{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":31263,"offset":31253,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u60a8\u4fee\u6539\u7684\u4ee3\u7801\u770b\u8d77\u6765\u5df2\u7ecf\u975e\u5e38\u63a5\u8fd1\u6b63\u786e\u5b9e\u73b0\u60a8\u7684\u9700\u6c42\u4e86\uff0c\u4f46\u662f\u53ef\u80fd\u5b58\u5728\u4e00\u4e9b\u91cd\u590d\u6216\u4e0d\u5fc5\u8981\u7684\u8c03\u7528\u3002\u6211\u4f1a\u5e2e\u60a8\u68b3\u7406\u8fd9\u4e9b\uff0c\u5e76\u7ed9\u51fa\u4e00\u4e9b\u53ef\u80fd\u9700\u8981\u6539\u8fdb\u7684\u5730\u65b9\uff1a\n\n1. **_get_event_sequence_path \u65b9\u6cd5**\uff1a\n   \u8fd9\u4e2a\u65b9\u6cd5\u73b0\u5728\u6b63\u786e\u5730\u8fd4\u56de\u6307\u5411 `_dvs` \u76ee\u5f55\u7684\u8def\u5f84\uff0c\u8fd9\u91cc\u5b58\u653e\u7684\u662f\u4ee5 `.png` \u6216 `.bmp` \u683c\u5f0f\u7684\u4e8b\u4ef6\u5e27\u3002\n\n2. **_get_event_frame \u65b9\u6cd5**\uff1a\n   \u60a8\u73b0\u5728\u7528 `_get_frame` \u65b9\u6cd5\uff08\u5b83\u8c03\u7528 `image_loader` \u52a0\u8f7d\u56fe\u7247\uff09\u6765\u52a0\u8f7d\u4e8b\u4ef6\u5e27\uff0c\u8fd9\u4e0e\u666e\u901a\u5e27\u4f7f\u7528\u7684\u65b9\u6cd5\u4e00\u81f4\u3002\u8fd9\u662f\u4e00\u4e2a\u5408\u7406\u7684\u7b80\u5316\uff0c\u786e\u4fdd\u4e86\u52a0\u8f7d\u548c\u5904\u7406\u7684\u4e00\u81f4\u6027\u3002\n\n3. **get_frames \u65b9\u6cd5**\uff1a\n   - \u60a8\u5df2\u7ecf\u5c06 `frame_event_list` \u7684\u83b7\u53d6\u8f6c\u79fb\u5230\u4e86 `_get_event_frame` \u7684\u8c03\u7528\u4e0a\uff0c\u800c\u8fd9\u4e9b\u56fe\u50cf\u662f\u4ece\u7531 `_get_event_sequence_path` \u6307\u5b9a\u7684\u8def\u5f84\u52a0\u8f7d\u7684\uff0c\u8fd9\u4e2a\u4fee\u6539\u662f\u6b63\u786e\u7684\u3002\n   - \u770b\u5230\u60a8\u6ce8\u91ca\u6389\u4e86 `frame_event_img_list` \u76f8\u5173\u7684\u4ee3\u7801\uff0c\u8fd9\u770b\u8d77\u6765\u662f\u6b63\u786e\u7684\uff0c\u56e0\u4e3a\u60a8\u5df2\u7ecf\u5c06\u8fd9\u90e8\u5206\u7684\u5904\u7406\u5408\u5e76\u5230\u4e86 `frame_event_list` \u4e2d\u3002\n\n\u4e00\u70b9\u5c0f\u4fee\u6539\uff1a\n- \u60a8\u5728 `get_frames` \u65b9\u6cd5\u4e2d\u7684\u6700\u540e\u8c03\u7528\u4e86 `_get_event_frame` \u4e24\u6b21\uff0c\u4e00\u6b21\u5728\u4e2d\u95f4\uff0c\u4e00\u6b21\u5728\u6700\u540e\u3002\u8fd9\u662f\u4e0d\u5fc5\u8981\u7684\uff0c\u60a8\u5e94\u8be5\u53ea\u4fdd\u7559\u4e00\u4e2a\u8fd9\u79cd\u8c03\u7528\u3002\n\n\u7b80\u5316\u540e\u7684 `get_frames` \u65b9\u6cd5\u5982\u4e0b\uff1a\n\n```python\ndef get_frames(self, seq_id, frame_ids, anno=None):\n    seq_path = self._get_sequence_path(seq_id)\n    frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n    # \u4f7f\u7528\u4fee\u6539\u540e\u7684\u65b9\u6cd5\u8bfb\u53d6\u4e8b\u4ef6\u6570\u636e\n    seq_event_path = self._get_event_sequence_path(seq_id)\n    frame_event_list = self._get_event_frame(seq_event_path, frame_ids)\n\n    if anno is None:\n        anno = self.get_sequence_info(seq_id)\n\n    anno_frames = {}\n    for key, value in anno.items():\n        anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n    object_meta = OrderedDict({\n        'object_class_name': None,\n        'motion_class': None,\n        'major_class': None,\n        'root_class': None,\n        'motion_adverb': None\n    })\n\n    return frame_list, anno_frames, object_meta, frame_event_list\n```\n\n\u8fd9\u6837\u4fee\u6539\u540e\uff0c\u4ee3\u7801\u5e94\u8be5\u66f4\u4e3a\u6e05\u6670\u548c\u9ad8\u6548\u3002\u786e\u4fdd\u5728\u5b9e\u9645\u73af\u5883\u4e2d\u5bf9\u8fd9\u4e9b\u4fee\u6539\u8fdb\u884c\u6d4b\u8bd5\uff0c\u4ee5\u9a8c\u8bc1\u6240\u6709\u529f\u80fd\u6309\u9884\u671f\u5de5\u4f5c\uff0c\u5e76\u786e\u4fdd\u6570\u636e\u8def\u5f84\u7b49\u8bbe\u7f6e\u6b63\u786e\u65e0\u8bef\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":7681,"n_turns_in_conversation":12,"n_words":603,"source_row_offset":31253,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"d4717b1ebcf12464485e8c22","turn_index":4,"user_text":"\u90a3\u4e48\u73b0\u5728\u6211\u4eec\u5c06\u4ee3\u7801\u4fee\u6539\u4e3a\uff1aimport os\nimport os.path\nimport numpy as np\nimport torch\nimport csv\nimport pandas\nimport random\nfrom collections import OrderedDict\nfrom .base_video_dataset import BaseVideoDataset\nfrom lib.train.data import jpeg4py_loader\nfrom lib.train.admin import env_settings\nimport scipy.io as scio\n\nclass Coesot(BaseVideoDataset):\n    def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\n\n        root = env_settings().got10k_dir if root is None else root\n        super().__init__('Coesot', root, image_loader)\n\n        self.sequence_list = self._get_sequence_list()\n\n        # seq_id is the index of the folder inside the got10k root path\n        if split is not None:\n            if seq_ids is not None:\n                raise ValueError('Cannot set both split_name and seq_ids.')\n            if split == 'train':\n                file_path = os.path.join(self.root, 'train.txt')\n            elif split == 'val':\n                file_path = os.path.join(self.root, 'val.txt')\n            else:\n                raise ValueError('Unknown split name')\n            seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze(\"columns\").values.tolist()\n        elif seq_ids is None:\n            seq_ids = list(range(0, len(self.sequence_list)))\n\n        self.sequence_list = [self.sequence_list[i] for i in seq_ids]\n\n    def get_name(self):\n        return 'coesot'\n\n    def _get_sequence_list(self):\n        with open(os.path.join(self.root, 'list.txt')) as f:\n            dir_list = list(csv.reader(f))\n        dir_list = [dir_name[0] for dir_name in dir_list]\n        return dir_list\n\n    def _read_bb_anno(self, seq_path):\n        bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n        gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\n        return torch.tensor(gt)\n\n    def _get_sequence_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_aps\")\n\n    def _get_event_img_sequence_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_dvs\")\n\n    def _get_grountgruth_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id])\n\n    def get_sequence_info(self, seq_id):\n        bbox_path = self._get_grountgruth_path(seq_id)\n        bbox = self._read_bb_anno(bbox_path)\n\n        valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n        visible = valid.clone().byte()\n        # return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\n        return {'bbox': bbox, 'valid': valid, 'visible': visible, }\n\n    def _get_frame_path(self, seq_path, frame_id):\n        if os.path.exists(os.path.join(seq_path, 'frame{:04}.png'.format(frame_id))):\n            return os.path.join(seq_path, 'frame{:04}.png'.format(frame_id))    # frames start from 0\n        else:\n            return os.path.join(seq_path, 'frame{:04}.bmp'.format(frame_id))    # some image is bmp\n\n    def _get_frame(self, seq_path, frame_id):\n        return self.image_loader(self._get_frame_path(seq_path, frame_id))\n\n    # def _get_event_sequence_path(self, seq_id):        ## get evemts' frames\n    #     return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_voxel\")\n    ################################ \u4fee\u6539 ###############\n    def _get_event_sequence_path(self, seq_id):\n        # \u8fd4\u56de\u6307\u5411DVS\u76ee\u5f55\u7684\u8def\u5f84\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + '_dvs')\n                        \n    # def _get_event_frame(self, seq_path, frame_id):\n    #     frame_event_list = []\n    #     for f_id in frame_id:\n    #         event_frame_file = os.path.join(seq_path, 'frame{:04}.mat'.format(f_id))\n    #         if os.path.getsize(event_frame_file) == 0:\n    #             event_features = np.zeros(4096, 19)\n    #             # need_data = [np.zeros([4096, 3]), np.zeros([4096, 16])]\n    #         else:\n    #             mat_data = scio.loadmat(event_frame_file)\n    #             # need_data = [mat_data['coor'], mat_data['features']]\n    #             event_features = np.concatenate((mat_data['coor'], mat_data['features']), axis=1)        # concat coorelate and features (x,y,z, feauture32/16)\n    #             if np.isnan(event_features).any():\n    #                 event_features = np.zeros(4096, 19)\n    #                 print(event_frame_file, 'exist nan value in voxel.')