text:  
  
  # model settings
  model = dict(
      type='MaskRCNN',
      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),
          norm_eval=True,
          style='pytorch',
          init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')),
      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],
              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='StandardRoIHead',
          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=1,
              bbox_coder=dict(
                  type='DeltaXYWHBBoxCoder',
                  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='L1Loss', loss_weight=1.0)),
          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='FCNMaskHead',
              num_convs=4,
              in_channels=256,
              conv_out_channels=256,
              num_classes=1,
              loss_mask=dict(
                  type='CrossEntropyLoss', use_mask=True, 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,
                  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_pre=2000,
              max_per_img=1000,
              nms=dict(type='nms', iou_threshold=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_pre=1000,
              max_per_img=1000,
              nms=dict(type='nms', iou_threshold=0.7),
              min_bbox_size=0),
          rcnn=dict(
              score_thr=0.05,
              nms=dict(type='nms', iou_threshold=0.5),
              max_per_img=100,
              mask_thr_binary=0.5)))
  
  
  ######################../_base_/datasets/coco_instance.py
  
  # dataset settings
  dataset_type = 'MyDataset'
  data_root = 'data/mydataset/'
  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=(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_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', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/train.json',
          img_prefix=data_root + 'train/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'val/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'annotations/val.json',
          img_prefix=data_root + 'val/',
          pipeline=test_pipeline))
  evaluation = dict(interval=400,metric=['bbox', 'segm'])
  
  
  
  # optimizer
  optimizer = dict(type='SGD', lr=0.02, 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=[8, 11])
  
  
  runner = dict(type='IterBasedRunner', max_iters=30000)
  checkpoint_config = dict(by_epoch=False, interval=800) 
  
  
   
  # yapf:disable
  log_config = dict(
      interval=50,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook')
      ])
  # yapf:enable
  custom_hooks = [dict(type='NumClassCheckHook')]
  
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  
  # disable opencv multithreading to avoid system being overloaded
  opencv_num_threads = 0
  # set multi-process start method as `fork` to speed up the training
  mp_start_method = 'fork'
  work_dir='/hpctmp/pyni/traininglog/'
  
  