text: _base_ = [
      './_base_/models/cascade_mask_rcnn_r50_fpn_fashion.py',
      # './_base_/schedules/schedule_1x.py',
  ]
  
  model = dict(
      backbone=dict(
          type='DetectoRS_ResNet',
          conv_cfg=dict(type='ConvAWS'),
          sac=dict(type='SAC', use_deform=True),
          stage_with_sac=(False, True, True, True),
          output_img=True),
      neck=dict(
          type='RFP',
          rfp_steps=2,
          aspp_out_channels=64,
          aspp_dilations=(1, 3, 6, 1),
          rfp_backbone=dict(
              rfp_inplanes=256,
              type='DetectoRS_ResNet',
              depth=50,
              num_stages=4,
              out_indices=(0, 1, 2, 3),
              frozen_stages=1,
              norm_cfg=dict(type='BN', requires_grad=True),
              norm_eval=True,
              conv_cfg=dict(type='ConvAWS'),
              sac=dict(type='SAC', use_deform=True),
              stage_with_sac=(False, True, True, True),
              pretrained='torchvision://resnet50',
              style='pytorch')))
  
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              match_low_quality=True,
              ignore_iof_thr=-1),
          sampler=dict(
              type='RandomSampler',
              num=256,
              pos_fraction=0.5,
              neg_pos_ub=-1,
              add_gt_as_proposals=False),
          allowed_border=0,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=2000,
          max_num=2000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=[
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.5,
                  neg_iou_thr=0.5,
                  min_pos_iou=0.5,
                  match_low_quality=False,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.6,
                  neg_iou_thr=0.6,
                  min_pos_iou=0.6,
                  match_low_quality=False,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False),
          dict(
              assigner=dict(
                  type='MaxIoUAssigner',
                  pos_iou_thr=0.7,
                  neg_iou_thr=0.7,
                  min_pos_iou=0.7,
                  match_low_quality=False,
                  ignore_iof_thr=-1),
              sampler=dict(
                  type='RandomSampler',
                  num=512,
                  pos_fraction=0.25,
                  neg_pos_ub=-1,
                  add_gt_as_proposals=True),
              mask_size=28,
              pos_weight=-1,
              debug=False)
      ])
  test_cfg = dict(
      rpn=dict(
          nms_across_levels=False,
          nms_pre=1000,
          nms_post=1000,
          max_num=1000,
          nms_thr=0.7,
          min_bbox_size=0),
      rcnn=dict(
          score_thr=0.6,
          nms=dict(type='nms', iou_threshold=0.5),
          max_per_img=100,
          mask_thr_binary=0.5)
  )
  
  dataset_type = 'FashionDataset'
  data_root = 'data/coco/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='Resize', img_scale=(800, 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=(800, 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(
      samples_per_gpu=3,
      workers_per_gpu=3,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'fashion/train.json',
          img_prefix=data_root + 'fashion/train_images',
          # classes=classes,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'fashion/val_split.json',
          img_prefix=data_root + 'fashion/train_images',
          # classes=classes,
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'fashion/test_pubilc.json',
          img_prefix=data_root + 'fashion/test_images',
          # classes=classes,
          pipeline=test_pipeline))
  evaluation = dict(metric=['bbox', 'segm'])
  
  work_dir = './work_dirs/fashion_detectors'
  
  optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[28, 32])
  total_epochs = 36
  
  checkpoint_config = dict(interval=1)
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
      ])
  # yapf:enable
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = 'work_dirs/fashion_detectors/epoch_24.pth'
  workflow = [('train', 1)]
  