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


  
1

	
2

	
3

	
4

	
5

	
6

	
7

	
8

	
9

	
10

	
11

	
12

	
13

	
14

	
15

	
16

	
17

	
18

	
19

	
20

	
21

	
22

	
23

	
24

	
            import super_gradients
from omegaconf import DictConfig
import hydra
import pkg_resources


@hydra.main(config_path=pkg_resources.resource_filename("conf", ""), config_name="imagenet_repvgg_conf")
def train(cfg: DictConfig) -> None:
    # INSTANTIATE ALL OBJECTS IN CFG
    cfg = hydra.utils.instantiate(cfg)

    # CONNECT THE DATASET INTERFACE WITH DECI MODEL
    cfg.sg_model.connect_dataset_interface(cfg.dataset_interface, data_loader_num_workers=cfg.data_loader_num_workers)

    # BUILD NETWORK
    cfg.sg_model.build_model(cfg.architecture, arch_params=cfg.arch_params, load_checkpoint=cfg.load_checkpoint)

    # TRAIN
    cfg.sg_model.train(training_params=cfg.training_params)


if __name__ == "__main__":
    super_gradients.init_trainer()
    train()