text: _base_ = [
      '_base_/models/cascade_mask_rcnn_pvtv2_b2_fpn.py',
      '_base_/datasets/coco_instance.py',
      '_base_/schedules/schedule_1x.py',
      '_base_/default_runtime.py'
  ]
  
  model = dict(
      # pretrained='pretrained/pvt_v2_b2_li.pth',
      pretrained='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2_li.pth',
      backbone=dict(
          type='pvt_v2_b2_li',
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[64, 128, 320, 512],
          out_channels=256,
          num_outs=5),
      roi_head=dict(
          bbox_head=[
              dict(
                  type='ConvFCBBoxHead',
                  num_shared_convs=4,
                  num_shared_fcs=1,
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  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=False,
                  reg_decoded_bbox=True,
                  norm_cfg=dict(type='SyncBN', requires_grad=True),
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
              dict(
                  type='ConvFCBBoxHead',
                  num_shared_convs=4,
                  num_shared_fcs=1,
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  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=False,
                  reg_decoded_bbox=True,
                  norm_cfg=dict(type='SyncBN', requires_grad=True),
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
              dict(
                  type='ConvFCBBoxHead',
                  num_shared_convs=4,
                  num_shared_fcs=1,
                  in_channels=256,
                  conv_out_channels=256,
                  fc_out_channels=1024,
                  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=False,
                  reg_decoded_bbox=True,
                  norm_cfg=dict(type='SyncBN', requires_grad=True),
                  loss_cls=dict(
                      type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                  loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
          ]))
  
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  
  # augmentation strategy originates from DETR / Sparse RCNN
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='AutoAugment',
           policies=[
               [
                   dict(type='Resize',
                        img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
                                   (608, 1333), (640, 1333), (672, 1333), (704, 1333),
                                   (736, 1333), (768, 1333), (800, 1333)],
                        multiscale_mode='value',
                        keep_ratio=True)
               ],
               [
                   dict(type='Resize',
                        img_scale=[(400, 1333), (500, 1333), (600, 1333)],
                        multiscale_mode='value',
                        keep_ratio=True),
                   dict(type='RandomCrop',
                        crop_type='absolute_range',
                        crop_size=(384, 600),
                        allow_negative_crop=True),
                   dict(type='Resize',
                        img_scale=[(480, 1333), (512, 1333), (544, 1333),
                                   (576, 1333), (608, 1333), (640, 1333),
                                   (672, 1333), (704, 1333), (736, 1333),
                                   (768, 1333), (800, 1333)],
                        multiscale_mode='value',
                        override=True,
                        keep_ratio=True)
               ]
           ]),
      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']),
  ]
  data = dict(train=dict(pipeline=train_pipeline))
  
  optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
                   paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                   'relative_position_bias_table': dict(decay_mult=0.),
                                                   'norm': dict(decay_mult=0.)}))
  lr_config = dict(step=[27, 33])
  
  # do not use apex fp16
  # runner = dict(type='EpochBasedRunner', max_epochs=36)
  
  # use apex fp16
  runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
  fp16 = None
  optimizer_config = dict(
      type="DistOptimizerHook",
      update_interval=1,
      grad_clip=None,
      coalesce=True,
      bucket_size_mb=-1,
      use_fp16=True,
  )
  
  find_unused_parameters = True
  