moey920
commented
2 years ago my config
from mmtrack import datasets
_base_ = [
'../../_base_/models/custom_yolox_x_8x8.py',
'../../_base_/datasets/lasot_drone.py', '../../_base_/default_runtime.py'
]
img_scale = (1280, 720)
samples_per_gpu = 2
"""
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth' # noqa: E501
)),
"""
model = dict(
type='ByteTrack',
detector=dict(
input_size=img_scale,
random_size_range=(18, 32),
bbox_head=dict(num_classes=1),
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.7)),
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth' # noqa: E501
)),
motion=dict(type='KalmanFilter'),
tracker=dict(
type='ByteTracker',
obj_score_thrs=dict(high=0.6, low=0.1),
init_track_thr=0.7,
weight_iou_with_det_scores=True,
match_iou_thrs=dict(high=0.1, low=0.5, tentative=0.3),
num_frames_retain=30))
train_pipeline = [
dict(
type='Mosaic',
img_scale=img_scale,
pad_val=114.0,
bbox_clip_border=False),
dict(
type='RandomAffine',
scaling_ratio_range=(0.1, 2),
border=(-img_scale[0] // 2, -img_scale[1] // 2),
bbox_clip_border=False),
dict(
type='MixUp',
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0,
bbox_clip_border=False),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Resize',
img_scale=img_scale,
keep_ratio=True,
bbox_clip_border=False),
dict(type='Pad', size_divisor=32, pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=img_scale,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[0.0, 0.0, 0.0],
std=[1.0, 1.0, 1.0],
to_rgb=False),
dict(
type='Pad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]
# musma_custom_dataset
# dataset_type = "CocoVideoDataset"
"""
ref_img_sampler=dict(
num_ref_imgs=1,
frame_range=10,
filter_key_img=True,
method='uniform'),
"""
# dataset_type = "LaSOTDataset"
dataset_type = "CocoVideoDataset"
classes = ('drone',)
data_root = 'data/lasot/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
classes=classes,
ann_file=data_root + "annotations/lasot_train.json",
img_prefix=data_root + "LaSOTBenchmark",
pipeline=train_pipeline),
val=dict(
type=dataset_type,
classes=classes,
ann_file=data_root + "annotations/lasot_test.json",
img_prefix=data_root + "LaSOTBenchmark",
pipeline=test_pipeline),
test=dict(
type=dataset_type,
classes=classes,
ann_file=data_root + "annotations/lasot_test.json",
img_prefix=data_root + "LaSOTBenchmark",
pipeline=test_pipeline))
# optimizer
# default 8 gpu
optimizer = dict(
type='SGD',
lr=0.0001 / 2 * samples_per_gpu,
momentum=0.9,
# 5e-4
weight_decay=0.0001,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config=dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
# some hyper parameters
total_epochs = 80
num_last_epochs = 10
resume_from = None
interval = 5
# learning policy
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=0.5,
warmup_iters=2000,
num_last_epochs=num_last_epochs,
min_lr_ratio=0.05)
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
num_last_epochs=num_last_epochs,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=num_last_epochs,
interval=interval,
priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=resume_from,
momentum=0.0001,
priority=49)
]
checkpoint_config = dict(interval=1)
evaluation = dict(metric=['bbox', 'track'], interval=1)
search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML']
# you need to set mode='dynamic' if you are using pytorch<=1.5.0
fp16 = dict(loss_scale=dict(init_scale=512.))
pixeli99
TheMadScientiist
Hello, I want to make a model that can track multiple drones in a video using only drone data from LaSOT dataset. I'd appreciate it if you let me know if this is a possible idea in the first place.
Preliminaries
'../../_base_/datasets/lasot_drone.py''../../_base_/models/custom_yolox_x_8x8.py'.===
Error
When training starts, after normal training for only 1 epoch,
data['category_id'] = self.cat_ids[label]error occurs in the evaluation process. I've looked all over for similar issues in mmDetection, but they didn't solve it. Do you know any solution?