5. 准备BufferinputHost = np.ascontiguousarray(inputData.reshape(-1)) outputHost = np.empty(context.get_tensor_shape(iTensorName[i]),trt.nptype(engine.get_tensor_dtype(iTensorName[1]))) inputDevice = cudart.cudaMalloc(inputHost.nbytes)[1] outputDevice = cudart.cudaMalloc(outputHost.nbytes)...
output_type = engine.get_tensor_dtype(output_name) output_shape = engine.get_tensor_shape(output_name) context = engine.create_execution_context() context.set_input_shape(input_name, [nB, nC, nH, nW]) _, stream = cudart.cudaStreamCreate() inputH0 = np.ascontiguousarray(data.reshape(-1...
is_input = engine.get_tensor_mode(name) == trt.TensorIOMode.INPUT dtype = np.dtype(trt.nptype(engine.get_tensor_dtype(name))) shape =self.context.get_tensor_shape(name)ifis_inputandshape[0] <0:assertengine.num_optimization_profiles >0,'Engine dynamic axes but no optimization profiles '...
context = engine.create_execution_context() #常见的方法 context.set_input_shape(lTensorName[0], [1, 1, nHeight, nWidth]) #设置输入张量的形状 context.get_tensor_shape(lTensorName[i]) #得到输出张量的形状 context.set_tensor_address(lTensorName[i], int(bufferD[i])) #设置tensor的地址 ...
Dims getTensorShape (char const *tensorName) const noexcept Return the shape of the given input or output. More... bool allInputDimensionsSpecified () const noexcept Whether all dynamic dimensions of input tensors have been specified. More... TRT_DEPRECATED bool allInputShapesSpecified () const...
Dims getTensorShape (char const *tensorName) const noexcept Get shape of an input or output tensor. More... DataType getTensorDataType (char const *tensorName) const noexcept Determine the required data type for a buffer from its tensor name. More... int32_t getNbLayers () const noexcep...
TF32:第三代Tensor Core支持的一种数据类型,是一种截短的 Float32 数据格式,将FP32中23个尾数位截短为10bits,而指数位仍为8bits,总长度为19(=1+8 +10)。保持了与FP16同样的精度(尾数位都是 10 位),同时还保持了FP32的动态范围指数位都是8位); ...
假设有一个很简单的模型,模型只有唯一的一层conv2d :kernel-size=2*2;stride=1;假设输入是shape为[1,1,4,4]的全1数据,那么输出就是shape为[1,1,3,3]的tensor,且数值都为4。 完整的demo代码如下: #include "NvInfer.h" #include <iostream> ...
// Creat the engine using only the API and not any parser.ICudaEngine*createMNISTEngine(unsigned int maxBatchSize,IBuilder*builder,DataType dt){INetworkDefinition*network=builder->createNetwork();// Create input tensor of shape { 1, 1, 28, 28 } with name INPUT_BLOB_NAMEITensor*data=networ...
conv1 = network.add_convolution(input=input_tensor, num_output_maps=20, kernel_shape=(5, 5), kernel=conv1_w, bias=conv1_b) conv1.stride = (1, 1) 添加一个池化层,指定输入(前一个卷积层的输出)、池化类型、窗口大小和步幅: pool1 = network.add_pooling(input=conv1.get_output(0), type...