win10上使用cuda10.1+TensorRT7.2解析pp_liteSeg（STDC2）的onnx模型失败

训练pp_liteSeg（STDC2）模型后，转换成onnx模型，然后在win10上使用cuda10.1+TensorRT7.2的环境，对转换后的onnx模型进行TRT解析，解析失败，报错如下

> Input filename: D:\program_files\11-RIIS1005\RIIS1015_Robot\algorithm\fastenerdetection\lib\model_onnx.onnx
> ONNX IR version: 0.0.8
> Opset version: 11
> Producer name:
> Producer version:
> Domain:
> Model version: 0
> Doc string:
> WARNING: ONNX model has a newer ir_version (0.0.8) than this parser was built against (0.0.3).
> While parsing node number 356 [Cast]:
> ERROR: builtin_op_importers.cpp:727 In function importCast:
> [8] Assertion failed: trt_dtype == nvinfer1::DataType::kHALF && cast_dtype == ::ONNX_NAMESPACE::TensorProto::FLOAT

TRT解析onnx代码如下：

>       // 创建构建器(即指向Ibuilder类型对象的指针)
>         nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger_);
>         if (builder == nullptr) {
>             LOG(ERROR) << "create builder failed!";
>             return false;
>         }
> 
>         // 创建网络
>         int explicit_batch = 1 << (int)(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
>         nvinfer1::INetworkDefinition* network = builder->createNetworkV2(1);// max_batch_size
>         if (network == nullptr) {
>             LOG(ERROR) << "create network failed!";
>             return false;
>         }
> 
>         //network->getLayer(network->);
>         // 创建解析器
>         nvinfer1::IBuilderConfig* config = builder->createBuilderConfig();
>         if (config == nullptr) {
>             LOG(ERROR) << "create config failed!";
>             return false;
>         }
> 
>         auto parser = nvonnxparser::createParser(*network, logger_.getTRTLogger());
>         if (parser == nullptr) {
>             LOG(ERROR) << "create parser failed!";
>             return false;
>         }
> 
>          //解析onnx文件，并填充网络
>         std::string onnx_model_file = model_path + "\\" + model_file;
>         if (!parser->parseFromFile(onnx_model_file.c_str(), static_cast<int>(logger_.getReportableSeverity()))) {
>             LOG(ERROR) << "failed to parse onnx file";
>             return false;
>         }
> 
>          //设置网络
>         builder->setMaxBatchSize(max_batch_size);
>         builder->setMaxWorkspaceSize(1 << 20); 
>         samplesCommon::enableDLA(builder, config, kUserDALCore_);
>         /****************************************************************/
> 
>         // 创建推理引擎（engine） 
>         ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
>         engine_ = engine;
>         if (engine_ == nullptr) {
>             LOG(ERROR) << "create engine failed!";
>             return false;
>         }
> 
>         // 释放解释器
>         parser->destroy();
> 
>         // 序列化推理引擎
>         trt_model_stream_ = engine_->serialize();
>         if (trt_model_stream_ == nullptr) {
>             LOG(ERROR) << "engine serialize failed!";
>             return false;
>         }
> 
>         // 获取模型名
>         std::vector<std::string> split_str_vec;
>         bool ret = StringSplit(model_file, '.', &split_str_vec);
>         if (!ret) {
>             LOG(ERROR) << "StringSplit " << model_file << " failed!";
>             return false;
>         }
> 
>         // 将序列化推理引擎保存到文件中
>         std::string model_name = split_str_vec[0];
>         std::string trt_model_str = model_path + "\\" + model_name + ".bin";
>         std::ofstream ofs(trt_model_str, std::ios::out | std::ios::binary);
>         ofs.write(static_cast<char*>(trt_model_stream_->data()), trt_model_stream_->size());
>         ofs.close();
>         LOG(INFO) << "Serialized model path: " << trt_model_str.c_str();
> 
>         network->destroy();
>         builder->destroy();
>

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