AI代码解释 RuntimeError: CUDA error: no kernel image is availableforexecution on the device 按照网络上常见的解释就是 torch 和 torchvision 版本不匹配,然而明明已经按照表格对应的版本进行了安装。 4. 解决方法:通过 whl 安装 在下载页面,按照表格对应的版本,分别下载 torch 和 torch vision 的.whl文件到本...
raiseAttributeError(__former_attrs__[attr])AttributeError: module'numpy'has no attribute'int'.`np.int` was a deprecated aliasforthe builtin `int`. To avoid this errorinexisting code, use `int` by itself. Doing this willnotmodify any behaviorandissafe. When replacing `np.int`, you may ...
错误描述:在安装或更新torchvision后,运行时出现版本不兼容的错误提示,如“RuntimeError: CUDA error: no kernel image is available for execution on the device”等。 解决方法: 确认你的CUDA和cuDNN版本与torchvision版本相兼容。你可以查阅PyTorch官方网站的安装指南,找到对应版本的匹配关系。 如果你的CUDA版本过高...
计算机 -> 属性 -> 高级系统设置 -> 环境变量 -> 系统变量(新建或者编辑) 然后就可以在pycharm中使用pip命令,但是还有报错WARNING: pip is configured with locations that require TLS/SSL, however the ssl module in Python is not available. 这是因为conda的环境变量也没有添加 正确应该是: 这样pycharm中...
importtorchimporttorchvisionimportcv2importosimporttimeimportargparseimportnumpyasnp from torchvision.transformsimportToTensor from deep_sort_realtime.deepsort_trackerimportDeepSort from utilsimportconvert_detections,annotate from coco_classesimportCOCO_91_CLASSESparser=argparse.ArgumentParser()parser.add_argument('...
AttributeError: module 'numpy' has no attribute 'int'. `` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing ``, you may wish to use e.g. `64` or `32`...
"numpy", "requests", pytorch_dep, ] 1.4 获取编译扩展信息,然后传递给setup函数,启动编译 if __name__ == "__main__": print(f"Building wheel {package_name}-{version}") write_version_file() with open("README.md") as f: readme = f.read() ...
device ="cuda"if torch.cuda.is_available()else"cpu"kwargs = {'num_workers': 1,'pin_memory':True} if device=='cuda'else{}train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST('/files/', train=True, download=True),batch_size=batch_size_train, **kwargs)test_loader = ...
import torch import torchvision import cv2 import os import time import numpy as np from torchvision.transforms import ToTensor from deep_sort_realtime.deepsort_tracker import DeepSort COCO_91_CLASSES = [ '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'tr...
from engine import train_one_epoch, evaluate import utils def main(): # 在GPU上训练,若无GPU,可选择在CPU上训练 device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # 我们的数据集只有两个类 - 背景和人 num_classes = 2 # 使用我们的数据集和定义的转换 ...