|
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' |
|
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) |
|
model = dict( |
|
backbone=dict( |
|
norm_cfg=norm_cfg, |
|
init_cfg=dict( |
|
type='Pretrained', |
|
checkpoint='open-mmlab://detectron/resnet50_gn')), |
|
neck=dict(norm_cfg=norm_cfg), |
|
roi_head=dict( |
|
bbox_head=dict( |
|
type='Shared4Conv1FCBBoxHead', |
|
conv_out_channels=256, |
|
norm_cfg=norm_cfg), |
|
mask_head=dict(norm_cfg=norm_cfg))) |
|
img_norm_cfg = dict( |
|
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) |
|
train_pipeline = [ |
|
dict(type='LoadImageFromFile'), |
|
dict(type='LoadAnnotations', with_bbox=True, with_mask=True), |
|
dict(type='Resize', img_scale=(1333, 800), 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=(1333, 800), |
|
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)) |
|
|
|
lr_config = dict(step=[16, 22]) |
|
runner = dict(type='EpochBasedRunner', max_epochs=24) |
|
|
