Datasets:
jblitzar
/
github-python-corpus

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for dset in datasets:
for vcr_task in ["qa", "qar"]:
if is_train:
assert len(dset['db']) == len(dset['img'])
assert len(dset['tasks']) == len(dset['mix_ratio'])
img_db, img_db_gt = [], []
for img_path in dset['img']:
curr_img_db, curr_img_db_gt = load_img_feat(
img_path, all_img_dbs, opts)
img_db.append(curr_img_db)
img_db_gt.append(curr_img_db_gt)
else:
assert len(dset['db']) == len(dset['img']) == 1
img_db, img_db_gt = load_img_feat(
dset['img'][0], all_img_dbs, opts)
for i, t in enumerate(dset['tasks']):
task = f'{t}_{dset["name"]}'
if is_train:
LOGGER.info(
f"Loading {task} train dataset with vcr_{vcr_task}, "
f"{dset['db']}, {[img.img_dir for img in img_db]},"
f"{[img.img_dir for img in img_db_gt]}")
txt_db = [VcrTxtTokLmdb(path, opts.max_txt_len,
task=vcr_task)
for path in dset['db']]
else:
LOGGER.info(
f"Loading {task} val dataset with vcr_{vcr_task}, "
f"{dset['db']}, {img_db.img_dir},"
f"{img_db_gt.img_dir}")
txt_db = VcrTxtTokLmdb(dset['db'][0], -1,
task=vcr_task)
if task.startswith('mlm'):
dataset = build_mlm_dataset(
txt_db, img_db_gt, img_db, is_train, opts)
elif task.startswith('mrfr'):
dataset = build_mrfr_dataset(
txt_db, img_db_gt, img_db, is_train, opts)
elif task.startswith('mrc'):
dataset = build_mrc_dataset(
txt_db, img_db_gt, img_db, is_train, opts)
else:
raise ValueError(f'Undefined task {task}')
LOGGER.info(f"{len(dataset[0])*hvd.size()} samples loaded")
loader = build_dataloader(*dataset, is_train, opts)
if is_train:
ratio = dset['mix_ratio'][i]
dataloaders[task] = (loader, ratio)
else:
dataloaders[task] = PrefetchLoader(loader)
return dataloaders, all_img_dbs
def main(opts):
hvd.init()
n_gpu = hvd.size()
device = torch.device("cuda", hvd.local_rank())
torch.cuda.set_device(hvd.local_rank())
rank = hvd.rank()
opts.rank = rank
LOGGER.info("device: {} n_gpu: {}, rank: {}, "
"16-bits training: {}".format(
device, n_gpu, hvd.rank(), opts.fp16))
if opts.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, "
"should be >= 1".format(
opts.gradient_accumulation_steps))
set_random_seed(opts.seed)
if rank == 0:
save_training_meta(opts)
TB_LOGGER.create(join(opts.output_dir, 'log'))
pbar = tqdm(total=opts.num_train_steps)
model_saver = ModelSaver(join(args.output_dir, 'ckpt'))
add_log_to_file(join(opts.output_dir, 'log', 'log.txt'))
else:
LOGGER.disabled = True
pbar = NoOp()
model_saver = NoOp()
all_dbs = [db for datasets in [opts.train_datasets, opts.val_datasets]
for dset in datasets for db in dset['db']]
tokenizer = json.load(open(f'{all_dbs[0]}/meta.json'))['bert']
assert all(tokenizer == json.load(open(f'{db}/meta.json'))['bert']
for db in all_dbs)
# build data loaders
train_dataloaders, all_img_dbs = create_dataloaders(
opts.train_datasets, True, opts)
val_dataloaders, _ = create_dataloaders(
opts.val_datasets, False, opts, all_img_dbs)
meta_loader = MetaLoader(train_dataloaders,
accum_steps=opts.gradient_accumulation_steps,