dict(decoder_state_dict)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Train the Models
try:
results = []
with torch.no_grad():
for step, (images, image_ids) in enumerate(tqdm(loader)):
images = utils.to_var(images)
features = encoder(images)
captions = beam_sample(decoder, features)
# captions = decoder.sample(features)
captions = captions.cpu().data.numpy()
captions = [
utils.convert_back_to_text(cap, vocab) for cap in captions
]
captions_formatted = [{
'image_id': int(img_id),
'caption': cap
} for img_id, cap in zip(image_ids, captions)]
results.extend(captions_formatted)
print('Sample:', captions_formatted[0])
except KeyboardInterrupt:
print('Ok bye!')
finally:
import json
file_name = 'captions_model.json'
with open(file_name, 'w') as f:
json.dump(results, f)
