我们提供的是pytorch保存的模型，onnx格式的模型可自通过我们开源的模型参数自行转换

请问你转换成功了吗，可以看下转onnx的代码嘛

这个是转换为onnx的代码，需要注意输入为13h*w
import os
from tkinter.messagebox import NO
import torch
import torch.onnx
import torch.nn as nn
import onnxruntime as ort
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
import onnx
from onnxsim import simplify
from model.locator import Crowd_locator
from collections import OrderedDict

# os.environ["CUDA_VISIBLE_DEVICES"]= "1"

GPU_ID = "0"
os.environ["CUDA_VISIBLE_DEVICES"] = GPU_ID
torch.backends.cudnn.benchmark = True

def onnx_export(model_path):
net = Crowd_locator(netName,GPU_ID,pretrained=False)
net.cuda()
state_dict = torch.load(model_path)
if len(GPU_ID.split(",")) > 1:
net.load_state_dict(state_dict)
else:
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "")
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
net.eval()
# 打印模型的每一层名称
for name, module in net.named_modules():
print(name,"nnnnn")
dummpy_input = torch.zeros(1, 3, 512, 1024).cuda() # 640 640
# dummpy_input = torch.zeros(1, 3, 512, 1024).cuda()
onnx_name = "HRnet_Crowd_count_512_1024_opset12.onnx"
# net = net(dummpy_input)
torch.onnx.export(
net, dummpy_input, onnx_name,
verbose=True,
input_names=["image"],
output_names=["predict"],
opset_version=12,
dynamic_axes=None
)

def onnx_sim(onnx_path):
model_onnx = onnx.load_model(onnx_path)
model_smi, check = simplify(model_onnx)
save_path = "HRnet_Crowd_count_512_1024_opset12-sim.onnx"
onnx.save(model_smi, save_path)
print("模型静态图简化完成")

if name == "main":
netName = "HR_Net" # VGG16_FPN HR_Net
model_path = "/IIM/Preweights/NWPU-HR-ep_241_F1_0.802_Pre_0.841_Rec_0.766_mae_55.6_mse_330.9.pth"

onnx_path = "/IIM/Preweights/1024_HRnet_Crowd_count_512_1024_opset12.onnx"
# save_model(pth_file)
onnx_export(model_path)
# onnx_sim(onnx_path)
print("Done")