face_mask_recognition.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
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        "<a href=\"https://colab.research.google.com/github/mk-armah/Face-Mask-Recognition-Model-Pytorch/blob/main/face_mask_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive/\")"
      ],
      "metadata": {
        "id": "jA76LKC3-QBm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1d7aa60-f14e-45fa-adc4-340835a6266a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SA5eFQbioQtE"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.io import read_image\n",
        "torch.manual_seed(0)\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import PIL,cv2\n",
        "from PIL import Image,ImageOps,ImageDraw\n",
        "import os\n",
        "from tqdm import tqdm,trange\n",
        "import time\n",
        "\n",
        "import albumentations"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CfGd9dFZX0na"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#create an annotation file for the labels\n",
        "default_dir = \"/content/drive/MyDrive/Coding Stuffs/Repository/Personal datasets/nose_mask_recognition/\"\n",
        "os.chdir(default_dir) #this will be the default working directory for this work.\n",
        "\n",
        "print(\"Working Directory \\n\",os.getcwd())\n",
        "training_path = \"Training_data\"\n",
        "testing_path = \"Testing_data\"\n",
        "validation_path = \"VAL\""
      ],
      "metadata": {
        "id": "dAhZIjICqaPn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f8c049b-0f1a-4add-9280-1d3190f1bb7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Working Directory \n",
            " /content/drive/MyDrive/Coding Stuffs/Repository/Personal datasets/nose_mask_recognition\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_map = {\"no_mask\":0,\n",
        "              \"incorrectly_masked\":1,\n",
        "              \"masked\":2}"
      ],
      "metadata": {
        "id": "Cx6mUZtl-tQr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def annotation_file(path):\n",
        "  \"\"\"generate an annotation file for the images\"\"\"\n",
        "  \"\"\"path argument takes in the directory of the images\"\"\"\n",
        "           \n",
        "  images = os.listdir(path)    #get the images in the directory\n",
        "\n",
        "  annote = []\n",
        "  for i in tqdm(images):\n",
        "    if i.startswith(\"no_mask\"):\n",
        "      annote.append(0)\n",
        "\n",
        "    elif i.startswith(\"incorrectly_masked\"):\n",
        "      annote.append(1)\n",
        "\n",
        "    elif i.startswith(\"masked\"):\n",
        "      annote.append(2)\n",
        "\n",
        "    else:\n",
        "      annote.append(i)\n",
        "\n",
        "  annotations = pd.DataFrame([images,annote]).T\n",
        "  annotations = annotations.sample(frac = 1).reset_index(drop = True) #shuffle the dataframe and reset the indices\n",
        "  print(\" Annotations Successful\")\n",
        "  return annotations"
      ],
      "metadata": {
        "id": "X3KLapjcAINH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#generate an annotatation file for the training images\n",
        "train_df = annotation_file(path = training_path) #\n",
        "train_df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Til17TvjE6aa",
        "outputId": "daf4b37b-dd62-4148-82a6-78c1b0210682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1500/1500 [00:00<00:00, 1276675.32it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Annotations Successful\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      "cell_type": "code",
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        "#generate an annotation file for the testing images\n",
        "test_df = annotation_file(path = testing_path)\n",
        "test_df.head(3)"
      ],
      "metadata": {
        "colab": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 300/300 [00:00<00:00, 468288.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Annotations Successful\n"
          ]
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        "#generate an annotation file for the validation images\n",
        "val_df = annotation_file(path = validation_path)\n",
        "val_df.head(3)"
      ],
      "metadata": {
        "colab": {
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          "name": "stderr",
          "text": [
            "100%|██████████| 300/300 [00:00<00:00, 667528.49it/s]"
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          "text": [
