{"id":1050,"date":"2026-05-14T17:11:22","date_gmt":"2026-05-14T09:11:22","guid":{"rendered":"https:\/\/www.eutaboo.com\/index.php\/2026\/05\/14\/%e3%80%8annu-net-0%e5%9f%ba%e7%a1%80%e5%85%a5%e9%97%a8%ef%bc%882%ef%bc%89%ef%bc%9a%e5%ae%89%e8%a3%85-nnu-net-v2%ef%bc%8c%e7%8e%af%e5%a2%83%e3%80%81pytorch-%e5%92%8c%e8%b7%af%e5%be%84%e5%8f%98%e9%87%8f\/"},"modified":"2026-05-14T17:11:22","modified_gmt":"2026-05-14T09:11:22","slug":"%e3%80%8annu-net-0%e5%9f%ba%e7%a1%80%e5%85%a5%e9%97%a8%ef%bc%882%ef%bc%89%ef%bc%9a%e5%ae%89%e8%a3%85-nnu-net-v2%ef%bc%8c%e7%8e%af%e5%a2%83%e3%80%81pytorch-%e5%92%8c%e8%b7%af%e5%be%84%e5%8f%98%e9%87%8f","status":"publish","type":"post","link":"https:\/\/www.eutaboo.com\/index.php\/2026\/05\/14\/%e3%80%8annu-net-0%e5%9f%ba%e7%a1%80%e5%85%a5%e9%97%a8%ef%bc%882%ef%bc%89%ef%bc%9a%e5%ae%89%e8%a3%85-nnu-net-v2%ef%bc%8c%e7%8e%af%e5%a2%83%e3%80%81pytorch-%e5%92%8c%e8%b7%af%e5%be%84%e5%8f%98%e9%87%8f\/","title":{"rendered":"\u300annU-Net 0\u57fa\u7840\u5165\u95e8\uff082\uff09\uff1a\u5b89\u88c5 nnU-Net v2\uff0c\u73af\u5883\u3001PyTorch \u548c\u8def\u5f84\u53d8\u91cf\u4e00\u6b21\u8bb2\u6e05\u300b"},"content":{"rendered":"<h2>\u672c\u7bc7\u5b66\u4e60\u76ee\u6807<\/h2>\n<p>\u8fd9\u662f\u300annU-Net 0\u57fa\u7840\u5165\u95e8\u300b\u7cfb\u5217\u7684\u7b2c 2 \u7bc7\u3002\u4e0a\u4e00\u7bc7\u6211\u4eec\u89e3\u91ca\u4e86 nnU-Net v2 \u4e3a\u4ec0\u4e48\u4e0d\u662f\u201c\u4e00\u4e2a U-Net \u7f51\u7edc\u201d\uff0c\u800c\u662f\u4e00\u6574\u5957\u81ea\u52a8\u914d\u7f6e\u533b\u5b66\u56fe\u50cf\u5206\u5272 pipeline\u3002\u672c\u6587\u5f00\u59cb\u8fdb\u5165\u5b9e\u64cd\uff1a\u5b89\u88c5 nnU-Net v2\u3002<\/p>\n<p>\u8bfb\u5b8c\u672c\u6587\uff0c\u4f60\u5e94\u8be5\u80fd\u591f\uff1a<\/p>\n<ol>\n<li>\u7406\u89e3\u4e3a\u4ec0\u4e48\u5b98\u65b9\u5efa\u8bae\u5148\u5b89\u88c5 PyTorch\uff0c\u518d\u5b89\u88c5 nnU-Net v2\u3002<\/li>\n<li>\u77e5\u9053 <code>nnUNet_raw<\/code>\u3001<code>nnUNet_preprocessed<\/code>\u3001<code>nnUNet_results<\/code> \u4e09\u4e2a\u8def\u5f84\u53d8\u91cf\u5206\u522b\u653e\u4ec0\u4e48\u3002<\/li>\n<li>\u5728 Linux \u4e0a\u5b8c\u6210\u4e00\u4e2a\u53ef\u7528\u4e8e\u540e\u7eed\u6559\u7a0b\u7684\u57fa\u7840\u73af\u5883\u914d\u7f6e\u3002<\/li>\n<li>\u7528\u7b80\u5355\u547d\u4ee4\u68c0\u67e5 Python\u3001PyTorch\u3001CUDA \u548c nnU-Net v2 \u662f\u5426\u53ef\u7528\u3002<\/li>\n<\/ol>\n<h2>1. \u5b89\u88c5\u524d\u5148\u7406\u89e3\uff1annU-Net v2 \u4f9d\u8d56\u4ec0\u4e48<\/h2>\n<p>nnU-Net v2 \u662f\u4e00\u4e2a Python \u5305\uff0c\u4f46\u5b83\u4e0d\u662f\u666e\u901a\u7684\u5c0f\u5de5\u5177\u3002\u5b83\u8981\u5b8c\u6210\u533b\u5b66\u56fe\u50cf\u5206\u5272\u8bad\u7ec3\uff0c\u80cc\u540e\u4f9d\u8d56 PyTorch\u3001\u56fe\u50cf\u8bfb\u5199\u3001\u9884\u5904\u7406\u3001\u591a\u8fdb\u7a0b\u6570\u636e\u589e\u5f3a\u3001GPU \u8bad\u7ec3\u548c\u6a21\u578b\u4fdd\u5b58\u3002\u56e0\u6b64\u5b89\u88c5\u65f6\u4e0d\u8981\u53ea\u8bb0\u4e00\u6761 <code>pip install nnunetv2<\/code>\u3002<\/p>\n<p>\u5b98\u65b9\u5b89\u88c5\u6587\u6863\u7ed9\u51fa\u7684\u5173\u952e\u8981\u6c42\u53ef\u4ee5\u6982\u62ec\u4e3a\uff1a<\/p>\n<ul>\n<li>\u4f7f\u7528 <strong>Python 3.10 \u6216\u66f4\u65b0\u7248\u672c<\/strong>\u3002<\/li>\n<li>Linux \u662f\u4e3b\u8981\u76ee\u6807\u5e73\u53f0\uff0cWindows \u548c macOS \u4e5f\u652f\u6301\u3002<\/li>\n<li>\u8bad\u7ec3\u5f3a\u70c8\u5efa\u8bae\u4f7f\u7528 GPU\u3002<\/li>\n<li>\u5fc5\u987b\u5148\u6839\u636e\u4f60\u7684\u786c\u4ef6\u5b89\u88c5 PyTorch\uff0c\u518d\u5b89\u88c5 <code>nnunetv2<\/code>\u3002<\/li>\n<li>\u5fc5\u987b\u8bbe\u7f6e\u4e09\u4e2a\u8def\u5f84\u53d8\u91cf\uff1a<code>nnUNet_raw<\/code>\u3001<code>nnUNet_preprocessed<\/code>\u3001<code>nnUNet_results<\/code>\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u91cc\u7684<strong>\u8def\u5f84\u53d8\u91cf<\/strong>\u6307\u7684\u662f\u73af\u5883\u53d8\u91cf\u3002\u73af\u5883\u53d8\u91cf\u53ef\u4ee5\u7406\u89e3\u4e3a\u5f53\u524d\u7cfb\u7edf\u6216\u7ec8\u7aef\u4f1a\u8bdd\u91cc\u7684\u201c\u5168\u5c40\u914d\u7f6e\u201d\u3002nnU-Net v2 \u901a\u8fc7\u8fd9\u4e09\u4e2a\u53d8\u91cf\u77e5\u9053\uff1a\u539f\u59cb\u6570\u636e\u5728\u54ea\u91cc\u3001\u9884\u5904\u7406\u7ed3\u679c\u653e\u54ea\u91cc\u3001\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u653e\u54ea\u91cc\u3002<\/p>\n<h2>2. \u63a8\u8350\u5b89\u88c5\u6d41\u7a0b\u603b\u89c8<\/h2>\n<p>\u672c\u6587\u4ee5 Linux + NVIDIA GPU \u4e3a\u4e3b\u7ebf\u3002Windows \u548c macOS \u7684\u8def\u5f84\u5199\u6cd5\u7565\u6709\u4e0d\u540c\uff0c\u4f46\u903b\u8f91\u4e00\u81f4\u3002<\/p>\n<pre><code class=\"language-mermaid\">flowchart TD\n    A[\u521b\u5efa Python \u73af\u5883] --> B[\u5b89\u88c5\u5339\u914d\u786c\u4ef6\u7684 PyTorch]\n    B --> C[\u9a8c\u8bc1 torch \u548c CUDA]\n    C --> D[\u5b89\u88c5 nnunetv2]\n    D --> E[\u521b\u5efa\u4e09\u4e2a nnU-Net \u76ee\u5f55]\n    E --> F[\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf]\n    F --> G[\u9a8c\u8bc1 nnU-Net \u547d\u4ee4\u53ef\u7528]\n<\/code><\/pre>\n<p>\u8fd9\u5f20\u56fe\u91cc\u6700\u5bb9\u6613\u51fa\u9519\u7684\u662f\u7b2c 2 \u6b65\u548c\u7b2c 6 \u6b65\uff1aPyTorch \u6ca1\u88c5\u5bf9\uff0c\u540e\u9762\u8bad\u7ec3\u5c31\u7528\u4e0d\u4e86 GPU\uff1b\u8def\u5f84\u53d8\u91cf\u6ca1\u8bbe\u5bf9\uff0cnnU-Net \u4f1a\u627e\u4e0d\u5230\u6570\u636e\u548c\u7ed3\u679c\u76ee\u5f55\u3002<\/p>\n<h2>3. \u521b\u5efa\u4e00\u4e2a\u5e72\u51c0\u7684 Python \u73af\u5883<\/h2>\n<p>\u5f3a\u70c8\u5efa\u8bae\u4e3a nnU-Net v2 \u5355\u72ec\u521b\u5efa\u73af\u5883\uff0c\u907f\u514d\u548c\u5176\u4ed6\u9879\u76ee\u7684 PyTorch\u3001CUDA\u3001numpy\u3001scipy \u7248\u672c\u4e92\u76f8\u5f71\u54cd\u3002\u4f60\u53ef\u4ee5\u7528 conda\uff0c\u4e5f\u53ef\u4ee5\u7528 venv\u3002\u4e0b\u9762\u7ed9\u51fa conda \u793a\u4f8b\uff1a<\/p>\n<pre><code class=\"language-bash\">conda create -n nnunetv2 python=3.10 -y\nconda activate nnunetv2\n\npython --version\npip --version\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u4f7f\u7528 venv\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<pre><code class=\"language-bash\">python3.10 -m venv ~\/venvs\/nnunetv2\nsource ~\/venvs\/nnunetv2\/bin\/activate\n\npython --version\npip --version\n<\/code><\/pre>\n<p>\u8fd9\u91cc\u7684 <code>python --version<\/code> \u81f3\u5c11\u5e94\u8be5\u663e\u793a Python 3.10\u3002\u4e0d\u8981\u5728\u4e00\u4e2a\u5df2\u7ecf\u88c5\u8fc7\u5f88\u591a\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u7684\u65e7\u73af\u5883\u91cc\u76f4\u63a5\u88c5 nnU-Net v2\uff0c\u540e\u7eed\u6392\u9519\u6210\u672c\u4f1a\u5f88\u9ad8\u3002<\/p>\n<h2>4. \u5148\u5b89\u88c5 PyTorch\uff1a\u6309\u4f60\u7684\u786c\u4ef6\u9009\u62e9<\/h2>\n<p>\u5b98\u65b9 nnU-Net \u5b89\u88c5\u9875\u660e\u786e\u8981\u6c42\uff1a\u5148\u5b89\u88c5\u9002\u5408\u4f60\u786c\u4ef6\u7684 PyTorch\uff0c\u518d\u5b89\u88c5 <code>nnunetv2<\/code>\u3002\u539f\u56e0\u5f88\u7b80\u5355\uff1aPyTorch \u7684\u5b89\u88c5\u5305\u548c CUDA\u3001ROCm\u3001CPU \u7248\u672c\u5f3a\u76f8\u5173\uff0c\u4e0d\u80fd\u8ba9\u4e0b\u6e38\u5305\u968f\u4fbf\u66ff\u4f60\u51b3\u5b9a\u3002<\/p>\n<p>\u63a8\u8350\u505a\u6cd5\u662f\u6253\u5f00 PyTorch \u5b98\u65b9\u5b89\u88c5\u9875\u9762\uff0c\u6839\u636e\u81ea\u5df1\u7684\u7cfb\u7edf\u9009\u62e9\uff1a<\/p>\n<ul>\n<li>Linux \/ Windows \/ macOS<\/li>\n<li>pip \u6216 conda<\/li>\n<li>Python<\/li>\n<li>CUDA\u3001ROCm\u3001MPS \u6216 CPU<\/li>\n<\/ul>\n<p>\u4f8b\u5982\uff0c\u5728 PyTorch \u5b98\u7f51\u9009\u62e9\u5668\u4e2d\uff0cLinux + pip + Python + CUDA \u67d0\u4e2a\u7248\u672c\u65f6\uff0c\u4f1a\u7ed9\u51fa\u7c7b\u4f3c\u8fd9\u6837\u7684\u547d\u4ee4\u3002\u8bf7\u4ee5\u5b98\u7f51\u5f53\u524d\u9009\u62e9\u5668\u4e3a\u51c6\uff0c\u4e0d\u8981\u673a\u68b0\u590d\u5236\u65e7\u535a\u5ba2\u91cc\u7684\u547d\u4ee4\uff1a<\/p>\n<pre><code class=\"language-bash\">pip3 install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u6ca1\u6709 NVIDIA GPU\uff0c\u53ea\u662f\u60f3\u5148\u5b66\u4e60\u6570\u636e\u683c\u5f0f\u548c\u547d\u4ee4\uff0c\u53ef\u4ee5\u5b89\u88c5 CPU \u7248\u672c\u3002\u4f46\u8981\u6ce8\u610f\uff1annU-Net v2 \u7684\u771f\u5b9e\u8bad\u7ec3\u975e\u5e38\u4f9d\u8d56 GPU\uff0cCPU \u8bad\u7ec3\u901a\u5e38\u4e0d\u9002\u5408\u5b9e\u9645\u9879\u76ee\u3002<\/p>\n<p>\u5b89\u88c5\u540e\u5148\u9a8c\u8bc1 PyTorch\uff1a<\/p>\n<pre><code class=\"language-bash\">python - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda available:\", torch.cuda.is_available())\nif torch.cuda.is_available():\n    print(\"gpu:\", torch.cuda.get_device_name(0))\nPY\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u671f\u671b\u4f7f\u7528 NVIDIA GPU\uff0c\u4f46 <code>torch.cuda.is_available()<\/code> \u8f93\u51fa <code>False<\/code>\uff0c\u5148\u4e0d\u8981\u7ee7\u7eed\u5b89\u88c5 nnU-Net\u3002\u5e94\u5148\u68c0\u67e5 GPU \u9a71\u52a8\u3001CUDA \u7248\u672c\u548c PyTorch \u5b89\u88c5\u547d\u4ee4\u662f\u5426\u5339\u914d\u3002<\/p>\n<h2>5. \u5b89\u88c5 nnU-Net v2\uff1a\u666e\u901a\u4f7f\u7528\u4e0e\u5f00\u53d1\u6a21\u5f0f<\/h2>\n<p>\u5982\u679c\u4f60\u53ea\u662f\u60f3\u4f7f\u7528 nnU-Net v2 \u8dd1\u5b9e\u9a8c\uff0c\u5b98\u65b9\u7ed9\u51fa\u7684\u666e\u901a\u5b89\u88c5\u65b9\u5f0f\u662f\uff1a<\/p>\n<pre><code class=\"language-bash\">pip install nnunetv2\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u540e\u9762\u51c6\u5907\u8ddf\u7740\u672c\u7cfb\u5217\u4fee\u6539 Trainer\u3001loss\u3001augmentation \u6216 network architecture\uff0c\u5efa\u8bae\u4f7f\u7528\u53ef\u7f16\u8f91\u5b89\u88c5\u3002<strong>editable install<\/strong> \u6307\u7684\u662f\uff1a\u4f60\u4ece GitHub \u514b\u9686\u6e90\u7801\uff0c\u7136\u540e\u7528 <code>pip install -e .<\/code> \u5b89\u88c5\u3002\u8fd9\u6837\u4f60\u4fee\u6539\u672c\u5730\u6e90\u7801\u540e\uff0cPython \u73af\u5883\u4f1a\u76f4\u63a5\u4f7f\u7528\u4fee\u6539\u540e\u7684\u4ee3\u7801\u3002<\/p>\n<pre><code class=\"language-bash\">git clone https:\/\/github.com\/MIC-DKFZ\/nnUNet.git\ncd nnUNet\npip install -e .