feat: refactor Jupyter notebook for convolution layer demonstration with updated input tensor and fixed kernel parameters

10.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-06-17T02:29:56.329629Z",
+     "start_time": "2025-06-17T02:29:53.851211Z"
+    }
+   },
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T02:30:23.709542Z",
+     "start_time": "2025-06-17T02:30:23.705090Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "x = torch.randn(3, 5, 5).unsqueeze(0)\n",
+    "print(x.shape)"
+   ],
+   "id": "19a429395361b901",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 3, 5, 5])\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T02:32:50.342968Z",
+     "start_time": "2025-06-17T02:32:50.335771Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# 请注意DW中，输入特征通道数与输出通道数是一样的\n",
+    "in_channels_dw = x.shape[1]\n",
+    "out_channels_dw = x.shape[1]\n",
+    "# 一般来讲DW卷积的kernel size 为3\n",
+    "kernel_size_dw = 3\n",
+    "stride_dw = 1\n",
+    "\n",
+    "# DW 卷积groups参数与输入通道数一样\n",
+    "dw = nn.Conv2d(in_channels_dw, out_channels_dw, kernel_size_dw, stride=stride_dw, groups=in_channels_dw)"
+   ],
+   "id": "23ca0000610f16a0",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-06-17T02:34:22.427522Z",
+     "start_time": "2025-06-17T02:34:22.369462Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "in_channels_pw = out_channels_dw\n",
+    "out_channels_pw = 4\n",
+    "kernel_size_pw = 1\n",
+    "\n",
+    "pw = nn.Conv2d(in_channels_pw, out_channels_pw, kernel_size_pw, stride=1)\n",
+    "\n",
+    "out = pw(dw(x))\n",
+    "print(out.shape)"
+   ],
+   "id": "d0bb1304d1f98d2e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 4, 3, 3])\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}