- pytorch判断NaN You can always leverage the fact that nan != nan: data = torch.tensor([1, 2, np.nan])tensor([ 1., 2., nan.])data[data != data]tensor([ 0, 0, 1], ... pytorch判断NaN You can always leverage the fact that nan != nan: data = torch.tensor([1, 2, np.nan])tensor([ 1., 2., nan.])data[data != data]tensor([ 0, 0, 1], ...
- https://nvidia.app.box.com/v/torch-stable-cp36-jetson-jp42 1.sudo apt-get install python3-pip 2.sudo apt install liblapacke-dev checkinstall # For OpenCV 3.sudo apt-get install l... https://nvidia.app.box.com/v/torch-stable-cp36-jetson-jp42 1.sudo apt-get install python3-pip 2.sudo apt install liblapacke-dev checkinstall # For OpenCV 3.sudo apt-get install l...
- nn.Module中定义参数:不需要加cuda,可以求导,反向传播 class BiFPN(nn.Module): def __init__(self, fpn_sizes): self.w1 = nn.Parameter(torch.rand(1)) print("no--... nn.Module中定义参数:不需要加cuda,可以求导,反向传播 class BiFPN(nn.Module): def __init__(self, fpn_sizes): self.w1 = nn.Parameter(torch.rand(1)) print("no--...
- pytorch 筛选数据(使用与或) import torch x = torch.linspace(1, 8, steps=8).view(4, 2) print(x) area1=(x[:,0]>5.5)&(x[:,1]>5.5) c=x[:,0]*x[:,1] area2=c>25 area=area1|area2 p... pytorch 筛选数据(使用与或) import torch x = torch.linspace(1, 8, steps=8).view(4, 2) print(x) area1=(x[:,0]>5.5)&(x[:,1]>5.5) c=x[:,0]*x[:,1] area2=c>25 area=area1|area2 p...
- 最简单的: state_dict = torch.load(weight_path) self.load_state_dict(state_dict,strict=False) 加载cpu: model = IResNet(IBasicBlock, [2, 2, 2, 2]) a_path=r"ms1mv3_ar... 最简单的: state_dict = torch.load(weight_path) self.load_state_dict(state_dict,strict=False) 加载cpu: model = IResNet(IBasicBlock, [2, 2, 2, 2]) a_path=r"ms1mv3_ar...
- 原文:https://github.com/jxgu1016/MNIST_center_loss_pytorch c++不知道什么框架的: https://github.com/BOBrown/SSD-Centerloss # coding: utf8import torchfrom torch.autograd import Varia... 原文:https://github.com/jxgu1016/MNIST_center_loss_pytorch c++不知道什么框架的: https://github.com/BOBrown/SSD-Centerloss # coding: utf8import torchfrom torch.autograd import Varia...
- 编译 FFWM时,报错了,只支持vs2013-2017之间的版本 换到vs2015后,报错: You need C++14 to compile PyTorch windows还没找到方案, liunx解决方法; https://stackoverflow.com/questions/3... 编译 FFWM时,报错了,只支持vs2013-2017之间的版本 换到vs2015后,报错: You need C++14 to compile PyTorch windows还没找到方案, liunx解决方法; https://stackoverflow.com/questions/3...
- class ResNet(nn.Module): def __init__(self, block, layers, use_se=True): self.inplanes = 64 self.use_se = use_se super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel... class ResNet(nn.Module): def __init__(self, block, layers, use_se=True): self.inplanes = 64 self.use_se = use_se super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel...
- pytorch方法测试——卷积(二维) 测试代码: import torchimport torch.nn as nn m = nn.Conv2d(2, 2, 3, stride=2)input = torch.randn(1, 2, 5, 7)output = m(input) print("输入图片(2张):")print(input)print("卷积的权重:")p... pytorch方法测试——卷积(二维) 测试代码: import torchimport torch.nn as nn m = nn.Conv2d(2, 2, 3, stride=2)input = torch.randn(1, 2, 5, 7)output = m(input) print("输入图片(2张):")print(input)print("卷积的权重:")p...
