pytorch_training_inference_on_image.ipynb

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      "metadata": {
        "id": "fwiBcV-1jdJN"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torchvision import datasets, transforms, models"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ltr0v4ZgjoTr",
        "outputId": "33b5e25b-f635-4a33-e99e-6c5eedcadee9"
      },
      "source": [
        "######################DONT NEED THESE LINES, USE PATH IN GCP DIRS######################################\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQAAwUZmjtkJ",
        "outputId": "0f4185b9-dcbc-4156-b798-f0f244f8b8fa"
      },
      "source": [
        "data_dir = \"/content/drive/MyDrive/Documents/imagenette2/train\" #########NEED TO CHANGE THIS PATH ACC TO GCP DIRS\n",
        "root_dir = data_dir\n",
        "def load_split_train_test(datadir, valid_size = .2):\n",
        "    train_transforms = transforms.Compose([transforms.Resize((224,224)),\n",
        "                                       transforms.ToTensor(),\n",
        "                                       ])\n",
        "    test_transforms = transforms.Compose([transforms.Resize((224,224)),\n",
        "                                      transforms.ToTensor(),\n",
        "                                      ])\n",
        "    train_data = datasets.ImageFolder(datadir,       \n",
        "                    transform=train_transforms)\n",
        "    test_data = datasets.ImageFolder(datadir,\n",
        "                    transform=test_transforms)\n",
        "    num_train = len(train_data)\n",
        "    indices = list(range(num_train))\n",
        "    split = int(np.floor(valid_size * num_train))\n",
        "    np.random.shuffle(indices)\n",
        "    from torch.utils.data.sampler import SubsetRandomSampler\n",
        "    train_idx, test_idx = indices[split:], indices[:split]\n",
        "    train_sampler = SubsetRandomSampler(train_idx)\n",
        "    test_sampler = SubsetRandomSampler(test_idx)\n",
        "    trainloader = torch.utils.data.DataLoader(train_data,\n",
        "                   sampler=train_sampler, batch_size=64)\n",
        "    testloader = torch.utils.data.DataLoader(test_data,\n",
        "                   sampler=test_sampler, batch_size=64)\n",
        "    return trainloader, testloader\n",
        "\n",
        "trainloader, testloader = load_split_train_test(data_dir, .2)\n",
        "print(trainloader.dataset.classes)\n",
        "\n",
        "#natural_img_dataset = datasets.ImageFolder(root = root_dir,transform = image_transforms[\"train\"])\n",
        "#natural_img_dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['n01440764', 'n02102040', 'n02979186', 'n03000684', 'n03028079', 'n03394916', 'n03417042', 'n03425413', 'n03445777', 'n03888257']\n"
          ],
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        }
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      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() \n",
        "                                  else \"cpu\")\n",
        "model = models.resnet50(pretrained=True)\n",
        "#print(model)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
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        "id": "hHPmFvKBj1d2",
        "outputId": "c3bdc926-3c2b-48ff-ee45-3dd01216c898"
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      "source": [
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "    \n",
        "model.fc = nn.Sequential(nn.Linear(2048, 512),\n",
        "                                 nn.ReLU(),\n",
        "                                 nn.Dropout(0.2),\n",
        "                                 nn.Linear(512, 10),\n",
        "                                 nn.LogSoftmax(dim=1))\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.fc.parameters(), lr=0.003)\n",
        "model.to(device)"
      ],
      "execution_count": 6,
      "outputs": [
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              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=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): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (3): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (4): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (5): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): Bottleneck(\n",
              "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "    (2): Bottleneck(\n",
              "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
              "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Sequential(\n",
              "    (0): Linear(in_features=2048, out_features=512, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.2, inplace=False)\n",
              "    (3): Linear(in_features=512, out_features=10, bias=True)\n",
              "    (4): LogSoftmax(dim=1)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVU8RIO4j4au",
        "outputId": "acdcba0e-0d06-41c8-95a5-55403f775934"
      },
      "source": [
        "import time\n",
        "\n",
        "t1 = time.time()\n",
        "epochs = 1\n",
        "steps = 0\n",
        "running_loss = 0\n",
        "print_every = 10\n",
        "train_losses, test_losses = [], []\n",
        "for epoch in range(epochs):\n",
        "  for inputs, labels in trainloader:\n",
        "    steps += 1\n",
        "    print(steps)\n",
        "    inputs, labels = inputs.to(device), labels.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    logps = model.forward(inputs)\n",
        "    loss = criterion(logps, labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    running_loss += loss.item()\n",
