7a.FCN.ipynb

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        "The following additional libraries are needed to run this\n",
        "notebook. Note that running on Colab is experimental, please report a Github\n",
        "issue if you have any problem."
      ]
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      "source": [
        "!pip install d2l==1.0.3\n"
      ]
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        "# Fully Convolutional Networks\n",
        ":label:`sec_fcn`\n",
        "\n",
        "As discussed in :numref:`sec_semantic_segmentation`,\n",
        "semantic segmentation\n",
        "classifies images in pixel level.\n",
        "A fully convolutional network (FCN)\n",
        "uses a convolutional neural network to\n",
        "transform image pixels to pixel classes :cite:`Long.Shelhamer.Darrell.2015`.\n",
        "Unlike the CNNs that we encountered earlier\n",
        "for image classification\n",
        "or object detection,\n",
        "a fully convolutional network\n",
        "transforms\n",
        "the height and width of intermediate feature maps\n",
        "back to those of the input image:\n",
        "this is achieved by\n",
        "the transposed convolutional layer\n",
        "introduced in :numref:`sec_transposed_conv`.\n",
        "As a result,\n",
        "the classification output\n",
        "and the input image\n",
        "have a one-to-one correspondence\n",
        "in pixel level:\n",
        "the channel dimension at any output pixel\n",
        "holds the classification results\n",
        "for the input pixel at the same spatial position.\n"
      ]
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      "source": [
        "%matplotlib inline\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from d2l import torch as d2l"
      ]
    },
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      "source": [
        "## The Model\n",
        "\n",
        "Here we describe the basic design of the fully convolutional network model.\n",
        "As shown in :numref:`fig_fcn`,\n",
        "this model first uses a CNN to extract image features,\n",
        "then transforms the number of channels into\n",
        "the number of classes\n",
        "via a $1\\times 1$ convolutional layer,\n",
        "and finally transforms the height and width of\n",
        "the feature maps\n",
        "to those\n",
        "of the input image via\n",
        "the transposed convolution introduced in :numref:`sec_transposed_conv`.\n",
        "As a result,\n",
        "the model output has the same height and width as the input image,\n",
        "where the output channel contains the predicted classes\n",
        "for the input pixel at the same spatial position.\n",
        "\n",
        "\n",
        "![Fully convolutional network.](http://d2l.ai/_images/fcn.svg)\n",
        ":label:`fig_fcn`\n",
        "\n",
        "Below, we [**use a ResNet-18 model pretrained on the ImageNet dataset to extract image features**]\n",
        "and denote the model instance as `pretrained_net`.\n",
        "The last few layers of this model\n",
        "include a global average pooling layer\n",
        "and a fully connected layer:\n",
        "they are not needed\n",
        "in the fully convolutional network.\n"
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            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/ci/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
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              "[Sequential(\n",
              "   (0): BasicBlock(\n",
              "     (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "     (relu): ReLU(inplace=True)\n",
              "     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "     (downsample): Sequential(\n",
              "       (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "     )\n",
              "   )\n",
              "   (1): BasicBlock(\n",
              "     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "     (relu): ReLU(inplace=True)\n",
              "     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "   )\n",
              " ),\n",
              " AdaptiveAvgPool2d(output_size=(1, 1)),\n",
              " Linear(in_features=512, out_features=1000, bias=True)]"
            ]
          },
          "execution_count": 2,
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      "source": [
        "pretrained_net = torchvision.models.resnet18(pretrained=True)\n",
        "list(pretrained_net.children())[-3:]"
      ]
    },
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        "Next, we [**create the fully convolutional network instance `net`**].\n",
        "It copies all the pretrained layers in the ResNet-18\n",
        "except for the final global average pooling layer\n",
        "and the fully connected layer that are closest\n",
        "to the output.\n"
      ]
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        "net = nn.Sequential(*list(pretrained_net.children())[:-2])"
      ]
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        "Given an input with height and width of 320 and 480 respectively,\n",
        "the forward propagation of `net`\n",
        "reduces the input height and width to 1/32 of the original, namely 10 and 15.\n"
      ]
    },
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              "torch.Size([1, 512, 10, 15])"
            ]
          },
          "execution_count": 4,
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      "source": [
        "X = torch.rand(size=(1, 3, 320, 480))\n",