            "Annotations Successful\n"
          ]
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "                              0  1\n",
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              "2               masked (96).jpg  2"
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          },
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sample_images(path,annotation_file):\n",
        "  os.chdir(os.path.join(os.getcwd(),path))\n",
        "  print(os.getcwd())\n",
        "  for i in tqdm(range(16)):\n",
        "    plt.subplot(4,4,i+1)\n",
        "    image = plt.imread(annotation_file.iloc[i,0])\n",
        "    plt.imshow(image)\n",
        "    plt.xticks([]) ; plt.yticks([])\n",
        "  os.chdir(default_dir) #restore to the working directory"
      ],
      "metadata": {
        "id": "YnP_0_2eHuih"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_images(training_path,train_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "wEiPpZm6rKB4",
        "outputId": "47e92182-2806-4f35-e92a-14b35e6b2c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/Coding Stuffs/Repository/Personal datasets/nose_mask_recognition/Training_data\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:00<00:00, 18.50it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class NoseMask(torch.utils.data.Dataset):\n",
        "  def __init__(self,annotation_file,img_dir,transform = None):\n",
        "    self.transform = transform\n",
        "    self.annotation_file = annotation_file\n",
        "    self.img_dir = img_dir\n",
        "    self.classes = labels_map#annotation_file[1].unique()\n",
        "  def __len__(self):\n",
        "    return len(self.annotation_file)\n",
        "\n",
        "  def __getitem__(self,idx):\n",
        "    image_path = os.path.join(self.img_dir,self.annotation_file.iloc[idx,0])\n",
        "    img = Image.open(image_path)\n",
        "\n",
        "    if self.transform:\n",
        "      img = self.transform(img)\n",
        "\n",
        "    return img,self.annotation_file.iloc[idx,1]"
      ],
      "metadata": {
        "id": "8lcHRxFOooJ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "pkR27fzX4GQW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#construct an image transformer\n",
        "from torchvision import transforms\n",
        "#Augment the training images for model generalization\n",
        "training_transforms = torchvision.transforms.Compose([(transforms.Resize((224,224))),\n",
        "                                                    (transforms.RandomRotation(30)),\n",
        "                                                   (transforms.RandomHorizontalFlip()),\n",
        "                                                    #(transforms.RandomChoice([transforms.RandomVerticalFlip(),transforms.RandomHorizontalFlip()], p=0.5)),\n",
        "                                                    (transforms.GaussianBlur(kernel_size=(5, 5), sigma=(0.1, 5))),\n",
        "                                                    (transforms.ToTensor()),\n",
        "                                                    (transforms.Normalize(mean = (0.485,0.456,0.406),std =(0.229,0.224,0.225)))])\n",
        "#define a transformation for the validation datasets\n",
        "validation_transforms = transforms.Compose([transforms.Resize(224),\n",
        "                                            transforms.CenterCrop(200),\n",
        "                                            transforms.ToTensor(),\n",
        "                                            transforms.Normalize([0.485, 0.456, 0.406], \n",
        "                                                                 [0.229, 0.224, 0.225])])\n",
        "\n",
        "#define a transformation for the validation_images\n",
        "testing_transforms = transforms.Compose([transforms.Resize(224),\n",
        "                                         transforms.CenterCrop(200),\n",
        "                                         transforms.ToTensor(),\n",
        "                                         transforms.Normalize([0.485, 0.456, 0.406], \n",
        "                                                              [0.229, 0.224, 0.225])])"
      ],
      "metadata": {
        "id": "2SsRqgSgO3PR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = NoseMask(annotation_file=train_df,img_dir = training_path,transform = training_transforms)\n",
        "testing_data = NoseMask(annotation_file = test_df, img_dir =  testing_path,transform = testing_transforms)\n",