- import torch def intersect(box_a, box_b): """ We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then... import torch def intersect(box_a, box_b): """ We resize both tensors to [A,B,2] without new malloc: [A,2] -> [A,1,2] -> [A,B,2] [B,2] -> [1,B,2] -> [A,B,2] Then...
- pytorch 多GPU训练 pytorch多GPU最终还是没搞通,可用的部分是前向计算,back propagation会出错,当时运行通过,也不太确定是如何通过了的。目前是这样,有机会再来补充 pytorch支持多GPU训练,官方文档(pytorch 0.30)给了一些说明:pytorch数据并行,但遗憾的是给出的说明并不详细。不过说的还是蛮清楚的,建... pytorch 多GPU训练 pytorch多GPU最终还是没搞通,可用的部分是前向计算,back propagation会出错,当时运行通过,也不太确定是如何通过了的。目前是这样,有机会再来补充 pytorch支持多GPU训练,官方文档(pytorch 0.30)给了一些说明:pytorch数据并行,但遗憾的是给出的说明并不详细。不过说的还是蛮清楚的,建...
- import torchimport numpy as npimport mathdata = [-0.9999,-0.5, -0.1, 0.8,0.9]tensor = torch.FloatTensor(data) # 转换成32位浮点 tensor # sin 三角函数 sinprint( '\nsin',torch.log(math.pi- to... import torchimport numpy as npimport mathdata = [-0.9999,-0.5, -0.1, 0.8,0.9]tensor = torch.FloatTensor(data) # 转换成32位浮点 tensor # sin 三角函数 sinprint( '\nsin',torch.log(math.pi- to...
- deep-high-resolution-net.pytorch 1070 100多ms from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function import argparseimp... deep-high-resolution-net.pytorch 1070 100多ms from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function import argparseimp...
- pytorch topk() torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) pytorch中文官网文档:http://www.mamicode.com/info-detail-2217311... pytorch topk() torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) pytorch中文官网文档:http://www.mamicode.com/info-detail-2217311...
- 传统方式需要10s,dat方式需要0.6s import os import time import torch import random from common.coco_dataset import COCODataset def gen_data(batch_size,data_path,target_path): os.maked... 传统方式需要10s,dat方式需要0.6s import os import time import torch import random from common.coco_dataset import COCODataset def gen_data(batch_size,data_path,target_path): os.maked...
上滑加载中
推荐直播
-
基于开源鸿蒙+海思星闪开发板:嵌入式系统开发实战(Day1)
2025/03/29 周六 09:00-18:00
华为开发者布道师
本次为期两天的课程将深入讲解OpenHarmony操作系统及其与星闪技术的结合应用,涵盖WS63E星闪开发板的详细介绍、“OpenHarmony+星闪”的创新实践、实验环境搭建以及编写首个“Hello World”程序等内容,旨在帮助学员全面掌握相关技术并进行实际操作
回顾中 -
基于开源鸿蒙+海思星闪开发板:嵌入式系统开发实战(Day2)
2025/03/30 周日 09:00-12:00
华为开发者布道师
本次为期两天的课程将深入讲解OpenHarmony操作系统及其与星闪技术的结合应用,涵盖WS63E星闪开发板的详细介绍、“OpenHarmony+星闪”的创新实践、实验环境搭建以及编写首个“Hello World”程序等内容,旨在帮助学员全面掌握相关技术并进行实际操作
回顾中 -
从AI基础到昇腾:大模型初探、DeepSeek解析与昇腾入门
2025/04/02 周三 16:00-17:30
不易 / 华为云学堂技术讲师
昇腾是华为研发的AI芯片,其具有哪些能力?我们如何基于其进行开发?本期直播将从AI以及大模型基础知识开始,介绍人工智能核心概念、昇腾AI基础软硬件平台以及昇腾专区,旨在为零基础或入门级学习者搭建从AI基础知识到昇腾技术的完整学习路径。
回顾中
热门标签