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- _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
- norm_cfg = dict(type='SyncBN', requires_grad=True)
- model = dict(
- # use ResNeSt img_norm
- data_preprocessor=dict(
- mean=[123.68, 116.779, 103.939],
- std=[58.393, 57.12, 57.375],
- bgr_to_rgb=True),
- backbone=dict(
- type='ResNeSt',
- stem_channels=64,
- depth=50,
- radix=2,
- reduction_factor=4,
- avg_down_stride=True,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
- roi_head=dict(
- bbox_head=[
- dict(
- type='Shared4Conv1FCBBoxHead',
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- norm_cfg=norm_cfg,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared4Conv1FCBBoxHead',
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- norm_cfg=norm_cfg,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.05, 0.05, 0.1, 0.1]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared4Conv1FCBBoxHead',
- in_channels=256,
- conv_out_channels=256,
- fc_out_channels=1024,
- norm_cfg=norm_cfg,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.033, 0.033, 0.067, 0.067]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
- ],
- mask_head=dict(norm_cfg=norm_cfg)))
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(
- type='LoadAnnotations',
- with_bbox=True,
- with_mask=True,
- poly2mask=False),
- dict(
- type='RandomChoiceResize',
- scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
- (1333, 768), (1333, 800)],
- keep_ratio=True),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
- ]
- train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
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