input1 = torch.LongTensor([[i for i in range(32)]]) input_names = ["input_1"] output_names = ["output_1"] torch.onnx.export(self.model, input1, "./data/TextCNN.onnx", verbose=True, input_names=input_names, out
接下来,我们需要对输出进行后处理以获得 softmax 向量,因为这不是由模型本身处理的: varoutput = results[0].GetTensorDataAsSpan<float>().ToArray();floatsum = output.Sum(x => (float)Math.Exp(x)); IEnumerable<float> softmax = output.Select(x => (float)Math.Exp(x) / sum); 其他型号可能...
后处理输出 接下来,我们需要对输出进行后处理以获得 softmax 向量,因为这不是由模型本身处理的: varoutput=results[0].GetTensorDataAsSpan<float>().ToArray();floatsum=output.Sum(x=>(float)Math.Exp(x));IEnumerable<float>softmax=output.Select(x=>(float)Math.Exp(x)/sum); 其他型号可能会在输出之...
推理(score model & input tensor, get back output tensor) auto output_tensors = session.Run(Ort::RunOptions{ nullptr }, input_node_names.data(), input_tensors.data(), input_names.size(), output_node_names.data(), output_node_names.size()); endTime = clock(); assert(output_tensors....
var output = results[0].GetTensorDataAsSpan<float>().ToArray();float sum = output.Sum(x => (float)Math.Exp(x));IEnumerable<float> softmax = output.Select(x => (float)Math.Exp(x) / sum); 其他型号可能会在输出之前应用 Softmax 节点,在这种情况下,您不需要此步骤。同样,您可以使用 Net...
size_t numItems = outputTensors[0].GetTensorTypeAndShapeInfo().GetElementCount(); std::vector<std::string> classNames = {"class1", "class2"}; int maxIndex = std::distance(floatArray, std::max_element(floatArray, floatArray + numItems)); std::cout << "Predicted class: " << cla...
input_x = blob.view(1, c, h, w) defto_numpy(tensor): returntensor.detach.cpu.numpyiftensor.requires_gradelsetensor.cpu.numpy # compute ONNX Runtime output prediction ort_inputs = {ort_session.get_inputs[0].name: to_numpy(input_x)} ...
在onnxruntime的C API中处理session的多输入和多输出,可以按照以下步骤进行操作:首先创建输入tensor和输出tensor的数组,并使用API设置它们的形状和数据。接下来,将输入tensor数组作为参数传递给会话的Run方法,同时传递输出tensor数组作为返回结果。运行会话后,可以依次访问输出tensor数组中的每个输出tensor,并获取其数据。这...
auto tensor_info_output0 = type_info_output0.GetTensorTypeAndShapeInfo(); _outputNodeDataType = tensor_info_output0.GetElementType(); _outputTensorShape = tensor_info_output0.GetShape(); //_outputMaskNodeDataType = tensor_info_output1.GetElementType(); //the same as output0 ...
*https://github.com/microsoft/onnxruntime/blob/rel-1.6.0/include/onnxruntime/core/session/onnxruntime_c_api.h#L93 * @param os * @param type * @return std::ostream&*/std::ostream&operator<<(std::ostream&os,constONNXTensorElementDataType&type) ...