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- _base_ = [
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- # model settings
- model = dict(
- type='SOLOv2',
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_mask=True,
- pad_size_divisor=32),
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- 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=0,
- num_outs=5),
- mask_head=dict(
- type='SOLOV2Head',
- num_classes=80,
- in_channels=256,
- feat_channels=512,
- stacked_convs=4,
- strides=[8, 8, 16, 32, 32],
- scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
- pos_scale=0.2,
- num_grids=[40, 36, 24, 16, 12],
- cls_down_index=0,
- mask_feature_head=dict(
- feat_channels=128,
- start_level=0,
- end_level=3,
- out_channels=256,
- mask_stride=4,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
- loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0)),
- # model training and testing settings
- test_cfg=dict(
- nms_pre=500,
- score_thr=0.1,
- mask_thr=0.5,
- filter_thr=0.05,
- kernel='gaussian', # gaussian/linear
- sigma=2.0,
- max_per_img=100))
- # optimizer
- optim_wrapper = dict(
- optimizer=dict(lr=0.01), clip_grad=dict(max_norm=35, norm_type=2))
- val_evaluator = dict(metric='segm')
- test_evaluator = val_evaluator
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