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.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/train_annfiles.json filter=lfs diff=lfs merge=lfs -text

data/split_data.py

@@ -0,0 +1,63 @@
+import os, json, argparse
+import numpy as np
+
+datasets_type = ["Underwater", "Low_altitude", "Indoor", "High_altitude"]
+
+
+def setup_parser():
+    parser = argparse.ArgumentParser(description='Split the MSVQA datasets')
+    parser.add_argument('--steps', type=int, default=10, help='Total number of steps (>=4)')
+    parser.add_argument('--all', type=bool, default=True, help='Learn all scenarios in the first step')
+    parser.add_argument('--shuffle', type=bool, default=True, help='Shuffle the order of learning scenarios.')
+    parser.add_argument('--random_seed', type=int, default=1993)
+    parser.add_argument('--train_anns_pth', type=str, default='./train_annfiles.json')
+    parser.add_argument('--save_pth', type=str, default='./workspace')
+    return parser
+
+
+def load_json(p): return json.load(open(p, 'r', encoding='utf-8'))
+
+
+def save_json(data_list, save_root, steps_num):
+    if not os.path.exists(save_root): os.mkdir(save_root)
+    for i, task_data in enumerate(data_list, 1):
+        json.dump(task_data, open(os.path.join(save_root, f"{steps_num}-steps-{i}.json"), "w"), indent=4)
+
+
+def split_datasets(img_data, ann_data, split_num, seed):
+    np.random.seed(seed)
+    np.random.shuffle(img_data)
+    img_num, split_single = len(img_data), len(img_data) // split_num
+    init_split = img_num - split_single * (split_num - 1)
+    return [[ann for ann in ann_data if
+             ann["image"] in img_data[init_split + split_single * (i - 1): init_split + split_single * i]] if i else
+            [ann for ann in ann_data if ann["image"] in img_data[:init_split]] for i in range(split_num)]
+
+
+if __name__ == "__main__":
+    args = setup_parser().parse_args()
+    if args.steps < 4: raise "Unsupported step size!"
+    train_data = load_json(args.train_anns_pth)
+    data, data_img = {}, {}
+    for t in datasets_type: data[t], data_img[t] = [], []
+    for ann in train_data:
+        p = ann["perspective"]
+        data[p].append(ann)
+        if ann["image"] not in data_img[p]: data_img[p].append(ann["image"])
+
+    omit_steps = args.steps - 4 * (args.steps // 4) - (1 if args.all else 0)
+    for dataset in datasets_type:
+        split_num = args.steps // 4 + (1 if omit_steps > 0 else 0) + (1 if args.all else 0)
+        if omit_steps > 0: omit_steps -= 1
+        data[dataset] = split_datasets(data_img[dataset], data[dataset], split_num, args.random_seed)
+
+    if args.all:
+        init_task = sum([data[t][0] for t in datasets_type], [])
+        sub_tasks = sum([data[t][1:] for t in datasets_type], [])
+        if args.shuffle: np.random.seed(args.random_seed); np.random.shuffle(sub_tasks)
+        task_data = [init_task, *sub_tasks]
+    else:
+        sub_tasks = sum([data[t] for t in datasets_type], [])
+        if args.shuffle: np.random.seed(args.random_seed); np.random.shuffle(sub_tasks)
+        task_data = sub_tasks
+    save_json(task_data, args.save_pth, args.steps)

data/train_annfiles.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:676c33bc7fe62639dd2674c5659c7ebd8bbb09d643403457ee67d7f8a10474bf
+size 20739872