\n<\/code><\/pre>\n<p>\u4e24\u79cd\u65b9\u5f0f\u600e\u4e48\u9009\uff1f\u53ef\u4ee5\u53c2\u8003\u4e0b\u8868\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<th>\u5b89\u88c5\u65b9\u5f0f<\/th>\n<th>\u9002\u5408\u4eba\u7fa4<\/th>\n<th>\u4f18\u70b9<\/th>\n<th>\u6ce8\u610f\u70b9<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>pip install nnunetv2<\/code><\/td>\n<td>\u53ea\u60f3\u8dd1\u901a\u8bad\u7ec3\u548c\u63a8\u7406<\/td>\n<td>\u7b80\u5355\u3001\u5e72\u51c0<\/td>\n<td>\u4e0d\u65b9\u4fbf\u6539\u6e90\u7801<\/td>\n<\/tr>\n<tr>\n<td><code>pip install -e .<\/code><\/td>\n<td>\u51c6\u5907\u5b66\u4e60\u5185\u90e8\u6846\u67b6\u548c\u81ea\u5b9a\u4e49\u6a21\u5757<\/td>\n<td>\u9002\u5408\u8c03\u8bd5\u548c\u4e8c\u6b21\u5f00\u53d1<\/td>\n<td>\u9700\u8981\u81ea\u5df1\u7ba1\u7406\u6e90\u7801\u76ee\u5f55<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>6. \u521b\u5efa\u4e09\u4e2a\u6838\u5fc3\u76ee\u5f55<\/h2>\n<p>nnU-Net v2 \u4e0d\u4f1a\u628a\u6240\u6709\u4e1c\u897f\u90fd\u585e\u8fdb\u5f53\u524d\u9879\u76ee\u76ee\u5f55\uff0c\u800c\u662f\u4f9d\u8d56\u4e09\u4e2a\u56fa\u5b9a\u542b\u4e49\u7684\u5b58\u50a8\u4f4d\u7f6e\u3002<\/p>\n<table>\n<thead>\n<tr>\n<th>\u73af\u5883\u53d8\u91cf<\/th>\n<th>\u4f5c\u7528<\/th>\n<th>\u91cc\u9762\u901a\u5e38\u6709\u4ec0\u4e48<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>nnUNet_raw<\/code><\/td>\n<td>\u4fdd\u5b58\u539f\u59cb\u6570\u636e\u96c6\uff0c\u4e14\u5fc5\u987b\u7b26\u5408 nnU-Net \u6570\u636e\u96c6\u683c\u5f0f<\/td>\n<td><code>Dataset001_MyDataset<\/code>\u3001<code>imagesTr<\/code>\u3001<code>labelsTr<\/code>\u3001<code>dataset.json<\/code><\/td>\n<\/tr>\n<tr>\n<td><code>nnUNet_preprocessed<\/code><\/td>\n<td>\u4fdd\u5b58\u9884\u5904\u7406\u540e\u7684\u8bad\u7ec3\u6570\u636e<\/td>\n<td>\u91cd\u91c7\u6837\u3001\u5f52\u4e00\u5316\u3001\u88c1\u526a\u4e4b\u540e\u7684\u4e2d\u95f4\u7ed3\u679c\u548c plans<\/td>\n<\/tr>\n<tr>\n<td><code>nnUNet_results<\/code><\/td>\n<td>\u4fdd\u5b58\u8bad\u7ec3\u7ed3\u679c\u548c\u6a21\u578b<\/td>\n<td>checkpoint\u3001\u65e5\u5fd7\u3001fold \u7ed3\u679c\u3001\u5b89\u88c5\u7684\u9884\u8bad\u7ec3\u6a21\u578b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u4f60\u53ef\u4ee5\u628a\u5b83\u4eec\u653e\u5728\u5927\u5bb9\u91cf\u786c\u76d8\u4e0a\uff0c\u4f8b\u5982\uff1a<\/p>\n<pre><code class=\"language-bash\">mkdir -p \/data\/nnUNet_raw\nmkdir -p \/data\/nnUNet_preprocessed\nmkdir -p \/data\/nnUNet_results\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u53ea\u662f\u672c\u673a\u5b66\u4e60\uff0c\u4e5f\u53ef\u4ee5\u653e\u5728\u5bb6\u76ee\u5f55\u4e0b\uff1a<\/p>\n<pre><code class=\"language-bash\">mkdir -p ~\/nnUNet_raw\nmkdir -p ~\/nnUNet_preprocessed\nmkdir -p ~\/nnUNet_results\n<\/code><\/pre>\n<h2>7. \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf<\/h2>\n<p>\u5728 Linux \u6216 macOS \u4e2d\uff0c\u53ef\u4ee5\u5148\u4e34\u65f6\u8bbe\u7f6e\uff1a<\/p>\n<pre><code class=\"language-bash\">export nnUNet_raw=\"\/data\/nnUNet_raw\"\nexport nnUNet_preprocessed=\"\/data\/nnUNet_preprocessed\"\nexport nnUNet_results=\"\/data\/nnUNet_results\"\n<\/code><\/pre>\n<p>\u4e34\u65f6\u8bbe\u7f6e\u53ea\u5bf9\u5f53\u524d\u7ec8\u7aef\u6709\u6548\u3002\u5173\u95ed\u7ec8\u7aef\u540e\u5c31\u4f1a\u6d88\u5931\u3002\u66f4\u5e38\u89c1\u7684\u505a\u6cd5\u662f\u5199\u5165 <code>~\/.bashrc<\/code> \u6216 <code>~\/.zshrc<\/code>\uff1a<\/p>\n<pre><code class=\"language-bash\">cat &gt;&gt; ~\/.bashrc &lt;&lt;'EOF'\nexport nnUNet_raw=\"\/data\/nnUNet_raw\"\nexport nnUNet_preprocessed=\"\/data\/nnUNet_preprocessed\"\nexport nnUNet_results=\"\/data\/nnUNet_results\"\nEOF\n\nsource ~\/.bashrc\n<\/code><\/pre>\n<p>\u5982\u679c\u4f60\u5b9e\u9645\u4f7f\u7528\u7684\u662f <code>~\/nnUNet_raw<\/code> \u8fd9\u7c7b\u8def\u5f84\uff0c\u5c31\u628a\u4e0a\u9762\u7684 <code>\/data\/...<\/code> \u6539\u6210\u4f60\u81ea\u5df1\u7684\u771f\u5b9e\u8def\u5f84\u3002<\/p>\n<p>Windows PowerShell \u7684\u5199\u6cd5\u4e0d\u540c\uff1a<\/p>\n<pre><code class=\"language-powershell\">$Env:nnUNet_raw = \"C:\/path\/to\/nnUNet_raw\"\n$Env:nnUNet_preprocessed = \"C:\/path\/to\/nnUNet_preprocessed\"\n$Env:nnUNet_results = \"C:\/path\/to\/nnUNet_results\"\n<\/code><\/pre>\n<p>\u672c\u7cfb\u5217\u540e\u7eed\u547d\u4ee4\u4ee5 Linux shell \u4e3a\u4e3b\u3002\u5982\u679c\u4f60\u5728 Windows \u4e0a\u5b66\u4e60\uff0c\u5efa\u8bae\u4f7f\u7528 WSL2 + NVIDIA GPU \u73af\u5883\uff0c\u8def\u5f84\u548c\u547d\u4ee4\u4f1a\u66f4\u63a5\u8fd1 Linux\u3002<\/p>\n<h2>8. \u9a8c\u8bc1\u5b89\u88c5\u662f\u5426\u6210\u529f<\/h2>\n<p>\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u4e0d\u8981\u6025\u7740\u51c6\u5907\u6570\u636e\u96c6\u3002\u5148\u505a\u4e00\u4e2a\u6700\u5c0f\u68c0\u67e5\uff1a<\/p>\n<pre><code class=\"language-bash\">echo \"$nnUNet_raw\"\necho \"$nnUNet_preprocessed\"\necho \"$nnUNet_results\"\n\npython - <<'PY'\nimport torch\nprint(\"torch:\", torch.