修改： doc/tutorial/TorchScript/jit.ipynb

doc/.gitignore

@@ -4,4 +4,5 @@ datasets/
 *.pkl
 *.params
 *.pth
-*.onnx
+*.onnx
+data/

doc/recipes/activation/index.md

@@ -1,6 +1,7 @@
 # 激活函数
 
 ```{toctree}
-tanh
+sigmoid
 silu
+tanh
 ```

doc/recipes/index.md

@@ -3,5 +3,5 @@
 ```{toctree}
 cifar/index
 benchmark
-activation/indexgit
+activation/index
 ```

doc/tutorial/TorchScript/jit.ipynb

@@ -117,32 +117,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "空列表被假设为 `List[Tensor]` 和空字典 `Dict[str, Tensor]`。要实例化其他类型的空列表或字典，请使用 Python 3 类型注解。\n",
+    "空列表被假设为 `list[Tensor]` 和空字典 `dict[str, Tensor]`。要实例化其他类型的空列表或字典，请使用 Python 3 类型注解。\n",
     "\n",
     "比如："
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
-    "import torch.nn as nn\n",
-    "from typing import Dict, List, Tuple\n",
+    "from torch import nn\n",
     "\n",
     "class EmptyDataStructures(torch.nn.Module):\n",
     "    def __init__(self):\n",
     "        super().__init__()\n",
     "\n",
-    "    def forward(self, x: torch.Tensor) -> Tuple[List[Tuple[int, float]], Dict[str, int]]:\n",
+    "    def forward(self, x: torch.Tensor) -> tuple[list[tuple[int, float]], dict[str, int]]:\n",
     "        # This annotates the list to be a `List[Tuple[int, float]]`\n",
-    "        my_list: List[Tuple[int, float]] = []\n",
+    "        my_list: list[tuple[int, float]] = []\n",
     "        for i in range(10):\n",
     "            my_list.append((i, x.item()))\n",
     "\n",
-    "        my_dict: Dict[str, int] = {}\n",
+    "        my_dict: dict[str, int] = {}\n",
     "        return my_list, my_dict\n",
     "\n",
     "x = torch.jit.script(EmptyDataStructures())"
@@ -164,12 +163,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "import torch\n",
-    "import torch.nn as nn\n",
+    "from torch import nn\n",
     "from typing import Optional\n",
     "\n",
     "class M(nn.Module):\n",
@@ -215,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +240,7 @@
        "        [1., 1.]])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -285,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +304,7 @@
        "120"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,7 +493,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.10.13"
   },
   "orig_nbformat": 4
  },

doc/tutorial/quant/fx-graph-mode-qat.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# FX Graph QAT\n",
     "\n",
-    "以 [resnet18_cifar10_relu_cutout](../cifar/cifar10-resnet18-relu) 网络为例展示如何进行 QAT FX Graph 训练。"
+    "以 [resnet18_cifar10_relu_cutout](cifar10-resnet18-relu) 网络为例展示如何进行 QAT FX Graph 训练。"
    ]
   },
   {