main.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "18D100005_Kaggle_Notebook.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvTqPFaPUBk2",
        "outputId": "dbacad60-3776-40a8-b852-6c6cd522cb02",
        "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": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNVPywcKoPZx"
      },
      "source": [
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.optim import lr_scheduler\n",
        "import numpy as np\n",
        "import torchvision\n",
        "from torchvision import datasets, models, transforms\n",
        "import torch.utils.data as data\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import os\n",
        "import copy\n",
        "import sklearn\n",
        "from google.colab.patches import cv2_imshow\n",
        "import cv2\n",
        "import torch\n",
        "from tqdm.auto import tqdm\n",
        "import pandas as pd\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable\n",
        "from torch import optim\n",
        "from torchvision.datasets import ImageFolder\n",
        "from torchvision.utils import make_grid\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xeK13rg3h2D",
        "outputId": "02152c3d-d728-40d5-d231-ccf9cb7db70f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "import shutil\n",
        "import random\n",
        "root_dir = \"/content/drive/My Drive/train\"\n",
        "classes_dir = ['basketball_court', 'beach', 'forest', 'others', 'railway', 'swimming_pool', 'tennis_court']\n",
        "val_ratio = 0.20\n",
        "for cls in classes_dir:\n",
        "    os.makedirs(root_dir +'/train/' + cls)\n",
        "    os.makedirs(root_dir +'/val/' + cls)\n",
        "    src = root_dir + \"/\" + cls \n",
        "    allFileNames = os.listdir(src)\n",
        "    np.random.shuffle(allFileNames)\n",
        "    train_FileNames, val_FileNames= np.split(np.array(allFileNames),\n",
        "                                                              [int(len(allFileNames)* (1 - val_ratio))])\n",
        "    train_FileNames = [src+'/'+ name for name in train_FileNames.tolist()]\n",
        "    val_FileNames = [src+'/' + name for name in val_FileNames.tolist()]\n",
        "    print('Total images: ', len(allFileNames))\n",
        "    print('Training: ', len(train_FileNames))\n",
        "    print('Validation: ', len(val_FileNames))\n",
        "    for name in train_FileNames:\n",
        "        shutil.copy(name, root_dir +'/train/' + cls)\n",
        "\n",
        "    for name in val_FileNames:\n",
        "        shutil.copy(name, root_dir +'/val/' + cls)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n",
            "Total images:  80\n",
            "Training:  64\n",
            "Validation:  16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZafXGf63VGZ",
        "outputId": "777f97fd-c77c-423c-dbb2-64324471bfb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "        rotation_range=10,\n",
        "        zoom_range=0.3,\n",
        "        vertical_flip=True,\n",
        "        horizontal_flip=True,\n",
        "        fill_mode='constant')\n",
        "classes = ['basketball_court', 'beach', 'forest', 'others', 'railway', 'swimming_pool', 'tennis_court']\n",
        "for cls in classes:\n",
        "  for filename in tqdm(os.listdir(\"/content/drive/My Drive/train/train/\"+cls)):\n",
        "    img = load_img(\"/content/drive/My Drive/train/train/\"+cls+\"/\"+filename)  \n",
        "    x = img_to_array(img) \n",
        "    x = x.reshape((1,) + x.shape) \n",
        "    i = 0\n",
        "    for batch in datagen.flow(x, batch_size=1,\n",
        "                              save_to_dir=\"/content/drive/My Drive/train/train/\"+cls, save_prefix=filename, save_format='jpeg'):\n",
        "        i += 1\n",
        "        if i > 10:\n",
        "            break  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 64/64 [00:18<00:00,  3.55it/s]\n",
            "100%|██████████| 64/64 [00:18<00:00,  3.51it/s]\n",
            "100%|██████████| 64/64 [00:19<00:00,  3.36it/s]\n",
            "100%|██████████| 64/64 [00:17<00:00,  3.56it/s]\n",
            "100%|██████████| 64/64 [00:18<00:00,  3.48it/s]\n",
            "100%|██████████| 64/64 [00:18<00:00,  3.38it/s]\n",
            "100%|██████████| 64/64 [00:18<00:00,  3.53it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WqjD8pTrgrh"
      },
      "source": [
        "data_transforms = {\n",
        "    'train': transforms.Compose([\n",
        "        transforms.Resize((224,224)),\n",
        "        transforms.RandomAffine(5, translate=(0.2,0.2), scale=(0.9,1.2), shear=(10,10), resample=False, fillcolor=0),\n",
        "        transforms.RandomPerspective(distortion_scale=0.1),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.3794, 0.3961, 0.3725], [0.1846, 0.1853, 0.1804])\n",
        "    ]),\n",
