knet_s3_upernet_resnet50_ade20k_512x512_80k.yml

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

batch_size: 4
iters: 80000

model:
  type: KNet
  backbone:
    type: ResNet50_vd
    output_stride: 8
    pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz
  backbone_indices: [0, 1, 2, 3]
  kernel_update_head_params:
    num_classes: 150
    num_heads: 8
    num_mask_fcs: 1
    feedforward_channels: 2048
    in_channels: 256
    out_channels: 256
    dropout: 0.0
    conv_kernel_size: 1
    with_ffn: True
  num_stages: 3
  kernel_generate_head_params:
    head_layer: UPerKernelHead
    num_classes: 150
    fpn_inplanes: [256, 512, 1024, 2048]
    channels: 256
    dropout_prob: 0.1
    enable_auxiliary_loss: False
    align_corners: False
  channels: 512
  dropout_prob: 0.1
  enable_auxiliary_loss: True

optimizer:
  _inherited_: False
  type: AdamW
  weight_decay: 0.0005
  grad_clip_cfg:
    name: ClipGradByNorm
    clip_norm: 1

lr_scheduler:
  _inherited_: False
  type: MultiStepDecay
  milestones: [60000, 72000]
  warmup_iters: 1000
  warmup_start_lr: 1.0e-5
  learning_rate: 0.0001

loss:
  types:
    - type: CrossEntropyLoss
  types:
    - type: CrossEntropyLoss
  types:
    - type: CrossEntropyLoss
  types:
    - type: CrossEntropyLoss
  types:
    - type: CrossEntropyLoss
  coef: [1, 1, 1, 1, 0.4]