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_base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='BoxInst',
data_preprocessor=dict(
type='BoxInstDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
mask_stride=4,
pairwise_size=3,
pairwise_dilation=2,
pairwise_color_thresh=0.3,
bottom_pixels_removed=10),
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=True),
norm_eval=True,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output', # use P5
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='BoxInstBboxHead',
num_params=593,
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=False,
center_sampling=True,
conv_bias=True,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.0),
loss_centerness=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
mask_head=dict(
type='BoxInstMaskHead',
num_layers=3,
feat_channels=16,
size_of_interest=8,
mask_out_stride=4,
topk_masks_per_img=64,
mask_feature_head=dict(
in_channels=256,
feat_channels=128,
start_level=0,
end_level=2,
out_channels=16,
mask_stride=8,
num_stacked_convs=4,
norm_cfg=dict(type='BN', requires_grad=True)),
loss_mask=dict(
type='DiceLoss',
use_sigmoid=True,
activate=True,
eps=5e-6,
loss_weight=1.0)),
# model training and testing settings
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,
mask_thr=0.5))
# optimizer
optim_wrapper = dict(optimizer=dict(lr=0.01))
# evaluator
val_evaluator = dict(metric=['bbox', 'segm'])
test_evaluator = val_evaluator
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