def DataloaderClevr(mode, opt={}, dl=False, opt_prefix='clevr_'):
from vqa_lab.config import CLEVR_QA_DIR, CLEVR_IMG_H5, CLEVR_IMG_PNG, CLEVR_IMG_NPY
parser = argparse.ArgumentParser(description='PyTorch CLEVR DataLoaer')
parser.add_argument('--clevr_image_source',
type=str,
default='h5',
help='h5|png')
parser.add_argument('--clevr_qa_dir', type=str, default=CLEVR_QA_DIR)
parser.add_argument('--clevr_img_h5', type=str, default=CLEVR_IMG_H5)
parser.add_argument('--clevr_img_png', type=str, default=CLEVR_IMG_PNG)
parser.add_argument('--clevr_load_trees', type=bool, default=True)
parser.add_argument('--clevr_load_png', type=bool, default=False)
my_opt, _ = parser.parse_known_args()
prefix_len = len(opt_prefix)
if isinstance(opt, dict): opt = argparse.Namespace(opt)
my_opt = update_opt_remove_prefix(
my_opt, opt, opt_prefix) # my_opt = { opt_prefix + '...' }
if 'logdir' in opt.__dict__:
print_save(opt.logdir, my_opt, to_screen=False)
from .dataset_clevr import clevrDataset
def my_collate(batch):
raw_samples = {key: [d[key] for d in batch] for key in batch[0]}
samples = {
'question': torch.stack(raw_samples['question']),
'image': torch.stack(raw_samples['image']),
'qid': torch.LongTensor(raw_samples['qid']),
}
if 'answer' in raw_samples:
samples['answer'] = torch.LongTensor(raw_samples['answer'])
if 'tree' in raw_samples:
samples['tree'] = raw_samples['tree']
if 'img_png' in raw_samples:
samples['img_png'] = torch.stack(raw_samples['img_png'])
return samples
dataset_opt = argparse.Namespace(**{
k[prefix_len:]: v
for k, v in my_opt.__dict__.items()
}) # remove opt prefix
from functools import reduce
dataset = reduce(lambda x, y: x + y,
[clevrDataset(dataset_opt, m) for m in mode.split('+')])
return DataLoader(dataset,
batch_size=opt.batch_size,
collate_fn=my_collate,
num_workers=opt.threads,
shuffle=(mode == 'train'),
drop_last=dl)
def DataloaderFigureQA(mode, opt={}, dl=False, opt_prefix='figureqa_'):
from vqa_lab.config import FIGUREQA_IMG_PNG, FIGUREQA_QA_DIR
parser = argparse.ArgumentParser(
description='PyTorch FigureQA DataLoader(Openended)')
parser.add_argument('--figureqa_img_png',
type=str,
default=FIGUREQA_IMG_PNG)
parser.add_argument('--figureqa_qa_dir', type=str, default=FIGUREQA_QA_DIR)
parser.add_argument('--figureqa_load_trees', type=bool, default=True)
my_opt, _ = parser.parse_known_args()
prefix_len = len(opt_prefix)
if isinstance(opt, dict): opt = argparse.Namespace(**opt)
my_opt = update_opt_remove_prefix(my_opt, opt, opt_prefix)
if 'logdir' in opt.__dict__:
print_save(opt.logdir, my_opt, to_screen=False)
def my_collate(batch):
raw_samples = {key: [d[key] for d in batch] for key in batch[0]}
samples = {
'question': torch.stack(raw_samples['question']),
'image': torch.stack(raw_samples['image']),
'qid': torch.LongTensor(raw_samples['qid']),
}
if 'answer' in raw_samples:
samples['answer'] = torch.LongTensor(raw_samples['answer'])
if 'tree' in raw_samples:
samples['tree'] = raw_samples['tree']
return samples
dataset_opt = argparse.Namespace(**{
k[prefix_len:]: v
for k, v in my_opt.__dict__.items()
}) # remove opt prefix
from .dataset_figureqa import figureqaDataset
from functools import reduce
dataset = reduce(
lambda x, y: x + y,
[figureqaDataset(dataset_opt, m) for m in mode.split('+')])
return DataLoader(dataset=dataset,
batch_size=opt.batch_size,
