5.4_ResNet_Torch.ipynb

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   "id": "8f9053df-8f2a-4d77-8087-1e6c93392b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed1e9d7f-ebc1-4a20-8d51-716dded924ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48652193-4825-4a4c-9f02-8fec3fb66e8e",
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  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3350da15-3abb-4cbc-98bb-c7c75667d734",
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   "source": [
    "class Residual(nn.Module):  #@save\n",
    "    \"\"\"The Residual block of ResNet.\"\"\"\n",
    "    def __init__(self, input_channels, num_channels, use_1x1conv=False,\n",
    "                 strides=1):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(input_channels, num_channels, kernel_size=3, padding=1, stride=strides)\n",
    "        self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1)\n",
    "        if use_1x1conv:\n",
    "            self.conv3 = nn.Conv2d(input_channels, num_channels, kernel_size=1, stride=strides)\n",
    "        else:\n",
    "            self.conv3 = None\n",
    "        self.bn1 = nn.BatchNorm2d(num_channels)\n",
    "        self.bn2 = nn.BatchNorm2d(num_channels)\n",
    "\n",
    "    def forward(self, X):\n",
    "        Y = F.relu(self.bn1(self.conv1(X)))\n",
    "        Y = self.bn2(self.conv2(Y))\n",
    "        if self.conv3:\n",
    "            X = self.conv3(X)\n",
    "        Y += X\n",
    "        return F.relu(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39be620c-3f3e-4acc-9781-183527fe6f4f",
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   "cell_type": "code",
   "execution_count": 23,
   "id": "68e9315d-00af-4724-a4c4-e7e3e7010d66",
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    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 6, 6])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = Residual(3, 3)\n",
    "X = torch.rand(4, 3, 6, 6)\n",
    "Y = blk(X)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b2c6852-70f3-4e57-a5a3-78f4cc432146",
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    {
     "data": {
      "text/plain": [
       "torch.Size([4, 6, 3, 3])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = Residual(3, 6, use_1x1conv=True, strides=2)\n",
    "blk(X).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4668266-f1b7-4286-8f1b-d4db4ac73d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 6, 6])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blk = Residual(3, 3)\n",
    "X = torch.rand(4, 3, 6, 6)\n",
    "Y = blk(X)\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a01d659f-8b1a-4a26-a883-f5bf54ea41d9",
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      "text/plain": [
       "torch.Size([4, 6, 3, 3])"
      ]
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     "execution_count": 32,
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   "source": [
    "blk = Residual(3, 6, use_1x1conv=True, strides=2)\n",
    "blk(X).shape"
   ]
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   "source": [
    "b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),\n",
    "                   nn.BatchNorm2d(64), nn.ReLU(),\n",
    "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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   "source": [
    "def resnet_block(input_channels, num_channels, num_residuals,\n",
    "                 first_block=False):\n",
    "    blk = []\n",
    "    for i in range(num_residuals):\n",
    "        if i == 0 and not first_block:\n",
    "            blk.append(\n",
    "                Residual(input_channels, num_channels, use_1x1conv=True,\n",
    "                         strides=2))\n",
    "        else:\n",
    "            blk.append(Residual(num_channels, num_channels))\n",
    "    return blk\n"
   ]
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   "cell_type": "code",
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   "source": [
    "b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))\n",
    "b3 = nn.Sequential(*resnet_block(64, 128, 2))\n",
    "b4 = nn.Sequential(*resnet_block(128, 256, 2))\n",
    "b5 = nn.Sequential(*resnet_block(256, 512, 2))\n"
   ]
  },
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   "cell_type": "code",
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   "id": "0ba47df1-2fb7-4f83-ad36-c337a527754c",
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   "source": [
    "net = nn.Sequential(b1, b2, b3, b4, b5, nn.AdaptiveAvgPool2d((1, 1)),\n",
    "                    nn.Flatten(), nn.Linear(512, 10))\n"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "Sequential output shape:\t torch.Size([1, 64, 56, 56])\n",
      "Sequential output shape:\t torch.Size([1, 64, 56, 56])\n",
      "Sequential output shape:\t torch.Size([1, 128, 28, 28])\n",
      "Sequential output shape:\t torch.Size([1, 256, 14, 14])\n",
      "Sequential output shape:\t torch.Size([1, 512, 7, 7])\n",
      "AdaptiveAvgPool2d output shape:\t torch.Size([1, 512, 1, 1])\n",
      "Flatten output shape:\t torch.Size([1, 512])\n",
      "Linear output shape:\t torch.Size([1, 10])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leon/opt/miniconda3/envs/torch/lib/python3.7/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(size=(1, 1, 224, 224))\n",
    "for layer in net:\n",
    "    X = layer(X)\n",
    "    print(layer.__class__.__name__, 'output shape:\\t', X.shape)\n"
   ]
  },
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   "execution_count": 38,
   "id": "9be38601-825f-46b9-b6e1-612ecd6b9d12",
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    {
     "name": "stdout",
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     "text": [
      "loss 0.009, train acc 0.998, test acc 0.916\n",
      "2174.7 examples/sec on cuda:0\n"
     ]
    },
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   "source": [
    "lr, num_epochs, batch_size = 0.05, 10, 256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=96)\n",
    "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())\n"
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   "source": []
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