Lecture4_Convolutional_Neural_Network.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cotton-ahn/TUM_20WS_Intro2Deep/blob/main/Lecture4_Convolutional_Neural_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kmIOkV65f5S0"
      },
      "source": [
        "### Import Packages\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU_uqfXvD_ew"
      },
      "source": [
        "# Load the TensorBoard notebook extension\n",
        "%load_ext tensorboard\n",
        "from tensorflow import summary\n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1f7bWu9lJtX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a94edb9e-d8a2-42c8-9476-3cd4c3ffdeb6"
      },
      "source": [
        "import os\n",
        "import csv\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "print(torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.7.0+cu101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA3MZRzNHclY"
      },
      "source": [
        "### Import your drive's contents!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXPZ3dc6W3kp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64e4dc75-30c8-4d2c-fe0e-fb52ac835239"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTj1RldNHgSQ"
      },
      "source": [
        "### Let's load the CIFAR10 dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcMuF_m9Ev8W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdaf8d12-c151-4e58-8bd3-c35fd6b9bff3"
      },
      "source": [
        "data_path = './cifar10'\n",
        "model_dir = './drive/MyDrive/Codes/models'\n",
        "batch_size = 64\n",
        "train_dataset = torchvision.datasets.CIFAR10(data_path, True, transform= torchvision.transforms.ToTensor(), download=True)\n",
        "test_dataset = torchvision.datasets.CIFAR10(data_path, False, transform= torchvision.transforms.ToTensor(), download=True)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYiku-SxHnA6"
      },
      "source": [
        "### Choose your device - use GPU or not?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k44xmCOHHmF4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21935c04-f39e-43c7-d770-64bb117877a5"
      },
      "source": [
        "# device = 'cpu'\n",
        "device = 'cuda'\n",
        "print('Current Device : {}'.format(device))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current Device : cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sT_N_IqnIQgQ"
      },
      "source": [
        "### Try to sample out one data point from dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAiU23NUKFl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2952d227-2c58-4986-a0cc-79ff096fce7e"
      },
      "source": [
        "sample = next(iter(train_dataloader))\n",
        "# sample = test_dataset[1]\n",
        "print(sample[0].shape)\n",
        "print(sample[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([64, 3, 32, 32])\n",
            "tensor([6, 4, 7, 9, 9, 2, 3, 8, 2, 3, 9, 8, 8, 1, 9, 3, 5, 1, 1, 4, 0, 6, 1, 0,\n",
            "        2, 3, 9, 6, 6, 3, 6, 0, 5, 7, 9, 1, 5, 2, 4, 8, 7, 7, 4, 7, 0, 9, 0, 6,\n",
            "        7, 6, 9, 7, 4, 2, 7, 2, 6, 6, 4, 4, 6, 4, 6, 4])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnDU90xVTpqC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "38ab02be-85d6-43de-9e38-0f7a25e7576c"
      },
      "source": [
        "fig, ax = plt.subplots(1, 10, figsize=(15, 4))\n",
        "for plot_idx in range(10):\n",
        "    ax[plot_idx].imshow(sample[0][plot_idx].permute(1, 2, 0))\n",
        "    ax[plot_idx].set_title('LABEL : {}'.format(sample[1][plot_idx]))\n",
        "    ax[plot_idx].set_xticks([])\n",
        "    ax[plot_idx].set_yticks([])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LEjuX7lWIYlb"
      },
      "source": [
        "### Define the Multiple Layer Perceptrons"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cx-fxCW5JD_w"
      },
      "source": [
        "class CNNs(nn.Module):\n",
        "    def __init__(self, channels=[32, 64, 128], img_size = 32, dim_output=10):\n",
        "        super(CNNs, self).__init__()\n",
        "        \n",
        "        self.channels = channels\n",
        "        self.conv1 = nn.Conv2d(3, self.channels[0], kernel_size=3, stride=1, padding=1)\n",
        "        self.conv2 = nn.Conv2d(self.channels[0], self.channels[1], kernel_size=3, stride=1, padding=1)\n",
        "        self.conv3 = nn.Conv2d(self.channels[1], self.channels[2], kernel_size=3, stride=1, padding=1)\n",
        "\n",
        "        self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.activation = nn.ReLU()\n",
        "        self.fc1 = nn.Linear(channels[2]*(img_size//8)**2, channels[2])\n",
        "        self.fc2 = nn.Linear(channels[2], dim_output)\n",
        "        \n",
        "\n",
        "    def forward(self, img):\n",
        "        batch_size = img.shape[0]\n",
        "        out = self.activation(self.max_pool(self.conv1(img)))\n",
        "        out = self.activation(self.max_pool(self.conv2(out)))\n",
        "        out = self.activation(self.max_pool(self.conv3(out)))\n",
        "        out = self.fc2(self.fc1(out.view(batch_size, -1)))\n",
        "        \n",
        "        return out"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4hjFl4-IsqO"
      },
      "source": [
        "### Define the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWtw-y-MKuv1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb5402b2-f0b9-461d-cf2c-f294e2d00656"
      },
      "source": [
        "model = CNNs()\n",
        "model = model.to(device)\n",
        "\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
        "\n",
        "print(model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CNNs(\n",
            "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "  (max_pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "  (activation): ReLU()\n",
            "  (fc1): Linear(in_features=2048, out_features=128, bias=True)\n",
            "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUSpLgVXIwqG"
      },
      "source": [
        "### Define functions for train/test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9pXkpAMK42y"
      },
      "source": [
        "def train(model, optimizer, sample):\n",
        "    model.train()\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    img = sample[0].float().to(device)\n",
        "    label = sample[1].long().to(device) \n",
        "    \n",
        "    pred = model(img)\n",
