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rtmdet_tiny_8xb32-300e_coco.py
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rtmdet_tiny_8xb32-300e_coco.py
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default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
train_cfg = dict(
type='EpochBasedTrainLoop',
max_epochs=300,
val_interval=10,
dynamic_intervals=[(280, 1)])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,
end=1000),
dict(
type='CosineAnnealingLR',
eta_min=0.0002,
begin=150,
end=300,
T_max=150,
by_epoch=True,
convert_to_iter_based=True)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=16)
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='CachedMosaic',
img_scale=(640, 640),
pad_val=114.0,
max_cached_images=20,
random_pop=False),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=10,
random_pop=False,
pad_val=(114, 114, 114),
prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=32,
num_workers=10,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='CachedMosaic',
img_scale=(640, 640),
pad_val=114.0,
max_cached_images=20,
random_pop=False),
dict(
type='RandomResize',
scale=(1280, 1280),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='CachedMixUp',
img_scale=(640, 640),
ratio_range=(1.0, 1.0),
max_cached_images=10,
random_pop=False,
pad_val=(114, 114, 114),
prob=0.5),
dict(type='PackDetInputs')
],
backend_args=None),
pin_memory=True)
val_dataloader = dict(
batch_size=5,
num_workers=10,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
test_dataloader = dict(
batch_size=5,
num_workers=10,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(640, 640), keep_ratio=True),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
val_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/instances_val2017.json',
metric='bbox',
format_only=False,
backend_args=None,
proposal_nums=(100, 1, 10))
test_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/instances_val2017.json',
metric='bbox',
format_only=False,
backend_args=None,
proposal_nums=(100, 1, 10))
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[[{
'type': 'Resize',
'scale': (640, 640),
'keep_ratio': True
}, {
'type': 'Resize',
'scale': (320, 320),
'keep_ratio': True
}, {
'type': 'Resize',
'scale': (960, 960),
'keep_ratio': True
}],
[{
'type': 'RandomFlip',
'prob': 1.0
}, {
'type': 'RandomFlip',
'prob': 0.0
}],
[{
'type': 'Pad',
'size': (960, 960),
'pad_val': {
'img': (114, 114, 114)
}
}],
[{
'type':
'PackDetInputs',
'meta_keys':
('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction')
}]])
]
model = dict(
type='RTMDet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
batch_augments=None),
backbone=dict(
type='CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=0.167,
widen_factor=0.375,
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True),
init_cfg=dict(
type='Pretrained',
prefix='backbone.',
checkpoint=
'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'
)),
neck=dict(
type='CSPNeXtPAFPN',
in_channels=[96, 192, 384],
out_channels=96,
num_csp_blocks=1,
expand_ratio=0.5,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
bbox_head=dict(
type='RTMDetSepBNHead',
num_classes=80,
in_channels=96,
stacked_convs=2,
feat_channels=96,
anchor_generator=dict(
type='MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
bbox_coder=dict(type='DistancePointBBoxCoder'),
loss_cls=dict(
type='QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
with_objectness=False,
exp_on_reg=False,
share_conv=True,
pred_kernel_size=1,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU', inplace=True)),
train_cfg=dict(
assigner=dict(type='DynamicSoftLabelAssigner', topk=13),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=30000,
min_bbox_size=0,
score_thr=0.001,
nms=dict(type='nms', iou_threshold=0.65),
max_per_img=300))
train_pipeline_stage2 = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(type='Pad', size=(640, 640), pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
]
max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.004
interval = 10
custom_hooks = [
dict(
type='EMAHook',
ema_type='ExpMomentumEMA',
momentum=0.0002,
update_buffers=True,
priority=49),
dict(
type='PipelineSwitchHook',
switch_epoch=280,
switch_pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(640, 640),
ratio_range=(0.5, 2.0),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(640, 640)),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='Pad', size=(640, 640),
pad_val=dict(img=(114, 114, 114))),
dict(type='PackDetInputs')
])
]
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth'