super-gradients/src/super_gradients/recipes/cityscapes_kd_base.yaml

# Distillation for semantic segmentation on Cityscapes dataset.
#
# Instructions:
#   0. Make sure that the data is stored in dataset_params.dataset_dir or add "dataset_params.data_dir=<PATH-TO-DATASET>" at the end of the command below (feel free to check ReadMe)
#   1. Move to the project root (where you will find the ReadMe and src folder)
#   2. Run the command:
#      DDRNet23:        python src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=cityscapes_kd_base student_architecture=ddrnet_23
#      DDRNet23-Slim:   python src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=cityscapes_kd_base student_architecture=ddrnet_23_slim
# Note: add "student_checkpoint_params.checkpoint_path=<ddrnet23-backbone-pretrained-path>" to use pretrained backbone
#
#  Teachers specifications:
#      DDRNet39-AL:     mIoU: 85.17     notes: trained with Cityscapes coarse data.
#
#  Validation mIoU results - Cityscapes, training time:
#      DDRNet23:        teacher: DDRNet39-AL  input-size: [1024, 2048]     mIoU: 81.48     4 X RTX A5000, 13 H
#      DDRNet23-Slim:   teacher: DDRNet39-AL  input-size: [1024, 2048]     mIoU: 79.41     4 X RTX A5000, 11 H
#
#  Pretrained backbones checkpoints:
#       https://deci-pretrained-models.s3.amazonaws.com/ddrnet/imagenet_pt_backbones/ddrnet23_bb_imagenet.pth
#       https://deci-pretrained-models.s3.amazonaws.com/ddrnet/imagenet_pt_backbones/ddrnet23_slim_bb_imagenet.pth
#
#  Logs, tensorboards and network checkpoints:
#       DDRNet23:       https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23_cwd/
#       DDRNet23-Slim:  https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23_slim_cwd/
#
#  Learning rate and batch size parameters, using 4 RTX A5000 with DDP:
#      DDRNet23:        input-size: [1024, 1024]     initial_lr: 0.0075    batch-size: 6 * 4gpus = 24
#      DDRNet23-Slim:   input-size: [1024, 1024]     initial_lr: 0.0075    batch-size: 6 * 4gpus = 24
#
#  Teachers checkpoints:
#       DDRNet39-AL:    https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet39_al/average_model_2023_02_20.pth
#
#  Comments:
#      * Pretrained backbones were used for the student models.
#      * Default hyper-parameters are based on DDRNet model train recipes, for full resolution training [1024 x 2048]

defaults:
  - training_hyperparams: cityscapes_default_train_params
  - dataset_params: cityscapes_ddrnet_dataset_params
  - checkpoint_params: default_checkpoint_params
  - _self_

train_dataloader: cityscapes_train
val_dataloader: cityscapes_val

resume: False
training_hyperparams:
  sync_bn: True
  max_epochs: 500
  initial_lr: 0.0075   # batch size 24
  resume: ${resume}
  loss:
    _target_: super_gradients.training.losses.seg_kd_loss.SegKDLoss
    weights: [ 1. ]
    kd_loss_weights: [1., 6.]

    kd_loss:
      _target_: super_gradients.training.losses.cwd_loss.ChannelWiseKnowledgeDistillationLoss
      temperature: 3.
      normalization_mode: channel_wise

    ce_loss:
      _target_: torch.nn.CrossEntropyLoss
      ignore_index: 19

student_arch_params:
  num_classes: 19
  use_aux_heads: False

teacher_arch_params:
  num_classes: 19
  use_aux_heads: False

# KD module arch params
arch_params:

teacher_checkpoint_params:
  load_backbone:
  checkpoint_path:
  strict_load: no_key_matching
  pretrained_weights: cityscapes

student_checkpoint_params:
  load_backbone: True
  checkpoint_path: ???    # ImageNet pretrained checkpoints
  strict_load: no_key_matching
  pretrained_weights:

run_teacher_on_eval: True

multi_gpu: DDP
num_gpus: 4

architecture: kd_module
student_architecture: ???
teacher_architecture: ddrnet_39

experiment_name: ${student_architecture}_teacher-${teacher_architecture}
ckpt_root_dir:

# THE FOLLOWING PARAMS ARE DIRECTLY USED BY HYDRA
hydra:
  run:
    # Set the output directory (i.e. where .hydra folder that logs all the input params will be generated)
    dir: ${hydra_output_dir:${ckpt_root_dir}, ${experiment_name}}