| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495 |
- _base_ = [
- '../_base_/models/cascade-mask-rcnn_r50_fpn.py',
- '../_base_/datasets/lvis_v1_instance.py',
- '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
- ]
- model = dict(
- backbone=dict(
- depth=101,
- init_cfg=dict(type='Pretrained',
- checkpoint='torchvision://resnet101')),
- roi_head=dict(
- bbox_head=[
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=1203,
- 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,
- cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
- loss_cls=dict(
- type='SeesawLoss',
- p=0.8,
- q=2.0,
- num_classes=1203,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=1203,
- 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,
- cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
- loss_cls=dict(
- type='SeesawLoss',
- p=0.8,
- q=2.0,
- num_classes=1203,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=1203,
- 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,
- cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
- loss_cls=dict(
- type='SeesawLoss',
- p=0.8,
- q=2.0,
- num_classes=1203,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
- ],
- mask_head=dict(num_classes=1203)),
- test_cfg=dict(
- rcnn=dict(
- score_thr=0.0001,
- # LVIS allows up to 300
- max_per_img=300)))
- # dataset settings
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- 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(dataset=dict(pipeline=train_pipeline)))
- train_cfg = dict(val_interval=24)
|
