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Deci-AI
/
super-gradients
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  1. unit_tests:
    2. 

  2. 	python -m unittest tests/deci_core_unit_test_suite_runner.py
    3. 

  3. 

  4. integration_tests:
    5. 

  5. 	python -m unittest tests/deci_core_integration_test_suite_runner.py
    6. 

  6. 

  7. yolo_nas_integration_tests:
    8. 

  8. 	python -m unittest tests/integration_tests/yolo_nas_integration_test.py
    9. 

  9. 

  10. recipe_accuracy_tests:
    11. 

  11. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_pose_dekr_w32_no_dc experiment_name=shortened_coco2017_pose_dekr_w32_ap_test epochs=1 batch_size=4 val_batch_size=8 training_hyperparams.lr_warmup_steps=0 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=1000 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
    12. 

  12. 	python src/super_gradients/train_from_recipe.py --config-name=cifar10_resnet               experiment_name=shortened_cifar10_resnet_accuracy_test   epochs=100 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
    13. 

  13. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolox               experiment_name=shortened_coco2017_yolox_n_map_test      epochs=10  architecture=yolox_n training_hyperparams.loss=yolox_fast_loss training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
    14. 

  14. 	python src/super_gradients/train_from_recipe.py --config-name=cityscapes_regseg48          experiment_name=shortened_cityscapes_regseg48_iou_test   epochs=10 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
    15. 

  15. 	python src/super_gradients/examples/convert_recipe_example/convert_recipe_example.py --config-name=cifar10_conversion_params experiment_name=shortened_cifar10_resnet_accuracy_test
    16. 

  16. 	coverage run --source=super_gradients -m unittest tests/deci_core_recipe_test_suite_runner.py
    17. 

  17. 

  18. 

  19. sweeper_test:
    20. 

  20. 	python -m super_gradients.train_from_recipe -m --config-name=cifar10_resnet \
    21. 

  21. 	  ckpt_root_dir=$$PWD \
    22. 

  22. 	  experiment_name=sweep_cifar10 \
    23. 

  23. 	  training_hyperparams.max_epochs=1 \
    24. 

  24. 	  training_hyperparams.initial_lr=0.001,0.01
    25. 

  25. 

  26. 	# Make sure that experiment_dir includes $$expected_num_dir subdirectories. If not, fail
    27. 

  27. 	subdir_count=$$(find "$$PWD/sweep_cifar10" -mindepth 1 -maxdepth 1 -type d | wc -l); \
    28. 

  28. 	if [ "$$subdir_count" -ne 2 ]; then \
    29. 

  29. 	  echo "Error: $$PWD/sweep_cifar10 should include 2 subdirectories but includes $$subdir_count."; \
    30. 

  30. 	  exit 1; \
    31. 

  31. 	fi
    32. 

  32. 

  33. WANDB_PARAMS = training_hyperparams.sg_logger=wandb_sg_logger +training_hyperparams.sg_logger_params.api_server=https://wandb.research.deci.ai +training_hyperparams.sg_logger_params.entity=super-gradients training_hyperparams.sg_logger_params.launch_tensorboard=false training_hyperparams.sg_logger_params.monitor_system=true +training_hyperparams.sg_logger_params.project_name=PoseEstimation training_hyperparams.sg_logger_params.save_checkpoints_remote=true training_hyperparams.sg_logger_params.save_logs_remote=true training_hyperparams.sg_logger_params.save_tensorboard_remote=false training_hyperparams.sg_logger_params.tb_files_user_prompt=false
    34. 

  34. 

  35. examples_to_docs:
    36. 

  36. 	jupyter nbconvert --to markdown --output-dir="documentation/source/" --execute src/super_gradients/examples/model_export/models_export.ipynb
    37. 

  37. 

  38. coco2017_yolo_nas_pose_s_128_512_512_2_2_3_0_1_0:
    39. 

  39. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_s_128_512_512_2_2_3_0_1_0 $(WANDB_PARAMS)
    40. 

  40. 

  41. coco2017_yolo_nas_pose_s_128_512_512_3_2_2_0_1_1:
    42. 

  42. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_s_128_512_512_3_2_2_0_1_1 $(WANDB_PARAMS)
    43. 

  43. 

  44. coco2017_yolo_nas_pose_m:
    45. 

  45. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_m $(WANDB_PARAMS)
    46. 

  46. 

  47. coco2017_yolo_nas_pose_m_resume:
    48. 

  48. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_m $(WANDB_PARAMS) resume=True
    49. 

  49. 

  50. coco2017_yolo_nas_pose_l:
    51. 

  51. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_l $(WANDB_PARAMS)
    52. 

  52. 

