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import json
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import pickle
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import os
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from tqdm import tqdm
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def build_referring_dataset(instance_path, refs_path, split, save_path):
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assert os.path.exists(instance_path), f'Path not found: {instance_path}'
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assert os.path.exists(refs_path), f'Path not found: {refs_path}'
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with open(instance_path) as f:
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instance = json.load(f)
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with open(refs_path, 'rb') as f:
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refs = pickle.load(f)
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images = instance['images']
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annotations = instance['annotations']
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img_id2info = {}
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for image in images:
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img_id2info[image['id']] = image
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anno_id2info = {}
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for annotation in annotations:
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anno_id2info[annotation['id']] = annotation
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outputs = []
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new_img_id = 0
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for sample in tqdm(refs):
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if sample['split'] != split:
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continue
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sample_annotation = anno_id2info[sample['ann_id']]
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sample_image = img_id2info[sample['image_id']]
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outputs.append(
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{
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'image': sample_image['file_name'],
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'image_info': sample_image,
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'instruction': sample['sentences'],
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'anns': [sample_annotation],
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'new_img_id': new_img_id,
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}
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)
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new_img_id += 1
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with open(save_path, 'w') as f:
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json.dump(outputs, f)
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print(f'Saving at {save_path}. Total sample: {len(outputs)}.')
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if __name__ == '__main__':
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root_path = 'datasets/refer_seg'
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datasets = ['refcoco', 'refcoco+', 'refcocog']
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splits = ['train', 'val', 'testA', 'testB']
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for dataset in datasets:
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if dataset == 'refcocog':
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splits = ['train', 'val', 'test']
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for split in splits:
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instance_path = os.path.join(root_path, f'{dataset}', 'instances.json')
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if dataset == 'refcocog':
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refs_name = 'refs(umd).p'
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else:
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refs_name = 'refs(unc).p'
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refs_path = os.path.join(root_path, f'{dataset}', refs_name)
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save_path = os.path.join(root_path, f'{dataset}', f'{split}_psalm.json')
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print(f'Processing {dataset}: {split}...')
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build_referring_dataset(instance_path, refs_path, split, save_path)
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print(f'Done')
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