所以我不用custom 符号，而是用这个opset19支持的算子。 结果export onnx失败，是为什么呢？
import pycuda.autoinit import tensorrt as trt import sys, os sys.path.insert(1, os.path.join(sys.path[0], "..")) import common import onnxruntime import dataset def runTensorRTModel(onnx_path='./new_crnn.onnx', engine_path = './new_crnn.engine', image_path = './data/demo....
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator - onnxruntime/include/onnxruntime/core/session/onnxruntime_c_api.h at main · microsoft/onnxruntime
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int trt_int8_enable; // enable TensorRT INT8 precision. Default 0 = false, nonzero = true const char* trt_int8_calibration_table_name; // TensorRT INT8 calibration table name. int trt_int8_use_native_calibration_table; // use native TensorRT generated calibration table. Defau...
TRT Python推理流程 下面将介绍如何使用TRT进行Python推理的流程。首先,我们需要安装TensorRT,并导入必要的库。 importtensorrtastrtimportpycuda.driverascudaimportpycuda.autoinitimportnumpyasnpimportcv2 1. 2. 3. 4. 5. 接下来,我们需要加载并解析模型。TRT支持多种模型格式,包括TensorFlow、PyTorch和ONNX等。在此...
cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out make -j$(nproc) 不要以为万事大吉,接下来会有一大堆奇怪的报错,报错1: 解决办法: 第一步:首先,手动将所需要的这个包给下载下来,放到指定文件夹中: 对应的下载地址为:https://github.com/google/protobuf/releases/download/v3.0....
x = self.conv(x) return x #把x差值变大到y的大小,再相加 def _upsample_add(self, x, y): return F.interpolate(x, size=y.size()[2:],mode='bilinear',align_corners=True) + y 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ...
if(NOT ONNX_NAMESPACE) set(ONNX_NAMESPACE "onnx2trt_onnx") endif() add_definitions("-DONNX_NAMESPACE=${ONNX_NAMESPACE}") add_subdirectory(third_party/onnx EXCLUDE_FROM_ALL) endif() # TensorRT find_path(TENSORRT_INCLUDE_DIR NvInfer.h HINTS ${TENSORRT_ROOT} ${CUDA_TOOLKIT...
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