YOLOX train训练提示CUDA out of memory 将YOLOX/yolox/exp/yolox_base.py中的 self.data_num_workers = 4 更改为: self.data_num_workers = 0 自己测试的时候,--fp16和-o并没有什么影响。
改的包括标签,改为“person”、类别数量为1,数据集的相关信息,就是源代码里有年份的信息,我们新建的数据集没有,需要改过来... 训练的时候直接在tools/train.py里修改了默认的参数,包括batchsize改为4,(8会报错,两个GPU带不起来,cuda out of memory),权重文件改为yolox_m.pth。 训练时间较长,跑了300个epo...
dataloader_kwargs = {"num_workers": self.data_num_workers,"pin_memory":True} dataloader_kwargs["batch_sampler"] = batch_sampler# Make sure each process has different random seed, especially for 'fork' methoddataloader_kwargs["worker_init_fn"] = worker_init_reset_seed train_loader = DataL...
we use half number of channels to save memory self.weight_level_0 = self._make_cbl(self...
#include<memory> #include <opencv2/opencv.hpp> #include "yolov8_utils.h" #include<onnxruntime_cxx_api.h> //#include <tensorrt_provider_factory.h> //if use OrtTensorRTProviderOptionsV2 //#include <onnxruntime_c_api.h> class Yolov8PoseOnnx { ...
flags.DEFINE_integer('num_classes', 80, 'number of classes in the model') def main(_argv): physical_devices = tf.config.experimental.list_physical_devices('GPU') if len(physical_devices) > 0: tf.config.experimental.set_memory_growth(physical_devices[0], True) ...
经过TensorRT优化后的序列化模型被保存到IHostMemory对象中,我们可以将其保存到磁盘中,下次使用时直接加载这个经过优化的模型即可,这样就可以省去漫长的等待模型优化的过程。我一般习惯把序列化模型保存到一个后缀为.engine的文件中。 nvinfer1::IHostMemory *serialized_model =builder->buildSerializedNetwork(*network,...
There are problems, confusion matrix and PR curve calculation of AP data may be about 1% to 2% error, but we do not know why. If you find out how to improve, welcome to discuss. This modification uses Grad_CAM and metrics.py from YOLOV5 ...
解决方法: 将文件E:\pythonFiles\YOLOX\yolox\evaluators\coco_evaluator.py中270行左右的 try: from yolox.layers import COCOeval_opt as COCOeval except ImportError: from pycocotools.cocoeval import COCOeval logger.warning("Use standard COCOeval.") ...
2.1. Memory Access Cost 对于CNN,能耗在内存访问而不是计算上。影响MAC的主要是是内存占用(intermediate activation memory footprint),它主要受卷积核和feature map大小的影响。c为输入输出通道。 2.2. GPUComputation 通过减少FLOP是来加速的前提是,每个flop point的计算效率是一致的。