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Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git

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pretrained_models_test.py 6.0 KB
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  1. import unittest
    2. 

  2. import super_gradients
    3. 

  3. from super_gradients.training import MultiGPUMode
    4. 

  4. from super_gradients.training import SgModel
    5. 

  5. from super_gradients.training.datasets.dataset_interfaces.dataset_interface import ImageNetDatasetInterface, \
    6. 

  6.     ClassificationTestDatasetInterface
    7. 

  7. from super_gradients.training.metrics import Accuracy
    8. 

  8. import os
    9. 

  9. import shutil
    10. 

  10. 

  11. 

  12. class PretrainedModelsTest(unittest.TestCase):
    13. 

  13.     def setUp(self) -> None:
    14. 

  14.         super_gradients.init_trainer()
    15. 

  15.         self.imagenet_pretrained_models = ["resnet50", "repvgg_a0", "regnetY800"]
    16. 

  16. 

  17.         self.imagenet_pretrained_arch_params = {"resnet50": {"pretrained_weights": "imagenet"},
    18. 

  18.                                                 "regnetY800": {"pretrained_weights": "imagenet"},
    19. 

  19.                                                 "repvgg_a0": {"pretrained_weights": "imagenet",
    20. 

  20.                                                               "build_residual_branches": True}}
    21. 

  21. 

  22.         self.imagenet_pretrained_accuracies = {"resnet50": 0.763,
    23. 

  23.                                                "repvgg_a0": 0.7205,
    24. 

  24.                                                "regnetY800": 0.7605}
    25. 

  25.         self.imagenet_dataset = ImageNetDatasetInterface(data_dir="/data/Imagenet", dataset_params={"batch_size": 128})
    26. 

  26. 

  27.         self.transfer_classification_dataset = ClassificationTestDatasetInterface(image_size=224)
    28. 

  28. 

  29.         self.transfer_classification_train_params = {"max_epochs": 3,
    30. 

  30.                                                      "lr_updates": [1],
    31. 

  31.                                                      "lr_decay_factor": 0.1,
    32. 

  32.                                                      "initial_lr": 0.6,
    33. 

  33.                                                      "loss": "cross_entropy",
    34. 

  34.                                                      "lr_mode": "step",
    35. 

  35.                                                      "optimizer_params": {"weight_decay": 0.000,
    36. 

  36.                                                                           "momentum": 0.9},
    37. 

  37.                                                      "train_metrics_list": [Accuracy()],
    38. 

  38.                                                      "valid_metrics_list": [Accuracy()],
    39. 

  39.                                                      "loss_logging_items_names": ["Loss"],
    40. 

  40.                                                      "metric_to_watch": "Accuracy",
    41. 

  41.                                                      "greater_metric_to_watch_is_better": True}
    42. 

  42. 

  43.     def test_pretrained_resnet50_imagenet(self):
    44. 

  44.         trainer = SgModel('imagenet_pretrained_resnet50', model_checkpoints_location='local',
    45. 

  45.                           multi_gpu=MultiGPUMode.OFF)
    46. 

  46.         trainer.connect_dataset_interface(self.imagenet_dataset, data_loader_num_workers=8)
    47. 

  47.         trainer.build_model("resnet50", arch_params=self.imagenet_pretrained_arch_params["resnet50"])
    48. 

  48.         res = trainer.test(test_loader=self.imagenet_dataset.val_loader, test_metrics_list=[Accuracy()],
    49. 

  49.                            metrics_progress_verbose=True)[0].cpu().item()
    50. 

  50.         self.assertAlmostEqual(res, self.imagenet_pretrained_accuracies["resnet50"])
    51. 

  51. 

  52.     def test_transfer_learning_resnet50_imagenet(self):
    53. 

  53.         trainer = SgModel('imagenet_pretrained_resnet50_transfer_learning', model_checkpoints_location='local',
    54. 

  54.                           multi_gpu=MultiGPUMode.OFF)
    55. 

  55.         trainer.connect_dataset_interface(self.transfer_classification_dataset, data_loader_num_workers=8)
    56. 

  56.         trainer.build_model("resnet50", arch_params=self.imagenet_pretrained_arch_params["resnet50"])
    57. 

  57.         trainer.train(training_params=self.transfer_classification_train_params)
    58. 

  58. 

  59.     def test_pretrained_regnetY800_imagenet(self):
    60. 

  60.         trainer = SgModel('imagenet_pretrained_regnetY800', model_checkpoints_location='local',
    61. 

  61.                           multi_gpu=MultiGPUMode.OFF)
    62. 

  62.         trainer.connect_dataset_interface(self.imagenet_dataset, data_loader_num_workers=8)
    63. 

  63.         trainer.build_model("regnetY800", arch_params=self.imagenet_pretrained_arch_params["regnetY800"])
    64. 

  64.         res = trainer.test(test_loader=self.imagenet_dataset.val_loader, test_metrics_list=[Accuracy()],
    65. 

  65.                            metrics_progress_verbose=True)[0].cpu().item()
    66. 

  66.         self.assertAlmostEqual(res, self.imagenet_pretrained_accuracies["regnetY800"])
    67. 

  67. 

  68.     def test_transfer_learning_regnetY800_imagenet(self):
    69. 

  69.         trainer = SgModel('imagenet_pretrained_regnetY800_transfer_learning', model_checkpoints_location='local',
    70. 

  70.                           multi_gpu=MultiGPUMode.OFF)
    71. 

  71.         trainer.connect_dataset_interface(self.transfer_classification_dataset, data_loader_num_workers=8)
    72. 

  72.         trainer.build_model("regnetY800", arch_params=self.imagenet_pretrained_arch_params["regnetY800"])
    73. 

  73.         trainer.train(training_params=self.transfer_classification_train_params)
    74. 

  74. 

  75.     def test_pretrained_repvgg_a0_imagenet(self):
    76. 

  76.         trainer = SgModel('imagenet_pretrained_repvgg_a0', model_checkpoints_location='local',
    77. 

  77.                           multi_gpu=MultiGPUMode.OFF)
    78. 

  78.         trainer.connect_dataset_interface(self.imagenet_dataset, data_loader_num_workers=8)
    79. 

  79.         trainer.build_model("repvgg_a0", arch_params=self.imagenet_pretrained_arch_params["repvgg_a0"])
    80. 

  80.         res = trainer.test(test_loader=self.imagenet_dataset.val_loader, test_metrics_list=[Accuracy()],
    81. 

  81.                            metrics_progress_verbose=True)[0].cpu().item()
    82. 

  82.         self.assertAlmostEqual(res, self.imagenet_pretrained_accuracies["repvgg_a0"])
    83. 

  83. 

  84.     def test_transfer_learning_repvgg_a0_imagenet(self):
    85. 

  85.         trainer = SgModel('imagenet_pretrained_repvgg_a0_transfer_learning', model_checkpoints_location='local',
    86. 

  86.                           multi_gpu=MultiGPUMode.OFF)
    87. 

  87.         trainer.connect_dataset_interface(self.transfer_classification_dataset, data_loader_num_workers=8)
    88. 

  88.         trainer.build_model("repvgg_a0", arch_params=self.imagenet_pretrained_arch_params["repvgg_a0"])
    89. 

  89.         trainer.train(training_params=self.transfer_classification_train_params)
    90. 

  90. 

  91.     def tearDown(self) -> None:
    92. 

  92.         if os.path.exists('~/.cache/torch/hub/'):
    93. 

  93.             shutil.rmtree('~/.cache/torch/hub/')
    94. 

  94. 

  95. 

  96. if __name__ == '__main__':
    97. 

  97.     unittest.main()
    98.
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