text: # norm_cfg = dict(type='SyncBN', requires_grad=True)
  norm_cfg = dict(type='BN', requires_grad=True)
  model = dict(
      type='EncoderDecoder',
      pretrained='open-mmlab://resnet50_v1c',
      backbone=dict(
          type='ResNetV1c',
          depth=50,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          dilations=(1, 1, 2, 4),
          strides=(1, 2, 1, 1),
          # norm_cfg=dict(type='SyncBN', requires_grad=True),
          norm_cfg=dict(type='BN', requires_grad=True),
          norm_eval=False,
          style='pytorch',
          contract_dilation=True),
      decode_head=dict(
          type='DepthwiseSeparableASPPHead',
          in_channels=2048,
          in_index=3,
          channels=512,
          dilations=(1, 12, 24, 36),
          c1_in_channels=256,
          c1_channels=48,
          dropout_ratio=0.1,
          num_classes=2,
          # norm_cfg=dict(type='SyncBN', requires_grad=True),
          norm_cfg=dict(type='BN', requires_grad=True),
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
      auxiliary_head=dict(
          type='FCNHead',
          in_channels=1024,
          in_index=2,
          channels=256,
          num_convs=1,
          concat_input=False,
          dropout_ratio=0.1,
          num_classes=2,
          # norm_cfg=dict(type='SyncBN', requires_grad=True),
          norm_cfg=dict(type='BN', requires_grad=True),
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)))
  train_cfg = dict()
  test_cfg = dict(mode='whole')
  dataset_type = 'CustomDataset'
  data_root = 'data/SIRLab_mars/'
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  crop_size = (512, 512)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations'),
      dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
      # dict(type='Resize', img_scale=(900, 900), ratio_range=(0.5, 2.0)),
      dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='PhotoMetricDistortion'),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_semantic_seg'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(2048, 512),
          # img_scale=(900, 900),
          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='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      # samples_per_gpu=4,
      samples_per_gpu=2,
      # workers_per_gpu=4,
      workers_per_gpu=2,
      train=dict(
          type='CustomDataset',
          data_root='data/SIRLab_mars/',
          img_dir='img_dir/train',
          ann_dir='ann_dir/train',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(type='LoadAnnotations'),
              dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
              # dict(type='Resize', img_scale=(900, 900), ratio_range=(0.5, 2.0)),
              dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75),
              dict(type='RandomFlip', flip_ratio=0.5),
              dict(type='PhotoMetricDistortion'),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255),
              dict(type='DefaultFormatBundle'),
              dict(type='Collect', keys=['img', 'gt_semantic_seg'])
          ]),
      val=dict(
          type='CustomDataset',
          data_root='data/SIRLab_mars/',
          img_dir='img_dir/val',
          ann_dir='ann_dir/val',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(2048, 512),
                  # img_scale=(900, 900),
                  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='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]),
      test=dict(
          type='CustomDataset',
          data_root='data/SIRLab_mars/',
          img_dir='img_dir/val',
          ann_dir='ann_dir/val',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=(2048, 512),
                  # img_scale=(900, 900),
                  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='ImageToTensor', keys=['img']),
                      dict(type='Collect', keys=['img'])
                  ])
          ]))
  log_config = dict(
      interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  cudnn_benchmark = True
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
  optimizer_config = dict()
  lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False)
  total_iters = 80000
  checkpoint_config = dict(by_epoch=False, interval=8000)
  evaluation = dict(interval=8000, metric='mIoU')
  work_dir = './work_dirs/deeplabv3plus_r50-d8_512x1024_80k_cityscapes_SIR'
  gpu_ids = range(0, 1)
  # gpu_ids = [0]
  