NotImplementedError: Please train `detector` and `reid` models firstly, then inference with SORT/DeepSORT.

[luke110]

When I used tools/train.py to train deepsort, the following error occurred: NotImplementedError: Please train 'detector' and 'reid' models first, then reference with SORT/DeepSORT.

I have trained the checkpoint for the detector and reid, but I don't know where to add the path to the checkpoint I have trained.

The configuration file I am using is: deepsort Faster rcnn Fpn 4e Mot17 public half. py

The configuration file details are as follows: model = dict( detector=dict( type='FasterRCNN', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch', init_cfg=dict( type='Pretrained', checkpoint='torchvision://resnet50')), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[1.0, 1.0, 1.0, 1.0], clip_border=False), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='StandardRoIHead', bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict( type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=1, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2], clip_border=False), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', loss_weight=1.0))), train_cfg=dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=-1, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=2000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)), test_cfg=dict( rpn=dict( nms_pre=1000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100)), init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth' )), type='DeepSORT', motion=dict(type='KalmanFilter', center_only=False), reid=dict( type='BaseReID', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1), head=dict( type='LinearReIDHead', num_fcs=1, in_channels=2048, fc_channels=1024, out_channels=128, num_classes=380, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), loss_pairwise=dict( type='TripletLoss', margin=0.3, loss_weight=1.0), norm_cfg=dict(type='BN1d'), act_cfg=dict(type='ReLU')), init_cfg=dict( type='Pretrained', checkpoint= 'https://download.openmmlab.com/mmtracking/mot/reid/tracktor_reid_r50_iter25245-a452f51f.pth' )), tracker=dict( type='SortTracker', obj_score_thr=0.5, reid=dict( num_samples=10, img_scale=(256, 128), img_norm_cfg=None, match_score_thr=2.0), match_iou_thr=0.5, momentums=None, num_tentatives=2, num_frames_retain=100)) dataset_type = 'MOTChallengeDataset' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadMultiImagesFromFile', to_float32=True), dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True), dict( type='SeqResize', img_scale=(1088, 1088), share_params=True, ratio_range=(0.8, 1.2), keep_ratio=True, bbox_clip_border=False), dict(type='SeqPhotoMetricDistortion', share_params=True), dict( type='SeqRandomCrop', share_params=False, crop_size=(1088, 1088), bbox_clip_border=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=32), dict(type='MatchInstances', skip_nomatch=True), dict( type='VideoCollect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids' ]), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadDetections'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img', 'public_bboxes']) ]) ] data_root = '../data/MOT17_tiny/' data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='MOTChallengeDataset', visibility_thr=-1, ann_file='../data/MOT17_tiny/annotations/half-train_cocoformat.json', img_prefix='../data/MOT17_tiny/train', ref_img_sampler=dict( num_ref_imgs=1, frame_range=10, filter_key_img=True, method='uniform'), pipeline=[ dict(type='LoadMultiImagesFromFile', to_float32=True), dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True), dict( type='SeqResize', img_scale=(1088, 1088), share_params=True, ratio_range=(0.8, 1.2), keep_ratio=True, bbox_clip_border=False), dict(type='SeqPhotoMetricDistortion', share_params=True), dict( type='SeqRandomCrop', share_params=False, crop_size=(1088, 1088), bbox_clip_border=False), dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5), dict( type='SeqNormalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='SeqPad', size_divisor=32), dict(type='MatchInstances', skip_nomatch=True), dict( type='VideoCollect', keys=[ 'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_instance_ids' ]), dict(type='SeqDefaultFormatBundle', ref_prefix='ref') ]), val=dict( type='MOTChallengeDataset', ann_file='../data/MOT17_tiny/annotations/half-val_cocoformat.json', img_prefix='../data/MOT17_tiny/train', ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadDetections'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img', 'public_bboxes']) ]) ], detection_file='../data/MOT17_tiny/annotations/half-val_detections.pkl'), test=dict( type='MOTChallengeDataset', ann_file='../data/MOT17_tiny/annotations/half-val_cocoformat.json', img_prefix='../data/MOT17_tiny/train', ref_img_sampler=None, pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadDetections'), dict( type='MultiScaleFlipAug', img_scale=(1088, 1088), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='VideoCollect', keys=['img', 'public_bboxes']) ]) ], detection_file='../data/MOT17_tiny/annotations/half-val_detections.pkl'))

optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] opencv_num_threads = 0 mp_start_method = 'fork' lr_config = dict( policy='step', warmup='linear', warmup_iters=100, warmup_ratio=0.01, step=[3]) total_epochs = 4 evaluation = dict(metric=['bbox', 'track'], interval=1) search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML'] work_dir = '../work-dir/deepsort' gpu_ids = [0]