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load_coco_objects_vqa_dataset.py
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load_coco_objects_vqa_dataset.py
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"""
Example script on how to load this dataset without depending on the entire framework.
"""
from pprint import pprint
from packg.paths import get_data_dir
from torch.utils.data import DataLoader
from ovqa.datasets.coco_objects_vqa_dataset import COCOObjectsVQADataset
from ovqa.processors import BlipImageEvalProcessor
def text_processor_noop(x):
return x
def main():
data_dir = get_data_dir()
coco_dir = data_dir / "coco"
vis_root = coco_dir / "images" / "val2017"
ann_paths = [coco_dir / "annotations/instances_val2017.json"]
vis_processor = None # None will give a pillow image back
# select which question the model will be asked
question_type = "what-seen-image" # "what-is-in-image", "whats-this"
# see ovqa/configs/datasets/coco.yaml
config = {
"question_type": question_type,
"class_name_key": "object",
"square_box": False,
"min_side": 40.0,
"margin_side": 2.0,
}
dataset = COCOObjectsVQADataset(
vis_processor=vis_processor,
text_processor=text_processor_noop,
vis_root=vis_root,
ann_paths=ann_paths,
config=config,
)
datapoint = dataset[0]
pprint(datapoint)
print()
# in order to use a dataloader, we need to transform the images to tensors, so we can stack them
dataset.vis_processor = BlipImageEvalProcessor(
image_size=224, mean=(0.5, 0.5, 0.5), std=(0.25, 0.25, 0.25)
)
dataloader = DataLoader(
dataset,
shuffle=False,
num_workers=0,
batch_size=2,
collate_fn=dataset.collater,
)
for i, batch in enumerate(dataloader):
image_tensor = batch.pop("image")
print("image:", image_tensor.shape, image_tensor.dtype, image_tensor.device)
pprint(batch)
print()
break
if __name__ == "__main__":
main()