        "  print(\"trainloader done\") \n",
        "  #if steps % print_every == 0:\n",
        "  test_loss = 0\n",
        "  accuracy = 0\n",
        "  model.eval()\n",
        "  with torch.no_grad():\n",
        "\n",
        "    for inputs, labels in testloader:\n",
        "      inputs, labels = inputs.to(device),labels.to(device)\n",
        "      logps = model.forward(inputs)\n",
        "      batch_loss = criterion(logps, labels)\n",
        "      test_loss += batch_loss.item()\n",
        "      \n",
        "      ps = torch.exp(logps)\n",
        "      top_p, top_class = ps.topk(1, dim=1)\n",
        "      equals = top_class == labels.view(*top_class.shape)\n",
        "      accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
        "  train_losses.append(running_loss/len(trainloader))\n",
        "  test_losses.append(test_loss/len(testloader))                    \n",
        "  print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
        "  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
        "  f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n",
        "  f\"Test accuracy: {accuracy/len(testloader):.3f}\")\n",
        "  running_loss = 0\n",
        "  model.train()\n",
        "print(\"Saving Model\")\n",
        "torch.save(model, '/content/drive/MyDrive/Documents/imagenette2/aerialmodel.pth') #########NEED TO CHANGE THIS PATH ACC TO GCP DIRS\n",
        "\n",
        "print(\"Training time per epoch is {} seconds\".format(time.time() - t1))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "trainloader done\n",
            "Epoch 1/1.. Train loss: 2.555.. Test loss: 0.055.. Test accuracy: 0.979\n",
            "Saving Model\n",
            "Training time per epoch is 5314.126418352127 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mob12X_U6bA2"
      },
      "source": [
        "import time\n",
        "from torch.autograd import Variable\n",
        "data_dir = \"/content/drive/MyDrive/Documents/imagenette2/train\" #########NEED TO CHANGE THIS PATH ACC TO GCP DIRS\n",
        "test_transforms = transforms.Compose([transforms.Resize((224, 224)),\n",
        "        transforms.ToTensor()\n",
        "                                     ])\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model=torch.load('/content/drive/MyDrive/Documents/imagenette2/aerialmodel.pth') #########NEED TO CHANGE THIS PATH ACC TO GCP DIRS\n",
        "model.eval()\n",
        "\n",
        "def predict_image(image):\n",
        "  image_tensor = test_transforms(image).float()\n",
        "  image_tensor = image_tensor.unsqueeze_(0)\n",
        "  input = Variable(image_tensor)\n",
        "  input = input.to(device)\n",
        "  output = model(input)\n",
        "  index = output.data.cpu().numpy().argmax()\n",
        "  return index\n",
        "\n",
        "def get_random_images(num):\n",
        "  data = datasets.ImageFolder(data_dir, transform=test_transforms)\n",
        "  classes = data.classes\n",
        "  indices = list(range(len(data)))\n",
        "  np.random.shuffle(indices)\n",
        "  idx = indices[:num]\n",
        "  from torch.utils.data.sampler import SubsetRandomSampler\n",
        "  sampler = SubsetRandomSampler(idx)\n",
        "  loader = torch.utils.data.DataLoader(data, \n",
        "                  sampler=sampler, batch_size=num)\n",
        "  dataiter = iter(loader)\n",
        "  images, labels = dataiter.next()\n",
        "  return images, labels"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXWiTIFp7jNy",
        "outputId": "d0249037-213d-43a4-8686-d9b991309f4c"
      },
      "source": [
        "t1 = time.time()\n",
        "to_pil = transforms.ToPILImage()\n",
        "images, labels = get_random_images(1000)\n",
        "#fig=plt.figure(figsize=(10,10))\n",
        "for ii in range(len(images)):\n",
        "    print(ii+1)\n",
        "    image = to_pil(images[ii])\n",
        "    index = predict_image(image)\n",
        "    #sub = fig.add_subplot(1, len(images), ii+1)\n",
        "    res = int(labels[ii]) == index\n",
        "    #sub.set_title(str(trainloader.dataset.classes[index]) + \":\" + str(res))\n",
        "    #plt.axis('off')\n",
        "    ##plt.imshow(image)\n",
        "#plt.show()\n",
        "print(\"Inference time is {} seconds\".format(time.time() - t1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Inference time is 246.6539294719696 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oc611w2UIXpH"
      },
      "source": [
        "#########################################################################################################\n",
        "####################################    VGG16 on Pytorch   ##############################################\n",
        "#########################################################################################################\n",
        "from torchvision import transforms\n",
        "\n",
        "# Image transformations\n",
        "image_transforms = {\n",
        "    # Train uses data augmentation\n",
        "    'train':\n",
        "    transforms.Compose([\n",
        "        transforms.RandomResizedCrop(size=256, scale=(0.8, 1.0)),\n",
        "        transforms.RandomRotation(degrees=15),\n",
        "        transforms.ColorJitter(),\n",
        "        transforms.RandomHorizontalFlip(),\n",
        "        transforms.CenterCrop(size=224),  # Image net standards\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406],\n",
        "                             [0.229, 0.224, 0.225])  # Imagenet standards\n",
        "    ]),\n",
        "    # Validation does not use augmentation\n",
