train_cifar10_cnn.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "DL_CS.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Y3Z4LCshWk0"
      },
      "source": [
        "## **Connect to drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IutWF1UAKyoO",
        "outputId": "f7b3796c-6dee-4d9f-fbd5-0e425def45b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAYsNgbNBUVe"
      },
      "source": [
        "## **Change the following paths**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTWJaTgrBTJU"
      },
      "source": [
        "import os",
        "\n",
        "\n",
        "# path where train csv file stored\n",
        "csv_file_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/train.truth.csv'\n",
        "\n",
        "\n",
        "# path of the training data\n",
        "train_data_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/train'\n",
        "\n",
        "\n",
        "# path to save the trained model \n",
        "save_model = '/content/drive/My Drive/Assignments/Deeper_Systems/cifar10_cnn'\n",
        "\n",
        "if not os.path.exists(save_model):\n",
        "    os.makedirs(save_model)\n",
        "\n",
        "\n",
        "# path of the testing data\n",
        "test_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/test'\n",
        "test_image_list = os.listdir(test_path)\n",
        "\n",
        "\n",
        "# path to save the testing images after correcting the orientation\n",
        "corrected_image_path = '/content/drive/My Drive/Assignments/Deeper_Systems/cifar10_cnn/corrected_images'\n",
        "\n",
        "if not os.path.exists(corrected_image_path):\n",
        "    os.makedirs(corrected_image_path)\n",
        "\n",
        "\n",
        "# path to save the results\n",
        "path_to_save = '/content/drive/My Drive/Assignments/Deeper_Systems/cifar10_cnn/result'\n",
        "\n",
        "if not os.path.exists(path_to_save):\n",
        "    os.makedirs(path_to_save)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1d1xw2t9hkbm"
      },
      "source": [
        "## **Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIpJWjDSNWdI"
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "from skimage import io\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmxFWuawhxRZ"
      },
      "source": [
        "## **Check device**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn2UEGaCNWe_",
        "outputId": "e23e8e35-2f36-4642-c953-45621b89b537",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9AcDYi5h15Y"
      },
      "source": [
        "## **Map labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-MuUpfHS79j"
      },
      "source": [
        "def map_label(l):\n",
        "  d = {'rotated_right':0, 'rotated_left':1, 'upright':2, 'upside_down':3}\n",
        "  return d[l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lSHAZUbg2Jc"
      },
      "source": [
        "def return_label(l):\n",
        "  d = {0:'rotated_right', 1:'rotated_left', 2:'upright', 3:'upside_down'}\n",
        "  return d[l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJDGWG0Dh69B"
      },
      "source": [
        "## **DataLoader**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_wD7Hq7OV7i"
      },
      "source": [
        "class create_dataloader(Dataset):\n",
        "\n",
        "    def __init__(self, csv_file, root_dir, transform):\n",
        "        self.data = pd.read_csv(csv_file)\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "\n",
        "        img_name = os.path.join(self.root_dir, self.data.fn[idx])\n",
        "        try:\n",
        "          image = io.imread(img_name)\n",
        "        except:\n",
        "          image = np.empty([64, 64, 3])\n",
        "        label = map_label(self.data.label[idx])\n",
        "        # image = torch.from_numpy(image)\n",
        "        image = self.transform(image)\n",
        "        sample = {'image': image, 'label': label}\n",
        "\n",
        "        return sample"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1O-7KzQpOpI9"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "                        [transforms.ToTensor()\n",
        "                        ])\n",
        "\n",
        "train_dataset = create_dataloader(csv_file=csv_file_path,\n",
        "                                  root_dir=train_data_path,\n",
        "                                  transform=transform)\n",
        "\n",
        "training_data = DataLoader(dataset=train_dataset, batch_size=4, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taJUcPT3iAC2"
      },
      "source": [
        "## **Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jBnT0bTNWdx"
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 32, 3)\n",
        "        self.conv2 = nn.Conv2d(32, 32, 3)\n",
        "        self.conv3 = nn.Conv2d(32, 64, 3)\n",
        "        self.conv4 = nn.Conv2d(64, 64, 3)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.drop1 = nn.Dropout(p=0.25)\n",
        "        self.drop2 = nn.Dropout(p=0.50)\n",
        "        self.fc1 = nn.Linear(10816, 512)\n",
        "        self.fc2 = nn.Linear(512, 4)\n",
        "\n",
        "    def forward(self, x):\n",
