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siamese_rpn_r50_20e_lasot.py
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cudnn_benchmark = False
deterministic = True
seed = 1
find_unused_parameters = True
crop_size = 511
exemplar_size = 127
search_size = 255
# model settings
model = dict(
type='SiamRPN',
backbone=dict(
type='SOTResNet',
depth=50,
out_indices=(1, 2, 3),
frozen_stages=4,
strides=(1, 2, 1, 1),
dilations=(1, 1, 2, 4),
norm_eval=True,
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmtracking/pretrained_weights/sot_resnet50.model' # noqa: E501
)),
neck=dict(
type='ChannelMapper',
in_channels=[512, 1024, 2048],
out_channels=256,
kernel_size=1,
norm_cfg=dict(type='BN'),
act_cfg=None),
head=dict(
type='SiameseRPNHead',
anchor_generator=dict(
type='SiameseRPNAnchorGenerator',
strides=[8],
ratios=[0.33, 0.5, 1, 2, 3],
scales=[8]),
in_channels=[256, 256, 256],
weighted_sum=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[1., 1., 1., 1.]),
loss_cls=dict(
type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
loss_bbox=dict(type='L1Loss', reduction='sum', loss_weight=1.2)),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.3,
min_pos_iou=0.6,
match_low_quality=False),
sampler=dict(
type='RandomSampler',
num=64,
pos_fraction=0.25,
add_gt_as_proposals=False),
num_neg=16,
exemplar_size=exemplar_size,
search_size=search_size)),
test_cfg=dict(
exemplar_size=exemplar_size,
search_size=search_size,
context_amount=0.5,
center_size=7,
rpn=dict(penalty_k=0.05, window_influence=0.42, lr=0.38)))
data_root = 'data/'
train_pipeline = [
dict(
type='PairSampling',
frame_range=100,
pos_prob=0.8,
filter_template_img=False),
dict(type='LoadMultiImagesFromFile', to_float32=True),
dict(type='SeqLoadAnnotations', with_bbox=True, with_label=False),
dict(
type='SeqCropLikeSiamFC',
context_amount=0.5,
exemplar_size=exemplar_size,
crop_size=crop_size),
dict(
type='SeqShiftScaleAug',
target_size=[exemplar_size, search_size],
shift=[4, 64],
scale=[0.05, 0.18]),
dict(type='SeqColorAug', prob=[1.0, 1.0]),
dict(type='SeqBlurAug', prob=[0.0, 0.2]),
dict(type='VideoCollect', keys=['img', 'gt_bboxes', 'is_positive_pairs']),
dict(type='ConcatSameTypeFrames'),
dict(type='SeqDefaultFormatBundle', ref_prefix='search')
]
test_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
dict(
type='MultiScaleFlipAug',
scale_factor=1,
flip=False,
transforms=[
dict(type='VideoCollect', keys=['img', 'gt_bboxes']),
dict(type='ImageToTensor', keys=['img'])
])
]
# dataset settings
data = dict(
samples_per_gpu=28,
workers_per_gpu=4,
persistent_workers=True,
samples_per_epoch=600000,
train=dict(
type='RandomSampleConcatDataset',
dataset_sampling_weights=[0.25, 0.2, 0.55],
dataset_cfgs=[
dict(
type='SOTImageNetVIDDataset',
ann_file=data_root +
'ILSVRC/annotations/imagenet_vid_train.json',
img_prefix=data_root + 'ILSVRC/Data/VID',
pipeline=train_pipeline,
split='train',
test_mode=False),
dict(
type='SOTCocoDataset',
ann_file=data_root +
'coco/annotations/instances_train2017.json',
img_prefix=data_root + 'coco/train2017',
pipeline=train_pipeline,
split='train',
test_mode=False),
dict(
type='SOTCocoDataset',
ann_file=data_root +
'ILSVRC/annotations/imagenet_det_30plus1cls.json',
img_prefix=data_root + 'ILSVRC/Data/DET',
pipeline=train_pipeline,
split='train',
test_mode=False)
]),
val=dict(
type='LaSOTDataset',
ann_file=data_root + 'lasot/annotations/lasot_test_infos.txt',
img_prefix=data_root + 'lasot/LaSOTBenchmark',
pipeline=test_pipeline,
split='test',
test_mode=True,
only_eval_visible=True),
test=dict(
type='LaSOTDataset',
ann_file=data_root + 'lasot/annotations/lasot_test_infos.txt',
img_prefix=data_root + 'lasot/LaSOTBenchmark',
pipeline=test_pipeline,
split='test',
test_mode=True,
only_eval_visible=True))
# optimizer
optimizer = dict(
type='SGD',
lr=0.005,
momentum=0.9,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0))))
optimizer_config = dict(
type='SiameseRPNOptimizerHook',
backbone_start_train_epoch=10,
backbone_train_layers=['layer2', 'layer3', 'layer4'],
grad_clip=dict(max_norm=10.0, norm_type=2))
# learning policy
lr_config = dict(
policy='SiameseRPN',
lr_configs=[
dict(type='step', start_lr_factor=0.2, end_lr_factor=1.0, end_epoch=5),
dict(type='log', start_lr_factor=1.0, end_lr_factor=0.1, end_epoch=20),
])
# checkpoint saving
checkpoint_config = dict(interval=1)
evaluation = dict(
metric=['track'],
interval=1,
start=10,
rule='greater',
save_best='success')
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 20
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/xxx'
load_from = None
resume_from = None
workflow = [('train', 1)]