        "    'valid':\n",
        "    transforms.Compose([\n",
        "        transforms.Resize(size=256),\n",
        "        transforms.CenterCrop(size=224),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ]),\n",
        "}\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEkzxQgyln8A",
        "outputId": "652fa8d6-f8bd-4714-a5bf-46cb3cef4056"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() \n",
        "                                  else \"cpu\")\n",
        "model = models.vgg16(pretrained=True)\n",
        "#print(model)\n",
        "\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "model.classifier[6] = nn.Sequential(\n",
        "                      nn.Linear(4096, 256), \n",
        "                      nn.ReLU(), \n",
        "                      nn.Dropout(0.4),\n",
        "                      nn.Linear(256, 10),                   \n",
        "                      nn.LogSoftmax(dim=1))\n",
        "\n",
        "criterion = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters())\n",
        "model.to(device)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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): Sequential(\n",
              "      (0): Linear(in_features=4096, out_features=256, bias=True)\n",
              "      (1): ReLU()\n",
              "      (2): Dropout(p=0.4, inplace=False)\n",
              "      (3): Linear(in_features=256, out_features=10, bias=True)\n",
              "      (4): LogSoftmax(dim=1)\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQJzeiNagJLa"
      },
      "source": [
        "t1 = time.time()\n",
        "# Early stopping details\n",
        "n_epochs_stop = 1\n",
        "min_val_loss = np.Inf\n",
        "epochs_no_improve = 0\n",
        "checkpoint_path = \"/content/drive/MyDrive/Documents/imagenette2/vgg16model.pth\"\n",
        "# Main loop\n",
        "for epoch in range(1):\n",
        "  # Initialize validation loss for epoch\n",
        "  val_loss = 0\n",
        "  \n",
        "  # Training loop\n",
        "  for data, targets in trainloader:\n",
        "    # Generate predictions\n",
        "    out = model(data)\n",
        "    # Calculate loss\n",
        "    loss = criterion(out, targets)\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "    # Update model parameters\n",
        "    optimizer.step()\n",
        "    \n",
        "# Validation loop\n",
        "for data, targets in testloader:\n",
        "  # Generate predictions \n",
        "  out = model(data)\n",
        "  # Calculate loss\n",
        "  loss = criterion(out, targets)\n",
        "  val_loss += loss\n",
        "\n",
        "# Average validation loss\n",
        "val_loss = val_loss / len(trainloader)\n",
        "\n",
        "# If the validation loss is at a minimum\n",
        "if val_loss < min_val_loss:\n",
        "  # Save the model\n",
        "  torch.save(model, checkpoint_path)\n",
        "  epochs_no_improve = 0\n",
        "  min_val_loss = val_loss\n",
        "  \n",
        "else:\n",
        "  epochs_no_improve += 1\n",
        "  # Check early stopping condition\n",
        "  if epochs_no_improve == n_epochs_stop:\n",
        "    print('Early stopping!')\n",
        "    \n",
        "    # Load in the best model\n",
        "    model = torch.load(checkpoint_path)\n",
        "print(\"Training time per epoch is {} seconds\".format(time.time() - t1))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ko-i9GyYfTR5",
        "outputId": "83ee7057-3d06-400d-e4ba-5a23c33072ea"
      },
      "source": [
        "model = torch.load(checkpoint_path)\n",
        "def predict_image(image):\n",
        "  image_tensor = test_transforms(image).float()\n",
        "  image_tensor = image_tensor.unsqueeze_(0)\n",
        "  input = Variable(image_tensor)\n",
        "  input = input.to(device)\n",
        "  output = model(input)\n",
        "  index = output.data.cpu().numpy().argmax()\n",
        "  return index\n",
        "\n",
        "def get_random_images(num):\n",
        "  data = datasets.ImageFolder(data_dir, transform=test_transforms)\n",
        "  classes = data.classes\n",
        "  indices = list(range(len(data)))\n",
        "  np.random.shuffle(indices)\n",
        "  idx = indices[:num]\n",
        "  from torch.utils.data.sampler import SubsetRandomSampler\n",
        "  sampler = SubsetRandomSampler(idx)\n",
        "  loader = torch.utils.data.DataLoader(data, \n",
        "                  sampler=sampler, batch_size=num)\n",
        "  dataiter = iter(loader)\n",
        "  images, labels = dataiter.next()\n",
        "  return images, labels\n",
        "\n",
        "t1 = time.time()\n",
        "to_pil = transforms.ToPILImage()\n",
        "images, labels = get_random_images(1000)\n",
        "#fig=plt.figure(figsize=(10,10))\n",
        "for ii in range(len(images)):\n",
        "    print(ii+1)\n",
        "    image = to_pil(images[ii])\n",
        "    index = predict_image(image)\n",
        "    #sub = fig.add_subplot(1, len(images), ii+1)\n",
        "    res = int(labels[ii]) == index\n",
        "    #sub.set_title(str(trainloader.dataset.classes[index]) + \":\" + str(res))\n",
        "    #plt.axis('off')\n",
        "    ##plt.imshow(image)\n",
        "#plt.show()\n",
        "print(\"Inference time is {} seconds\".format(time.time() - t1))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
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            "Inference time is 627.9544966220856 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLHv4YjVhRVG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}