{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-06-16T08:22:25.477936Z",
     "start_time": "2025-06-16T08:22:25.474514Z"
    }
   },
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-16T09:46:23.991077Z",
     "start_time": "2025-06-16T09:46:23.982065Z"
    }
   },
   "cell_type": "code",
   "source": [
    "input_feat = torch.tensor([[4, 1, 7, 5], [4, 4, 2, 5], [7, 7, 2, 4], [1, 0, 2, 4]], dtype=torch.float32).unsqueeze(0).unsqueeze(0)\n",
    "print(input_feat)\n",
    "print(input_feat.shape)"
   ],
   "id": "c2fef52f697ea63",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[[[4., 1., 7., 5.],\n",
      "          [4., 4., 2., 5.],\n",
      "          [7., 7., 2., 4.],\n",
      "          [1., 0., 2., 4.]]]])\n",
      "torch.Size([1, 1, 4, 4])\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-16T09:47:40.137555Z",
     "start_time": "2025-06-16T09:47:40.131166Z"
    }
   },
   "cell_type": "code",
   "source": [
    "conv2d = nn.Conv2d(1, 1, (2, 2), stride=1, padding='same', bias=False)\n",
    "# 卷积核要有四个维度：输出通道数，输入通道数，卷积核高度，卷积核宽度\n",
    "kernels = torch.tensor([[[[1, 0], [2, 1]]]], dtype=torch.float32)\n",
    "conv2d.weight = nn.Parameter(kernels, requires_grad=False)  # 设置卷积核\n",
    "# 默认情况随机初始化参数\n",
    "print(conv2d.weight)\n",
    "print(conv2d.bias)"
   ],
   "id": "1903942bae26fde7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[[[1., 0.],\n",
      "          [2., 1.]]]])\n",
      "None\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-16T09:47:42.159880Z",
     "start_time": "2025-06-16T09:47:42.153928Z"
    }
   },
   "cell_type": "code",
   "source": [
    "output = conv2d(input_feat)\n",
    "output"
   ],
   "id": "8ebf518ec7c7bc70",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[16., 11., 16., 15.],\n",
       "          [25., 20., 10., 13.],\n",
       "          [ 9.,  9., 10., 12.],\n",
       "          [ 1.,  0.,  2.,  4.]]]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  }
 ],
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython2",
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