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})(window)</script><meta name="generator" content="Hexo 6.3.0"><link href="https://cdn.bootcss.com/KaTeX/0.11.1/katex.min.css" rel="stylesheet" /></head><body><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="avatar-img is-center"><img src="/img/avatar.jpg" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="sidebar-site-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">95</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">118</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">37</div></a></div><hr/><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div></div></div></div><div class="page" id="body-wrap"><header class="full_page" id="page-header" style="background: linear-gradient(20deg, #0062be, #925696, #cc426e, #fb0347)"><nav id="nav"><span id="blog_name"><a id="site-name" href="/">文羊羽</a></span><div id="menus"><div id="search-button"><a class="site-page social-icon search"><i class="fas fa-search fa-fw"></i><span> 搜索</span></a></div><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> 首页</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> 归档</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> 标签</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> 分类</span></a></div></div><div id="toggle-menu"><a class="site-page"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">文羊羽</h1><div id="site-subtitle"><span id="subtitle"></span></div><div id="site_social_icons"><a class="social-icon" href="https://github.com/whut-zhangwx" target="_blank" title="Github"><i class="fab fa-github"></i></a></div></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/IntermediateLayerGetter/" title="IntermediateLayerGetter 获取中间结果">IntermediateLayerGetter 获取中间结果</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-02-02T16:00:00.000Z" title="发表于 2025-02-03 00:00:00">2025-02-03</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/deep-learning/">deep learning</a></span></div><div class="content">获取中间结果
利用 IntermediateLayerGetter 方法可以非常方便的获取中间结果，例如获取 resnet 的中间结果——features，这一点很有用，因为很多模型都会使用 resnet 作为 backbone 来提取 features。
123456789101112131415161718192021import torchfrom torchvision.models import resnet18from torchvision.models._utils import IntermediateLayerGetterinput = torch.rand(size=(1, 3, 224, 224))model = resnet18()output = model(input)print(output.shape)# torch.Size([1, 1000])'''extract layer1, layer2, layer3 and layer4, giving as names feat1, feat2, feat3 and fea ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-packing-and-unpacking/" title="Python 包裹传递和解包传递">Python 包裹传递和解包传递</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-01-11T16:00:00.000Z" title="发表于 2025-01-12 00:00:00">2025-01-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">简介
在 Python 中，参数传递方式有两种主要形式：位置参数和关键字参数。通过使用包裹传递（packing）和解包传递（unpacking），可以灵活地处理任意数量的参数。
单星号 *
单星号 * 用于处理位置参数的打包和解包。
单星号打包
单星号 * 打包会将多个位置参数打包成一个 tuple 对象
123456def sum_all(*args): print(f"{args = }, {type(args) = }") return sum(args)result = sum_all(1, 2, 3, 4)# args = (1, 2, 3, 4), type(args) = <class 'tuple'>
单星号解包
单星号 * 解包用于将一个可迭代对象解包为位置参数
123456def add(a, b, c): return a + b + cdata = [1, 2, 3]print(*data) # 1 2 3result = add(*data) # add(1, 2 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/resnet-implement-of-pytorch/" title="Pytorch 对 ResNet 的实现">Pytorch 对 ResNet 的实现</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2025-01-02T16:00:00.000Z" title="发表于 2025-01-03 00:00:00">2025-01-03</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/deep-learning/">deep learning</a></span></div><div class="content">参考
pytorch vision resnet
resnet.py | pytorch github
借助 torch hub
可以直接借助 torch hub pytorch vision resnet 来加载预训练的 resnet。
12345678import torchmodel = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)'''or any of these variants'''# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet34', pretrained=True)# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)# mod ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-formatted-output/" title="Python 标准化输出">Python 标准化输出</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-12T16:00:00.000Z" title="发表于 2024-12-13 00:00:00">2024-12-13</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">Document
Input and Output - formatted-string-literals | python docs
Python format 格式化函数 | 菜鸟教程
使用 f-strings
f-strings 是 Python 从 3.6 版本开始引入的一种字符串格式化方式，其全称为“格式化字符串字面量”。f-strings 使用更加简洁的语法，并且具有更高的可读性和性能。
f-strings 的语法使得可以直接在字符串中使用 {} 嵌入变量和python表达式，并允许在 : 后添加格式化字符说明。
123name, age, height = "zhang", 18, 1.7print(f"My name is {name}, I'm {age} years old and {height:.2f} meter tall.")# My name is zhang, I'm 18 years old and 1.70 me ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/evaluation-metrics-for-segmentation/" title="图像分割的评价指标">图像分割的评价指标</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-11T16:00:00.000Z" title="发表于 2024-12-12 00:00:00">2024-12-12</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/deep-learning/">deep learning</a></span></div><div class="content">TP TN FP FN
The following are the 4 basic terminologies you need to know.