        "\n",
        "    num_correct = sum(torch.argmax(pred, dim=1) == label)\n",
        "    \n",
        "    pred_loss = criterion(pred, label)\n",
        "    \n",
        "    # Try to add the regularization loss!\n",
        "\n",
        "    pred_loss.backward()\n",
        "       \n",
        "    optimizer.step()\n",
        "\n",
        "    return pred_loss.item(), num_correct.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhcKwq8WOUGu"
      },
      "source": [
        "def test(model, sample):\n",
        "    model.eval()\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "    with torch.no_grad():\n",
        "        img = sample[0].float().to(device)\n",
        "        label = sample[1].long().to(device) \n",
        "\n",
        "        pred = model(img)\n",
        "        pred_loss = criterion(pred, label)\n",
        "\n",
        "        num_correct = sum(torch.argmax(pred, dim=1) == label)\n",
        "\n",
        "    return pred_loss.item(), num_correct.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjfAYAPsI2j4"
      },
      "source": [
        "### Prepare the Tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLjrayaAMyb5"
      },
      "source": [
        "train_log_dir = './runs/train_normal'\n",
        "train_summary_writer = summary.create_file_writer(train_log_dir)\n",
        "test_log_dir = './runs/test_normal'\n",
        "test_summary_writer = summary.create_file_writer(test_log_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rb5fy_VVRjUT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "outputId": "72e74ab2-9e83-495b-e7ec-fc02935a1b9f"
      },
      "source": [
        "%tensorboard --logdir runs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T23Toz8vI7Gu"
      },
      "source": [
        "### Run Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kTrpt4YMRS6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413,
          "referenced_widgets": [
            "9474254b3e674624ae5a4f33501d43d3",
            "821202da723d43179d7121f7705ede91",
            "b9862dba9a3848dc96c35a550663cccd",
            "52fa15e8feda4b27ad8fc83c31f7cd93",
            "36d0bedafc5e402fb9534dac3e022a6b",
            "2e48efe9f51049638e6d966afbc160ba",
            "f54a883b4a274756802163beadef6672",
            "a056fcc1d5f94c39837f985cad71c03d"
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        "outputId": "4060291d-1450-450c-efbb-9406534d6a59"
      },
      "source": [
        "max_epoch = 200\n",
        "tmp_path = './checkpoint.pth'\n",
        "\n",
        "for epoch in tqdm(range(max_epoch)):        \n",
        "    ###Train Phase\n",
        "    \n",
        "    # Initialize Loss and Accuracy\n",
        "    train_loss = 0.0\n",
        "    train_accu = 0.0\n",
        "\n",
        "    # Load the saved MODEL AND OPTIMIZER after evaluation.\n",
        "    if epoch > 0:\n",
        "        checkpoint = torch.load(tmp_path)\n",
        "        model.load_state_dict(checkpoint['model_state_dict'])\n",
        "        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
        "        # how about learning rate scheduler?\n",
        "\n",
        "    # Iterate over the train_dataloader\n",
        "    for idx, sample in enumerate(train_dataloader):\n",
        "        curr_loss, num_correct = train(model, optimizer, sample)\n",
        "        train_loss += curr_loss / len(train_dataloader)\n",
        "        train_accu += num_correct / len(train_dataset)\n",
        "\n",
        "    # Write the current loss and accuracy to the Tensorboard\n",
        "    with train_summary_writer.as_default():\n",
        "        tf.summary.scalar('loss', train_loss, step=epoch)                \n",
        "        tf.summary.scalar('accuracy', train_accu, step=epoch)                \n",
        "\n",
        "    # save the model and optimizer's information before the evaulation\n",
        "    checkpoint = {\n",
        "        'model_state_dict': model.state_dict(),\n",
        "        'optimizer_state_dict': optimizer.state_dict(),\n",
        "        # how about learning rate scheduler?\n",
        "    }\n",
        "    torch.save(checkpoint, tmp_path)\n",
        "    torch.save(model.state_dict(), os.path.join(model_dir, 'recent.pth'))\n",
        "    \n",
        "    ### Test Phase\n",
        "    # Initialize Loss and Accuracy\n",
        "    test_loss = 0.0\n",
        "    test_accu = 0.0\n",
        "\n",
        "    # Iterate over the test_dataloader\n",
        "    for idx, sample in enumerate(test_dataloader):\n",
        "        curr_loss, num_correct = test(model, sample)\n",
        "        test_loss += curr_loss / len(test_dataloader)\n",
        "        test_accu += num_correct / len(test_dataloader)\n",
        "\n",
        "    # Write the current loss and accuracy to the Tensorboard\n",
        "    with test_summary_writer.as_default():\n",
        "        tf.summary.scalar('loss', test_loss, step=epoch)\n",
        "        tf.summary.scalar('accuracy', test_accu, step=epoch) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9474254b3e674624ae5a4f33501d43d3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=200.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-219760e61f13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;31m# Iterate over the test_dataloader\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0mcurr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_correct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m         \u001b[0mtest_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcurr_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mtest_accu\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnum_correct\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dataloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-40-d07a9430713e>\u001b[0m in \u001b[0;36mtest\u001b[0;34m(model, sample)\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mpred_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0mnum_correct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpred_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_correct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverrides\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mTensor\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhas_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOeYopHTkhvJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}