Dive-into-DL-PyTorch/code/chapter05_CNN/5.3_channels.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.3 多输入通道和多输出通道\n",
    "## 5.3.1 多输入通道"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import sys\n",
    "sys.path.append(\"..\") \n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def corr2d_multi_in(X, K):\n",
    "    # 沿着X和K的第0维（通道维）分别计算再相加\n",
    "    res = d2l.corr2d(X[0, :, :], K[0, :, :])\n",
    "    for i in range(1, X.shape[0]):\n",
    "        res += d2l.corr2d(X[i, :, :], K[i, :, :])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 56.,  72.],\n",
       "        [104., 120.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.tensor([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],\n",
    "              [[1, 2, 3], [4, 5, 6], [7, 8, 9]]])\n",
    "K = torch.tensor([[[0, 1], [2, 3]], [[1, 2], [3, 4]]])\n",
    "\n",
    "corr2d_multi_in(X, K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.2 多输出通道"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def corr2d_multi_in_out(X, K):\n",
    "    # 对K的第0维遍历，每次同输入X做互相关计算。所有结果使用stack函数合并在一起\n",
    "    return torch.stack([corr2d_multi_in(X, k) for k in K])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 2, 2, 2])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = torch.stack([K, K + 1, K + 2])\n",
    "K.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 56.,  72.],\n",
       "         [104., 120.]],\n",
       "\n",
       "        [[ 76., 100.],\n",
       "         [148., 172.]],\n",
       "\n",
       "        [[ 96., 128.],\n",
       "         [192., 224.]]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr2d_multi_in_out(X, K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3.3 $1\\times 1$卷积层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def corr2d_multi_in_out_1x1(X, K):\n",
    "    c_i, h, w = X.shape\n",
    "    c_o = K.shape[0]\n",
    "    X = X.view(c_i, h * w)\n",
    "    K = K.view(c_o, c_i)\n",
    "    Y = torch.mm(K, X)  # 全连接层的矩阵乘法\n",
    "    return Y.view(c_o, h, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.rand(3, 3, 3)\n",
    "K = torch.rand(2, 3, 1, 1)\n",
    "\n",
    "Y1 = corr2d_multi_in_out_1x1(X, K)\n",
    "Y2 = corr2d_multi_in_out(X, K)\n",
    "\n",
    "(Y1 - Y2).norm().item() < 1e-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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