\n    #         frame_event_list.append(event_features)\n    #     return frame_event_list\n    def _get_event_frame(self, seq_path, frame_ids):\n        # \u4f7f\u7528\u56fe\u50cf\u52a0\u8f7d\u5668\u8bfb\u53d6\u6bcf\u4e00\u4e2a\u4e8b\u4ef6\u5e27\n        frame_event_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n        return frame_event_list  \n\n    def get_frames(self, seq_id, frame_ids, anno=None):\n        seq_path = self._get_sequence_path(seq_id)\n        # obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n        frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n        # \u4f7f\u7528\u4fee\u6539\u540e\u7684\u65b9\u6cd5\u8bfb\u53d6\u4e8b\u4ef6\u6570\u636e\n        seq_event_path = self._get_event_sequence_path(seq_id)\n        frame_event_list = self._get_event_frame(seq_event_path, frame_ids)\n\n        # seq_event_path = self._get_event_img_sequence_path(seq_id)\n        # frame_event_img_list = [self._get_frame(seq_event_path, f_id) for f_id in frame_ids]\n        if anno is None:\n            anno = self.get_sequence_info(seq_id)\n\n        anno_frames = {}\n        for key, value in anno.items():\n            anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n        object_meta = OrderedDict({'object_class_name': None,\n                                   'motion_class': None,\n                                   'major_class': None,\n                                   'root_class': None,\n                                   'motion_adverb': None})\n\n        seq_event_path = self._get_event_sequence_path(seq_id)\n        frame_event_list = self._get_event_frame(seq_event_path, frame_ids)\n\n        return frame_list, anno_frames, object_meta, frame_event_list # , frame_event_img_list\n\n\u68c0\u67e5\u4e00\u4e0b\u6709\u65e0\u9519\u8bef\uff1f"},{"assistant_initiates":false,"asst_text":"\u62b1\u6b49\u672a\u80fd\u660e\u786e\u6307\u51fa\u4fee\u6539\u4e4b\u5904\u3002\u6211\u5728\u4e0a\u4e00\u6761\u56de\u590d\u4e2d\u63d0\u5230\u7684\u4fee\u6539\u4e3b\u8981\u662f\u5728\u60a8\u7684 `get_frames` \u65b9\u6cd5\u4e2d\u79fb\u9664\u91cd\u590d\u7684 `_get_event_frame` \u8c03\u7528\u3002\u5728\u60a8\u539f\u59cb\u63d0\u4ea4\u7684\u4ee3\u7801\u4e2d\uff0c\u8be5\u65b9\u6cd5\u88ab\u8c03\u7528\u4e86\u4e24\u6b21\uff1a\n\n```python\nframe_event_list = self._get_event_frame(seq_event_path, frame_ids)\n...\nframe_event_list = self._get_event_frame(seq_event_path, frame_ids)\n```\n\n\u8fd9\u662f\u4e0d\u5fc5\u8981\u7684\uff0c\u56e0\u4e3a\u5728\u8fd4\u56de\u51fd\u6570\u7ed3\u679c\u524d\uff0c\u540c\u6837\u7684\u64cd\u4f5c\u88ab\u6267\u884c\u4e86\u4e24\u6b21\u800c\u6ca1\u6709\u4efb\u4f55\u53d8\u5316\u3002\u8fd9\u4e0d\u4ec5\u4f1a\u5f71\u54cd\u6027\u80fd\uff0c\u4e5f\u4f1a\u589e\u52a0\u4e0d\u5fc5\u8981\u7684\u8ba1\u7b97\u590d\u6742\u6027\u3002\u6211\u7684\u5efa\u8bae\u662f\u4fdd\u7559\u4e00\u6b21\u8c03\u7528\uff0c\u5e76\u786e\u4fdd\u7ed3\u679c\u6b63\u786e\u4f7f\u7528\u3002\n\n\u6240\u4ee5\uff0c\u5b9e\u9645\u7684\u4fee\u6539\u662f\u5c06 `get_frames` \u65b9\u6cd5\u4e2d\u591a\u4f59\u7684 `_get_event_frame` \u8c03\u7528\u79fb\u9664\uff0c\u66f4\u65b0\u540e\u7684 `get_frames` \u65b9\u6cd5\u53ea\u5305\u542b\u4e00\u6b21 `_get_event_frame` \u7684\u8c03\u7528\uff1a\n\n```python\ndef get_frames(self, seq_id, frame_ids, anno=None):\n    seq_path = self._get_sequence_path(seq_id)\n    frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n    # \u4f7f\u7528\u4fee\u6539\u540e\u7684\u65b9\u6cd5\u8bfb\u53d6\u4e8b\u4ef6\u6570\u636e\n    seq_event_path = self._get_event_sequence_path(seq_id)\n    frame_event_list = self._get_event_frame(seq_event_path, frame_ids)\n\n    if anno is None:\n        anno = self.get_sequence_info(seq_id)\n\n    anno_frames = {}\n    for key, value in anno.items():\n        anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n    object_meta = OrderedDict({\n        'object_class_name': None,\n        'motion_class': None,\n        'major_class': None,\n        'root_class': None,\n        'motion_adverb': None\n    })\n\n    return frame_list, anno_frames, object_meta, frame_event_list\n```\n\n\u786e\u4fdd\u8fd9\u4e00\u4fee\u6539\u5df2\u7ecf\u8db3\u591f\u6e05\u695a\uff0c\u5e76\u4e14\u8fd9\u662f\u907f\u514d\u6267\u884c\u4e0d\u5fc5\u8981\u64cd\u4f5c\u7684\u6539\u8fdb\u3002\u9664\u6b64\u4e4b\u5916\uff0c\u60a8\u7684\u4ee3\u7801\u5df2\u7ecf\u6539\u53d8\u6570\u636e\u52a0\u8f7d\u65b9\u5f0f\u4ece\u8bfb\u53d6 `.mat` \u6587\u4ef6\u53d8\u6210\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6\uff0c\u4e14\u9002\u5e94\u4e86\u65b0\u7684\u6570\u636e\u8868\u793a\u65b9\u5f0f\u3002\u786e\u4fdd\u5728\u90e8\u7f72\u524d\u6d4b\u8bd5\u6570\u636e\u52a0\u8f7d\u3001\u5904\u7406\u662f\u5426\u6b63\u5e38\u4ee5\u786e\u4fdd\u6a21\u578b\u9884\u671f\u7684\u8f93\u51fa\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":1367,"n_turns_in_conversation":12,"n_words":99,"source_row_offset":31254,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"5be900cb28a9e90e444f4686","turn_index":5,"user_text":"\u4fee\u6539\u4e86\u54ea\u91cc\uff1f"},{"assistant_initiates":false,"asst_text":"\u60a8\u7684\u4fee\u6539\u5df2\u7ecf\u5f88\u5b8c\u6574\uff0c\u4f7f\u5f97\u4ee3\u7801\u53ef\u4ee5\u4ece `.dvs` \u76ee\u5f55\u52a0\u8f7d `.png` \u6216 `.bmp` \u683c\u5f0f\u7684\u4e8b\u4ef6\u5e27\uff0c\u800c\u4e0d\u662f\u4ece `.voxel` \u76ee\u5f55\u52a0\u8f7d `.mat` \u6587\u4ef6\u3002\u8fd9\u8868\u660e\u4e86\u60a8\u5df2\u9876\u66ff\u4e4b\u524d\u5904\u7406 `.mat` \u6587\u4ef6\u7684\u903b\u8f91\uff0c\u79fb\u52a8\u5230\u4f7f\u7528\u56fe\u50cf\u683c\u5f0f\u5904\u7406\uff0c\u8fd9\u5bf9\u4e8e\u4e8b\u4ef6\u6444\u50cf\u673a\u6570\u636e\u6765\u8bf4\u662f\u4e2a\u9002\u5f53\u7684\u6539\u8fdb\u3002\n\n### \u786e\u4fdd\u7684\u51e0\u70b9\u4e8b\u9879\uff1a\n\n1. **\u56fe\u50cf\u52a0\u8f7d**:\n   - \u786e\u4fdd `self.image_loader` \u5f53\u52a0\u8f7d `.png` \u6216 `.bmp` \u6587\u4ef6\u65f6\u8868\u73b0\u6b63\u5e38\u3002\u5982\u679c\u4e0d\u786e\u5b9a\uff0c\u53ef\u4ee5\u9a8c\u8bc1\u4e00\u4e0b\u662f\u5426\u8fd9\u4e2a\u52a0\u8f7d\u51fd\u6570\u5bf9\u6240\u6709\u56fe\u7247\u683c\u5f0f\u90fd\u6709\u6548\uff0c\u6216\u8003\u8651\u5b9e\u73b0\u6216\u4f7f\u7528\u9488\u5bf9\u7279\u6b8a\u56fe\u50cf\u683c\u5f0f\u7684\u52a0\u8f7d\u65b9\u5f0f\u3002\n\n2. **\u8def\u5f84\u7684\u6b63\u786e\u6027**:\n   - \u786e\u4fdd `_get_event_sequence_path` \u548c `_get_sequence_path` \u8fd4\u56de\u7684\u8def\u5f84\u6b63\u786e\u6307\u5411\u5305\u542b\u9002\u5f53\u6587\u4ef6\u7684\u76ee\u5f55\u3002\u5982\u679c\u76ee\u5f55\u7ed3\u6784\u6709\u6240\u4e0d\u540c\uff0c\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u8c03\u6574\u3002\n\n3. **\u5f02\u5e38\u5904\u7406**:\n   - \u60a8\u5df2\u7ecf\u79fb\u9664\u4e86\u5904\u7406 `.mat` \u6587\u4ef6\u65f6\u5bf9\u7a7a\u6587\u4ef6\u548c NaN \u503c\u7684\u5904\u7406\u903b\u8f91\u3002\u786e\u4fdd\u65b0\u7684\u56fe\u50cf\u6570\u636e\u4e2d\u4e0d\u4f1a\u9047\u5230\u7c7b\u4f3c\u7684\u95ee\u9898\uff0c\u6216\u5728\u53d1\u73b0\u95ee\u9898\u65f6\u6709\u6070\u5f53\u7684\u5f02\u5e38\u5904\u7406\u903b\u8f91\u3002\n\n4. **\u8f93\u51fa\u9a8c\u8bc1**:\n   - \u9a8c\u8bc1 `get_frames` \u65b9\u6cd5\u8fd4\u56de\u7684 `frame_list` \u548c `frame_event_list` \u662f\u5426\u5305\u542b\u4e86\u9884\u671f\u7684\u5e27\u6570\u636e\uff0c\u5e76\u4e14\u6570\u636e\u683c\u5f0f\u6b63\u786e\uff08\u4f8b\u5982\u5c3a\u5bf8\u3001\u7c7b\u578b\u7b49\u90fd\u7b26\u5408\u540e\u7eed\u5904\u7406\u7684\u8981\u6c42\uff09\u3002\n\n5. **\u6027\u80fd\u5f71\u54cd**:\n   - \u56fe\u50cf\u6587\u4ef6\u901a\u5e38\u6bd4 `.mat` \u6587\u4ef6\u66f4\u5bb9\u6613\u52a0\u8f7d\u548c\u5904\u7406\uff0c\u4f46\u4f9d\u65e7\u9700\u8981\u7559\u610f\u52a0\u8f7d\u548c\u5904\u7406\u7684\u6027\u80fd\uff0c\u7279\u522b\u662f\u5728\u5927\u89c4\u6a21\u6570\u636e\u96c6\u6216\u5728\u8f83\u4f4e\u6027\u80fd\u7684\u786c\u4ef6\u4e0a\u8fd0\u884c\u65f6\u3002\n\n6. **\u6ce8\u91ca\u548c\u6587\u6863**:\n   - \u66f4\u65b0\u7c7b\u548c\u65b9\u6cd5\u7684\u6ce8\u91ca\uff0c\u786e\u4fdd\u6587\u6863\u53cd\u6620\u4e86\u5f53\u524d\u7684\u5b9e\u73b0\u72b6\u6001\uff0c\u7279\u522b\u662f\u5bf9\u4e8e\u90a3\u4e9b\u4e0e\u52a0\u8f7d\u548c\u5904\u7406\u6570\u636e\u96c6\u76f8\u5173\u7684\u51fd\u6570\u3002\n\n\u8003\u8651\u5230\u4ee5\u4e0a\u70b9\uff0c\u60a8\u7684\u4ee3\u7801\u4fee\u6539\u770b\u8d77\u6765\u662f\u51c6\u786e\u548c\u9002\u5f53\u7684\u3002\u5efa\u8bae\u8fdb\u884c\u5b9e\u9645\u7684\u6d4b\u8bd5\uff0c\u5305\u62ec\u5355\u5143\u6d4b\u8bd5\u548c\u96c6\u6210\u6d4b\u8bd5\uff0c\u4ee5\u786e\u4fdd\u6240\u6709\u4fee\u6539\u7684\u90e8\u5206\u90fd\u6309\u9884\u671f\u5de5\u4f5c\uff0c\u6ca1\u6709\u5f15\u5165\u65b0\u7684\u9519\u8bef\u3002\u8fd9\u6837\u7684\u6d4b\u8bd5\u4e0d\u4ec5\u53ef\u4ee5\u5e2e\u52a9\u9a8c\u8bc1\u529f\u80fd\u7684\u6b63\u786e\u6027\uff0c\u8fd8\u53ef\u4ee5\u786e\u4fdd\u6027\u80fd\u6ee1\u8db3\u8981\u6c42\u3002\u5982\u679c\u4e00\u5207\u529f\u80fd\u6b63\u5e38\uff0c\u60a8\u5e94\u8be5\u80fd\u591f\u65e0\u969c\u788d\u5730\u4f7f\u7528\u4fee\u6539\u540e\u7684\u4ee3\u7801\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":6915,"n_turns_in_conversation":12,"n_words":541,"source_row_offset":31255,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"d901243c5d29488f3ac10627","turn_index":6,"user_text":"\u73b0\u5728\u6539\u7684\u662f\uff1aimport os\nimport os.path\nimport numpy as np\nimport torch\nimport csv\nimport pandas\nimport random\nfrom collections import OrderedDict\nfrom .base_video_dataset import BaseVideoDataset\nfrom lib.train.data import jpeg4py_loader\nfrom lib.train.admin import env_settings\nimport scipy.io as scio\n\nclass Coesot(BaseVideoDataset):\n    def __init__(self, root=None, image_loader=jpeg4py_loader, split=None, seq_ids=None, data_fraction=None):\n\n        root = env_settings().got10k_dir if root is None else root\n        super().__init__('Coesot', root, image_loader)\n\n        self.sequence_list = self._get_sequence_list()\n\n        # seq_id is the index of the folder inside the got10k root path\n        if split is not None:\n            if seq_ids is not None:\n                raise ValueError('Cannot set both split_name and seq_ids.')