__version__)\nprint(\"cuda available:\", torch.cuda.is_available())\nPY\n\nnnUNetv2_plan_and_preprocess -h\nnnUNetv2_train -h\nnnUNetv2_predict -h\n<\/code><\/pre>\n<p>\u4f60\u5e0c\u671b\u770b\u5230\uff1a<\/p>\n<ul>\n<li>\u4e09\u4e2a <code>echo<\/code> \u547d\u4ee4\u90fd\u8f93\u51fa\u4e86\u4f60\u914d\u7f6e\u7684\u8def\u5f84\uff0c\u800c\u4e0d\u662f\u7a7a\u884c\u3002<\/li>\n<li><code>torch.cuda.is_available()<\/code> \u5728 GPU \u73af\u5883\u4e2d\u4e3a <code>True<\/code>\u3002<\/li>\n<li><code>nnUNetv2_plan_and_preprocess -h<\/code>\u3001<code>nnUNetv2_train -h<\/code>\u3001<code>nnUNetv2_predict -h<\/code> \u80fd\u6253\u5370\u5e2e\u52a9\u4fe1\u606f\u3002<\/li>\n<\/ul>\n<h2>9. \u5e38\u89c1\u5b89\u88c5\u95ee\u9898<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u73b0\u8c61<\/th>\n<th>\u5e38\u89c1\u539f\u56e0<\/th>\n<th>\u5efa\u8bae\u68c0\u67e5<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>torch.cuda.is_available()<\/code> \u662f <code>False<\/code><\/td>\n<td>PyTorch \u88c5\u6210 CPU \u7248\uff0c\u6216\u9a71\u52a8\/CUDA \u4e0d\u5339\u914d<\/td>\n<td>\u91cd\u65b0\u7528 PyTorch \u5b98\u7f51\u9009\u62e9\u5668\u751f\u6210\u547d\u4ee4\uff1b\u68c0\u67e5 <code>nvidia-smi<\/code><\/td>\n<\/tr>\n<tr>\n<td>\u627e\u4e0d\u5230 <code>nnUNetv2_train<\/code><\/td>\n<td>\u73af\u5883\u672a\u6fc0\u6d3b\uff0c\u6216\u5b89\u88c5\u5931\u8d25<\/td>\n<td>\u786e\u8ba4 <code>conda activate nnunetv2<\/code>\uff1b\u8fd0\u884c <code>pip show nnunetv2<\/code><\/td>\n<\/tr>\n<tr>\n<td>nnU-Net \u62a5\u8def\u5f84\u672a\u8bbe\u7f6e<\/td>\n<td>\u4e09\u4e2a\u73af\u5883\u53d8\u91cf\u4e3a\u7a7a\u6216\u53ea\u5728\u522b\u7684\u7ec8\u7aef\u8bbe\u7f6e\u8fc7<\/td>\n<td>\u8fd0\u884c <code>echo \"$nnUNet_raw\"<\/code> \u7b49\u547d\u4ee4<\/td>\n<\/tr>\n<tr>\n<td>\u9884\u5904\u7406\u6216\u8bad\u7ec3\u65f6\u78c1\u76d8\u7206\u6ee1<\/td>\n<td><code>nnUNet_preprocessed<\/code> \u6216 <code>nnUNet_results<\/code> \u653e\u5728\u5c0f\u78c1\u76d8<\/td>\n<td>\u628a\u4e09\u4e2a\u76ee\u5f55\u653e\u5230\u5927\u5bb9\u91cf\u786c\u76d8\uff1b\u63d0\u524d\u786e\u8ba4\u5269\u4f59\u7a7a\u95f4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>10. \u5b98\u65b9\u8d44\u6599\u5165\u53e3<\/h2>\n<p>\u672c\u6587\u4e3b\u8981\u53c2\u8003\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/github.com\/MIC-DKFZ\/nnUNet\/blob\/master\/documentation\/getting-started\/installation-and-setup.md\" target=\"_blank\"  rel=\"nofollow\" >nnU-Net v2 \u5b98\u65b9\u5b89\u88c5\u4e0e\u8bbe\u7f6e\u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/MIC-DKFZ\/nnUNet\/tree\/master\/documentation\/getting-started\" target=\"_blank\"  rel=\"nofollow\" >nnU-Net Getting Started \u6587\u6863\u76ee\u5f55<\/a><\/li>\n<li><a href=\"https:\/\/pytorch.org\/get-started\/locally\/\" target=\"_blank\"  rel=\"nofollow\" >PyTorch \u5b98\u65b9\u672c\u5730\u5b89\u88c5\u9875\u9762<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/MIC-DKFZ\/nnUNet\" target=\"_blank\"  rel=\"nofollow\" >MIC-DKFZ\/nnUNet \u5b98\u65b9\u4ed3\u5e93<\/a><\/li>\n<\/ul>\n<h2>\u672c\u7bc7\u603b\u7ed3<\/h2>\n<p>\u5b89\u88c5 nnU-Net v2 \u7684\u5173\u952e\u4e0d\u662f\u6b7b\u8bb0\u4e00\u6761 pip \u547d\u4ee4\uff0c\u800c\u662f\u6309\u987a\u5e8f\u5b8c\u6210\uff1a\u521b\u5efa\u5e72\u51c0 Python \u73af\u5883\u3001\u5148\u5b89\u88c5\u5339\u914d\u786c\u4ef6\u7684 PyTorch\u3001\u518d\u5b89\u88c5 <code>nnunetv2<\/code>\u3001\u521b\u5efa\u4e09\u4e2a\u5b58\u50a8\u76ee\u5f55\u3001\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf\uff0c\u6700\u540e\u7528\u5e2e\u52a9\u547d\u4ee4\u548c PyTorch \u68c0\u67e5\u811a\u672c\u786e\u8ba4\u73af\u5883\u53ef\u7528\u3002<\/p>\n<p>\u53ea\u8981\u8fd9\u4e00\u6b65\u6253\u597d\u57fa\u7840\uff0c\u540e\u9762\u51c6\u5907\u6570\u636e\u96c6\u3001\u9884\u5904\u7406\u3001\u8bad\u7ec3\u548c\u63a8\u7406\u90fd\u4f1a\u987a\u5f88\u591a\u3002<\/p>\n<h2>\u4e0b\u4e00\u7bc7\u9884\u544a<\/h2>\n<p>\u4e0b\u4e00\u7bc7\u6211\u4eec\u8fdb\u5165 nnU-Net v2 \u6700\u5bb9\u6613\u8e29\u5751\u7684\u90e8\u5206\uff1a\u6570\u636e\u96c6\u683c\u5f0f\u3002\u6211\u4eec\u4f1a\u4ece <code>imagesTr<\/code>\u3001<code>labelsTr<\/code>\u3001<code>imagesTs<\/code>\u3001case id\u3001\u901a\u9053\u7f16\u53f7\u548c <code>dataset.json<\/code> \u5f00\u59cb\uff0c\u624b\u628a\u624b\u89e3\u91ca\u4e00\u4e2a\u6570\u636e\u96c6\u600e\u6837\u6574\u7406\u6210 nnU-Net \u80fd\u8bc6\u522b\u7684\u683c\u5f0f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u7bc7\u8bb2\u6e05 nnU-Net v2 \u5b89\u88c5\u987a\u5e8f\u3001PyTorch \u9009\u62e9\u3001\u4e09\u4e2a\u6838\u5fc3\u8def\u5f84\u53d8\u91cf\u4ee5\u53ca\u5b89\u88c5\u540e\u7684\u6700\u5c0f\u9a8c\u8bc1\u65b9\u6cd5\u3002<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[83],"tags":[],"class_list":["post-1050","post","type-post","status-publish","format-standard","hentry","category-83"],"views":4,"_links":{"self":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts\/1050","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/comments?post=1050"}],"version-history":[{"count":0,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts\/1050\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/media?parent=1050"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/categories?post=1050"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/tags?post=1050"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}