        "      x = self.conv1(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.conv2(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.pool(x)\n",
        "      x = self.drop1(x)\n",
        "      x = self.conv3(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.conv4(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.pool(x)\n",
        "      x = self.drop1(x)\n",
        "      x = x.view(x.size(0), -1)\n",
        "      x = self.fc1(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.drop2(x)\n",
        "      x = self.fc2(x)\n",
        "      x = F.softmax(x)\n",
        "\n",
        "      return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NG8ZboNjLR_"
      },
      "source": [
        "## **Initialize Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLyw2QsTjJ5I"
      },
      "source": [
        "''' Network '''\n",
        "net = Net().to(device)\n",
        "\n",
        "''' Loss Function '''\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "''' Optimizer '''\n",
        "optimizer = optim.RMSprop(net.parameters(), lr=0.0001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlBcFyPdiIo6"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiDuNJZ9NWeD",
        "outputId": "0130d0a0-24fe-488c-affb-f522c8a50c65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for epoch in range(2):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, batch in enumerate(training_data):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        image= batch['image']\n",
        "        label= batch['label']\n",
        "        image = image.to(device)\n",
        "        label = label.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        logits = net(image.float())\n",
        "        loss = criterion(logits, label)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        print('Epoch Number -> {} Iteration Number: {} | Loss: {}'.format(epoch, i+1, loss))\n",
        "\n",
        "    PATH = os.path.join(save_model, 'train_net.pth')\n",
        "    torch.save(net.state_dict(), PATH)\n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Epoch Number -> 1 Iteration Number: 7227 | Loss: 1.3564581871032715\n",
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            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HFbJLAJiLQ6"
      },
      "source": [
        "## **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbNRP9sBcE1X",
        "outputId": "5911f9c0-2c08-441a-8d39-9a03744ad964",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "PATH = os.path.join(save_model, 'train_net.pth')\n",
        "\n",
        "net = Net().to(device)\n",
        "net.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yOP8J3Sf7Ct"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "                        [transforms.ToTensor()\n",
        "                        ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7nry2SINWev",
        "outputId": "439303d4-130e-4eac-d651-a6035219acef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "test_image_array = list()\n",
        "\n",
        "predicted_label = list()\n",
        "\n",
        "for image_name in test_image_list:\n",
        "  img = os.path.join(test_path, image_name)\n",
        "  img = io.imread(img)\n",
        "  image = transform(img)\n",
        "  image = image.to(device)\n",
        "\n",
        "  image = torch.reshape(image, (1, image.shape[0], image.shape[1], image.shape[2]))\n",
        "\n",
        "  outputs = net(image)\n",
        "  _, predicted = torch.max(outputs, 1)\n",
        "  pred_label = predicted.item()\n",
        "  label = return_label(pred_label)\n",
        "  predicted_label.append(label)\n",
        "\n",
        "  # rotated_right\n",
        "  if pred_label==0:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)\n",
        "  # rotated_left\n",
        "  if pred_label==1:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n",
        "  # upside_down\n",
        "  if pred_label==3:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_180)\n",
        "  # upright\n",
        "  if pred_label==2:\n",
        "    corrected_img = img\n",
        "\n",
        "  cv2.imwrite(os.path.join(corrected_image_path, image_name), corrected_img)\n",
        "  test_image_array.append(corrected_img)\n",
        "\n",
        "  print(image_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:32: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "91-7194491_1893-05-08_1954.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M20gvaDLgJ8B",
        "outputId": "680085f7-f8e7-4489-a470-c3f3983f163e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Create a dataframe from 2 lists\n",
        "\n",
        "data_df = {'fn': image_name, 'label': predicted_label}\n",
        "df = pd.DataFrame(data_df)\n",
        "\n",
        "df.to_csv(os.path.join(path_to_save, 'test.preds.csv'), index=False)\n",
        "\n",
        "print('File generated...')\n",
        "\n",
        "# save numpy array of corrected test image\n",
        "np.save(os.path.join(path_to_save, 'corrected_test_image_array'), test_image_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File generated...\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}