model = dict(
type='Tracktor', # 多目标跟踪器的名称
detector=dict(
# 检测器的详细配置说明请查看 https://github.com/open-mmlab/mmdetection/blob/master/docs_zh-CN/tutorials/config.md#mask-r-cnn-配置文件示例
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))),
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth'
# noqa: E501
), # 检测器预训练权重，它也会在测试中使用
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_across_levels=False,
nms_pre=2000,
nms_post=1000,
max_num=1000,
nms_thr=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_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100))),
reid=dict( # 重识别模型的配置
type='BaseReID', # 重识别模型的名称
backbone=dict( # 重识别模型的主干网络配置
type='ResNet',
# 详细请查看 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L288 了解更多的主干网络
depth=50, # 主干网络的深度，对于 ResNet 以及 ResNext 网络，通常使用50或者101深度
num_stages=4, # 主干网络中阶段的数目
out_indices=(3,), # 每个阶段产生的输出特征图的索引
style='pytorch'), # 主干网络的形式，'pytorch' 表示步长为2的网络层在3x3的卷积中，'caffe' 表示步长为2的网络层在1x1卷积中。
neck=dict(type='GlobalAveragePooling', kernel_size=(8, 4), stride=1), # 重识别模型的颈部,通常是全局池化层。
head=dict( # 重识别模型的头部
type='LinearReIDHead', # 分类模型头部的名称
num_fcs=1, # 模型头部的全连接层数目
in_channels=2048, # 输入通道的数目
fc_channels=1024, # 全连接层通道数目
out_channels=128, # 输出通道数目
norm_cfg=dict(type='BN1d'), # 规一化模块的配置
act_cfg=dict(type='ReLU')), # 激活函数模块的配置
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/reid/reid_r50_6e_mot17-4bf6b63d.pth' # noqa: E501
)), # 重识别模型预训练权重，它也会在测试中使用
motion=dict( # 运动模型配置
type='CameraMotionCompensation', # 运动模型名称
warp_mode='cv2.MOTION_EUCLIDEAN', # 包装模式
num_iters=100, # 迭代次数
stop_eps=1e-05), # 停止迭代阈值
tracker=dict( # 跟踪器配置
type='TracktorTracker', # 跟踪器名称
obj_score_thr=0.5, # 检测目标的分类分数阈值
regression=dict( # Tracktor 跟踪器的回归模块
obj_score_thr=0.5, # 检测目标的分类分数阈值
nms=dict(type='nms', iou_threshold=0.6), # 回归器非极大值抑制配置
match_iou_thr=0.3), # 检测目标框的交并比阈值
reid=dict( # 测试阶段重识别模块配置
num_samples=10, # 计算特征相似性的最大样本数目
img_scale=(256, 128), # 重识别模型输入的图片大小
img_norm_cfg=None, # 重识别网络输入的标准化配置，None 表示与主干网络一致
match_score_thr=2.0, # 特征相似性阈值
match_iou_thr=0.2), # 交并比匹配阈值
momentums=None, # 更新缓冲区的动量
num_frames_retain=10)) # 保留消失轨迹的最大帧数
# 以下配置与视频目标检测一致。 详情请参考 `config_vid.md`
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='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'])
])
]
data_root = 'data/MOT17/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='MOTChallengeDataset',
visibility_thr=-1,
ann_file='data/MOT17/annotations/train_cocoformat.json',
img_prefix='data/MOT17/train',
ref_img_sampler=dict(
num_ref_imgs=1,
frame_range=10,
filter_key_img=True,
method='uniform'),
pipeline=train_pipeline),
val=dict(
type='MOTChallengeDataset',
ann_file='data/MOT17/annotations/train_cocoformat.json',
img_prefix='data/MOT17/train',
ref_img_sampler=None,
pipeline=test_pipeline),
test=dict(
type='MOTChallengeDataset',
ann_file='data/MOT17/annotations/train_cocoformat.json',
img_prefix='data/MOT17/train',
ref_img_sampler=None,
pipeline=test_pipeline))
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', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False),
dict(type='WandbLoggerHook', by_epoch=False,
init_kwargs={'entity': "OpenMMLab",
'project': "MMTracking",
'config': cfg_dict}),
])
dist_params = dict(backend='nccl', port='29500')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
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']
test_set = 'train'
work_dir = None