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decoupled_solo_light_r50_fpn_3x_coco.py
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decoupled_solo_light_r50_fpn_3x_coco.py
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_base_ = './decoupled_solo_r50_fpn_3x_coco.py'
# model settings
model = dict(
mask_head=dict(
type='DecoupledSOLOLightHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 8, 16, 32, 32],
scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
pos_scale=0.2,
num_grids=[40, 36, 24, 16, 12],
cls_down_index=0,
loss_mask=dict(
type='DiceLoss', use_sigmoid=True, activate=False,
loss_weight=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
(852, 352)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(852, 512),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))