Thanks for answering!
my output is like the below.
1. ONNX model
: too large to load up, I just only show the ByteTrack-ONNXRuntime python script.
from loguru import logger
import torch
from torch import nn
from yolox.exp import get_exp
from yolox.models.network_blocks import SiLU
from yolox.utils import replace_module
import argparse
import os
def make_parser():
parser = argparse.ArgumentParser("YOLOX onnx deploy")
parser.add_argument(
"--output-name", type=str, default="bytetrack_s.onnx", help="output name of models"
)
parser.add_argument(
"--input", default="images", type=str, help="input node name of onnx model"
)
parser.add_argument(
"--output", default="output", type=str, help="output node name of onnx model"
)
parser.add_argument(
"-o", "--opset", default=11, type=int, help="onnx opset version"
)
parser.add_argument("--no-onnxsim", action="store_true", help="use onnxsim or not")
parser.add_argument(
"-f",
"--exp_file",
default=None,
type=str,
help="expriment description file",
)
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("-c", "--ckpt", default=None, type=str, help="ckpt path")
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
return parser
@logger.catch
def main():
args = make_parser().parse_args()
logger.info("args value: {}".format(args))
exp = get_exp(args.exp_file, args.name)
exp.merge(args.opts)
if not args.experiment_name:
args.experiment_name = exp.exp_name
model = exp.get_model()
if args.ckpt is None:
file_name = os.path.join(exp.output_dir, args.experiment_name)
ckpt_file = os.path.join(file_name, "best_ckpt.pth.tar")
else:
ckpt_file = args.ckpt
# load the model state dict
ckpt = torch.load(ckpt_file, map_location="cpu")
model.eval()
if "model" in ckpt:
ckpt = ckpt["model"]
model.load_state_dict(ckpt)
model = replace_module(model, nn.SiLU, SiLU)
model.head.decode_in_inference = False
logger.info("loading checkpoint done.")
dummy_input = torch.randn(1, 3, exp.test_size[0], exp.test_size[1])
torch.onnx._export(
model,
dummy_input,
args.output_name,
input_names=[args.input],
output_names=[args.output],
opset_version=args.opset,
)
logger.info("generated onnx model named {}".format(args.output_name))
if not args.no_onnxsim:
import onnx
from onnxsim import simplify
# use onnxsimplify to reduce reduent model.
onnx_model = onnx.load(args.output_name)
model_simp, check = simplify(onnx_model)
assert check, "Simplified ONNX model could not be validated"
onnx.save(model_simp, args.output_name)
logger.info("generated simplified onnx model named {}".format(args.output_name))
if __name__ == "__main__":
main()
2. check_model.py
: my Onnx model runs well with onnx.checker.check_model(model).
3. TRT EXEC perf.
: this is my TRT EXEC performance with -verbose options
verbose.txt (2.3 MB)
