upload dataset file to repo

lisa_data/grefcoco.py

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+import json
+from grefer import G_REFER
+import random
+from pycocotools import mask as maskUtils
+from tqdm import tqdm
+def annToMask(mask_ann, h=None, w=None):
+    if isinstance(mask_ann, list):
+        
+        rles = maskUtils.frPyObjects(mask_ann, h, w)
+        rle = maskUtils.merge(rles)
+    elif isinstance(mask_ann['counts'], list):
+        # uncompressed RLE
+        rle = maskUtils.frPyObjects(mask_ann, h, w)
+    else:
+        # rle
+        rle = mask_ann
+    mask = maskUtils.decode(rle)
+    return mask
+
+
+refer_api = G_REFER('/mnt/workspace/workgroup/yuanyq/code/LISA/dataset/refer_seg', 'grefs', 'unc')
+ref_ids_train = refer_api.getRefIds(split="train")
+images_ids_train = refer_api.getImgIds(ref_ids=ref_ids_train)
+refs_train = refer_api.loadRefs(ref_ids=ref_ids_train)
+# loaded_images = refer_api.loadImgs(image_ids=images_ids_train)     
+annotation = refer_api.Anns     
+img2refs = {}
+for ref in refs_train:
+    image_id = ref["image_id"]
+    img2refs[image_id] = img2refs.get(image_id, []) + [
+        ref,
+    ]
+
+final_data = []
+for idx in tqdm(img2refs):
+    dic = {}
+    data = img2refs[idx]
+    img_idx = data[0]['image_id']
+    image = refer_api.loadImgs(image_ids=img_idx)[0]    
+    dic['image'] = 'refer_seg/images/mscoco/images/train2014/'+image['file_name']
+    # dic['image'] = 'refer_seg/images/saiapr_tc-12/'+image['file_name']
+    dic['height']= image['height']
+    dic['width'] = image['width']
+    cats = []
+    masks = []
+    for ann in data:
+        ann_idx = ann['ann_id']
+        annotation_ = annotation[ann_idx]
+        cat_list = set()
+        for cat in ann['sentences']:
+            cat_list.add(cat['sent'])
+        cat_list = list(cat_list)
+        cats+=cat_list
+        for i in range(len(cat_list)):
+            masks.append(annotation_['segmentation'])   
+            
+    dic['cat'] = cats
+    dic['masks'] = masks
+    import pdb 
+    pdb.set_trace()
+    final_data.append(dic)
+    
+print(len(final_data))
+with open('./refcocog.json', 'w') as f:
+    f.write(json.dumps(final_data))
+
+# conversations.append({'from': 'human', 'value': random.choice(SHORT_QUESTION).format(class_name=text.lower())})
+# conversations.append({'from': 'gpt', 'value': random.choice(ANSWER_LIST)})