text: _base_ = [
      '../_base_/models/mask_rcnn_r50_fpn.py',
      '../_base_/datasets/coco_instance.py',
      '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
  ]
  classes = ('Small 1-piece vehicle',
                      'Large 1-piece vehicle',
                      'Extra-large 2-piece truck',)
  scale_factor = 0.5
  img_scale = (3840*scale_factor, 2160*scale_factor)
  model = dict(
      type='MaskRCNN',
      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',
          init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
      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='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=3,
              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=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=3,
              loss_mask=dict(
                  type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
      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)))
  dataset_type = 'CocoDataset'
  #data_root = '/data2/qilei_chen/DATA/trans_drone/'
  #data_root = '/home/qilei/DATASETS/trans_drone/'
  #data_root = '/home/qilei/DATASETS/trans_drone/worcester_site/'
  data_root = '/home/qilei/DATASETS/trans_drone/andover_worster/'
  
  albu_train_transforms = [
      dict(
          type='ShiftScaleRotate',
          shift_limit=0.0625,
          scale_limit=0.0,
          rotate_limit=90,
          interpolation=1,
          p=0.5),
      dict(
          type='RandomBrightnessContrast',
          brightness_limit=[0.1, 0.3],
          contrast_limit=[0.1, 0.3],
          p=0.2),
      dict(
          type='OneOf',
          transforms=[
              dict(
                  type='RGBShift',
                  r_shift_limit=10,
                  g_shift_limit=10,
                  b_shift_limit=10,
                  p=1.0),
              dict(
                  type='HueSaturationValue',
                  hue_shift_limit=20,
                  sat_shift_limit=30,
                  val_shift_limit=20,
                  p=1.0)
          ],
          p=0.1),
      dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
      dict(type='ChannelShuffle', p=0.1),
      dict(
          type='OneOf',
          transforms=[
              dict(type='Blur', blur_limit=3, p=1.0),
              dict(type='MedianBlur', blur_limit=3, p=1.0)
          ],
          p=0.1),
  ]
  
  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=img_scale, keep_ratio=True),
      dict(type='Pad', size_divisor=32),
      dict(
          type='Albu',
          transforms=albu_train_transforms,
          bbox_params=dict(
              type='BboxParams',
              format='pascal_voc',
              label_fields=['gt_labels'],
              min_visibility=0.0,
              filter_lost_elements=True),
          keymap={
              'img': 'image',
              'gt_masks': 'masks',
              'gt_bboxes': 'bboxes'
          },
          update_pad_shape=False,
          skip_img_without_anno=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='RandomFlip', direction ='vertical',flip_ratio=0.5),
      dict(type='RandomFlip', direction ='diagonal',flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=img_scale,
          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 = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/train_AW.json',
          img_prefix=data_root+'images/',
          pipeline=train_pipeline,
          classes=classes),
      val=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/test_AW.json',
          img_prefix=data_root+'images/',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=img_scale,
                  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'])
                  ])
          ],classes=classes),
      test=dict(
          type='CocoDataset',
          ann_file=data_root+'annotations/test_AW.json',
          img_prefix=data_root+'images/',
          pipeline=[
              dict(type='LoadImageFromFile'),
              dict(
                  type='MultiScaleFlipAug',
                  img_scale=img_scale,
                  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'])
                  ])
          ],classes=classes))
  evaluation = dict(interval=1,metric=['bbox', 'segm'])
  optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
  optimizer_config = dict(grad_clip=None)
  lr_config = dict(
      policy='step',
      warmup='linear',
      warmup_iters=500,
      warmup_ratio=0.001,
      step=[16, 22])
  runner = dict(type='EpochBasedRunner', max_epochs=36)
  checkpoint_config = dict(interval=1)
  log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
  custom_hooks = [dict(type='NumClassCheckHook')]
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  work_dir = data_root+'work_dirs/mask_rcnn_r50_fpn_2x_coco_mix_realalbu_'+str(scale_factor)+'_andover_worster/'