本文主要是介绍pytorch转onnx转mnn并验证，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
pytorch训练的模型在实际使用时往往需要转换成onnx或mnn部署，训练好的模型需先转成onnx：
import sys
import argparse
import torch
import torchvision
import torch.onnxfrom mobilenetv2 import MobileNetV2if __name__ == '__main__':model=MobileNetV2(2)model_path='./model/mobilenetv2.mdl'model.eval()model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))dummy_input = torch.randn([1,3,32,32]) #batch,channel,height,widthtorch.onnx.export(model, dummy_input, model_path.replace('mdl', 'onnx'), verbose=True, input_names=['input'], output_names=['output'],opset_version=11)print('Done!')转换成功后，再转mnn，通过MNN转换工具:
.MNNConvert -f ONNX --modelFile XXX.onnx --MNNModel XXX.mnn --bizCode biz
测试pytorch的结果：
import argparse
import os
from glob import glob
import cv2
import torch
import torch.backends.cudnn as cudnn
import yaml
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm import tqdm
from PIL import Image
from mobilenetv2 import MobileNetV2
import numpy as npdef parse_args():parser = argparse.ArgumentParser()parser.add_argument('--image_path', default=None,help='the path of imgae')args = parser.parse_args()return argsdef main():args = parse_args()device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")start = cv2.getTickCount()# create modelmodel = MobileNetV2(2).to(device)model.load_state_dict(torch.load('models/best-mobilenetv2.mdl',map_location=torch.device('cpu')))model.eval()img = args.image_pathcut_size = 48tf = transforms.Compose([lambda x: Image.open(x).convert('RGB'), # string path= > image datatransforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])img = tf(img).unsqueeze(0)x = img.to(device)outputs = model(x)# 输出概率最大的类别_, indices = torch.max(outputs, 1)percentage = torch.nn.functional.softmax(outputs, dim=1)[0] * 100perc = percentage[int(indices)].item()print('predicted:', perc)print('id:', int(indices))end = cv2.getTickCount()during = (end - start) / cv2.getTickFrequency()print("avg_time:", during)if __name__ == '__main__':main()
测试ONNX的结果,与pytorch结果一致：
import argparse
import os
from glob import glob
import onnxruntime
import onnx
import cv2
import torch
import torch.backends.cudnn as cudnn
import yaml
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm import tqdm
from PIL import Image
from mobilenetv2 import MobileNetV2
import numpy as npdef parse_args():parser = argparse.ArgumentParser()parser.add_argument('--image_path', default=None,help='the path of imgae')args = parser.parse_args()return argsdef to_numpy(tensor):return tensor.detach().cpu.numpy() if tensor.requires_grad else tensor.cpu().numpy()def main():args = parse_args()device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")start = cv2.getTickCount()model = 'models/best-mobilenetv2.onnx'onet_seeion=onnxruntime.InferenceSession(model)img = args.image_pathcut_size = 48tf = transforms.Compose([lambda x: Image.open(x).convert('RGB'), # string path= > image datatransforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])img = tf(img).unsqueeze(0)x = img.to(device)inputs={onet_seeion.get_inputs()[0].name:to_numpy(img)}outputs=onet_seeion.run(None,inputs)print(outputs)end = cv2.getTickCount()during = (end - start) / cv2.getTickFrequency()print("avg_time:", during)if __name__ == '__main__':main()
测试mnn的结果,与前面的结果一致，但是速度快了近20倍：
import argparse
import os
from glob import glob
import MNN
import cv2
import torch
import torch.backends.cudnn as cudnn
import yaml
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from tqdm import tqdm
from PIL import Image
from mobilenetv2 import MobileNetV2