        "validation_data = NoseMask(annotation_file = val_df,img_dir = validation_path,transform = validation_transforms)"
      ],
      "metadata": {
        "id": "M0IibPBuc7u2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_transformed_images(dataset):\n",
        "  img_loader = torch.utils.data.DataLoader(dataset,batch_size = 9)\n",
        "  img_grid = next(iter(img_loader))\n",
        "  images,labels = img_grid\n",
        "  torch_grid = torchvision.utils.make_grid(images,nrow = 3)\n",
        "  plt.imshow(np.transpose(torch_grid,[1,2,0]))"
      ],
      "metadata": {
        "id": "LjBIdSPnr-K6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_transformed_images(dataset = validation_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "vE4G0CH_LDnX",
        "outputId": "6f78321e-96b2-4843-8408-f544f3c43285"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_dataloader = torch.utils.data.DataLoader(dataset = training_data,batch_size=64,shuffle = True)\n",
        "validation_dataloader = torch.utils.data.DataLoader(dataset = validation_data,batch_size =64)\n",
        "testing_dataloader = torch.utils.data.DataLoader(dataset = testing_data,batch_size = 64)"
      ],
      "metadata": {
        "id": "YE-N9f9-fFI4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dsets = torchvision.datasets.ImageFolder(os.path.join(default_dir,\"remainder\"))\n",
        "#Image.open(dsets.imgs[0][0])"
      ],
      "metadata": {
        "id": "P-Aq-TNvyK1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torchvision.models import vgg16\n",
        "model = vgg16(pretrained = True)"
      ],
      "metadata": {
        "id": "auNqXdOuyTAY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "ad68206b99bb4e05b8d2fa89a9421108",
            "924744efaeec4b89bb5cf8b8652ca627",
            "5a53a6a5389f454bb06a104591a1be5b",
            "f15c0efc9d9248049a77cbd2f9af6e6c",
            "9adfe2a400c14366915cabef7fabb1f6",
            "3115a4f3b877441cbfffb295f4e75a37",
            "1888c2ea1c8e402d864aaccc76ffa4e6",
            "b2d0851930ff4f0bac114e367a3efb13",
            "f83f4a1d576b4928b1614aef2d9a4365",
            "0bf494b43d9e4d43a0b19a23f7402bd0",
            "5a339a395e90430e96e6b30ce89d8f5b"
          ]
        },
        "outputId": "29ad9b4b-fa73-4aef-e084-29466ba0d060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ad68206b99bb4e05b8d2fa89a9421108",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0.00/528M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "daKL2NZ_wtVq",
        "outputId": "81229fb7-7cf7-41e0-b382-a83e252eb49b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Module.parameters of VGG(\n",
              "  (features): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (3): ReLU(inplace=True)\n",
              "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (6): ReLU(inplace=True)\n",
              "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (8): ReLU(inplace=True)\n",
              "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (11): ReLU(inplace=True)\n",
              "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (13): ReLU(inplace=True)\n",
              "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (15): ReLU(inplace=True)\n",
              "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (18): ReLU(inplace=True)\n",
              "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (20): ReLU(inplace=True)\n",
              "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (22): ReLU(inplace=True)\n",
              "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (25): ReLU(inplace=True)\n",
              "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (27): ReLU(inplace=True)\n",
              "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (29): ReLU(inplace=True)\n",
              "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
              "    (1): ReLU(inplace=True)\n",
              "    (2): Dropout(p=0.5, inplace=False)\n",
              "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
              "    (4): ReLU(inplace=True)\n",
              "    (5): Dropout(p=0.5, inplace=False)\n",
              "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
              "  )\n",
              ")>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for params in model.parameters():\n",
        "  params.requires_grad = False\n",
        "print(\"\\n Operation Successful\")"
      ],
      "metadata": {
