87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
import os
|
|
import sys
|
|
import argparse
|
|
import warnings
|
|
import onnx
|
|
import torch
|
|
import torch.nn as nn
|
|
from damo.base_models.core.ops import RepConv, SiLU
|
|
from damo.config.base import parse_config
|
|
from damo.detectors.detector import build_local_model
|
|
from damo.utils.model_utils import replace_module
|
|
|
|
|
|
class DeepStreamOutput(nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
|
|
def forward(self, x):
|
|
boxes = x[1]
|
|
scores, classes = torch.max(x[0], 2, keepdim=True)
|
|
return torch.cat((boxes, scores, classes), dim=2)
|
|
|
|
|
|
def suppress_warnings():
|
|
warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
|
|
warnings.filterwarnings('ignore', category=UserWarning)
|
|
warnings.filterwarnings('ignore', category=DeprecationWarning)
|
|
|
|
|
|
def damoyolo_export(weights, config_file, device):
|
|
config = parse_config(config_file)
|
|
config.model.head.export_with_post = True
|
|
model = build_local_model(config, device)
|
|
ckpt = torch.load(weights, map_location=device)
|
|
model.eval()
|
|
if 'model' in ckpt:
|
|
ckpt = ckpt['model']
|
|
model.load_state_dict(ckpt, strict=True)
|
|
model = replace_module(model, nn.SiLU, SiLU)
|
|
for layer in model.modules():
|
|
if isinstance(layer, RepConv):
|
|
layer.switch_to_deploy()
|
|
model.head.nms = False
|
|
return config, model
|
|
|
|
|
|
def main(args):
|
|
suppress_warnings()
|
|
device = torch.device('cpu')
|
|
cfg, model = damoyolo_export(args.weights, args.config, device)
|
|
|
|
model = nn.Sequential(model, DeepStreamOutput())
|
|
|
|
img_size = args.size * 2 if len(args.size) == 1 else args.size
|
|
|
|
onnx_input_im = torch.zeros(1, 3, *img_size).to(device)
|
|
onnx_output_file = cfg.miscs['exp_name'] + '.onnx'
|
|
|
|
torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
|
|
do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes=None)
|
|
|
|
if args.simplify:
|
|
import onnxsim
|
|
model_onnx = onnx.load(onnx_output_file)
|
|
model_onnx, _ = onnxsim.simplify(model_onnx)
|
|
onnx.save(model_onnx, onnx_output_file)
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description='DeepStream DAMO-YOLO conversion')
|
|
parser.add_argument('-w', '--weights', required=True, help='Input weights (.pth) file path (required)')
|
|
parser.add_argument('-c', '--config', required=True, help='Input config (.py) file path (required)')
|
|
parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
|
|
parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
|
|
parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
|
|
args = parser.parse_args()
|
|
if not os.path.isfile(args.weights):
|
|
raise SystemExit('Invalid weights file')
|
|
if not os.path.isfile(args.config):
|
|
raise SystemExit('Invalid config file')
|
|
return args
|
|
|
|
|
|
if __name__ == '__main__':
|
|
args = parse_args()
|
|
sys.exit(main(args))
|