True Positives (TP): when the actual value is Positive and predicted is also Positive.
True Negatives (TN): when the actual value is Negative and prediction is also Negative.
False Positives (FP): When the actual is negative but prediction is Positive. Also known as the Type 1 error.
False Negatives (FN): When the actual is Positive but the prediction is Negative. Also known as the Type 2 error.
OA, Overall Accuracy, ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-configuration/" title="Python Config">Python Config</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-08T16:00:00.000Z" title="发表于 2024-12-09 00:00:00">2024-12-09</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a><i class="fas fa-angle-right article-meta-link"></i><a class="article-meta__categories" href="/categories/python/deep-learning/">deep learning</a></span></div><div class="content">简介
使用 Python 编写一些深度学习算法时，经常会涉及到大量超参数的配置。通过 Python 的两个标准库，ml_collections 和 dataclasses，可以实现对这些配置进行管理。
ml_collections
ml_collections 是一个 Python 库，主要用于管理实验配置，其中的 ConfigDict 是一个核心工具。ConfigDict 提供了一种结构化的、易于管理的方式来定义和操作配置。它类似于 Python 的字典，但增强了功能，以便更方便地进行深度学习实验或其他场景中的配置管理。
1234567891011121314from ml_collections import ConfigDictdef get_config() -> ConfigDict: config = ConfigDict() config.model = ConfigDict() config.model.type = 'ResNet' config.model.num_layers = 50 config.optimizer = &# ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-datetime/" title="Python 格式化时间">Python 格式化时间</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-08T16:00:00.000Z" title="发表于 2024-12-09 00:00:00">2024-12-09</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">datetime
在 Python 中使用 datetime 库可以获取当前时间
12345678from datetime import datetime# 获取当前时间now = datetime.now()# 格式化时间为指定格式formatted_time = now.strftime("%Y-%m-%d %H:%M:%S %p")print(formatted_time) # 2024-12-09 20:34:41 PM
其中各个字母的含义如下
%Y - 年份（四位数，例如 2024）
%m - 月份（两位数，例如 01 到 12）
%d - 日期（两位数，例如 01 到 31）
%H - 小时（24 小时制，两位数，例如 00 到 23）
%I - 小时（12 小时制，两位数，例如 01 到 12）
%M - 分钟（两位数，例如 00 到 59）
%S - 秒数（两位数，例如 00 到 59）
%p - 上下午标识（AM 或 PM）
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-logging/" title="Python Logging">Python Logging</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-05T16:00:00.000Z" title="发表于 2024-12-06 00:00:00">2024-12-06</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">参考
logging — Python 的日志记录工具{target="_blank"}
通过 logging 调用
123456789101112131415161718192021222324import loggingdef main() -> None: # logging.basicConfig(level=logging.DEBUG) logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S") # logging.basicConfig(level=logging.WARNING) logging.debug("This is a debug message.") logging.info("This is a in ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/python-type-hints/" title="Python 类型提示">Python 类型提示</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-12-05T16:00:00.000Z" title="发表于 2024-12-06 00:00:00">2024-12-06</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/python/">python</a></span></div><div class="content">简介
Python 的类型提示（Type Hints）是在 Python 3.5 中正式引入的，作为 PEP 484 – Type Hints 的一部分。它是一种在代码中显式声明变量、参数、返回值等的类型信息的方式。它对代码运行没有实际影响，只是作为一种可选的静态类型标注，意在为开发者提供变量、参数和返回值的类型信息，方便代码阅读。
可以把类型提示当作一种注释，就是告诉开发者这个变量“应该”是提示的类型，但是 python 编译器本身不做类型检查，即便代码运行时这个变量不是标注的类型，也不会有任何提示。
普通变量的类型提示
在创建变量时可以进行类型提示
123x: int = 1y: float = 1.0z: str = "hello"
还可以对一些自定义的数据类型进行提示
