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"""Exports a pytorch *.pt model to *.onnx format
Usage:
$ export PYTHONPATH="$PWD" && python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
"""
import argparse
import onnx
from models.common import *
from utils import google_utils
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1
print(opt)
# Parameters
f = opt.weights.replace('.pt', '.onnx') # onnx filename
img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size, (1, 3, 320, 192) iDetection
# Load pytorch model
google_utils.attempt_download(opt.weights)
model = torch.load(opt.weights, map_location=torch.device('cpu'))['model'].float()
model.eval()
model.fuse()
# Export to onnx
model.model[-1].export = True # set Detect() layer export=True
_ = model(img) # dry run
torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['images'],
output_names=['output']) # output_names=['classes', 'boxes']
# Check onnx model
model = onnx.load(f) # load onnx model
onnx.checker.check_model(model) # check onnx model
print(onnx.helper.printable_graph(model.graph)) # print a human readable representation of the graph
print('Export complete. ONNX model saved to %s\nView with https://github.com/lutzroeder/netron' % f)
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