get_input_tensors() 返回的列表的每个元素都与一个 DPU 运行器输入相对应。每个列表元素都具有若干个类属性,用法如下所示: inputTensors = dpu_runner.get_input_tensors() print(dir(inputTensors[0])这些属性中最实用的有 name、dims 和 dtype: for inputTensor in inputTensors: print(inputTensor.name)...
AippInputShape AippPaddingPara AippResizePara BuildOptions DynamicShapeConfig NativeHandle 模型管家V2接口 Overview 模型编译类 Build CreateModelBuilder 已编译模型类 CreateBuiltModel CheckCompatibility GetInputTensorDescs GetName GetOutputTensorDescs RestoreFromBuffer RestoreFromFile Sa...
AippInputShape AippPaddingPara AippResizePara BuildOptions DynamicShapeConfig NativeHandle 模型管家V2接口 Overview 模型编译类 Build CreateModelBuilder 已编译模型类 CreateBuiltModel CheckCompatibility GetInputTensorDescs GetName GetOutputTensorDescs RestoreFromBuffer RestoreFromFile Sa...
Allocate memory for the input and output tensors. Run inference on the input data. This involves using the TensorFlow Lite API to execute the model. Interpret the output. How do I use the TensorFlow Lite model? You can use TensorFlow Lite models for a variety of activities like network trai...
If you are working with ONNX models, it is important to know how to retrieve the input and output shapes of the model. Using this information, you can prepare input data and process your model's outputs. This tutorial shows how to get the model input and output shapes using ONNX Run...
name: "output" data_type: TYPE_FP32 dims: [ 1, 3 ] } ] instance_group [ { count: 1 kind: KIND_GPU } ] parameters: { key: "FORCE_CPU_ONLY_INPUT_TENSORS" value: {string_value:"no"}} I run the server with tritonserver --model-repository `pwd`/models --model-control-mode=po...
output=tf.add(input1, input2) result=output.eval()printresult Tensorflow的计算必须要在一个Session的上下文中。Session会包含一个计算图,而这个图你添加的Tensors和Operations。当然,你在添加Tensor和Operation的时候,它们都不会立即进行计算,而是等到最后需要计算Session的结果的时候。当Tensorflow之后了计算图中的所...
Sentis runs a model Create input for a model Convert a texture to a tensor Create an engine to run a model Run a model Split inference over multiple frames Use a command buffer Get output from a model Read output asynchronously Use output data Manage memory Use Tensors Profile and optimize...
#The value returned by the constructor represents the output#of the Constant op.matrix1 = tf.constant([[3., 3.]])#Create another Constant that produces a 2x1 matrix.matrix2 = tf.constant([[2.],[2.]])#Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.#The returne...
: tensor_info.GetShape()[j]; dims.emplace_back(dim); } output_dims.emplace_back(dims); } std::vector<Ort::Value> ort_inputs; for (auto i = 0; i < input_dims.size(); i++) { int count = 1; for (auto j = 0; j < input_dims[i].size(); j++) { ...