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| from loguru import logger | |
| import tensorrt as trt | |
| import torch | |
| from torch2trt import torch2trt | |
| from yolox.exp import get_exp | |
| import argparse | |
| import os | |
| import shutil | |
| def make_parser(): | |
| parser = argparse.ArgumentParser("YOLOX ncnn deploy") | |
| parser.add_argument("-expn", "--experiment-name", type=str, default=None) | |
| parser.add_argument("-n", "--name", type=str, default=None, help="model name") | |
| parser.add_argument( | |
| "-f", | |
| "--exp_file", | |
| default=None, | |
| type=str, | |
| help="pls input your expriment description file", | |
| ) | |
| parser.add_argument("-c", "--ckpt", default=None, type=str, help="ckpt path") | |
| return parser | |
| def main(): | |
| args = make_parser().parse_args() | |
| exp = get_exp(args.exp_file, args.name) | |
| if not args.experiment_name: | |
| args.experiment_name = exp.exp_name | |
| model = exp.get_model() | |
| file_name = os.path.join(exp.output_dir, args.experiment_name) | |
| os.makedirs(file_name, exist_ok=True) | |
| if args.ckpt is None: | |
| ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar") | |
| else: | |
| ckpt_file = args.ckpt | |
| ckpt = torch.load(ckpt_file, map_location="cpu") | |
| # load the model state dict | |
| model.load_state_dict(ckpt["model"]) | |
| logger.info("loaded checkpoint done.") | |
| model.eval() | |
| model.cuda() | |
| model.head.decode_in_inference = False | |
| x = torch.ones(1, 3, exp.test_size[0], exp.test_size[1]).cuda() | |
| model_trt = torch2trt( | |
| model, | |
| [x], | |
| fp16_mode=True, | |
| log_level=trt.Logger.INFO, | |
| max_workspace_size=(1 << 32), | |
| ) | |
| torch.save(model_trt.state_dict(), os.path.join(file_name, "model_trt.pth")) | |
| logger.info("Converted TensorRT model done.") | |
| engine_file = os.path.join(file_name, "model_trt.engine") | |
| engine_file_demo = os.path.join("deploy", "TensorRT", "cpp", "model_trt.engine") | |
| with open(engine_file, "wb") as f: | |
| f.write(model_trt.engine.serialize()) | |
| shutil.copyfile(engine_file, engine_file_demo) | |
| logger.info("Converted TensorRT model engine file is saved for C++ inference.") | |
| if __name__ == "__main__": | |
| main() | |