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_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
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=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5,
# There is a chance to get 40.3 after switching init_cfg,
# otherwise it is about 39.9~40.1
init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'),
relu_before_extra_convs=True),
bbox_head=dict(
type='CenterNetUpdateHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
hm_min_radius=4,
hm_min_overlap=0.8,
more_pos_thresh=0.2,
more_pos_topk=9,
soft_weight_on_reg=False,
loss_cls=dict(
type='GaussianFocalLoss',
pos_weight=0.25,
neg_weight=0.75,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
),
train_cfg=None,
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# single-scale training is about 39.3
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.00025,
by_epoch=False,
begin=0,
end=4000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
optim_wrapper = dict(
optimizer=dict(lr=0.01),
# Experiments show that there is no need to turn on clip_grad.
paramwise_cfg=dict(norm_decay_mult=0.))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
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