        "    'validation': transforms.Compose([                                    \n",
        "        transforms.Resize((224, 224)),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.3794, 0.3961, 0.3725], [0.1846, 0.1853, 0.1804])\n",
        "    ]),\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-1_5U1ko6ey"
      },
      "source": [
        "train_dataset = datasets.ImageFolder(\"/content/drive/My Drive/train/train/\", data_transforms['train'])\n",
        "val_dataset = datasets.ImageFolder(\"/content/drive/My Drive/train/val\", data_transforms['validation'])\n",
        "\n",
        "\n",
        "dataloaders = {}\n",
        "dataloaders['train'] = torch.utils.data.DataLoader(\n",
        "                                    train_dataset,                                        \n",
        "                                    batch_size=32,\n",
        "                                    shuffle=True, num_workers=4)\n",
        "dataloaders['validation'] = torch.utils.data.DataLoader(\n",
        "                                    val_dataset,                                        \n",
        "                                    batch_size=4,\n",
        "                                    shuffle=True, num_workers=4)\n",
        "\n",
        "dataset_sizes = {}\n",
        "dataset_sizes['train'] = len(train_dataset)\n",
        "dataset_sizes['validation'] = len(val_dataset)\n",
        "classes = train_dataset.classes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBvoRMRBKdj5",
        "outputId": "1a37b428-752f-4c87-b32e-32af05713157",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(dataset_sizes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'train': 5374, 'validation': 112}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "saXIjxDqlHUV"
      },
      "source": [
        "data = next(iter(dataloaders['train']))\n",
        "data[0].mean(axis = (0, 2, 3)), data[0].std(axis = (0, 2, 3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP52flpplmk1",
        "outputId": "0b750ae6-adde-422a-970d-42ebea1527cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "plt.hist(data[0].flatten())\n",
        "plt.axvline(data[0].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f6f14e53a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KvsHDJ6saHa",
        "outputId": "06618bb7-f47a-4895-d6ab-fd198ec45283",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "def imshow(img):\n",
        "    img = img / 2 + 0.5\n",
        "    npimg = img.numpy()\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "    plt.show()\n",
        "dataiter = iter(dataloaders['train'])\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "tennis_court forest tennis_court railway\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "466sczA-zxiN"
      },
      "source": [
        "class AlexNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.ConvLayer1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 3, out_channels = 64, kernel_size = 3, stride = 1, padding = 1),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.BatchNorm2d(64)\n",
        "        )\n",
        "        self.ConvLayer2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 3, stride = 1, padding = 1),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.BatchNorm2d(64),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "        )\n",
        "        self.ConvLayer3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 64, out_channels = 128, kernel_size = 3, stride = 1, padding = 1),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.BatchNorm2d(128),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "        )\n",
        "        self.ConvLayer4 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 128, out_channels = 256, kernel_size = 3, stride = 1, padding = 1),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.BatchNorm2d(256),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "        )\n",
        "        self.ConvLayer5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels = 256, out_channels = 512, kernel_size = 3, stride = 1, padding = 1),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.BatchNorm2d(512),\n",
        "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
        "        )\n",
        "        self.FC = nn.Sequential(\n",
        "            nn.Linear(100352, 1000),\n",
        "            nn.LeakyReLU(inplace = True),\n",
        "            nn.Dropout(p=0.5),\n",
        "            nn.Linear(1000, 7)\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        x = self.ConvLayer1(x)\n",
        "        x = self.ConvLayer2(x)\n",
        "        x = self.ConvLayer3(x)\n",
        "        x = self.ConvLayer4(x)\n",
        "        x = self.ConvLayer5(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.FC(x)\n",
        "        return F.log_softmax(x, dim = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIqWTdWd3_Tt"
