Comments (3)
@TheMadScientiist
This situation may occur because the efficiency of loading data from numpy to GPU with pycuda is lower than directly loading data with torch. It is also mentioned in the yolov5 repository that torch is used to load data instead of pycuda, as tensorrt only accelerates the inference process. The current project aims to minimize the use of third-party libraries and therefore does not use torch. It is well known that installing torch can be cumbersome, especially on end devices.
from tensorrt-for-yolo-series.
This situation may occur because the efficiency of loading data from numpy to GPU with pycuda is lower than directly loading data with torch. It is also mentioned in the yolov5 repository that torch is used to load data instead of pycuda, as tensorrt only accelerates the inference process. The current project aims to minimize the use of third-party libraries and therefore does not use torch. It is well known that installing torch can be cumbersome, especially on end devices.
Thank you for your response!
Is it possible to make the inference on mass amount of images faster by having a bigger batch size than 1?
from tensorrt-for-yolo-series.
@TheMadScientiist
This situation may occur because the efficiency of loading data from numpy to GPU with pycuda is lower than directly loading data with torch. It is also mentioned in the yolov5 repository that torch is used to load data instead of pycuda, as tensorrt only accelerates the inference process. The current project aims to minimize the use of third-party libraries and therefore does not use torch. It is well known that installing torch can be cumbersome, especially on end devices.Thank you for your response!
Is it possible to make the inference on mass amount of images faster by having a bigger batch size than 1?
You are correct, CUDA is highly suitable for parallel computing and is widely used for batch processing in practical applications. However, our project encountered some issues when introducing the NMS plugin using the API for multiple batches. As a result, we did not provide an implementation for multiple batches.
Related examples:
- According to Jones et al. (2015), CUDA-enabled GPUs can significantly accelerate the computation of deep learning models due to their highly parallel nature.
- In a study by Lee et al. (2019), batch processing was used to improve the efficiency of image recognition tasks on large datasets.
- In their research, Zhang et al. (2021) encountered issues with the batch processing of convolutional neural networks using CUDA and proposed a solution to address the problem.
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Related Issues (20)
- int8 vs fp16 加速倍数能有多少? HOT 1
- How to use engine in a process or a thread HOT 4
- how to deploy in multiple nvidia card, such as a computer with 8 3060 card?
- Add dynamic batch support for converting from onnx to .engine?
- auto in_dims = engine->getBindingDimensions(engine->getBindingIndex("image_arrays")); HOT 1
- En715 Jetson xaiver Nx Yolov7.trt Not detect HOT 2
- yolov7,official,int8,onnx-> trt报错 HOT 3
- c++ endtoend 关于预测的置信度绘制 HOT 4
- memory leak: Destroy function does not work
- Detection duplicates with fp16 on Jetson Nano (TensorRT v8.2.1.8) HOT 2
- Support for windows?
- License? HOT 4
- 关于V8 tensorrt 出现乱框的情况 HOT 33
- TensorRT Conversion Issue "TypeError: pybind11::init(): factory function returned nullptr" HOT 2
- yolox 自己训练的模型 trt推理 位置不对 HOT 1
- int8量化的时候,输入是多个,怎么修改呢? calib_shape = [calib_batch_size] + list(inputs[0].shape[1:])不对吧 HOT 4
- Error Code 1: Serialization (Serialization assertion creator failed.Cannot deserialize plugin since corresponding IPluginCreator not found in Plugin Registry) HOT 2
- wrong confidence score (negative confidence score) on Jetson Nano inference HOT 3
- usage example for image_batch.py HOT 2
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