1234import numpyimport torchx: numpy.ndarray = numpy.array([1, 2, 3])y: torch.tensor = torch.tensor([1, 2, 3])
函数参数和返回值类型
在函数定义中，可以为参数和返回值添加类型提示：
1 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/should-image-shape-be-CxHxW-or-HxWxC/" title="关于image的shape是CxHxW还是HxWxC">关于image的shape是CxHxW还是HxWxC</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">发表于</span><time datetime="2024-11-28T16:00:00.000Z" title="发表于 2024-11-29 00:00:00">2024-11-29</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/deep-learing/">deep learing</a></span></div><div class="content">H×W×C Or C×H×W
123456789101112131415from PIL import Imageimport numpy as npfrom torchvision import transformsimage_path = r"path/to/image"mask_path = r"path/to/mask"img = Image.open(image_path) # size: (width, height)mask = Image.open(mask_path) # size: (width, height)np_img = np.array(img) # shape: (height, width, 3)np_mask = np.array(img) # shape: (height, width, 3)ts_img = transforms.PILToTensor()(img) # shape: torch.Size([3, height, width])ts_mask = transforms.PILToTensor ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">…</span><a class="page-number" href="/page/10/#content-inner">10</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">文羊羽</div><div class="author-info__description"></div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">95</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">118</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">37</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://www.zhihu.com/people/onexiaophai"><i class="fa-brands fa-zhihu"></i><span></span></a><div class="card-info-social-icons is-center"><a class="social-icon" href="https://github.com/whut-zhangwx" target="_blank" title="Github"><i class="fab fa-github"></i></a></div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/IntermediateLayerGetter/" title="IntermediateLayerGetter 获取中间结果">IntermediateLayerGetter 获取中间结果</a><time datetime="2025-02-02T16:00:00.000Z" title="发表于 2025-02-03 00:00:00">2025-02-03</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/python-packing-and-unpacking/" title="Python 包裹传递和解包传递">Python 包裹传递和解包传递</a><time datetime="2025-01-11T16:00:00.000Z" title="发表于 2025-01-12 00:00:00">2025-01-12</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/resnet-implement-of-pytorch/" title="Pytorch 对 ResNet 的实现">Pytorch 对 ResNet 的实现</a><time datetime="2025-01-02T16:00:00.000Z" title="发表于 2025-01-03 00:00:00">2025-01-03</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/python-formatted-output/" title="Python 标准化输出">Python 标准化输出</a><time datetime="2024-12-12T16:00:00.000Z" title="发表于 2024-12-13 00:00:00">2024-12-13</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/evaluation-metrics-for-segmentation/" title="图像分割的评价指标">图像分割的评价指标</a><time datetime="2024-12-11T16:00:00.000Z" title="发表于 2024-12-12 00:00:00">2024-12-12</time></div></div></div></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">95</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2025-02-04T07:39:02.501Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总字数 :</div><div class="item-count">353.6k</div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-02-04T07:39:02.684Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer"><div id="footer-wrap"><div class="copyright">©2020 - 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