        "net(X).shape"
      ]
    },
    {
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        "Next, we [**use a $1\\times 1$ convolutional layer to transform the number of output channels into the number of classes (21) of the Pascal VOC2012 dataset.**]\n",
        "Finally, we need to (**increase the height and width of the feature maps by 32 times**) to change them back to the height and width of the input image.\n",
        "Recall how to calculate\n",
        "the output shape of a convolutional layer in :numref:`sec_padding`.\n",
        "Since $(320-64+16\\times2+32)/32=10$ and $(480-64+16\\times2+32)/32=15$, we construct a transposed convolutional layer with stride of $32$,\n",
        "setting\n",
        "the height and width of the kernel\n",
        "to $64$, the padding to $16$.\n",
        "In general,\n",
        "we can see that\n",
        "for stride $s$,\n",
        "padding $s/2$ (assuming $s/2$ is an integer),\n",
        "and the height and width of the kernel $2s$,\n",
        "the transposed convolution will increase\n",
        "the height and width of the input by $s$ times.\n"
      ]
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      "source": [
        "num_classes = 21\n",
        "net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))\n",
        "net.add_module('transpose_conv', nn.ConvTranspose2d(num_classes, num_classes,\n",
        "                                    kernel_size=64, padding=16, stride=32))"
      ]
    },
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      "source": [
        "## [**Initializing Transposed Convolutional Layers**]\n",
        "\n",
        "\n",
        "We already know that\n",
        "transposed convolutional layers can increase\n",
        "the height and width of\n",
        "feature maps.\n",
        "In image processing, we may need to scale up\n",
        "an image, i.e., *upsampling*.\n",
        "*Bilinear interpolation*\n",
        "is one of the commonly used upsampling techniques.\n",
        "It is also often used for initializing transposed convolutional layers.\n",
        "\n",
        "To explain bilinear interpolation,\n",
        "say that\n",
        "given an input image\n",
        "we want to\n",
        "calculate each pixel\n",
        "of the upsampled output image.\n",
        "In order to calculate the pixel of the output image\n",
        "at coordinate $(x, y)$,\n",
        "first map $(x, y)$ to coordinate $(x', y')$ on the input image, for example, according to the ratio of the input size to the output size.\n",
        "Note that the mapped $x'$ and $y'$ are real numbers.\n",
        "Then, find the four pixels closest to coordinate\n",
        "$(x', y')$ on the input image.\n",
        "Finally, the pixel of the output image at coordinate $(x, y)$ is calculated based on these four closest pixels\n",
        "on the input image and their relative distance from $(x', y')$.\n",
        "\n",
        "Upsampling of bilinear interpolation\n",
        "can be implemented by the transposed convolutional layer\n",
        "with the kernel constructed by the following `bilinear_kernel` function.\n",
        "Due to space limitations, we only provide the implementation of the `bilinear_kernel` function below\n",
        "without discussions on its algorithm design.\n"
      ]
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      "source": [
        "def bilinear_kernel(in_channels, out_channels, kernel_size):\n",
        "    factor = (kernel_size + 1) // 2\n",
        "    if kernel_size % 2 == 1:\n",
        "        center = factor - 1\n",
        "    else:\n",
        "        center = factor - 0.5\n",
        "    og = (torch.arange(kernel_size).reshape(-1, 1),\n",
        "          torch.arange(kernel_size).reshape(1, -1))\n",
        "    filt = (1 - torch.abs(og[0] - center) / factor) * \\\n",
        "           (1 - torch.abs(og[1] - center) / factor)\n",
        "    weight = torch.zeros((in_channels, out_channels,\n",
        "                          kernel_size, kernel_size))\n",
        "    weight[range(in_channels), range(out_channels), :, :] = filt\n",
        "    return weight"
      ]
    },
    {
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      "source": [
        "Let's [**experiment with upsampling of bilinear interpolation**]\n",
        "that is implemented by a transposed convolutional layer.\n",
        "We construct a transposed convolutional layer that\n",
        "doubles the height and weight,\n",
        "and initialize its kernel with the `bilinear_kernel` function.\n"
      ]
    },
    {
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      "source": [
        "conv_trans = nn.ConvTranspose2d(3, 3, kernel_size=4, padding=1, stride=2,\n",
        "                                bias=False)\n",
        "conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4));"
      ]
    },
    {
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      "id": "4ce526ac",
      "metadata": {
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      "source": [
        "Read the image `X` and assign the upsampling output to `Y`. In order to print the image, we need to adjust the position of the channel dimension.\n"
      ]
    },
    {
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      "outputs": [],
      "source": [
        "img = torchvision.transforms.ToTensor()(d2l.Image.open('../img/catdog.jpg'))\n",
        "X = img.unsqueeze(0)\n",
        "Y = conv_trans(X)\n",
        "out_img = Y[0].permute(1, 2, 0).detach()"
      ]
    },
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      "source": [
