super-gradients/tests/integration_tests/deci_lab_export_test.py

import unittest
from super_gradients import Trainer
from super_gradients.common.plugins.deci_client import DeciClient
from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.models import ResNet18
from torch.optim import SGD
from super_gradients.training.utils.callbacks import DeciLabUploadCallback, ModelConversionCheckCallback
from deci_lab_client.models import Metric, QuantizationLevel, ModelMetadata, OptimizationRequestForm


class DeciLabUploadTest(unittest.TestCase):
    def setUp(self) -> None:
        self.trainer = Trainer("deci_lab_export_test_model")

    def test_train_with_deci_lab_integration(self):
        model_meta_data = ModelMetadata(
            name="model_for_deci_lab_upload_test",
            primary_batch_size=1,
            architecture="Resnet18",
            framework="pytorch",
            dl_task="classification",
            input_dimensions=(3, 224, 224),
            primary_hardware="XEON",
            dataset_name="imagenet",
            description="ResNet18 ONNX deci.ai Test",
            tags=["imagenet", "resnet18"],
        )

        optimization_request_form = OptimizationRequestForm(
            target_hardware="XEON",
            target_batch_size=1,
            target_metric=Metric.LATENCY,
            optimize_model_size=True,
            quantization_level=QuantizationLevel.FP16,
            optimize_autonac=True,
        )

        model_conversion_callback = ModelConversionCheckCallback(model_meta_data=model_meta_data)
        deci_lab_callback = DeciLabUploadCallback(model_meta_data=model_meta_data, optimization_request_form=optimization_request_form)

        net = ResNet18(num_classes=5, arch_params={})
        train_params = {
            "max_epochs": 2,
            "lr_updates": [1],
            "lr_decay_factor": 0.1,
            "lr_mode": "step",
            "lr_warmup_epochs": 0,
            "initial_lr": 0.1,
            "loss": "cross_entropy",
            "optimizer": self.optimizer,
            "criterion_params": {},
            "train_metrics_list": [Accuracy(), Top5()],
            "valid_metrics_list": [Accuracy(), Top5()],
            "metric_to_watch": "Accuracy",
            "greater_metric_to_watch_is_better": True,
            "phase_callbacks": [model_conversion_callback, deci_lab_callback],
        }
        self.optimizer = SGD(params=net.parameters(), lr=0.1)

        self.trainer.train(
            model=net, training_params=train_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader()
        )

        # CLEANUP

        # FIXME: MISUSE OF DECI_PLATFROM CALLBACK:
        #  https://github.com/Deci-AI/deci_trainer/pull/106/files/2ed12b78adc9886faabad9d952969ff5479e9237#r708092979
        new_model_from_repo_name = model_meta_data.name + "_1_1"

        your_model_from_repo = deci_lab_callback.platform_client.get_model_by_name(name=new_model_from_repo_name).data
        deci_lab_callback.platform_client.delete_model(your_model_from_repo.model_id)

    def test_upload_function(self):
        model_meta_data = ModelMetadata(
            name="model_for_deci_lab_upload_test",
            primary_batch_size=1,
            architecture="Resnet18",
            framework="pytorch",
            dl_task="classification",
            input_dimensions=(3, 224, 224),
            primary_hardware="K80",
            dataset_name="ImageNet",
            description="ResNet18 ONNX deci.ai Test",
            tags=[""],
        )

        optimization_request_form = OptimizationRequestForm(
            target_hardware="XEON",
            target_batch_size=1,
            target_metric=Metric.LATENCY,
            optimize_model_size=True,
            quantization_level=QuantizationLevel.FP16,
            optimize_autonac=True,
        )

        net = ResNet18(num_classes=5, arch_params={})
        client = DeciClient()
        client.upload_model(model=net, model_meta_data=model_meta_data, optimization_request_form=optimization_request_form)


if __name__ == "__main__":
    unittest.main()