\n            if split == 'train':\n                file_path = os.path.join(self.root, 'train.txt')\n            elif split == 'val':\n                file_path = os.path.join(self.root, 'val.txt')\n            else:\n                raise ValueError('Unknown split name')\n            seq_ids = pandas.read_csv(file_path, header=None, dtype=np.int64).squeeze(\"columns\").values.tolist()\n        elif seq_ids is None:\n            seq_ids = list(range(0, len(self.sequence_list)))\n\n        self.sequence_list = [self.sequence_list[i] for i in seq_ids]\n\n    def get_name(self):\n        return 'coesot'\n\n    def _get_sequence_list(self):\n        with open(os.path.join(self.root, 'list.txt')) as f:\n            dir_list = list(csv.reader(f))\n        dir_list = [dir_name[0] for dir_name in dir_list]\n        return dir_list\n\n    def _read_bb_anno(self, seq_path):\n        bb_anno_file = os.path.join(seq_path, \"groundtruth.txt\")\n        gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False, low_memory=False).values\n        return torch.tensor(gt)\n\n    def _get_sequence_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_aps\")\n\n    def _get_event_img_sequence_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_dvs\")\n\n    def _get_grountgruth_path(self, seq_id):\n        return os.path.join(self.root, self.sequence_list[seq_id])\n\n    def get_sequence_info(self, seq_id):\n        bbox_path = self._get_grountgruth_path(seq_id)\n        bbox = self._read_bb_anno(bbox_path)\n\n        valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)\n        visible = valid.clone().byte()\n        # return {'bbox': bbox, 'valid': valid, 'visible': visible, 'visible_ratio': visible_ratio}\n        return {'bbox': bbox, 'valid': valid, 'visible': visible, }\n\n    def _get_frame_path(self, seq_path, frame_id):\n        if os.path.exists(os.path.join(seq_path, 'frame{:04}.png'.format(frame_id))):\n            return os.path.join(seq_path, 'frame{:04}.png'.format(frame_id))    # frames start from 0\n        else:\n            return os.path.join(seq_path, 'frame{:04}.bmp'.format(frame_id))    # some image is bmp\n\n    def _get_frame(self, seq_path, frame_id):\n        return self.image_loader(self._get_frame_path(seq_path, frame_id))\n\n    # def _get_event_sequence_path(self, seq_id):        ## get evemts' frames\n    #     return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + \"_voxel\")\n    ################################ \u4fee\u6539 ###############\n    def _get_event_sequence_path(self, seq_id):\n        # \u8fd4\u56de\u6307\u5411DVS\u76ee\u5f55\u7684\u8def\u5f84\n        return os.path.join(self.root, self.sequence_list[seq_id], self.sequence_list[seq_id] + '_dvs')\n                        \n    # def _get_event_frame(self, seq_path, frame_id):\n    #     frame_event_list = []\n    #     for f_id in frame_id:\n    #         event_frame_file = os.path.join(seq_path, 'frame{:04}.mat'.format(f_id))\n    #         if os.path.getsize(event_frame_file) == 0:\n    #             event_features = np.zeros(4096, 19)\n    #             # need_data = [np.zeros([4096, 3]), np.zeros([4096, 16])]\n    #         else:\n    #             mat_data = scio.loadmat(event_frame_file)\n    #             # need_data = [mat_data['coor'], mat_data['features']]\n    #             event_features = np.concatenate((mat_data['coor'], mat_data['features']), axis=1)        # concat coorelate and features (x,y,z, feauture32/16)\n    #             if np.isnan(event_features).any():\n    #                 event_features = np.zeros(4096, 19)\n    #                 print(event_frame_file, 'exist nan value in voxel.')\n    #         frame_event_list.append(event_features)\n    #     return frame_event_list\n    def _get_event_frame(self, seq_path, frame_ids):\n        # \u4f7f\u7528\u56fe\u50cf\u52a0\u8f7d\u5668\u8bfb\u53d6\u6bcf\u4e00\u4e2a\u4e8b\u4ef6\u5e27\n        frame_event_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n        return frame_event_list  \n\n    def get_frames(self, seq_id, frame_ids, anno=None):\n        seq_path = self._get_sequence_path(seq_id)\n        # obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]\n\n        frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]\n\n        # \u4f7f\u7528\u4fee\u6539\u540e\u7684\u65b9\u6cd5\u8bfb\u53d6\u4e8b\u4ef6\u6570\u636e\n        seq_event_path = self._get_event_sequence_path(seq_id)\n        frame_event_list = self._get_event_frame(seq_event_path, frame_ids)\n\n        # seq_event_path = self._get_event_img_sequence_path(seq_id)\n        # frame_event_img_list = [self._get_frame(seq_event_path, f_id) for f_id in frame_ids]\n        if anno is None:\n            anno = self.get_sequence_info(seq_id)\n\n        anno_frames = {}\n        for key, value in anno.items():\n            anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]\n\n        object_meta = OrderedDict({'object_class_name': None,\n                                   'motion_class': None,\n                                   'major_class': None,\n                                   'root_class': None,\n                                   'motion_adverb': None})\n\n        return frame_list, anno_frames, object_meta, frame_event_list # , frame_event_img_list\n\n"},{"assistant_initiates":false,"asst_text":"\u60a8\u63d0\u4f9b\u7684Python\u4ee3\u7801\u5b9e\u73b0\u4e86\u4e00\u4e2a\u540d\u4e3a`STARKProcessing`\u7684\u5904\u7406\u7c7b\uff0c\u7528\u4e8e\u56fe\u50cf\u6570\u636e\u9884\u5904\u7406\uff0c\u7279\u522b\u662f\u7528\u4e8e\u76ee\u6807\u8ddf\u8e2a\u6846\u67b6\u4e2d\u7684\u9884\u5904\u7406\u8fc7\u7a0b\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u89e3\u91ca\u6bcf\u4e2a\u90e8\u5206\u7684\u529f\u80fd\u548c\u76ee\u7684\uff1a\n\n### \u7c7b `BaseProcessing`\n\u8be5\u7c7b\u5b9a\u4e49\u4e86\u57fa\u672c\u7684\u6570\u636e\u9884\u5904\u7406\u529f\u80fd\u3002\u5b83\u4f7f\u7528\u5404\u79cd\u56fe\u50cf\u53d8\u6362\uff0c\u53ef\u4ee5\u5206\u522b\u5e94\u7528\u4e8e\u6a21\u677f\u56fe\u50cf(`template_transform`)\u3001\u641c\u7d22\u56fe\u50cf(`search_transform`)\uff0c\u4ee5\u53ca\u540c\u65f6\u4f5c\u7528\u4e8e\u6a21\u677f\u548c\u641c\u7d22\u56fe\u50cf\u7684\u8054\u5408\u53d8\u6362(`joint_transform`)\u3002\n\n### \u7c7b `STARKProcessing`\n\u7ee7\u627f\u81ea`BaseProcessing`\uff0c\u8be5\u7c7b\u4e13\u95e8\u7528\u4e8e\u5c0f\u6570\u70b9\u56fe\u50cf\u5904\u7406\uff0c\u5b9e\u73b0\u4e86\u4e00\u4e9b\u5177\u4f53\u7684\u9884\u5904\u7406\u6b65\u9aa4\uff1a\n\n1. **\u76ee\u6807\u6296\u52a8 (Jittering)\uff1a** \u76ee\u6807\u8fb9\u754c\u6846\u901a\u8fc7\u6dfb\u52a0\u566a\u58f0\uff08\u5927\u5c0f\u548c\u4e2d\u5fc3\u4f4d\u7f6e\u6296\u52a8\uff09\u6765\u5b9e\u73b0\u6296\u52a8\uff0c\u4ece\u800c\u907f\u514d\u6a21\u578b\u4ec5\u5b66\u4e60\u5230\u76ee\u6807\u59cb\u7ec8\u4f4d\u4e8e\u641c\u7d22\u533a\u57df\u4e2d\u5fc3\u7684\u504f\u5dee\u3002\n\n2. **\u641c\u7d22\u533a\u57df\u88c1\u526a\uff1a** \u6839\u636e\u7ecf\u8fc7\u6296\u52a8\u7684\u76ee\u6807\u6846\uff0c\u4ee5\u53ca\u9884\u5b9a\u4e49\u7684\u641c\u7d22\u533a\u57df\u56e0\u5b50(`search_area_factor`)\uff0c\u88c1\u526a\u51fa\u4e00\u4e2a\u56f4\u7ed5\u76ee\u6807\u4e2d\u5fc3\u7684\u6b63\u65b9\u5f62\u641c\u7d22\u533a\u57df\u3002\u8be5\u533a\u57df\u968f\u540e\u88ab\u8c03\u6574\u5230\u56fa\u5b9a\u5927\u5c0f(`output_sz`)\u3002\n\n3. **\u53d8\u6362\u5e94\u7528\uff1a** \u5728\u6a21\u677f\u548c\u641c\u7d22\u56fe\u50cf\u4e0a\u5e94\u7528\u9884\u5b9a\u4e49\u7684\u56fe\u50cf\u53d8\u6362\u3002\u8fd9\u5305\u62ec\u6807\u51c6\u56fe\u50cf\u53d8\u6362\u4ee5\u53ca\u9488\u5bf9\u7279\u5b9a\u6a21\u6001\uff08\u4f8b\u5982\u4e8b\u4ef6\u6444\u50cf\u673a\u6570\u636e\uff09\u7684\u7279\u6b8a\u5904\u7406\u3002 \n\n4. **\u4e8b\u4ef6\u6570\u636e\u5904\u7406\uff1a** \u9488\u5bf9\u4e8b\u4ef6\u6570\u636e\uff08\u4f8b\u5982\u6765\u81ea\u4e8b\u4ef6\u6444\u50cf\u673a\u7684\u6570\u636e\uff09\uff0c\u5b9e\u73b0\u4e86\u4e00\u7cfb\u5217\u7279\u6b8a\u7684\u5904\u7406\u6b65\u9aa4\uff0c\u5982\u5750\u6807\u8f6c\u6362\u3001\u88c1\u526a\u3001\u89c4\u8303\u5316\u548c\u586b\u5145\uff0c\u4ee5\u4f7f\u6570\u636e\u9002\u914d\u4e8e\u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3\u3002\n\n5. **\u6709\u6548\u6027\u68c0\u67e5\uff1a** \u68c0\u67e5\u5904\u7406\u540e\u7684\u6570\u636e\u662f\u5426\u6709\u6548\uff0c\u4f8b\u5982\u786e\u8ba4\u88c1\u526a\u7684\u8fb9\u754c\u6846\u5927\u5c0f\u8db3\u591f\u5927\uff0c\u4ee5\u53ca\u6ce8\u610f\u529b\u63a9\u7801\u6ca1\u6709\u5b8c\u5168\u906e\u76d6\u6574\u4e2a\u533a\u57df\u3002\n\n6. **\u8f93\u51fa\u683c\u5f0f\u5904\u7406\uff1a** \u6839\u636e\u5904\u7406\u6a21\u5f0f\uff08\u6210\u5bf9\u6216\u5e8f\u5217\uff09\uff0c\u5bf9\u8f93\u51fa\u6570\u636e\u8fdb\u884c\u683c\u5f0f\u8c03\u6574\u3002\u8fd9\u53ef\u80fd\u6d89\u53ca\u5806\u53e0\u5f20\u91cf\u6216\u4ec5\u9009\u62e9\u5217\u8868\u4e2d\u7684\u7b2c\u4e00\u4e2a\u5143\u7d20\u3002\n\n### \u51fd\u6570 `stack_tensors`\n\u8fd9\u662f\u4e00\u4e2a\u8f85\u52a9\u51fd\u6570\uff0c\u7528\u4e8e\u5c06\u5f20\u91cf\u5217\u8868\u5806\u53e0\u6210\u4e00\u4e2a\u65b0\u7684\u5f20\u91cf\u3002\u5982\u679c\u8f93\u5165\u4e0d\u662f\u5f20\u91cf\u5217\u8868\uff0c\u5219\u76f4\u63a5\u8fd4\u56de\u8f93\u5165\u3002\n\n### \u603b\u7ed3\n\u8fd9\u6bb5\u4ee3\u7801\u5b9e\u73b0\u4e86\u590d\u6742\u7684\u56fe\u50cf\u9884\u5904\u7406\u903b\u8f91\uff0c\u9002\u7528\u4e8e\u9ad8\u7ea7\u89c6\u89c9\u4efb\u52a1\u5982\u76ee\u6807\u8ddf\u8e2a\u3002\u5b83\u7279\u522b\u8003\u8651\u4e86\u6570\u636e\u589e\u5f3a\u3001\u566a\u58f0\u6291\u5236\u548c\u6570\u636e\u6709\u6548\u6027\u68c0\u67e5\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u5bf9\u73b0\u5b9e\u4e16\u754c\u53d8\u5316\u7684\u9c81\u68d2\u6027\u3002\u4ee3\u7801\u4e2d\u5145\u5206\u4f7f\u7528\u4e86PyTorch\u7684\u529f\u80fd\uff0c\u5982\u5f20\u91cf\u64cd\u4f5c\u548c\u529f\u80fd\u63a5\u53e3\uff0c\u5e76\u7ed3\u5408\u4e86\u591a\u79cd\u81ea\u5b9a\u4e49\u6570\u636e\u5904\u7406\u6280\u672f\uff0c\u4ece\u800c\u4f18\u5316\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\u8fc7\u7a0b\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":11865,"n_turns_in_conversation":12,"n_words":1178,"source_row_offset":31256,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"cb2dace6fff7630af3e2ca17","turn_index":7,"user_text":"\u89e3\u8bfb\u4e00\u4e0b\uff1aimport copy\n\nimport torch\nimport torchvision.transforms as transforms\nfrom lib.utils import TensorDict\nimport lib.train.data.processing_utils as prutils\nimport torch.nn.functional as F\n\n\ndef stack_tensors(x):\n    if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor):\n        return torch.stack(x)\n    return x\n\n\nclass BaseProcessing:\n    \"\"\" Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it\n     through the network. For example, it can be used to crop a search region around the object, apply various data\n     augmentations, etc.\"\"\"\n\n    def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None,\n                 joint_transform=None):\n        \"\"\"\n        args:\n            transform       - The set of transformations to be applied on the images. Used only if template_transform or\n                                search_transform is None.\n            template_transform - The set of transformations to be applied on the template images. If None, the 'transform'\n                                argument is used instead.\n            search_transform  - The set of transformations to be applied on the search images. If None, the 'transform'\n                                argument is used instead.\n            joint_transform - The set of transformations to be applied 'jointly' on the template and search images.  For\n                                example, it can be used to convert both template and search images to grayscale.\n        \"\"\"\n        self.transform = {'template': transform if template_transform is None else template_transform,\n                          'search': transform if search_transform is None else search_transform,\n                          'joint': joint_transform}\n\n    def __call__(self, data: TensorDict):\n        raise NotImplementedError\n\n\nclass STARKProcessing(BaseProcessing):\n    \"\"\" The processing class used for training LittleBoy. The images are processed in the following way.\n    First, the target bounding box is jittered by adding some noise. Next, a square region (called search region )\n    centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is\n    cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is\n    always at the center of the search region. The search region is then resized to a fixed size given by the\n    argument output_sz.\n    \"\"\"\n\n    def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor,\n                 mode='pair', settings=None, *args, **kwargs):\n        \"\"\"\n        args:\n            search_area_factor - The size of the search region  relative to the target size.\n            output_sz - An integer, denoting the size to which the search region is resized. The search region is always\n                        square.\n            center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before\n                                    extracting the search region. See _get_jittered_box for how the jittering is done.\n            scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before\n                                    extracting the search region. See _get_jittered_box for how the jittering is done.\n            mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames\n        \"\"\"\n        super().__init__(*args, **kwargs)\n        self.search_area_factor = search_area_factor\n        self.output_sz = output_sz\n        self.center_jitter_factor = center_jitter_factor\n        self.scale_jitter_factor = scale_jitter_factor\n        self.mode = mode\n        self.settings = settings\n\n    def _get_jittered_box(self, box, mode):\n        \"\"\" Jitter the input box\n        args:\n            box - input bounding box\n            mode - string 'template' or 'search' indicating template or search data\n\n        returns:\n            torch.Tensor - jittered box\n        \"\"\"\n\n        jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode])\n        max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float())\n        jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5)\n\n        return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0)\n\n    def __call__(self, data: TensorDict):\n        \"\"\"\n        args:\n            data - The input data, should contain the following fields:\n                'template_images', search_images', 'template_anno', 'search_anno'\n        returns:\n            TensorDict - output data block with following fields:\n                'template_images', 'search_images', 'template_anno', 'search_anno', 'test_proposals', 'proposal_iou'\n        \"\"\"\n        # Apply joint transforms\n        if self.transform['joint'] is not None:\n            data['template_images'], data['template_anno'], data['template_masks'] = self.transform['joint'](\n                image=data['template_images'], bbox=data['template_anno'], mask=data['template_masks'])\n            data['search_images'], data['search_anno'], data['search_masks'] = self.transform['joint'](\n                image=data['search_images'], bbox=data['search_anno'], mask=data['search_masks'], new_roll=False)\n\n        for s in ['template', 'search']:\n            assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \\\n                \"In pair mode, num train/test frames must be 1\"\n\n            # Add a uniform noise to the center pos\n            jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']]\n\n            # 2021.1.9 Check whether data is valid. Avoid too small bounding boxes\n            w, h = torch.stack(jittered_anno, dim=0)[:, 2], torch.stack(jittered_anno, dim=0)[:, 3]\n\n            crop_sz = torch.ceil(torch.sqrt(w * h) * self.search_area_factor[s])\n            if (crop_sz < 1).any():\n                data['valid'] = False\n                # print(\"Too small box is found. Replace it with new data.\")\n                return data\n\n            # Crop image region centered at jittered_anno box and get the attention mask\n            crops, boxes, att_mask, mask_crops, crop_coor = prutils.jittered_center_crop(data[s + '_images'],\n                                                                                         jittered_anno,\n                                                                                         data[s + '_anno'],\n                                                                                         self.search_area_factor[s],\n                                                                                         self.output_sz[s],\n                                                                                         masks=data[s + '_masks'])\n            # Apply transforms\n            data[s + '_images'], data[s + '_anno'], data[s + '_att'], data[s + '_masks'] = self.transform[s](\n                image=crops, bbox=boxes, att=att_mask, mask=mask_crops, joint=False)\n\n            data[s + '_event'] = torch.from_numpy(data[s + '_event'][0])\n            z = copy.deepcopy(data[s + '_event'][:, 0])\n            x, y = data[s + '_event'][:, 1], data[s + '_event'][:, 2]\n            data[s + '_event'][:, 0] = x\n            data[s + '_event'][:, 1] = y\n            data[s + '_event'][:, 2] = z\n            # crop to select voxels; template crop and search crop into the four times region.  // 10 resize\n            x1, x2 = crop_coor[0][0] / 10, crop_coor[0][1] / 10\n            y1, y2 = crop_coor[0][2] / 10, crop_coor[0][3] / 10\n            ### coor normalized to 0-1 becasue of box coor\n            x_range, y_range = x2 - x1, y2 - y1\n            data[s + '_event'][:, 0] = (data[s + '_event'][:, 0]+0.5 - x1) / x_range\n            data[s + '_event'][:, 1] = (data[s + '_event'][:, 1]+0.5 - y1) / y_range\n            data[s + '_event'][:, 2] = (data[s + '_event'][:, 2]+0.5) / 19\n            indices = (data[s + '_event'][:, 0] >= 0) & (data[s + '_event'][:, 0] <= 1) & \\\n                      (data[s + '_event'][:, 1] >= 0) & (data[s + '_event'][:, 1] <= 1)\n            data[s + '_event'] = torch.index_select(data[s + '_event'], dim=0, index=indices.nonzero().squeeze(1))\n\n            # padding to 1024/4096\n            data[s + '_event'] = data[s + '_event'].unsqueeze(0).unsqueeze(0)\n            if s in 'template' and (data[s + '_event'].shape[2] >= 1024):\n                data[s + '_event'], _ = torch.topk(data[s + '_event'], k=1024, dim=2)\n                pad_len = 0\n            elif (s in 'template') and (data[s + '_event'].shape[2] < 1024):\n                pad_len = 1024 - data[s + '_event'].shape[2]\n            elif (s in 'search') and (data[s + '_event'].shape[2] < 4096):\n                pad_len = 4096 - data[s + '_event'].shape[2]\n            elif (s in 'search') and (data[s + '_event'].shape[2] >= 4096):\n                data[s + '_event'], _ = torch.topk(data[s + '_event'], k=4096, dim=2)\n                pad_len = 0\n            else:\n                print('the dataset is wrong.')\n            data[s + '_event'] = F.pad(data[s + '_event'].transpose(-1, -2), (0, pad_len), mode='constant', value=0)\n\n            for ele in data[s + '_att']:\n                if (ele == 1).all():\n                    data['valid'] = False\n                    # print(\"Values of original attention mask are all one. Replace it with new data.\")\n                    return data\n            # 2021.1.10 more strict conditions: require the donwsampled masks not to be all 1\n            for ele in data[s + '_att']:\n                feat_size = self.output_sz[s] // 16  # 16 is the backbone stride\n                # (1,1,128,128) (1,1,256,256) --> (1,1,8,8) (1,1,16,16)\n                mask_down = F.interpolate(ele[None, None].float(), size=feat_size).to(torch.bool)[0]\n                if (mask_down == 1).all():\n                    data['valid'] = False\n                    # print(\"Values of down-sampled attention mask are all one. \"\n                    #       \"Replace it with new data.\")\n                    return data\n\n        data['valid'] = True\n        # if we use copy-and-paste augmentation\n        if data[\"template_masks\"] is None or data[\"search_masks\"] is None:\n            data[\"template_masks\"] = torch.zeros((1, self.output_sz[\"template\"], self.output_sz[\"template\"]))\n            data[\"search_masks\"] = torch.zeros((1, self.output_sz[\"search\"], self.output_sz[\"search\"]))\n        # Prepare output\n        if self.mode == 'sequence':\n            data = data.apply(stack_tensors)\n        else:\n            data = data.apply(lambda x: x[0] if isinstance(x, list) else x)\n\n        return data\n"},{"assistant_initiates":false,"asst_text":"\u5728\u60a8\u63d0\u4f9b\u7684\u4ee3\u7801\u4e2d\uff0c\u9488\u5bf9\u4e8b\u4ef6\u6570\u636e\u7684\u5904\u7406\u90e8\u5206\u8bbe\u8ba1\u5f97\u76f8\u5f53\u8be6\u7ec6\uff0c\u4ee5\u786e\u4fdd\u8be5\u7c7b\u578b\u7279\u6709\u7684\u6570\u636e\u7279\u6027\u5f97\u4ee5\u6709\u6548\u5904\u7406\u5e76\u9002\u5e94\u4e8e\u7528\u4e8e\u76ee\u6807\u8ddf\u8e2a\u7684\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002\u4e0b\u9762\u662f\u4e8b\u4ef6\u6570\u636e\u5904\u7406\u90e8\u5206\u7684\u5177\u4f53\u903b\u8f91\u548c\u6b65\u9aa4\u89e3\u91ca\uff1a\n\n### \u5904\u7406\u6b65\u9aa4\n\n1. **\u6570\u636e\u8f6c\u6362\u548c\u91cd\u5b9a\u4f4d**\n   - \u5728\u5904\u7406\u6d41\u7a0b\u5f00\u59cb\u65f6\uff0c\u5bf9\u4e8b\u4ef6\u6570\u636e\u8fdb\u884c\u57fa\u672c\u7684\u683c\u5f0f\u5316\u3002\u4e8b\u4ef6\u6570\u636e\u901a\u5e38\u6709\u4e09\u4e2a\u5173\u952e\u4fe1\u606f\uff1a\u65f6\u95f4\u6233\u3001`x`\u5750\u6807\u548c`y`\u5750\u6807\u3002\u4ee3\u7801\u4e2d\u53ef\u80fd\u5c06\u5176\u4ece\u4e00\u4e2a\u683c\u5f0f\u8f6c\u6362\u81f3\u5f20\u91cf\u683c\u5f0f\uff0c\u5e76\u9002\u5f53\u8c03\u6574\u7ef4\u5ea6\uff0c\u5982\u8f6c\u7f6e\u7b49\u3002\n   - \u8c03\u6574`x`\u3001`y`\u548c\u65f6\u95f4(`z`)\u7684\u987a\u5e8f\uff0c\u5c06\u5b83\u4eec\u5339\u914d\u5230\u6a21\u578b\u6216\u540e\u7eed\u6b65\u9aa4\u9700\u8981\u7684\u683c\u5f0f\u3002\n\n2. **\u88c1\u526a\u548c\u89c4\u8303\u5316**\n   - \u4f7f\u7528\u8ba1\u7b97\u5f97\u5230\u7684`crop_coor`\uff08\u88c1\u526a\u5750\u6807\uff09\uff0c\u8fd9\u901a\u5e38\u57fa\u4e8e\u76ee\u6807\u7684\u566a\u58f0\u8fb9\u754c\u6846\u548c\u641c\u7d22\u533a\u57df\u56e0\u5b50\u6765\u5b9a\u4e49\u3002\u8fd9\u4e9b\u5750\u6807\u7528\u4e8e\u51b3\u5b9a\u4e8b\u4ef6\u6570\u636e\u5e94\u88ab\u88c1\u526a\u7684\u8303\u56f4\u3002\n   - \u5750\u6807\u8303\u56f4(`x_range` \u548c `y_range`)\u88ab\u7528\u4e8e\u89c4\u8303\u5316\u4e8b\u4ef6\u6570\u636e\u70b9\u7684\u5750\u6807\uff0c\u4f7f\u5176\u843d\u57280\u52301\u4e4b\u95f4\uff0c\u8fd9\u662f\u795e\u7ecf\u7f51\u7edc\u5904\u7406\u7684\u5e38\u89c1\u505a\u6cd5\u3002\n\n3. **\u8fc7\u6ee4\u548c\u9009\u62e9**\n   - \u901a\u8fc7\u68c0\u67e5`x`\u548c`y`\u503c\u786e\u4fdd\u5b83\u4eec\u5728[0, 1]\u8303\u56f4\u5185\u3002\u8fd9\u6b65\u9aa4\u5e2e\u52a9\u53bb\u9664\u90a3\u4e9b\u5728\u7ecf\u8fc7\u88c1\u526a\u548c\u89c4\u8303\u5316\u540e\u4ecd\u7136\u4f4d\u4e8e\u56fe\u50cf\u5916\u90e8\u7684\u4e8b\u4ef6\u70b9\u3002\n   - \u4f7f\u7528`torch.index_select`\u6765\u9009\u62e9\u6709\u6548\u7684\u4e8b\u4ef6\u70b9\uff0c\u5e76\u8fdb\u4e00\u6b65\u5904\u7406\u3002\n\n4. **\u586b\u5145\u548c\u88c1\u526a**\n   - \u6839\u636e\u6a21\u677f (`template`) \u6216\u641c\u7d22 (`search`) \u7684\u9700\u8981\uff0c\u4e8b\u4ef6\u5f20\u91cf\u53ef\u80fd\u4f1a\u88ab\u586b\u5145\u81f3\u7279\u5b9a\u957f\u5ea6\uff08\u59821024\u62164096\uff09\uff0c\u4fdd\u6301\u8f93\u5165\u6570\u636e\u7684\u4e00\u81f4\u6027\u548c\u5b8c\u6574\u6027\u3002 \n   - \u4f7f\u7528 `torch.topk` \u88c1\u526a\u6216\u9009\u62e9\u6700\u91cd\u8981\u7684\u4e8b\u4ef6\uff0c\u4ee5\u6ee1\u8db3\u6a21\u578b\u7684\u8f93\u5165\u8981\u6c42\u3002\n\n5. **\u6700\u7ec8\u8c03\u6574**\n   - \u8fdb\u884c\u5fc5\u8981\u7684\u586b\u5145\u64cd\u4f5c\u6765\u786e\u4fdd\u6240\u6709\u5904\u7406\u540e\u7684\u4e8b\u4ef6\u6570\u636e\u5177\u6709\u76f8\u540c\u7684\u5c3a\u5bf8\uff0c\u4fbf\u4e8e\u6279\u5904\u7406\u548c\u7f51\u7edc\u5904\u7406\u3002\n\n### \u8bbe\u8ba1\u610f\u56fe\n\n\u8fd9\u79cd\u5904\u7406\u65b9\u5f0f\u4e3b\u8981\u662f\u4e3a\u4e86\u786e\u4fdd\u4e8b\u4ef6\u6570\u636e\u80fd\u591f\u88ab\u6b63\u786e\u5730\u8c03\u6574\u548c\u683c\u5f0f\u5316\u4ee5\u9002\u5e94\u76ee\u6807\u8ddf\u8e2a\u6a21\u578b\u7684\u9700\u8981\u3002\u901a\u8fc7\u6b63\u89c4\u5316\u3001\u88c1\u526a\u3001\u9009\u53d6\u548c\u586b\u5145\u6b65\u9aa4\uff0c\u539f\u59cb\u7684\u4e8b\u4ef6\u6570\u636e\u88ab\u8f6c\u6362\u6210\u7f51\u7edc\u53ef\u4ee5\u63a5\u53d7\u7684\u6807\u51c6\u683c\u5f0f\u3002\u6b64\u5916\uff0c\u8fd9\u6837\u7684\u5904\u7406\u4e5f\u8003\u8651\u4e86\u8ba1\u7b97\u6548\u7387\u548c\u6570\u636e\u5b8c\u6574\u6027\uff0c\u786e\u4fdd\u4e86\u6570\u636e\u5728\u7ecf\u8fc7\u5f3a\u5316\u5b66\u4e60\u6216\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u5904\u7406\u524d\uff0c\u80fd\u591f\u4fdd\u6301\u5176\u5173\u952e\u7279\u6027\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":916,"n_turns_in_conversation":12,"n_words":44,"source_row_offset":31257,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"40a6e440a030226509c5ee69","turn_index":8,"user_text":"\u5173\u4e8eevent\u90e8\u5206\u7684\u5904\u7406\u662f\u5982\u4f55\u8bbe\u8ba1\u7684\uff1f"},{"assistant_initiates":false,"asst_text":"\u5bf9\u4e8eRGB\u56fe\u50cf\u5e27\u7684\u5904\u7406\uff0c\u60a8\u7684\u4ee3\u7801\u4e3b\u8981\u4fa7\u91cd\u4e8e\u88c1\u526a\u3001\u53d8\u6362\u548c\u589e\u5f3a\u7684\u64cd\u4f5c\uff0c\u786e\u4fdd\u56fe\u50cf\u6570\u636e\u80fd\u591f\u6709\u6548\u5730\u88ab\u6a21\u578b\u5229\u7528\u3002\u5728\u60a8\u63d0\u4f9b\u7684`STARKProcessing`\u7c7b\u4e2d\uff0c\u9488\u5bf9RGB\u5e27\u7684\u5904\u7406\u4e3b\u8981\u5206\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a\n\n### \u56fe\u50cf\u88c1\u526a\u548c\u53d8\u6362\n1. **\u8054\u5408\u53d8\u6362**: \u8fd9\u901a\u5e38\u662f\u7b2c\u4e00\u6b65\uff0c\u4f7f\u7528`joint_transform`\u6765\u5bf9\u6a21\u677f\u56fe\u50cf(`template_images`)\u548c\u641c\u7d22\u56fe\u50cf(`search_images`)\u8fdb\u884c\u53ef\u80fd\u7684\u5171\u540c\u9884\u5904\u7406\uff0c\u4f8b\u5982\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u7070\u5ea6\u4ee5\u51cf\u5c11\u989c\u8272\u53d8\u5316\u7684\u5f71\u54cd\uff0c\u6216\u5e94\u7528\u5176\u4ed6\u589e\u5f3a\u6280\u672f\u3002\u8fd9\u6837\u505a\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u5728\u641c\u7d22\u9636\u6bb5\u96c6\u4e2d\u5728\u7ed3\u6784\u4fe1\u606f\u4e0a\uff0c\u800c\u975e\u989c\u8272\u53d8\u5316\u3002\n\n2. **\u6296\u52a8\u548c\u5b9a\u4f4d\u53d8\u6362**: \u5bf9\u6a21\u677f\u548c\u641c\u7d22\u76ee\u6807\u6846(`template_anno` \u548c `search_anno`)\u5e94\u7528\u6296\u52a8\uff0c\u5305\u62ec\u4e2d\u5fc3\u4f4d\u7f6e\u548c\u5c3a\u5bf8\u7684\u968f\u673a\u8c03\u6574\u3002\u8fd9\u901a\u8fc7`_get_jittered_box`\u51fd\u6570\u5b9e\u73b0\uff0c\u76ee\u7684\u662f\u6a21\u62df\u76ee\u6807\u7269\u4f53\u4f4d\u7f6e\u548c\u5927\u5c0f\u7684\u81ea\u7136\u53d8\u5316\uff0c\u589e\u52a0\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\n\n3. **\u641c\u7d22\u533a\u57df\u7684\u786e\u5b9a\u548c\u88c1\u526a**: \u6839\u636e\u6296\u52a8\u540e\u7684\u8fb9\u754c\u6846\u548c\u5b9a\u4e49\u7684\u641c\u7d22\u533a\u57df\u56e0\u5b50(`search_area_factor`)\u786e\u5b9a\u641c\u7d22\u533a\u57df\u7684\u5927\u5c0f\u548c\u4f4d\u7f6e\uff0c\u7136\u540e\u4ece\u539f\u59cb\u56fe\u50cf\u4e2d\u88c1\u526a\u51fa\u8fd9\u4e00\u533a\u57df\u3002\u88c1\u526a\u540e\u7684\u56fe\u50cf\u5927\u5c0f\u901a\u8fc7`output_sz`\u8fdb\u4e00\u6b65\u7edf\u4e00\u6807\u51c6\u5316\uff0c\u786e\u4fdd\u8f93\u5165\u7f51\u7edc\u7684\u56fe\u50cf\u5927\u5c0f\u4e00\u81f4\u3002\n\n### \u6570\u636e\u9a8c\u8bc1\u548c\u5904\u7406\n4. **\u9a8c\u8bc1\u6709\u6548\u6027**: \u5728\u88c1\u526a\u56fe\u50cf\u540e\uff0c\u5bf9\u88c1\u526a\u5f97\u5230\u7684\u65b0\u76ee\u6807\u6846\u7684\u5bbd\u5ea6\u548c\u9ad8\u5ea6\u8fdb\u884c\u68c0\u67e5\uff0c\u786e\u4fdd\u6ca1\u6709\u56e0\u4e3a\u88c1\u526a\u8fc7\u5ea6\u800c\u5bfc\u81f4\u76ee\u6807\u8fc7\u5c0f\u65e0\u6cd5\u8bc6\u522b\u3002\n\n5. **\u56fe\u50cf\u548c\u6807\u6ce8\u7684\u6700\u7ec8\u5904\u7406**: \u5bf9\u88c1\u526a\u5f97\u5230\u7684\u56fe\u50cf\u5e94\u7528\u72ec\u7acb\u7684\u6a21\u677f\u6216\u641c\u7d22\u56fe\u50cf\u53d8\u6362(`template` \u6216 `search`)\uff0c\u8fd9\u53ef\u80fd\u5305\u62ec\u5f52\u4e00\u5316\u3001\u6570\u636e\u589e\u5f3a\u7b49\u6b65\u9aa4\u3002\u540c\u65f6\uff0c\u5bf9\u5e94\u7684\u76ee\u6807\u6846\u3001\u6ce8\u610f\u529b\u63a9\u7801\u548c\u5176\u4ed6\u53ef\u80fd\u7684\u6807\u6ce8\u6570\u636e\u4e5f\u6309\u9700\u5904\u7406\u3002\n\n### \u6ce8\u610f\u529b\u63a9\u7801\u548c\u5176\u4ed6\u5143\u6570\u636e\u7684\u5904\u7406\n6. **\u6ce8\u610f\u529b\u63a9\u7801**: \u5728\u4e00\u4e9b\u60c5\u51b5\u4e0b\uff0c\u9664\u4e86\u56fe\u50cf\u548c\u76ee\u6807\u6846\u5916\uff0c\u8fd8\u9700\u8981\u5904\u7406\u9644\u52a0\u7684\u6ce8\u610f\u529b\u63a9\u7801\uff0c\u8fd9\u53ef\u4ee5\u5e2e\u52a9\u6a21\u578b\u66f4\u597d\u5730\u805a\u7126\u4e8e\u5173\u952e\u533a\u57df\uff0c\u5e76\u5ffd\u7565\u4e0d\u76f8\u5173\u7684\u80cc\u666f\u3002\u6ce8\u610f\u529b\u63a9\u7801\u53ef\u4ee5\u662f\u624b\u52a8\u6807\u6ce8\u7684\uff0c\u4e5f\u53ef\u4ee5\u662f\u81ea\u52a8\u751f\u6210\u7684\u3002\n\n7. **\u5c3a\u5bf8\u89c4\u8303\u5316\u548c\u586b\u5145**: \u6839\u636e\u9700\u8981\u5bf9\u56fe\u50cf\u548c\u76f8\u5173\u7684\u63a9\u7801\u8fdb\u884c\u5c3a\u5bf8\u8c03\u6574\u548c\u586b\u5145\uff0c\u786e\u4fdd\u5b83\u4eec\u9002\u5408\u795e\u7ecf\u7f51\u7edc\u5904\u7406\uff0c\u6ee1\u8db3\u6279\u5904\u7406\u548c\u7f51\u7edc\u7ed3\u6784\u7684\u9700\u6c42\u3002\n\n### \u8f93\u51fa\u548c\u6a21\u5f0f\n8. **\u8f93\u51fa\u5904\u7406**: \u6839\u636e\u5904\u7406\u6a21\u5f0f(`pair` \u6216 `sequence`)\uff0c\u5bf9\u5904\u7406\u540e\u7684\u6570\u636e\u8fdb\u884c\u6700\u7ec8\u683c\u5f0f\u5316\u3002\u4f8b\u5982\uff0c\u5728\u5e8f\u5217\u6a21\u5f0f\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u5c06\u591a\u5e27\u6570\u636e\u5806\u53e0\u5728\u4e00\u8d77\u3002\n\n\u8fd9\u4e9b\u6b65\u9aa4\u786e\u4fdd\u4e86\u4ece\u539f\u59cb\u56fe\u50cf\u6355\u83b7\u5230\u5177\u4f53\u7528\u4e8e\u8bad\u7ec3\u6216\u63a8\u7406\u7684\u6570\u636e\u7684\u6574\u4e2a\u5904\u7406\u6d41\u7a0b\u90fd\u662f\u7cbe\u786e\u548c\u9ad8\u6548\u7684\uff0c\u540c\u65f6\u63d0\u9ad8\u4e86\u6a21\u578b\u5bf9\u4e8e\u5b9e\u9645\u5e94\u7528\u4e2d\u53d8\u5316\u7684\u9002\u5e94\u6027\u548c\u9c81\u68d2\u6027\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0cRGB\u5e27\u6570\u636e\u4e0d\u4ec5\u4f5c\u4e3a\u89c6\u89c9\u4fe1\u606f\u8f93\u5165\u6a21\u578b\uff0c\u8fd8\u901a\u8fc7\u5404\u79cd\u9884\u5904\u7406\u6b65\u9aa4\u589e\u5f3a\u4e86\u5176\u5bf9\u76ee\u6807\u8ddf\u8e2a\u4efb\u52a1\u7684\u6709\u6548\u6027\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":1159,"n_turns_in_conversation":12,"n_words":42,"source_row_offset":31258,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"480744167a34027b4db55d2a","turn_index":9,"user_text":"\u90a3\u4e48\u5173\u4e8eRGB frame\u7684\u5904\u7406\u662f\u5982\u4f55\u8bbe\u8ba1\u7684\uff1f"},{"assistant_initiates":false,"asst_text":"\u5982\u679c\u4e8b\u4ef6\u6570\u636e\u683c\u5f0f\u4ece\u4e4b\u524d\u7684voxel\u683c\u5f0f\uff08\u5305\u62ec\u7279\u5f81\u548c\u5750\u6807\u77e9\u9635\uff09\u8f6c\u53d8\u4e3a\u4e8b\u4ef6\u5e27\u683c\u5f0f\uff08\u5373\u7c7b\u4f3c\u4e8eRGB\u5e27\u7684\u56fe\u50cf\u683c\u5f0f\uff09\uff0c\u90a3\u4e48\u5bf9\u4e8b\u4ef6\u6570\u636e\u7684\u5904\u7406\u65b9\u6cd5\u5c31\u9700\u8981\u4e0eRGB\u56fe\u50cf\u5e27\u7684\u5904\u7406\u65b9\u6cd5\u7c7b\u4f3c\u3002\u60a8\u5c06\u9700\u8981\u8fdb\u884c\u51e0\u9879\u5173\u952e\u4fee\u6539\u4ee5\u9002\u5e94\u65b0\u7684\u6570\u636e\u683c\u5f0f\uff1a\n\n### \u4fee\u6539\u4e8b\u4ef6\u6570\u636e\u5904\u7406\u65b9\u5f0f\n\n1. **\u7edf\u4e00\u8bfb\u53d6\u65b9\u5f0f**\uff1a\n   \u9996\u5148\u786e\u4fdd\u4e8b\u4ef6\u5e27\u6570\u636e\u4e0eRGB\u5e27\u6570\u636e\u7684\u8bfb\u53d6\u65b9\u5f0f\u4e00\u81f4\u3002\u5982\u679c\u4e8b\u4ef6\u5e27\u662f\u4ee5\u56fe\u50cf\u683c\u5f0f\u5b58\u50a8\uff08\u5982`.png`\u6216`.bmp`\uff09\uff0c\u5219\u53ef\u4ee5\u4f7f\u7528\u4e0eRGB\u5e27\u76f8\u540c\u7684\u56fe\u50cf\u52a0\u8f7d\u5668\u8bfb\u53d6\u5b83\u4eec\u3002\n\n2. **\u53d6\u6d88\u7279\u6b8a\u7684\u4e8b\u4ef6\u6570\u636e\u5750\u6807\u548c\u7279\u5f81\u89e3\u6790**\uff1a\n   \u4e0d\u518d\u9700\u8981\u4ecematrices\u4e2d\u63d0\u53d6\u7279\u5f81\u548c\u5750\u6807\uff0c\u56e0\u4e3a\u4e8b\u4ef6\u5e27\u5df2\u7ecf\u662f\u6807\u51c6\u7684\u56fe\u50cf\u683c\u5f0f\u3002\u56e0\u6b64\uff0c\u6240\u6709\u4e4b\u524d\u9488\u5bf9\u7279\u6b8a\u683c\u5f0f\u5904\u7406\u7684\u4ee3\u7801\uff08\u5982\u5750\u6807\u8f6c\u6362\u548c\u7279\u5f81\u63d0\u53d6\uff09\u90fd\u5e94\u8be5\u88ab\u79fb\u9664\u6216\u66ff\u6362\u4e3a\u901a\u7528\u7684\u56fe\u50cf\u9884\u5904\u7406\u6b65\u9aa4\u3002\n\n3. **\u5e94\u7528\u76f8\u540c\u7684\u56fe\u50cf\u9884\u5904\u7406**\uff1a\n   \u5e94\u7528\u548cRGB\u5e27\u76f8\u540c\u7684\u56fe\u50cf\u9884\u5904\u7406\u6d41\u7a0b\uff0c\u5305\u62ec\uff1a\n   - \u56fe\u50cf\u88c1\u526a\uff1a\u6839\u636e\u76ee\u6807\u7684\u4f4d\u7f6e\u548c\u5927\u5c0f\u8fdb\u884c\u88c1\u526a\u3002\n   - \u56fe\u50cf\u7f29\u653e\uff1a\u6839\u636e\u6a21\u578b\u7684\u8f93\u5165\u8981\u6c42\u8c03\u6574\u56fe\u50cf\u5927\u5c0f\u3002\n   - \u6570\u636e\u589e\u5f3a\uff1a\u5982\u989c\u8272\u53d8\u6362\u3001\u65cb\u8f6c\u3001\u7ffb\u8f6c\u7b49\uff0c\u7528\u4ee5\u63d0\u5347\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002\n\n4. **\u8c03\u6574\u8054\u5408\u53d8\u6362**\uff1a\n   \u5982\u679c\u6709\u4f7f\u7528\u8054\u5408\u53d8\u6362\uff08\u6bd4\u5982\u5c06RGB\u56fe\u50cf\u548c\u4e8b\u4ef6\u5e27\u56fe\u50cf\u90fd\u8f6c\u6362\u4e3a\u7070\u5ea6\u56fe\u50cf\uff09\uff0c\u786e\u4fdd\u8fd9\u4e9b\u53d8\u6362\u540c\u6837\u9002\u7528\u4e8e\u4e8b\u4ef6\u5e27\u6570\u636e\u3002\n\n5. **\u66f4\u65b0\u6570\u636e\u9a8c\u8bc1\u903b\u8f91**\uff1a\n   \u4fdd\u8bc1\u4e8b\u4ef6\u5e27\u6570\u636e\u5728\u7ecf\u8fc7\u88c1\u526a\u548c\u53d8\u6362\u7b49\u9884\u5904\u7406\u6b65\u9aa4\u540e\u4ecd\u7136\u6709\u6548\uff08\u4f8b\u5982\uff0c\u68c0\u67e5\u88c1\u526a\u540e\u7684\u56fe\u50cf\u662f\u5426\u8fc7\u5c0f\uff09\u3002\n\n### \u793a\u4f8b\u4ee3\u7801\u8c03\u6574\n\n\u5047\u8bbe`_get_event_frame`\u51fd\u6570\u7528\u4e8e\u52a0\u8f7d\u4e8b\u4ef6\u5e27\uff0c\u90a3\u4e48\u60a8\u53ef\u4ee5\u76f4\u63a5\u8c03\u7528\u76f8\u540c\u7684\u56fe\u50cf\u52a0\u8f7d\u5668\u5904\u7406\u4e8b\u4ef6\u5e27\uff1a\n\n```python\ndef _get_event_frame(self, seq_path, frame_id):\n    # \u76f4\u63a5\u4f7f\u7528\u56fe\u50cf\u52a0\u8f7d\u5668\u8bfb\u53d6\u4e8b\u4ef6\u5e27\u56fe\u50cf\n    return self.image_loader(self._get_frame_path(seq_path, frame_id))\n```\n\n\u7136\u540e\u5728\u4e3b\u5904\u7406\u6d41\u7a0b`__call__`\u4e2d\u5bf9\u4e8b\u4ef6\u5e27\u548cRGB\u5e27\u6267\u884c\u76f8\u540c\u7684\u5904\u7406\uff1a\n\n```python\ndef __call__(self, data: TensorDict):\n    # \u5148\u5bf9RGB\u5e27\u548c\u4e8b\u4ef6\u5e27\u8fdb\u884c\u8054\u5408\u53d8\u6362\n    if self.transform['joint'] is not None:\n        data['template_images'], data['template_anno'], data['template_event'] = self.transform['joint'](\n            image=data['template_images'], bbox=data['template_anno'], event=data['template_event'])\n        data['search_images'], data['search_anno'], data['search_event'] = self.transform['joint'](\n            image=data['search_images'], bbox=data['search_anno'], event=data['search_event'])\n\n    # \u5bf9\u6a21\u677f\u548c\u641c\u7d22\u56fe\u50cf\u53ca\u4e8b\u4ef6\u5e27\u8fdb\u884c\u66f4\u5177\u4f53\u7684\u53d8\u6362\n    for s in ['template', 'search']:\n        # \u5e94\u7528\u72ec\u7acb\u53d8\u6362\n        data[s + '_images'], data[s + '_anno'] = self.transform[s](\n            image=data[s + '_images'], bbox=data[s + '_anno'])\n        data[s + '_event'] = self.transform[s](image=data[s + '_event'])\n\n    # \u6839\u636e\u6a21\u5f0f\u5904\u7406\u8f93\u51fa\u6570\u636e\n    if self.mode == 'sequence':\n        data = data.apply(stack_tensors)\n    else:\n        data = data.apply(lambda x: x[0] if isinstance(x, list) else x)\n\n    return