        "As we can see, the transposed convolutional layer increases both the height and width of the image by a factor of two.\n",
        "Except for the different scales in coordinates,\n",
        "the image scaled up by bilinear interpolation and the original image printed in :numref:`sec_bbox` look the same.\n"
      ]
    },
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            "input image shape: torch.Size([561, 728, 3])\n",
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              "<Figure size 350x250 with 1 Axes>"
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      ],
      "source": [
        "d2l.set_figsize()\n",
        "print('input image shape:', img.permute(1, 2, 0).shape)\n",
        "d2l.plt.imshow(img.permute(1, 2, 0));\n",
        "print('output image shape:', out_img.shape)\n",
        "d2l.plt.imshow(out_img);"
      ]
    },
    {
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      "id": "e85f4c76",
      "metadata": {
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      "source": [
        "In a fully convolutional network, we [**initialize the transposed convolutional layer with upsampling of bilinear interpolation. For the $1\\times 1$ convolutional layer, we use Xavier initialization.**]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ae40200",
      "metadata": {
        "execution": {
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          "iopub.status.busy": "2023-08-18T19:34:28.371880Z",
          "iopub.status.idle": "2023-08-18T19:34:28.381788Z",
          "shell.execute_reply": "2023-08-18T19:34:28.380642Z"
        },
        "origin_pos": 29,
        "tab": [
          "pytorch"
        ],
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      },
      "outputs": [],
      "source": [
        "W = bilinear_kernel(num_classes, num_classes, 64)\n",
        "net.transpose_conv.weight.data.copy_(W);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fbc76cde",
      "metadata": {
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      },
      "source": [
        "## [**Reading the Dataset**]\n",
        "\n",
        "We read\n",
        "the semantic segmentation dataset\n",
        "as introduced in :numref:`sec_semantic_segmentation`.\n",
        "The output image shape of random cropping is\n",
        "specified as $320\\times 480$: both the height and width are divisible by $32$.\n"
      ]
    },
    {
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      "execution_count": null,
      "id": "0bdc2a20",
      "metadata": {
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "read 1114 examples\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "read 1078 examples\n"
          ]
        }
      ],
      "source": [
        "batch_size, crop_size = 32, (320, 480)\n",
        "train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6654107c",
      "metadata": {
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      "source": [
        "## [**Training**]\n",
        "\n",
        "\n",
        "Now we can train our constructed\n",
        "fully convolutional network.\n",
        "The loss function and accuracy calculation here\n",
        "are not essentially different from those in image classification of earlier chapters.\n",
        "Because we use the output channel of the\n",
        "transposed convolutional layer to\n",
        "predict the class for each pixel,\n",
        "the channel dimension is specified in the loss calculation.\n",
        "In addition, the accuracy is calculated\n",
        "based on correctness\n",
        "of the predicted class for all the pixels.\n"
      ]
    },
    {
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      "execution_count": null,
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      "metadata": {
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          "iopub.status.busy": "2023-08-18T19:35:21.377599Z",
          "iopub.status.idle": "2023-08-18T19:36:21.659017Z",
          "shell.execute_reply": "2023-08-18T19:36:21.657836Z"
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        "id": "b65f6226",
        "outputId": "ce3e8683-393e-4a94-d84a-b782199abed8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loss 0.449, train acc 0.861, test acc 0.852\n",
            "226.7 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
          ]
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            "text/plain": [
              "<Figure size 350x250 with 1 Axes>"
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      ],
      "source": [
        "def loss(inputs, targets):\n",
        "    return F.cross_entropy(inputs, targets, reduction='none').mean(1).mean(1)\n",
        "\n",
        "num_epochs, lr, wd, devices = 5, 0.001, 1e-3, d2l.try_all_gpus()\n",
        "trainer = torch.optim.SGD(net.parameters(), lr=lr, weight_decay=wd)\n",
        "d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)"
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      "id": "9c0c7f12",
      "metadata": {
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      "source": [
        "## [**Prediction**]\n",
        "\n",
        "\n",
        "When predicting, we need to standardize the input image\n",
        "in each channel and transform the image into the four-dimensional input format required by the CNN.\n"
      ]
    },
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          "shell.execute_reply": "2023-08-18T19:36:21.668481Z"
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        "origin_pos": 37,
        "tab": [
          "pytorch"
        ],
