text: model = dict(
      type='MaskRCNN',
      pretrained='torchvision://resnet50',
      backbone=dict(
          type='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,
          style='pytorch'),
      neck=dict(
          type='FPN',
          in_channels=[256, 512, 1024, 2048],
          out_channels=256,
          num_outs=5),
      rpn_head=dict(
          type='RPNHead',
          in_channels=256,
          feat_channels=256,
          anchor_generator=dict(
              type='AnchorGenerator',
              scales=[8],
              ratios=[0.5, 1.0, 2.0],
              strides=[4, 8, 16, 32, 64]),
          bbox_coder=dict(
              type='DeltaXYWHBBoxCoder',
              target_means=[0.0, 0.0, 0.0, 0.0],
              target_stds=[1.0, 1.0, 1.0, 1.0]),
          loss_cls=dict(
              type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
          loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
      roi_head=dict(
          type='RefineRoIHead',
          bbox_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          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, 0.0, 0.0],
                  target_stds=[0.1, 0.1, 0.2, 0.2]),
              reg_class_agnostic=False,
              loss_cls=dict(
                  type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
              loss_bbox=dict(type='L1Loss', loss_weight=2.0)),
          mask_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[4, 8, 16, 32]),
          mask_head=dict(
              type='RefineMaskHead',
              num_convs_instance=2,
              num_convs_semantic=4,
              conv_in_channels_instance=256,
              conv_in_channels_semantic=256,
              conv_kernel_size_instance=3,
              conv_kernel_size_semantic=3,
              conv_out_channels_instance=256,
              conv_out_channels_semantic=256,
              conv_cfg=None,
              norm_cfg=None,
              dilations=[1, 3, 5],
              semantic_out_stride=4,
              mask_use_sigmoid=True,
              stage_num_classes=[1203, 1203, 1203, 1],
              stage_sup_size=[14, 28, 56, 112],
              upsample_cfg=dict(type='bilinear', scale_factor=2),
              loss_cfg=dict(
                  type='RefineCrossEntropyLoss',
                  stage_instance_loss_weight=[0.25, 0.5, 0.75, 1.0],
                  semantic_loss_weight=1.0,
                  boundary_width=2,
                  start_stage=1))))
  
  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=-1,
          pos_weight=-1,
          debug=False),
      rpn_proposal=dict(
          nms_across_levels=False,
          nms_pre=2000,
          nms_post=1000,
          max_num=1000,
          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=True,
              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=0.0001,
          nms=dict(type='nms', iou_threshold=0.5),
          max_per_img=300,
          mask_thr_binary=0.5))
  
  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,
          poly2mask=False),
      dict(
          type='Resize',
          img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
                     (1333, 768), (1333, 800)],
          multiscale_mode='value',
          keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      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=(1333, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  
  data_root = 'DATASET/lvis' # change this to your own path
  data = dict(
      samples_per_gpu=1,
      workers_per_gpu=1,
      train=dict(
          type='ClassBalancedDataset',
          oversample_thr=0.001,
          dataset=dict(
              type='LVISV1Dataset',
              ann_file='annotations/lvis_v1_train.json',
              img_prefix='',
              pipeline=[
                  dict(type='LoadImageFromFile'),
                  dict(
                      type='LoadAnnotations',
                      with_bbox=True,
                      with_mask=True,
                      poly2mask=False),
                  dict(
                      type='Resize',
                      img_scale=[(1333, 640), (1333, 672), (1333, 704),
                                 (1333, 736), (1333, 768), (1333, 800)],
                      multiscale_mode='value',
                      keep_ratio=True),
                  dict(type='RandomFlip', flip_ratio=0.5),
                  dict(
                      type='Normalize',
                      mean=[123.675, 116.28, 103.53],
                      std=[58.395, 57.12, 57.375],
                      to_rgb=True),
                  dict(type='Pad', size_divisor=32),
                  dict(type='DefaultFormatBundle'),
                  dict(
                      type='Collect',
                      keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
              ],
              data_root=data_root)
      ),
      val=dict(
          type='LVISV1Dataset',
          ann_file='annotations/lvis_v1_val.json',
          img_prefix='',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(1333, 800),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ],
          data_root=data_root),
      test=dict(
          type='LVISV1Dataset',
          ann_file='annotations/lvis_v1_val.json',
          img_prefix='',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(1333, 800),
                  flip=False,
                  transforms=[
                      dict(type='Resize', keep_ratio=True),
                      dict(type='RandomFlip'),
                      dict(
                          type='Normalize',
                          mean=[123.675, 116.28, 103.53],
                          std=[58.395, 57.12, 57.375],
                          to_rgb=True),
                      dict(type='Pad', size_divisor=32),
                      dict(type='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ],
          data_root=data_root))
  
  evaluation = dict(metric=['bbox', 'segm'], classwise=True, interval=12)
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  total_epochs = 12
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[8, 11])
  
  checkpoint_config = dict(interval=1)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from=None
  
  workflow = [('train', 1)]
  work_dir = None
  gpu_ids = range(2, 1)
  