data\n```\n\n\u901a\u8fc7\u8fd9\u6837\u7684\u4fee\u6539\uff0c\u60a8\u7684\u5904\u7406\u7c7b\u5c31\u80fd\u591f\u652f\u6301\u65b0\u7684\u4e8b\u4ef6\u5e27\u683c\u5f0f\uff0c\u7c7b\u4f3c\u4e8e\u5904\u7406\u666e\u901a\u7684RGB\u56fe\u50cf\u5e27\uff0c\u4ece\u800c\u7b80\u5316\u6574\u4f53\u7684\u6570\u636e\u5904\u7406\u6d41\u7a0b\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":2031,"n_turns_in_conversation":12,"n_words":124,"source_row_offset":31259,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"8e3f617ae6cd2b8f736d51d1","turn_index":10,"user_text":"\u539f\u59cb\u7684event\u6570\u636e\u683c\u5f0f\u662fvoxel\u7684\u683c\u5f0f\uff0c\u5305\u542bfeatures\u548ccoor\u7684\u77e9\u9635\uff0c\u73b0\u5728event\u7684\u683c\u5f0f\u662fevent frame\u7684\u683c\u5f0f\uff0c\u662f\u4e00\u5f20\u4e00\u5f20\u7684\u56fe\u50cf\uff0c\u540cRGB frame\uff0c\u90a3\u4e48\u5c06event\u90e8\u5206\u7684\u5904\u7406\u8fdb\u884c\u4fee\u6539"},{"assistant_initiates":false,"asst_text":"\u4e3a\u4e86\u9002\u5e94\u4e8b\u4ef6\u6570\u636e\u683c\u5f0f\u4ecevoxel\u5230event frame\u7684\u53d8\u5316\uff0c\u5e76\u7edf\u4e00\u5904\u7406RGB\u548c\u4e8b\u4ef6\u5e27\uff08event frame\uff09\u6570\u636e\u7684\u9884\u5904\u7406\u6b65\u9aa4\uff0c\u60a8\u9700\u8981\u5bf9\u73b0\u6709\u7684\u5904\u7406\u6d41\u7a0b\u8fdb\u884c\u9002\u5f53\u7684\u8c03\u6574\u3002\u4e0b\u9762\u662f\u5efa\u8bae\u7684\u4fee\u6539\u65b9\u6848\uff1a\n\n### 1. \u66f4\u65b0\u6570\u636e\u52a0\u8f7d\u65b9\u6cd5\n\u9996\u5148\u9700\u8981\u786e\u4fdd\u4e8b\u4ef6\u6570\u636e\u4ee5\u56fe\u50cf\u5f62\u5f0f\u52a0\u8f7d\uff0c\u8fd9\u6d89\u53ca\u5230\u4fee\u6539\u6216\u786e\u4fdd\u6570\u636e\u52a0\u8f7d\u51fd\u6570\u80fd\u591f\u63a5\u53d7\u4e8b\u4ef6\u5e27\u56fe\u50cf\u8def\u5f84\uff0c\u5e76\u6b63\u786e\u52a0\u8f7d\u56fe\u50cf\u3002\u5047\u8bbe\u60a8\u5df2\u7ecf\u6709\u4e00\u4e2a`_get_frame`\u51fd\u6570\u52a0\u8f7dRGB\u5e27\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u76f8\u540c\u7684\u51fd\u6570\u6765\u52a0\u8f7d\u4e8b\u4ef6\u5e27\uff0c\u56e0\u4e3a\u5b83\u4eec\u73b0\u5728\u90fd\u662f\u56fe\u50cf\u683c\u5f0f\u3002\n\n```python\ndef _get_event_frame(self, seq_path, frame_id):\n    # \u4f7f\u7528\u76f8\u540c\u7684\u52a0\u8f7d\u51fd\u6570\u52a0\u8f7d\u4e8b\u4ef6\u5e27\n    return self._get_frame(seq_path, frame_id)\n```\n\n### 2. \u8c03\u6574\u56fe\u50cf\u5904\u7406\u6d41\u7a0b\n\u8c03\u6574\u56fe\u50cf\u5904\u7406\u6b65\u9aa4\u4ee5\u786e\u4fdd\u5b83\u540c\u65f6\u9002\u7528\u4e8eRGB\u56fe\u50cf\u548c\u4e8b\u4ef6\u56fe\u50cf\u3002\u7531\u4e8e\u4e8b\u4ef6\u56fe\u50cf\u73b0\u5728\u662f\u4e0e\u666e\u901aRGB\u56fe\u50cf\u76f8\u540c\u7684\u683c\u5f0f\uff0c\u60a8\u53ef\u4ee5\u7528\u76f8\u540c\u7684\u53d8\u6362\u5bf9\u5b83\u4eec\u8fdb\u884c\u5904\u7406\u3002\n\n```python\ndef __call__(self, data: TensorDict):\n    # \u5e94\u7528\u8054\u5408\u53d8\u6362\n    if self.transform['joint'] is not None:\n        data['template_images'], data['template_anno'] = self.transform['joint'](\n            image=data['template_images'], bbox=data['template_anno'])\n        data['search_images'], data['search_anno'] = self.transform['joint'](\n            image=data['search_images'], bbox=data['search_anno'])\n\n        # \u5e94\u7528\u8054\u5408\u53d8\u6362\u5230\u4e8b\u4ef6\u5e27\n        data['template_event'], _ = self.transform['joint'](\n            image=data['template_event'], bbox=data['template_anno'])\n        data['search_event'], _ = self.transform['joint'](\n            image=data['search_event'], bbox=data['search_anno'])\n\n    for s in ['template', 'search']:\n        # \u5bf9\u6a21\u677f\u548c\u641c\u7d22\u533a\u8fdb\u884c\u6296\u52a8\u548c\u88c1\u526a\u5904\u7406\n        jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']]\n        crops, boxes = prutils.jittered_center_crop(data[s + '_images'], jittered_anno, self.search_area_factor[s], self.output_sz[s])\n        event_crops, _ = prutils.jittered_center_crop(data[s + '_event'], jittered_anno, self.search_area_factor[s], self.output_sz[s])\n\n        # \u5e94\u7528\u72ec\u7acb\u53d8\u6362\n        data[s + '_images'], data[s + '_anno'] = self.transform[s](image=crops, bbox=boxes)\n        data[s + '_event'], _ = self.transform[s](image=event_crops, bbox=boxes)\n\n    # \u68c0\u67e5\u6570\u636e\u6709\u6548\u6027\u7b49\u5176\u5b83\u5904\u7406\n    ...\n\n    return data\n```\n\n### 3. \u66f4\u65b0\u9a8c\u8bc1\u548c\u5176\u4ed6\u5904\u7406\u903b\u8f91\n\u7531\u4e8e\u4e8b\u4ef6\u6570\u636e\u73b0\u5728\u4e0eRGB\u6570\u636e\u5177\u6709\u4e00\u81f4\u7684\u683c\u5f0f\uff0c\u786e\u4fdd\u6240\u6709\u5904\u7406RGB\u6570\u636e\u7684\u9a8c\u8bc1\u548c\u903b\u8f91\u68c0\u67e5\u4e5f\u540c\u6837\u9002\u7528\u4e8e\u4e8b\u4ef6\u6570\u636e\u3002\n\n### \u603b\u7ed3\n\u901a\u8fc7\u4e0a\u8ff0\u4fee\u6539\uff0c\u60a8\u53ef\u4ee5\u786e\u4fdd\u4e8b\u4ef6\u6570\u636e\u548cRGB\u6570\u636e\u4f7f\u7528\u76f8\u540c\u7684\u9884\u5904\u7406\u6d41\u7a0b\uff0c\u7b80\u5316\u4e86\u4ee3\u7801\u7ed3\u6784\uff0c\u4e5f\u4f7f\u5904\u7406\u903b\u8f91\u66f4\u52a0\u7edf\u4e00\u3002\u52a1\u5fc5\u5728\u8fdb\u884c\u8fd9\u4e9b\u66f4\u6539\u540e\u5bf9\u7cfb\u7edf\u8fdb\u884c\u5f7b\u5e95\u6d4b\u8bd5\uff0c\u786e\u4fdd\u6240\u6709\u6570\u636e\u90fd\u88ab\u6b63\u786e\u5904\u7406\uff0c\u5e76\u4e14\u6a21\u578b\u7684\u6027\u80fd\u6ca1\u6709\u53d7\u5230\u8d1f\u9762\u5f71\u54cd\u3002","behavior":"degenerate_repetition","conversation_hash":"07360441701c827f8099642e72432b96","conversation_redacted":false,"conversation_toxic":false,"country":"Hong Kong","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-turbo-2024-04-09","n_chars":12838,"n_turns_in_conversation":12,"n_words":1270,"source_row_offset":31260,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:42 GMT","turn_id":"cab2ff452ea405d83df4e6a2","turn_index":11,"user_text":"\u76f4\u63a5\u5728import copy\n\nimport torch\nimport torchvision.transforms as transforms\nfrom lib.utils import TensorDict\nimport lib.train.data.processing_utils as prutils\nimport torch.nn.functional as F\n\n\ndef stack_tensors(x):\n    if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor):\n        return torch.stack(x)\n    return x\n\n\nclass BaseProcessing:\n    \"\"\" Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it\n     through the network. For example, it can be used to crop a search region around the object, apply various data\n     augmentations, etc.\"\"\"\n\n    def __init__(self, transform=transforms.ToTensor(), template_transform=None, search_transform=None,\n                 joint_transform=None):\n        \"\"\"\n        args:\n            transform       - The set of transformations to be applied on the images. Used only if template_transform or\n                                search_transform is None.\n            template_transform - The set of transformations to be applied on the template images. If None, the 'transform'\n                                argument is used instead.\n            search_transform  - The set of transformations to be applied on the search images. If None, the 'transform'\n                                argument is used instead.\n            joint_transform - The set of transformations to be applied 'jointly' on the template and search images.  For\n                                example, it can be used to convert both template and search images to grayscale.\n        \"\"\"\n        self.transform = {'template': transform if template_transform is None else template_transform,\n                          'search': transform if search_transform is None else search_transform,\n                          'joint': joint_transform}\n\n    def __call__(self, data: TensorDict):\n        raise NotImplementedError\n\n\nclass STARKProcessing(BaseProcessing):\n    \"\"\" The processing class used for training LittleBoy. The images are processed in the following way.\n    First, the target bounding box is jittered by adding some noise. Next, a square region (called search region )\n    centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is\n    cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is\n    always at the center of the search region. The search region is then resized to a fixed size given by the\n    argument output_sz.\n    \"\"\"\n\n    def __init__(self, search_area_factor, output_sz, center_jitter_factor, scale_jitter_factor,\n                 mode='pair', settings=None, *args, **kwargs):\n        \"\"\"\n        args:\n            search_area_factor - The size of the search region  relative to the target size.\n            output_sz - An integer, denoting the size to which the search region is resized. The search region is always\n                        square.\n            center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before\n                                    extracting the search region. See _get_jittered_box for how the jittering is done.\n            scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before\n                                    extracting the search region. See _get_jittered_box for how the jittering is done.\n            mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames\n        \"\"\"\n        super().