text: # dataset settings
  dataset_type = 'VrdDataset'
  data_root = 'data/vrd/'
  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_rel=True),
      dict(type='Resize', img_scale=(1333, 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_rels', 'gt_relmaps']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      # Since the forward process may need gt info, annos must be loaded.
      dict(type='LoadAnnotations', with_bbox=True, with_rel=True),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1333, 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),
              # NOTE: Do not change the img to DC.
              dict(type='ImageToTensor', keys=['img']),
              dict(type='ToTensor', keys=['gt_bboxes', 'gt_labels']),
              dict(type='ToDataContainer', fields=(dict(key='gt_bboxes'), dict(key='gt_labels'))),
              dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
          ])
  ]
  data = dict(
      imgs_per_gpu=8,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'sg_annotations/train_sgs.json',
          dict_file=data_root + 'sg_annotations/labels.json',
          image_file=data_root + 'sg_annotations/train_images.json',
          pipeline=train_pipeline,
          num_im=-1,
          split='train',
          img_prefix=data_root + 'sg_dataset/sg_train_images'),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'sg_annotations/test_sgs.json',
          dict_file=data_root + 'sg_annotations/labels.json',
          image_file=data_root + 'sg_annotations/test_images.json',
          pipeline=test_pipeline,
          num_im=-1,
          split='val',
          img_prefix=data_root + 'sg_dataset/sg_test_images/'),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'sg_annotations/test_sgs.json',
          dict_file=data_root + 'sg_annotations/labels.json',
          image_file=data_root + 'sg_annotations/test_images.json',
          pipeline=test_pipeline,
          num_im=-1,
          split='test',
          img_prefix=data_root + 'sg_dataset/sg_test_images/'))
  # model settings
  dataset_config = data['train'].copy()
  dataset_config.update(dict(cache=data_root + 'VRD_statistics.cache'))
  model = dict(
      type='FasterRCNN',
      pretrained='checkpoints/mmlab/imnet/resnext101_64x4d-ee2c6f71.pth',
      backbone=dict(
          type='ResNeXt',
          depth=101,
          groups=64,
          base_width=4,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=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_scales=[8],
          anchor_ratios=[0.5, 1.0, 2.0],
          anchor_strides=[4, 8, 16, 32, 64],
          target_means=[.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='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
      bbox_roi_extractor=dict(
          type='SingleRoIExtractor',
          roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
          out_channels=256,
          featmap_strides=[4, 8, 16, 32]),
      bbox_head=dict(
          type='SharedFCBBoxHead',
          num_fcs=2,
          in_channels=256,
          fc_out_channels=1024,
          roi_feat_size=7,
          num_classes=101,
          target_means=[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=1.0),
          loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
      relation_head=dict(
          type='HETHead',
          dataset_config=dataset_config,
          num_classes=101,
          num_predicates=71,
          use_bias=True,
          head_config=dict(
              use_gt_box=True,
              use_gt_label=False,
              use_vision=True,
              embed_dim=200,
              hidden_dim=512,
              roi_dim=1024,
              context_pooling_dim=4096,
              dropout_rate=0.2,
              context_object_layer=1,
              context_edge_layer=2,
              glove_dir='data/glove/',
              pick_parent='area',
              isc_thresh=0.9,
              child_order='confidence',
              with_chain=False,
              causal_effect_analysis=False),
          bbox_roi_extractor=dict(
              type='VisualSpatialExtractor',
              bbox_roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
              # mask_roi_layer=dict(type='ShapeAwareRoIAlign', out_size=7, sample_num=2),
              with_visual_bbox=True,
              with_visual_mask=False,
              with_visual_point=False,
              with_spatial=False,
              in_channels=256,
              fc_out_channels=1024,
              featmap_strides=[4, 8, 16, 32]),
          relation_roi_extractor=dict(
              type='VisualSpatialExtractor',
              bbox_roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
              # mask_roi_layer=dict(type='ShapeAwareRoIAlign', out_size=7, sample_num=2),
              with_visual_bbox=True,
              with_visual_mask=False,
              with_visual_point=False,
              with_spatial=True,
              separate_spatial=False,
              in_channels=256,
              fc_out_channels=1024,
              featmap_strides=[4, 8, 16, 32]),
          relation_sampler=dict(
              type='Motif',
              pos_iou_thr=0.5,
              require_overlap=False,  # for sgdet training, not require
              num_sample_per_gt_rel=4,
              num_rel_per_image=1024,
              pos_fraction=0.25,
              test_overlap=True    # for testing
          ),
          loss_object=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
          loss_relation=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)))
  # model training and testing settings
  train_cfg = dict(
      rpn=dict(
          assigner=dict(
              type='MaxIoUAssigner',
              pos_iou_thr=0.7,
              neg_iou_thr=0.3,
              min_pos_iou=0.3,
              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,
              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=0.05,
          nms=dict(type='nms', iou_thr=0.5),
          max_per_img=50,  # Follow the setting in TDE, 80 Bboxes are selected.
          mask_thr_binary=0.5,
          rle_mask_encode=False,  # do not transform the mask into rle.
          crop_mask=True,  # so that the mask shape is the same as bbox, instead of image shape
          format_mask_result=False,  # do not transform to the result format like bbox
          to_tensor=True))
  find_unused_parameters = True
  evaluation = dict(interval=1, metric='sgcls', relation_mode=True, classwise=True, nogc_thres_num=[10, 70])
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001,
                   freeze_modules=['backbone', 'neck', 'rpn_head', 'bbox_head', 'mask_head'])
  optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
  # learning policy
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=50,
      warmup_ratio=1.0 / 3,
      step=[7, 10])
  checkpoint_config = dict(interval=1)
  
  # yapf:enable
  # runtime settings
  total_epochs = 12
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './new_experiments/VRD_SgCls_heth_area_Xchain_faster_rcnn_x101_64x4d_fpn_1x'
  load_from = './experiments/VRD_Detection_faster_rcnn_x101_64x4d_fpn_1x_ftCOCO/latest.pth'
  # load_mapping = dict(align_dict={'relation_head.bbox_roi_extractor.visual_bbox_head': 'bbox_head.shared_fcs',
  #                                'relation_head.relation_roi_extractor.visual_bbox_head': 'bbox_head.shared_fcs'})
  resume_from = None
  workflow = [('train', 1), ('val', 1)]
  
  # yapf:disable
  log_config = dict(
      interval=10,
      hooks=[
          dict(type='TextLoggerHook'),
          dict(type='TensorboardLoggerHook'),
          dict(type='WandbLoggerHook',
               init_kwargs=dict(
                   project=work_dir.split('/')[-1],
                   name='train-1',
                   config=work_dir + '/cfg.yaml'))
      ])
  