import numpy as npdef parse_args():parser = argparse.ArgumentParser()parser.add_argument('--image_path', default=None,help='the path of imgae')args = parser.parse_args()return argsdef to_numpy(tensor):return tensor.detach().cpu.numpy() if tensor.requires_grad else tensor.cpu().numpy()def main():args = parse_args()device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")start = cv2.getTickCount()model = 'models/best-mobilenetv2.mnn'interpreter = MNN.Interpreter(model)mnn_session = interpreter.createSession()input_tensor = interpreter.getSessionInput(mnn_session)img = args.image_pathcut_size = 48tf = transforms.Compose([lambda x: Image.open(x).convert('RGB'), # string path= > image datatransforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])img = tf(img).unsqueeze(0)tmp_input = MNN.Tensor((1, 3, 32, 32), MNN.Halide_Type_Float, \to_numpy(img[0]), MNN.Tensor_DimensionType_Caffe)print(tmp_input.getShape())# print(tmp_input.getData())print(input_tensor.copyFrom(tmp_input))input_tensor.printTensorData()interpreter.runSession(mnn_session)output_tensor = interpreter.getSessionOutput(mnn_session, 'output')output_tensor.printTensorData()output_data = np.array(output_tensor.getData())print('mnn result is:', output_data)print("output belong to class: {}".format(np.argmax(output_tensor.getData())))end = cv2.getTickCount()during = (end - start) / cv2.getTickFrequency()print("avg_time:", during)if __name__ == '__main__':main()
用c++进行mnn重写测试,结果一致，这样就可以编库了：
// mnn_test.cpp : 定义控制台应用程序的入口点。#include "stdafx.h"
#include <iostream>
#include <opencv2/opencv.hpp>
#include <MNN/Interpreter.hpp>
#include <MNN/MNNDefine.h>
#include <MNN/Tensor.hpp>
#include <MNN/ImageProcess.hpp>
#include <memory>#define IMAGE_VERIFY_SIZE 32
#define CLASSES_SIZE 2
#define INPUT_NAME "input"
#define OUTPUT_NAME "output"cv::Mat BGRToRGB(cv::Mat img)
{cv::Mat image(img.rows, img.cols, CV_8UC3);for (int i = 0; i<img.rows; ++i) {cv::Vec3b *p1 = img.ptr<cv::Vec3b>(i);cv::Vec3b *p2 = image.ptr<cv::Vec3b>(i);for (int j = 0; j<img.cols; ++j) {p2[j][2] = p1[j][0];p2[j][1] = p1[j][1];p2[j][0] = p1[j][2];}}return image;
}int main(int argc, char* argv[]) {if (argc < 2) {printf("Usage:\n\t%s mnn_model_path image_path\n", argv[0]);return -1;}// create net and sessionconst char *mnn_model_path = argv[1];const char *image_path = argv[2];auto mnnNet = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_model_path));MNN::ScheduleConfig netConfig;netConfig.type = MNN_FORWARD_CPU;netConfig.numThread = 4;auto session = mnnNet->createSession(netConfig);auto input = mnnNet->getSessionInput(session, INPUT_NAME);if (input->elementSize() <= 4) {mnnNet->resizeTensor(input, { 1, 3, IMAGE_VERIFY_SIZE, IMAGE_VERIFY_SIZE });mnnNet->resizeSession(session);}std::cout << "input shape: " << input->shape()[0] << " " << input->shape()[1] << " " << input->shape()[2] << " " << input->shape()[3] << std::endl;// preprocess imageMNN::Tensor givenTensor(input, MNN::Tensor::CAFFE);// const int inputSize = givenTensor.elementSize();// std::cout << inputSize << std::endl;auto inputData = givenTensor.host<float>();cv::Mat bgr_image = cv::imread(image_path);bgr_image = BGRToRGB(bgr_image);cv::Mat norm_image;cv::resize(bgr_image, norm_image, cv::Size(IMAGE_VERIFY_SIZE, IMAGE_VERIFY_SIZE));for (int k = 0; k < 3; k++) {for (int i = 0; i < norm_image.rows; i++) {for (int j = 0; j < norm_image.cols; j++) {const auto src = norm_image.at<cv::Vec3b>(i, j)[k];auto dst = 0.0;if (k == 0) dst = (float(src) / 255.0f - 0.485) / 0.229;if (k == 1) dst = (float(src) / 255.0f - 0.456) / 0.224;if (k == 2) dst = (float(src) / 255.0f - 0.406) / 0.225;inputData[k * IMAGE_VERIFY_SIZE * IMAGE_VERIFY_SIZE + i * IMAGE_VERIFY_SIZE + j] = dst;}}}input->copyFromHostTensor(&givenTensor);double st = cvGetTickCount();// run sessionmnnNet->runSession(session);double et = cvGetTickCount() - st;et = et / ((double)cvGetTickFrequency() * 1000);std::cout << " speed: " << et << " ms" << std::endl;// get output dataauto output = mnnNet->getSessionOutput(session, OUTPUT_NAME);// std::cout << "output shape: " << output->shape()[0] << " " << output->shape()[1] << std::endl;auto output_host = std::make_shared<MNN::Tensor>(output, MNN::Tensor::CAFFE);output->copyToHostTensor(output_host.get());auto values = output_host->host<float>();// post processstd::vector<float> output_values;auto exp_sum = 0.0;auto max_index = 0;for (int i = 0; i < CLASSES_SIZE; i++) {if (values[i] > values[max_index]) max_index = i;output_values.push_back(values[i]);exp_sum += std::exp(values[i]);}std::cout << "output: " << output_values[0]<<","<< output_values[1] << std::endl;std::cout << "id: " << max_index << std::endl;std::cout << "prob: " << std::exp(output_values[max_index]) / exp_sum << std::endl;system("pause");return 0;
}
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