__init__(*args, **kwargs)\n        self.search_area_factor = search_area_factor\n        self.output_sz = output_sz\n        self.center_jitter_factor = center_jitter_factor\n        self.scale_jitter_factor = scale_jitter_factor\n        self.mode = mode\n        self.settings = settings\n\n    def _get_jittered_box(self, box, mode):\n        \"\"\" Jitter the input box\n        args:\n            box - input bounding box\n            mode - string 'template' or 'search' indicating template or search data\n\n        returns:\n            torch.Tensor - jittered box\n        \"\"\"\n\n        jittered_size = box[2:4] * torch.exp(torch.randn(2) * self.scale_jitter_factor[mode])\n        max_offset = (jittered_size.prod().sqrt() * torch.tensor(self.center_jitter_factor[mode]).float())\n        jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (torch.rand(2) - 0.5)\n\n        return torch.cat((jittered_center - 0.5 * jittered_size, jittered_size), dim=0)\n\n    def __call__(self, data: TensorDict):\n        \"\"\"\n        args:\n            data - The input data, should contain the following fields:\n                'template_images', search_images', 'template_anno', 'search_anno'\n        returns:\n            TensorDict - output data block with following fields:\n                'template_images', 'search_images', 'template_anno', 'search_anno', 'test_proposals', 'proposal_iou'\n        \"\"\"\n        # Apply joint transforms\n        if self.transform['joint'] is not None:\n            data['template_images'], data['template_anno'], data['template_masks'] = self.transform['joint'](\n                image=data['template_images'], bbox=data['template_anno'], mask=data['template_masks'])\n            data['search_images'], data['search_anno'], data['search_masks'] = self.transform['joint'](\n                image=data['search_images'], bbox=data['search_anno'], mask=data['search_masks'], new_roll=False)\n\n        for s in ['template', 'search']:\n            assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \\\n                \"In pair mode, num train/test frames must be 1\"\n\n            # Add a uniform noise to the center pos\n            jittered_anno = [self._get_jittered_box(a, s) for a in data[s + '_anno']]\n\n            # 2021.1.9 Check whether data is valid. Avoid too small bounding boxes\n            w, h = torch.stack(jittered_anno, dim=0)[:, 2], torch.stack(jittered_anno, dim=0)[:, 3]\n\n            crop_sz = torch.ceil(torch.sqrt(w * h) * self.search_area_factor[s])\n            if (crop_sz < 1).any():\n                data['valid'] = False\n                # print(\"Too small box is found. Replace it with new data.\")\n                return data\n\n            # Crop image region centered at jittered_anno box and get the attention mask\n            crops, boxes, att_mask, mask_crops, crop_coor = prutils.jittered_center_crop(data[s + '_images'],\n                                                                                         jittered_anno,\n                                                                                         data[s + '_anno'],\n                                                                                         self.search_area_factor[s],\n                                                                                         self.output_sz[s],\n                                                                                         masks=data[s + '_masks'])\n            # Apply transforms\n            data[s + '_images'], data[s + '_anno'], data[s + '_att'], data[s + '_masks'] = self.transform[s](\n                image=crops, bbox=boxes, att=att_mask, mask=mask_crops, joint=False)\n\n            data[s + '_event'] = torch.from_numpy(data[s + '_event'][0])\n            z = copy.deepcopy(data[s + '_event'][:, 0])\n            x, y = data[s + '_event'][:, 1], data[s + '_event'][:, 2]\n            data[s + '_event'][:, 0] = x\n            data[s + '_event'][:, 1] = y\n            data[s + '_event'][:, 2] = z\n            # crop to select voxels; template crop and search crop into the four times region.  // 10 resize\n            x1, x2 = crop_coor[0][0] / 10, crop_coor[0][1] / 10\n            y1, y2 = crop_coor[0][2] / 10, crop_coor[0][3] / 10\n            ### coor normalized to 0-1 becasue of box coor\n            x_range, y_range = x2 - x1, y2 - y1\n            data[s + '_event'][:, 0] = (data[s + '_event'][:, 0]+0.5 - x1) / x_range\n            data[s + '_event'][:, 1] = (data[s + '_event'][:, 1]+0.5 - y1) / y_range\n            data[s + '_event'][:, 2] = (data[s + '_event'][:, 2]+0.5) / 19\n            indices = (data[s + '_event'][:, 0] >= 0) & (data[s + '_event'][:, 0] <= 1) & \\\n                      (data[s + '_event'][:, 1] >= 0) & (data[s + '_event'][:, 1] <= 1)\n            data[s + '_event'] = torch.index_select(data[s + '_event'], dim=0, index=indices.nonzero().squeeze(1))\n\n            # padding to 1024/4096\n            data[s + '_event'] = data[s + '_event'].unsqueeze(0).unsqueeze(0)\n            if s in 'template' and (data[s + '_event'].shape[2] >= 1024):\n                data[s + '_event'], _ = torch.topk(data[s + '_event'], k=1024, dim=2)\n                pad_len = 0\n            elif (s in 'template') and (data[s + '_event'].shape[2] < 1024):\n                pad_len = 1024 - data[s + '_event'].shape[2]\n            elif (s in 'search') and (data[s + '_event'].shape[2] < 4096):\n                pad_len = 4096 - data[s + '_event'].shape[2]\n            elif (s in 'search') and (data[s + '_event'].shape[2] >= 4096):\n                data[s + '_event'], _ = torch.topk(data[s + '_event'], k=4096, dim=2)\n                pad_len = 0\n            else:\n                print('the dataset is wrong.')\n            data[s + '_event'] = F.pad(data[s + '_event'].transpose(-1, -2), (0, pad_len), mode='constant', value=0)\n\n            for ele in data[s + '_att']:\n                if (ele == 1).all():\n                    data['valid'] = False\n                    # print(\"Values of original attention mask are all one. Replace it with new data.\")\n                    return data\n            # 2021.1.10 more strict conditions: require the donwsampled masks not to be all 1\n            for ele in data[s + '_att']:\n                feat_size = self.output_sz[s] // 16  # 16 is the backbone stride\n                # (1,1,128,128) (1,1,256,256) --> (1,1,8,8) (1,1,16,16)\n                mask_down = F.interpolate(ele[None, None].float(), size=feat_size).to(torch.bool)[0]\n                if (mask_down == 1).all():\n                    data['valid'] = False\n                    # print(\"Values of down-sampled attention mask are all one. \"\n                    #       \"Replace it with new data.\")\n                    return data\n\n        data['valid'] = True\n        # if we use copy-and-paste augmentation\n        if data[\"template_masks\"] is None or data[\"search_masks\"] is None:\n            data[\"template_masks\"] = torch.zeros((1, self.output_sz[\"template\"], self.output_sz[\"template\"]))\n            data[\"search_masks\"] = torch.zeros((1, self.output_sz[\"search\"], self.output_sz[\"search\"]))\n        # Prepare output\n        if self.mode == 'sequence':\n            data = data.apply(stack_tensors)\n        else:\n            data = data.apply(lambda x: x[0] if isinstance(x, list) else x)\n\n        return data\n\u4e2d\u8fdb\u884c\u4fee\u6539"},{"assistant_initiates":false,"asst_text":"Hello! How can I assist you today?","behavior":"degenerate_repetition","conversation_hash":"37bb054b64aaf40c67bba0a53d44b68d","conversation_redacted":false,"conversation_toxic":false,"country":"Albania","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-turbo-2024-04-09","n_chars":43,"n_turns_in_conversation":3,"n_words":9,"source_row_offset":31261,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:47 GMT","turn_id":"9c29d1141a54b5bf1cb8066e","turn_index":0,"user_text":"Hi there"},{"assistant_initiates":false,"asst_text":"I can definitely help you brainstorm ideas and guide you through the process of creating a logo, but I can't create images or visual content directly. If you tell me a bit about your brand, business, or the idea for the logo, I can suggest concepts, color schemes, typography, and other design elements you might consider. What is the logo for, and do you have any specific ideas or styles in mind?","behavior":"degenerate_repetition","conversation_hash":"37bb054b64aaf40c67bba0a53d44b68d","conversation_redacted":false,"conversation_toxic":false,"country":"Albania","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-turbo-2024-04-09","n_chars":423,"n_turns_in_conversation":3,"n_words":78,"source_row_offset":31262,"source_shard":"turns-00021.parquet","timestamp":"Thu, 09 May 2024 13:26:47 GMT","turn_id":"d84f3b8779d0e05290557d3c","turn_index":1,"user_text":"Can you create me a logo"}],"shard":{"index":21,"name":"turns-00021.parquet","rows":89135}}