        "id": "hylWm1u5w1us",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3886a1bc-b579-49d4-ddb4-383427978c9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Operation Successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#obtain the input and out_features value for the last layer \n",
        "in_features = model.classifier[6].out_features #this will be used as the input feature for our custom last layer\n",
        "out_features = len(training_dataloader.dataset.classes) #this will be used as the output features for our custom last layer"
      ],
      "metadata": {
        "id": "WrqNhFFxhnHA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#construct the last layer by adding one additional layer to the vgg16 model architecture\n",
        "model.classifier.add_module(\"last_layer\",torch.nn.Linear(in_features = in_features,out_features = 3))"
      ],
      "metadata": {
        "id": "tp2EoRkKyr3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.classifier.last_layer.requires_grad_(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiFyGCtfrcKr",
        "outputId": "8518e592-3b1e-4c3c-89e7-9562a1c4fc09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Linear(in_features=1000, out_features=3, bias=True)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else \"cpu\"\n",
        "print(device)\n",
        "print(torch.cuda.current_device())\n",
        "print(torch.cuda.get_device_name(0))\n",
        "\n",
        "print(torch.__version__)\n",
        "print(torch.version.cuda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0QWeT9XhJP-",
        "outputId": "bbc3bf87-a661-4036-96c8-9586297d0623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n",
            "0\n",
            "Tesla K80\n",
            "1.10.0+cu111\n",
            "11.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lets move the model to cuda\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "id": "9wRERGI54jnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#optimizer and loss function\n",
        "learning_rate = 0.004\n",
        "optimizer = torch.optim.Adam(params = model.parameters(),lr = learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "oceekYa4tiwB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "next(iter(training_dataloader));"
      ],
      "metadata": {
        "id": "IqFQDEwXABV3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for image,label in training_dataloader:\n",
        "  #print(image,label)\n",
        "  print('shape of images :',image.shape)\n",
        "  print('shape of labels :' ,label.shape)\n",
        "  break"
      ],
      "metadata": {
        "id": "MwCohWCUB6DK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24f7fa32-5214-4db9-c38d-889ab3e76eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of images : torch.Size([64, 3, 224, 224])\n",
            "shape of labels : torch.Size([64])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def training_loop(model,dataloader,optimizer,loss_fn):\n",
        "\n",
        "  model.train()\n",
        "\n",
        "  num_batches = len(dataloader)\n",
        "  data_size = len(dataloader.dataset)\n",
        "\n",
        "  training_loss,training_accuracy = 0,0          #training loss and accuracy\n",
        "\n",
        "  for batch,(images,labels) in enumerate(dataloader):\n",
        "    images = images.type(torch.FloatTensor)\n",
        "    images = images.to(device)\n",
        "\n",
        "    labels = labels.type(torch.int64)\n",
        "    labels= labels.to(device)\n",
        "    \n",
        "    #forward pass\n",
        "    y_pred = model(images)\n",
        "\n",
        "    loss = loss_fn(y_pred,labels)       #compute the loss\n",
        "    optimizer.zero_grad()               #empyt/zero the gradient\n",
        "    loss.backward()                     #do a backpropagation\n",
        "    optimizer.step()                    #updata the weights\n",
        "\n",
        "    acc = (y_pred.argmax(1)==labels).sum().type(torch.float).item()\n",
        "    training_accuracy+= acc\n",
        "    training_loss+= loss\n",
        "\n",
        "  training_accuracy/= data_size\n",
        "  training_loss/= num_batches\n",
        "\n",
        "  print(f\"Training Loss   :::>    {(training_loss):.3f}     ||    Training Accuracy   :::>    {(training_accuracy):.3f}\")"
      ],
      "metadata": {
        "id": "diN7LuG_iHO3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cTBKlmKyRxJ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def testing_loop(dataloader,model,loss_fn):\n",
        "\n",
        "  model.eval()\n",
        "\n",
        "  num_batches = len(dataloader)\n",
        "  testing_size = len(dataloader.dataset)\n",
