How to run MixFormer on my own video?

[HJ1722]

MixFormer$ bash tracking/run_video_demo.sh {'model': 'mixformer_online_22k.pth.tar', 'update_interval': 25, 'online_sizes': 3, 'search_area_scale': 4.5, 'max_score_decay': 1.0, 'vis_attn': 0} test config: {'MODEL': {'HEAD_TYPE': 'CORNER', 'HIDDEN_DIM': 384, 'NUM_OBJECT_QUERIES': 1, 'POSITION_EMBEDDING': 'sine', 'PREDICT_MASK': False, 'BACKBONE': {'PRETRAINED': True, 'PRETRAINED_PATH': '/home/roxign-usr/Badminton/MixFormer/CvT_pretrain_weights/CvT-21-384x384-IN-22k.pth', 'INIT': 'trunc_norm', 'NUM_STAGES': 3, 'PATCH_SIZE': [7, 3, 3], 'PATCH_STRIDE': [4, 2, 2], 'PATCH_PADDING': [2, 1, 1], 'DIM_EMBED': [64, 192, 384], 'NUM_HEADS': [1, 3, 6], 'DEPTH': [1, 4, 16], 'MLP_RATIO': [4.0, 4.0, 4.0], 'ATTN_DROP_RATE': [0.0, 0.0, 0.0], 'DROP_RATE': [0.0, 0.0, 0.0], 'DROP_PATH_RATE': [0.0, 0.0, 0.1], 'QKV_BIAS': [True, True, True], 'CLS_TOKEN': [False, False, False], 'POS_EMBED': [False, False, False], 'QKV_PROJ_METHOD': ['dw_bn', 'dw_bn', 'dw_bn'], 'KERNEL_QKV': [3, 3, 3], 'PADDING_KV': [1, 1, 1], 'STRIDE_KV': [2, 2, 2], 'PADDING_Q': [1, 1, 1], 'STRIDE_Q': [1, 1, 1], 'FREEZE_BN': True}, 'PRETRAINED_STAGE1': True, 'NLAYER_HEAD': 3, 'HEAD_FREEZE_BN': True}, 'TRAIN': {'TRAIN_SCORE': True, 'SCORE_WEIGHT': 1.0, 'LR': 0.0001, 'WEIGHT_DECAY': 0.0001, 'EPOCH': 30, 'LR_DROP_EPOCH': 20, 'BATCH_SIZE': 32, 'NUM_WORKER': 8, 'OPTIMIZER': 'ADAMW', 'BACKBONE_MULTIPLIER': 0.1, 'GIOU_WEIGHT': 2.0, 'L1_WEIGHT': 5.0, 'DEEP_SUPERVISION': False, 'FREEZE_STAGE0': False, 'PRINT_INTERVAL': 50, 'VAL_EPOCH_INTERVAL': 5, 'GRAD_CLIP_NORM': 0.1, 'SCHEDULER': {'TYPE': 'step', 'DECAY_RATE': 0.1}}, 'DATA': {'SAMPLER_MODE': 'trident_pro', 'MEAN': [0.485, 0.456, 0.406], 'STD': [0.229, 0.224, 0.225], 'MAX_SAMPLE_INTERVAL': [200], 'TRAIN': {'DATASETS_NAME': ['GOT10K_vottrain', 'LASOT', 'COCO17', 'TRACKINGNET'], 'DATASETS_RATIO': [1, 1, 1, 1], 'SAMPLE_PER_EPOCH': 60000}, 'VAL': {'DATASETS_NAME': ['GOT10K_votval'], 'DATASETS_RATIO': [1], 'SAMPLE_PER_EPOCH': 10000}, 'SEARCH': {'SIZE': 320, 'FACTOR': 5.0, 'CENTER_JITTER': 4.5, 'SCALE_JITTER': 0.5}, 'TEMPLATE': {'SIZE': 128, 'FACTOR': 2.0, 'NUMBER': 2, 'CENTER_JITTER': 0, 'SCALE_JITTER': 0}}, 'TEST': {'TEMPLATE_FACTOR': 2.0, 'TEMPLATE_SIZE': 128, 'SEARCH_FACTOR': 5.0, 'SEARCH_SIZE': 384, 'EPOCH': 40, 'UPDATE_INTERVALS': {'LASOT': [200], 'GOT10K_TEST': [10], 'TRACKINGNET': [25], 'VOT20': [10], 'VOT20LT': [200], 'OTB': [6], 'UAV': [200]}, 'ONLINE_SIZES': {'LASOT': [2], 'GOT10K_TEST': [2], 'TRACKINGNET': [1], 'VOT20': [5], 'VOT20LT': [3], 'OTB': [3], 'UAV': [1]}}} search_area_scale: 4.5 missing keys: [] unexpected keys: ['stage2.cls_token'] Loading pretrained CVT done. head channel: 384 Online size is: 3 Update interval is: 25 max score decay = 1.0 Traceback (most recent call last): File "tracking/video_demo.py", line 53, in main() File "tracking/video_demo.py", line 49, in main args.save_results, tracker_params=tracker_params) File "tracking/video_demo.py", line 21, in run_video tracker.run_video(videofilepath=videofile, optional_box=optional_box, debug=debug, save_results=save_results) File "tracking/../lib/test/evaluation/tracker.py", line 229, in run_video out = tracker.track(frame) File "tracking/../lib/test/tracker/mixformer_online.py", line 135, in track outdict, = self.network.forward_test(search, run_score_head=True) File "tracking/../lib/models/mixformer/mixformer_online.py", line 858, in forward_test out, outputs_coord_new = self.forward_head(search, template, run_score_head, gt_bboxes) File "tracking/../lib/models/mixformer/mixformer_online.py", line 876, in forward_head out_dict_box, outputs_coord = self.forward_box_head(search) File "tracking/../lib/models/mixformer/mixformer_online.py", line 896, in forward_box_head outputs_coord = box_xyxy_to_cxcywh(self.box_head(search)) File "/home/roxign-usr/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, kwargs) File "tracking/../lib/models/mixformer/head.py", line 62, in forward coorx_tl, coory_tl = self.soft_argmax(score_map_tl) File "tracking/../lib/models/mixformer/head.py", line 86, in soft_argmax score_vec = score_map.view((-1, self.feat_sz self.feat_sz)) # (batch, feat_sz feat_sz) RuntimeError: shape '[-1, 400]' is invalid for input of size 576**

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I encountered some problems when i running this command.

It show the RuntimeError.

Do I need any other settings to run this?

Thanks!!