topformer_small_ade20k_512x512_160k.yml

_base_: '../_base_/ade20k.yml'

batch_size: 4  # total batch size is 16
iters: 160000

train_dataset:
  transforms:
    - type: ResizeStepScaling
      min_scale_factor: 0.5
      max_scale_factor: 2.0
      scale_step_size: 0.25
    - type: RandomPaddingCrop
      crop_size: [512, 512]
    - type: RandomHorizontalFlip
    - type: RandomDistort
      brightness_range: 0.4
      contrast_range: 0.4
      saturation_range: 0.4
    - type: Normalize
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]

val_dataset:
  transforms:
    - type: Resize
      target_size: [2048, 512]
      keep_ratio: True
      size_divisor: 32
    - type: Normalize
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]
  
export:
  transforms:
    - type: Resize
      target_size: [2048, 512]
      keep_ratio: True
      size_divisor: 32
    - type: Normalize
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]

optimizer:
  _inherited_: False
  type: AdamW
  weight_decay: 0.01

lr_scheduler:
  type: PolynomialDecay
  learning_rate: 0.0012
  end_lr: 0
  power: 1.0
  warmup_iters: 1500
  warmup_start_lr: 1.0e-6

loss:
  types:
    - type: CrossEntropyLoss
  coef: [1]

model:
  type: TopFormer
  backbone:
    type: TopTransformer_Small
    lr_mult: 0.1
    pretrained: https://paddleseg.bj.bcebos.com/dygraph/backbone/topformer_small_imagenet_pretrained.zip