text: _base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
  model = dict(
      roi_head=dict(
          semantic_roi_extractor=dict(
              type='SingleRoIExtractor',
              roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
              out_channels=256,
              featmap_strides=[8]),
          semantic_head=dict(
              type='FusedSemanticHead',
              num_ins=5,
              fusion_level=1,
              num_convs=4,
              in_channels=256,
              conv_out_channels=256,
              num_classes=183,
              loss_seg=dict(
                  type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2))))
  data_root = '/data/vision/polina/scratch/clintonw/datasets/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, with_seg=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='SegRescale', scale_factor=1 / 8),
      dict(type='DefaultFormatBundle'),
      dict(
          type='Collect',
          keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1333, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      train=dict(
          seg_prefix=data_root + 'stuffthingmaps/train2017/',
          pipeline=train_pipeline),
      val=dict(pipeline=test_pipeline),
      test=dict(pipeline=test_pipeline))