collate_fn=my_collate,
num_workers=opt.threads,
shuffle=('train' in mode),
drop_last=dl)
def DataloaderVQAv2(mode, opt = {}, dl = False, opt_prefix = 'vqav2_'):
from vqa_lab.config import VQAV2_QA_DIR, VQAV2_IMG_H5, VQAV2_IMG_BU, VQAV2_IMG_JPG
parser = argparse.ArgumentParser(description='PyTorch VQAv2 DataLoader(Openended)')
parser.add_argument('--vqav2_image_source', type=str, default='BU', help='h5|jpg|BU')
parser.add_argument('--vqav2_img_h5', type=str, default=VQAV2_IMG_H5)
parser.add_argument('--vqav2_img_BU', type=str, default=VQAV2_IMG_BU)
parser.add_argument('--vqav2_img_jpg', type=str, default=VQAV2_IMG_JPG)
parser.add_argument('--vqav2_qa_dir', type=str, default=VQAV2_QA_DIR)
parser.add_argument('--vqav2_load_trees', type=bool, default=True)
parser.add_argument('--vqav2_load_jpg', type=bool, default=False)
my_opt, _ = parser.parse_known_args()
prefix_len = len(opt_prefix)
if isinstance(opt, dict) : opt = argparse.Namespace(**opt)
my_opt = update_opt_remove_prefix(my_opt, opt, opt_prefix)
if 'logdir' in opt.__dict__: print_save(opt.logdir, my_opt, to_screen=False)
def my_collate(batch):
raw_samples = { key: [d[key] for d in batch] for key in batch[0] }
samples = {
'question' : torch.stack(raw_samples['question']) ,
'image' : torch.stack(raw_samples['image']) ,
'qid' : torch.LongTensor(raw_samples['qid']) ,
}
if 'answer' in raw_samples :
samples['answer'] = torch.LongTensor(raw_samples['answer'])
if 'raw_answer' in raw_samples :
samples['raw_answer'] = torch.stack(raw_samples['raw_answer'])
if 'tree' in raw_samples :
samples['tree'] = raw_samples['tree']
if 'img_jpg' in raw_samples :
samples['img_jpg'] = torch.stack(raw_samples['img_jpg'])
return samples
dataset_opt = argparse.Namespace(**{ k[prefix_len:] : v for k, v in my_opt.__dict__.items() }) # remove opt prefix
from .dataset_vqav2 import vqa2Dataset
from functools import reduce
dataset = reduce(lambda x, y : x + y, [vqa2Dataset(dataset_opt, m) for m in mode.split('+')])
return DataLoader(dataset = dataset ,
batch_size = opt.batch_size ,
collate_fn = my_collate ,
num_workers = opt.threads ,
shuffle = ('train' in mode),
drop_last = dl)
def DataloaderSclevr(mode, opt={}, dl=False, opt_prefix='sclevr_'):
from vqa_lab.config import SCLEVR_QA_DIR
parser = argparse.ArgumentParser(
description='PyTorch Sort-of-CLEVR DataLoader(Openended)')
parser.add_argument('--sclevr_qa_dir', type=str, default=SCLEVR_QA_DIR)
my_opt, _ = parser.parse_known_args()
prefix_len = len(opt_prefix)
if isinstance(opt, dict): opt = argparse.Namespace(**opt)
my_opt = update_opt_remove_prefix(my_opt, opt, opt_prefix)
if 'logdir' in opt.__dict__:
print_save(opt.logdir, my_opt, to_screen=False)
def my_collate(batch):
raw_samples = {key: [d[key] for d in batch] for key in batch[0]}
return {
'question': torch.stack(raw_samples['question']),
'image': torch.stack(raw_samples['image']),
'qid': torch.LongTensor(raw_samples['qid']),
'answer': torch.LongTensor(raw_samples['answer']),
}
dataset_opt = argparse.Namespace(**{
k[prefix_len:]: v
for k, v in my_opt.__dict__.items()
}) # remove opt prefix
from .dataset_sclevr import sclevrDataset
dataset = sclevrDataset(dataset_opt, mode)
return DataLoader(dataset=dataset,
batch_size=opt.batch_size,
collate_fn=my_collate,
num_workers=opt.threads,
shuffle=(mode == 'train'),
drop_last=dl)