|
|
|
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) |
|
model = dict( |
|
type='RepPointsV2MaskDetector', |
|
pretrained=None, |
|
backbone=dict( |
|
type='SwinTransformer', |
|
embed_dim=96, |
|
depths=[2, 2, 6, 2], |
|
num_heads=[3, 6, 12, 24], |
|
window_size=7, |
|
mlp_ratio=4., |
|
qkv_bias=True, |
|
qk_scale=None, |
|
drop_rate=0., |
|
attn_drop_rate=0., |
|
drop_path_rate=0.2, |
|
ape=False, |
|
patch_norm=True, |
|
out_indices=(1, 2, 3), |
|
use_checkpoint=False), |
|
neck=dict( |
|
type='BiFPN', |
|
in_channels=[192, 384, 768], |
|
out_channels=256, |
|
start_level=0, |
|
add_extra_convs=False, |
|
num_outs=5, |
|
no_norm_on_lateral=False, |
|
num_repeat=2, |
|
norm_cfg=norm_cfg |
|
), |
|
bbox_head=dict( |
|
type='RepPointsV2Head', |
|
num_classes=80, |
|
in_channels=256, |
|
feat_channels=256, |
|
point_feat_channels=256, |
|
stacked_convs=3, |
|
shared_stacked_convs=1, |
|
first_kernel_size=3, |
|
kernel_size=1, |
|
corner_dim=64, |
|
num_points=9, |
|
gradient_mul=0.1, |
|
point_strides=[8, 16, 32, 64, 128], |
|
point_base_scale=4, |
|
norm_cfg=norm_cfg, |
|
loss_cls=dict( |
|
type='RPDQualityFocalLoss', |
|
use_sigmoid=True, |
|
beta=2.0, |
|
loss_weight=1.0), |
|
loss_bbox_init=dict(type='RPDGIoULoss', loss_weight=1.0), |
|
loss_bbox_refine=dict(type='RPDGIoULoss', loss_weight=2.0), |
|
loss_heatmap=dict( |
|
type='GaussianFocalLoss', |
|
alpha=2.0, |
|
gamma=4.0, |
|
loss_weight=0.25), |
|
loss_offset=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), |
|
loss_sem=dict( |
|
type='SEPFocalLoss', |
|
gamma=2.0, |
|
alpha=0.25, |
|
loss_weight=0.1), |
|
transform_method='exact_minmax', |
|
|
|
coord_pos='center', |
|
mask_head=dict( |
|
type='CondConvMaskHead', |
|
branch_cfg=dict( |
|
in_channels=256, |
|
channels=128, |
|
in_features=[0,1,2], |
|
out_stride=[8,16,32], |
|
norm=dict(type='BN', requires_grad=True), |
|
num_convs=4, |
|
out_channels=8, |
|
semantic_loss_on=False, |
|
num_classes=80, |
|
loss_sem=dict( |
|
type='FocalLoss', |
|
use_sigmoid=True, |
|
gamma=2.0, |
|
alpha=0.25, |
|
loss_weight=1.0, |
|
prior_prob=0.01) |
|
), |
|
head_cfg=dict( |
|
channels=8, |
|
disable_rel_coords=False, |
|
num_layers=3, |
|
use_fp16=False, |
|
mask_out_stride=4, |
|
max_proposals=500, |
|
aux_loss=True, |
|
mask_loss_weight=[0.,0.6,1.], |
|
sizes_of_interest=[64, 128, 256, 512, 1024] |
|
), |
|
)), |
|
train_cfg = dict( |
|
init=dict( |
|
assigner=dict(type='PointAssignerV2', scale=4, pos_num=1, mask_center_sample=True, use_center=True), |
|
allowed_border=-1, |
|
pos_weight=-1, |
|
debug=False), |
|
heatmap=dict( |
|
assigner=dict(type='PointHMAssigner', gaussian_bump=True, gaussian_iou=0.7), |
|
allowed_border=-1, |
|
pos_weight=-1, |
|
debug=False), |
|
refine=dict( |
|
assigner=dict(type='ATSSAssignerV2', topk=9, mask_center_sample=True), |
|
allowed_border=-1, |
|
pos_weight=-1, |
|
debug=False)), |
|
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) |
|
) |
