ONNX转FP16 ONNX支持FP32模型转换为FP16模型,接口如下: import onnxmltools from onnxmltools.utils.float16_converter import convert_float_to_float16 # Update the input name and path for your ONNX model input_onnx_model = 'model.onnx' # Change this path to the output name and path for ...
fp16_model = float16_converter.convert_float_to_float16(onnx_model, keep_io_types=False) save_model(_model, os.path.join(dir, "resnet50v1_FP16.onnx")) OpenCV Mat支持float16数据类型(CV_16F),支持ONNX float16模型只需将模型中的float16的tensor转换成OpenCV的float16 Mat即可。 但是,ONNX...
fp16_model = float16_converter.convert_float_to_float16(onnx_model, keep_io_types=False) save_model(_model, os.path.join(dir, "resnet50v1_FP16.onnx")) 1. 2. 3. 4. 5. 6. 7. OpenCV Mat支持float16数据类型(CV_16F),支持ONNX float16模型只需将模型中的float16的tensor转换成OpenCV...
manickavela29 deleted the zip_onnx_fp16 branch June 27, 2024 08:13 yfyeung pushed a commit to yfyeung/icefall that referenced this pull request Aug 9, 2024 Zipformer Onnx FP16 (k2-fsa#1671) … f8c4983 Sign up for free to join this conversation on GitHub. Already have an acco...
d: cd Stable-Diffusion-ONNX-FP16 sd_env\scripts\activate Remember this for whenver you want to use your installation. Let's now get to the fun part and convert some models: mkdir model python conv_sd_to_onnx.py --model_path "stabilityai/stable-diffusion-2-1-base" --output_path "....
使用resnet50模型进行试验。打开bin文件夹，在终端执行命令查看帮助信息。执行命令查看resnet50的性能。执行命令将resnet50转换为fp16格式并保存为resnet50_fp16.trt，查看吞吐量。执行命令将resnet50转换为int8格式并保存为resnet50_int8.trt，再次查看吞吐量。模型Python TRT部署：利用上一部分量化得到...
ONNX转FP16 ONNX支持FP32模型转换为FP16模型,接口如下: import onnxmltools from onnxmltools.utils.float16_converter import convert_float_to_float16 # Update the input name and path for your ONNX model input_onnx_model = 'model.onnx' ...
Part 2: tensorrt fp32 fp16 tutorial Part 3: tensorrt int8 tutorial Code Example include headers #include<assert.h>#include<sys/stat.h>#include#include<iostream>#include<fstream>#include<sstream>#include<iomanip>#include<cmath>#include<algorithm>#include<cuda_runtime_api.h>#include"NvCaffeParse...
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For fp16, you can run trtexec--onnx=/path/to/model.onnx \--maxShapes="input_1:0":16x3x544x960 \--minShapes="input_1:0":1x3x544x960 \--optShapes="input_1:0":8x3x544x960 \--fp16 \--saveEngine=/path/to/save/trt/model.engine The 544x960 can be modified to the actual ...