super-gradients/src/super_gradients/recipes/imagenet_resnet50_kd.yaml

#  ResNet50 Imagenet classification training:
#  This example trains with batch_size = 192 * 8 GPUs, total 1536.
#  Training time on 8 x GeForce RTX A5000 is 9min / epoch.
#  Reach => 81.91 Top1 accuracy.
#
#  Log and tensorboard at s3://deci-pretrained-models/KD_ResNet50_Beit_Base_ImageNet/average_model.pth

# 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:
#       python src/super_gradients/examples/train_from_kd_recipe_example/train_from_kd_recipe.py --config-name=imagenet_resnet50_kd


defaults:
  - training_hyperparams: imagenet_resnet50_kd_train_params
  - dataset_params: imagenet_resnet50_kd_dataset_params
  - checkpoint_params: default_checkpoint_params

train_dataloader: imagenet_train
val_dataloader: imagenet_val

resume: False
training_hyperparams:
  resume: ${resume}
  loss: kd_loss
  criterion_params:
    distillation_loss_coeff: 0.8
    task_loss_fn:
      _target_: super_gradients.training.losses.label_smoothing_cross_entropy_loss.LabelSmoothingCrossEntropyLoss

arch_params:
  teacher_input_adapter:
    _target_: super_gradients.training.utils.kd_trainer_utils.NormalizationAdapter
    mean_original: [0.485, 0.456, 0.406]
    std_original: [0.229, 0.224, 0.225]
    mean_required: [0.5, 0.5, 0.5]
    std_required: [0.5, 0.5, 0.5]

student_arch_params:
  num_classes: 1000

teacher_arch_params:
  num_classes: 1000
  image_size: [224, 224]
  patch_size: [16, 16]

teacher_checkpoint_params:
  load_backbone: False # whether to load only backbone part of checkpoint
  checkpoint_path: # checkpoint path that is not located in super_gradients/checkpoints
  strict_load: # key matching strictness for loading checkpoint's weights
    _target_: super_gradients.training.sg_trainer.StrictLoad
    value: True
  pretrained_weights: imagenet

checkpoint_params:
  teacher_pretrained_weights: imagenet

student_checkpoint_params:
  load_backbone: False # whether to load only backbone part of checkpoint
  checkpoint_path: # checkpoint path that is not located in super_gradients/checkpoints
  strict_load: # key matching strictness for loading checkpoint's weights
    _target_: super_gradients.training.sg_trainer.StrictLoad
    value: True
  pretrained_weights: # a string describing the dataset of the pretrained weights (for example "imagenent").





run_teacher_on_eval: True

experiment_name: resnet50_imagenet_KD_Model

ckpt_root_dir:

multi_gpu: DDP
num_gpus: 8

architecture: kd_module
student_architecture: resnet50
teacher_architecture: beit_base_patch16_224


# 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}}