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      "outputs": [],
      "source": [
        "def predict(img):\n",
        "    X = test_iter.dataset.normalize_image(img).unsqueeze(0)\n",
        "    pred = net(X.to(devices[0])).argmax(dim=1)\n",
        "    return pred.reshape(pred.shape[1], pred.shape[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27b9364c",
      "metadata": {
        "origin_pos": 38,
        "id": "27b9364c"
      },
      "source": [
        "To [**visualize the predicted class**] of each pixel, we map the predicted class back to its label color in the dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "88b09f25",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-18T19:36:21.673578Z",
          "iopub.status.busy": "2023-08-18T19:36:21.672677Z",
          "iopub.status.idle": "2023-08-18T19:36:21.678207Z",
          "shell.execute_reply": "2023-08-18T19:36:21.677171Z"
        },
        "origin_pos": 40,
        "tab": [
          "pytorch"
        ],
        "id": "88b09f25"
      },
      "outputs": [],
      "source": [
        "def label2image(pred):\n",
        "    colormap = torch.tensor(d2l.VOC_COLORMAP, device=devices[0])\n",
        "    X = pred.long()\n",
        "    return colormap[X, :]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "db007d32",
      "metadata": {
        "origin_pos": 41,
        "id": "db007d32"
      },
      "source": [
        "Images in the test dataset vary in size and shape.\n",
        "Since the model uses a transposed convolutional layer with stride of 32,\n",
        "when the height or width of an input image is indivisible by 32,\n",
        "the output height or width of the\n",
        "transposed convolutional layer will deviate from the shape of the input image.\n",
        "In order to address this issue,\n",
        "we can crop multiple rectangular areas with height and width that are integer multiples of 32 in the image,\n",
        "and perform forward propagation\n",
        "on the pixels in these areas separately.\n",
        "Note that\n",
        "the union of these rectangular areas needs to completely cover the input image.\n",
        "When a pixel is covered by multiple rectangular areas,\n",
        "the average of the transposed convolution outputs\n",
        "in separate areas for this same pixel\n",
        "can be input to\n",
        "the softmax operation\n",
        "to predict the class.\n",
        "\n",
        "\n",
        "For simplicity, we only read a few larger test images,\n",
        "and crop a $320\\times480$ area for prediction starting from the upper-left corner of an image.\n",
        "For these test images, we\n",
        "print their cropped areas,\n",
        "prediction results,\n",
        "and ground-truth row by row.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f780e21",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-18T19:36:21.682056Z",
          "iopub.status.busy": "2023-08-18T19:36:21.681351Z",
          "iopub.status.idle": "2023-08-18T19:36:53.274281Z",
          "shell.execute_reply": "2023-08-18T19:36:53.273369Z"
        },
        "origin_pos": 43,
        "tab": [
          "pytorch"
        ],
        "id": "8f780e21",
        "outputId": "99b5a9a5-a360-44b0-b190-2940478de204"
      },
      "outputs": [
        {
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              " </defs>\n",
              "</svg>\n"
            ],
            "text/plain": [
              "<Figure size 800x600 with 12 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')\n",
        "test_images, test_labels = d2l.read_voc_images(voc_dir, False)\n",
        "n, imgs = 4, []\n",
        "for i in range(n):\n",
        "    crop_rect = (0, 0, 320, 480)\n",
        "    X = torchvision.transforms.functional.crop(test_images[i], *crop_rect)\n",
        "    pred = label2image(predict(X))\n",
        "    imgs += [X.permute(1,2,0), pred.cpu(),\n",
        "             torchvision.transforms.functional.crop(\n",
        "                 test_labels[i], *crop_rect).permute(1,2,0)]\n",
        "d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f4076170",
      "metadata": {
        "origin_pos": 44,
        "id": "f4076170"
      },
      "source": [
        "## Summary\n",
        "\n",
        "* The fully convolutional network first uses a CNN to extract image features, then transforms the number of channels into the number of classes via a $1\\times 1$ convolutional layer, and finally transforms the height and width of the feature maps to those of the input image via the transposed convolution.\n",
        "* In a fully convolutional network, we can use upsampling of bilinear interpolation to initialize the transposed convolutional layer.\n",
        "\n",
        "\n",
        "## Exercises\n",
        "\n",
        "1. If we use Xavier initialization for the transposed convolutional layer in the experiment, how does the result change?\n",
        "1. Can you further improve the accuracy of the model by tuning the hyperparameters?\n",
        "1. Predict the classes of all pixels in test images.\n",
        "1. The original fully convolutional network paper also uses outputs of some intermediate CNN layers :cite:`Long.Shelhamer.Darrell.2015`. Try to implement this idea.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4ba1b83",
      "metadata": {
        "origin_pos": 46,
        "tab": [
          "pytorch"
        ],
        "id": "a4ba1b83"
      },
      "source": [
        "[Discussions](https://discuss.d2l.ai/t/1582)\n"
      ]
    }
  ],
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