        "\n",
        "  testing_loss,testing_accuracy = 0,0\n",
        "\n",
        "  with torch.no_grad():\n",
        "\n",
        "    for images,labels in dataloader:\n",
        "        images,labels = images.to(device),labels.to(device)\n",
        "\n",
        "        y_pred = model(images)                 #make predictions\n",
        "        loss = loss_fn(y_pred,labels)          #compute the difference between actual outputs and predicted outputs\n",
        "\n",
        "        batch_accuracy = (y_pred.argmax(1)==labels).sum().type(torch.float).item()        #test models accuracy -- how many were correctly predicted\n",
        "\n",
        "        testing_loss+= loss                    #sum up the loss obtained from each batch iteration\n",
        "        testing_accuracy+= batch_accuracy      #sum up the accuracy from each batch iteration\n",
        "\n",
        "    testing_accuracy/= testing_size            # accuracy = correctly predicted samples/all available samples\n",
        "    testing_loss/= num_batches                 #compute the cost (average loss) -- total loss/ sum of individual batches\n",
        "\n",
        "    print(f\"Testing Loss    :::>    {(testing_loss):.3f}     ||    Testing Accuracy   :::>    {(testing_accuracy):.3f}\")"
      ],
      "metadata": {
        "id": "XcEVlNpS5rw_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 10\n",
        "import time\n",
        "start_time = time.time()\n",
        "for epochs in torch.arange(num_epochs):\n",
        "  print(f\"Epoch {(epochs+1).item()}/{num_epochs}\")\n",
        "  training_loop(model,training_dataloader,optimizer,loss_fn)\n",
        "  testing_loop(testing_dataloader,model,loss_fn)\n",
        "  print(\"\\n\")\n",
        "print(\"Epochs Completed\",\"time ::: \",time.time() - start_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V55OqlvGp3iL",
        "outputId": "71950235-9590-4d55-bf44-51b9b88a9125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "Training Loss   :::>    1.598     ||    Training Accuracy   :::>    0.643\n",
            "Testing Loss    :::>    0.436     ||    Testing Accuracy   :::>    0.860\n",
            "\n",
            "\n",
            "Epoch 2/10\n",
            "Training Loss   :::>    0.496     ||    Training Accuracy   :::>    0.829\n",
            "Testing Loss    :::>    0.404     ||    Testing Accuracy   :::>    0.820\n",
            "\n",
            "\n",
            "Epoch 3/10\n",
            "Training Loss   :::>    0.423     ||    Training Accuracy   :::>    0.853\n",
            "Testing Loss    :::>    0.261     ||    Testing Accuracy   :::>    0.880\n",
            "\n",
            "\n",
            "Epoch 4/10\n",
            "Training Loss   :::>    0.349     ||    Training Accuracy   :::>    0.870\n",
            "Testing Loss    :::>    0.209     ||    Testing Accuracy   :::>    0.920\n",
            "\n",
            "\n",
            "Epoch 5/10\n",
            "Training Loss   :::>    0.390     ||    Training Accuracy   :::>    0.863\n",
            "Testing Loss    :::>    0.460     ||    Testing Accuracy   :::>    0.797\n",
            "\n",
            "\n",
            "Epoch 6/10\n",
            "Training Loss   :::>    0.390     ||    Training Accuracy   :::>    0.860\n",
            "Testing Loss    :::>    0.333     ||    Testing Accuracy   :::>    0.883\n",
            "\n",
            "\n",
            "Epoch 7/10\n",
            "Training Loss   :::>    0.386     ||    Training Accuracy   :::>    0.862\n",
            "Testing Loss    :::>    0.331     ||    Testing Accuracy   :::>    0.857\n",
            "\n",
            "\n",
            "Epoch 8/10\n",
            "Training Loss   :::>    0.410     ||    Training Accuracy   :::>    0.861\n",
            "Testing Loss    :::>    0.400     ||    Testing Accuracy   :::>    0.867\n",
            "\n",
            "\n",
            "Epoch 9/10\n",
            "Training Loss   :::>    0.450     ||    Training Accuracy   :::>    0.853\n",
            "Testing Loss    :::>    0.354     ||    Testing Accuracy   :::>    0.890\n",
            "\n",
            "\n",
            "Epoch 10/10\n",
            "Training Loss   :::>    0.392     ||    Training Accuracy   :::>    0.861\n",
            "Testing Loss    :::>    0.201     ||    Testing Accuracy   :::>    0.930\n",
            "\n",
            "\n",
            "Epochs Completed time :::  907.3612520694733\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Z_LF8uNJsCld"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}