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_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/nuim-instance.py',
'../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
roi_head=dict(
bbox_head=dict(num_classes=10), mask_head=dict(num_classes=10)))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1280, 720), (1920, 1080)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='PackDetInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 900),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
]),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor')),
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' # noqa
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