  53. coco2017_yolo_nas_pose_l_no_ema:
    54. 

  54. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_l $(WANDB_PARAMS) training_hyperparams.ema=False
    55. 

  55. 

  56. coco2017_yolo_nas_pose_n:
    57. 

  57. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_n $(WANDB_PARAMS)
    58. 

  58. 

  59. coco2017_yolo_nas_pose_n_resume:
    60. 

  60. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_n $(WANDB_PARAMS) resume=True
    61. 

  61. 

  62. coco2017_yolo_nas_pose_l_resume:
    63. 

  63. 	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_l $(WANDB_PARAMS) resume=True
    64. 

  64. 

  65. #coco2017_yolo_nas_pose_s:
    66. 

  66. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_s_weights_and_biases dataset_params=coco_pose_estimation_yolo_nas_dataset_params
    67. 

  67. #
    68. 

  68. #coco2017_yolo_nas_pose_shared_s:
    69. 

  69. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_shared_s
    70. 

  70. #
    71. 

  71. #coco2017_yolo_nas_pose_shared_m:
    72. 

  72. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_shared_m
    73. 

  73. #
    74. 

  74. #
    75. 

  75. #coco2017_yolo_nas_pose_shared_s_ema_less_mosaic_lr_bce_local:
    76. 

  76. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_shared_s_ema_less_mosaic_lr_bce_local  dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/coco2017 dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/coco2017 num_gpus=4
    77. 

  77. #
    78. 

  78. #coco2017_yolo_nas_pose_s_local:
    79. 

  79. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_s_local  dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/coco2017 dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/coco2017 num_gpus=4
    80. 

  80. #
    81. 

  81. #coco2017_yolo_nas_pose_shared_s_local:
    82. 

  82. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_shared_s_local dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/coco2017 dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/coco2017 num_gpus=4
    83. 

  83. #
    84. 

  84. #coco2017_yolo_nas_pose_shared_s_384_short:
    85. 

  85. #	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolo_nas_pose_shared_s_384_short dataset_params=coco_pose_estimation_yolo_nas_dataset_params  dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/coco2017 dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/coco2017 num_gpus=4 multi_gpu=DDP
    86. 

  86. 

  87. 

  88. crowdpose_yolo_nas_pose_s_no_crowd_local:
    89. 

  89. 	python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_no_crowd \
    90. 

  90.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    91. 

  91.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    92. 

  92.     num_gpus=4
    93. 

  93. 

  94. crowdpose_yolo_nas_pose_s_no_crowd_no_ema_local:
    95. 

  95. 	python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_no_crowd_no_ema \
    96. 

  96. 	checkpoint_params.checkpoint_path=/home/bloodaxe/develop/super-gradients/checkpoints/crowdpose_yolo_nas_pose_s_box_focal_1.0_ciou_2.5_dfl_0.01_pose_focal_1.0_reg_34__default_640no_crowd/RUN_20230919_212216_740555/average_model.pth \
    97. 

  97.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    98. 

  98.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    99. 

  99.     num_gpus=4
    100. 

  100. 

  101. crowdpose_yolo_nas_pose_s_proxy:
    102. 

  102. 	CUDA_VISIBLE_DEVICES=0 python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_proxy \
    103. 

  103.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    104. 

  104.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    105. 

  105.     num_gpus=1 multi_gpu=Off training_hyperparams.initial_lr=3e-4 training_hyperparams.criterion_params.classification_loss_type=focal &
    106. 

  106. 

  107. 	CUDA_VISIBLE_DEVICES=1 python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_proxy \
    108. 

  108.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    109. 

  109.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    110. 

  110.     num_gpus=1 multi_gpu=Off training_hyperparams.initial_lr=3e-4 training_hyperparams.criterion_params.classification_loss_type=bce &
    111. 

  111. 

  112. 	CUDA_VISIBLE_DEVICES=2 python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_proxy \
    113. 

  113.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    114. 

  114.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    115. 

  115.     num_gpus=1 multi_gpu=Off training_hyperparams.initial_lr=3e-4 training_hyperparams.criterion_params.classification_loss_weight=10 &
    116. 

  116. 

  117. 	CUDA_VISIBLE_DEVICES=3 python src/super_gradients/train_from_recipe.py --config-name=crowdpose_yolo_nas_pose_s_proxy \
    118. 

  118.     dataset_params.train_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    119. 

  119.     dataset_params.val_dataset_params.data_dir=/home/bloodaxe/data/crowdpose \
    120. 

  120.     num_gpus=1 multi_gpu=Off training_hyperparams.initial_lr=3e-4 training_hyperparams.criterion_params.assigner_multiply_by_pose_oks=True &
    121.
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