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "model = AlexNet()\n",
        "model = model.to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer_fc = torch.optim.SGD([param for (name, param) in model.named_parameters() if \"FC\" in name], lr=0.0001, weight_decay=0.05)\n",
        "optimizer_conv = torch.optim.SGD([param for (name, param) in model.named_parameters() if \"FC\" not in name], lr=0.001, weight_decay=0.05)\n",
        "exp_lr_scheduler_fc = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_fc)\n",
        "exp_lr_scheduler_conv = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer_conv)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gV-MKWFnsE-5",
        "outputId": "e4bda992-9290-4524-c6db-a5920dee51f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GNRNet(\n",
              "  (ConvLayer1): Sequential(\n",
              "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (ConvLayer2): Sequential(\n",
              "    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (ConvLayer3): Sequential(\n",
              "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (ConvLayer4): Sequential(\n",
              "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (ConvLayer5): Sequential(\n",
              "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              "  )\n",
              "  (FC): Sequential(\n",
              "    (0): Linear(in_features=100352, out_features=1000, bias=True)\n",
              "    (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
              "    (2): Dropout(p=0.5, inplace=False)\n",
              "    (3): Linear(in_features=1000, out_features=7, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMf6MUYUvYoD"
      },
      "source": [
        "def train_model(model, criterion, optimizer_fc, optimizer_conv, scheduler_fc, scheduler_conv, num_epochs=10,resume=None, model_name='AlexNet'):\n",
        "    since = time.time()\n",
        "    train_loss = []\n",
        "    val_loss = []\n",
        "    best_acc = 0.0\n",
        "\n",
        "    for epoch in range(num_epochs):\n",
        "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
        "        print('-' * 10)\n",
        "\n",
        "        for phase in ['train', 'validation']:#\n",
        "            if phase == 'train':\n",
        "                model.train()  \n",
        "            else:\n",
        "                model.eval() \n",
        "            running_loss = 0.0\n",
        "            running_corrects = 0\n",
        "            ground_truth = []\n",
        "            predictions = []\n",
        "            confidence = []\n",
        "            new_soft = []\n",
        "            new_live_images = []\n",
        "            new_ground_truth = []\n",
        "            new_predictions = []\n",
        "            temp = 0\n",
        "            \n",
        "            for inputs, labels in dataloaders[phase]:\n",
        "                inputs = inputs.to(device)\n",
        "                labels = labels.to(device)\n",
        "                optimizer_fc.zero_grad()\n",
        "                optimizer_conv.zero_grad()\n",
        "                with torch.set_grad_enabled(phase == 'train'):\n",
        "                    outputs = model(inputs)\n",
        "\n",
        "                    _, preds = torch.max(outputs, dim=1)\n",
        "                    loss = criterion(outputs, labels)\n",
        "\n",
        "                    if phase == 'train':\n",
        "                        loss.backward()\n",
        "                        optimizer_fc.step()\n",
        "                        optimizer_conv.step()\n",
        "\n",
        "                    else:\n",
        "                        ground_truth.append(labels)\n",
        "                        predictions.append(preds)\n",
        "                        conf_scores,idx = torch.max(torch.nn.functional.softmax(outputs,dim=1),dim=1)\n",
        "                        confidence.append(conf_scores)\n",
        "\n",
        "                \n",
        "                running_loss += loss.item() * inputs.size(0)\n",
        "                running_corrects += torch.sum(preds == labels.data)                \n",
        "            if phase == 'validation':\n",
        "                scheduler_fc.step(running_loss)\n",
        "                scheduler_conv.step(running_loss)\n",
        "            if phase == 'validation':\n",
        "                a,b,c = torch.cat(ground_truth), torch.cat(predictions), torch.cat(confidence)\n",
        "                a = a.detach().cpu().numpy()\n",
        "                b = b.detach().cpu().numpy()\n",
        "                c = c.detach().cpu().numpy()\n",
        "\n",
        "                a = [0 if x==0 else 1 for x in a]\n",
        "                b = [0 if x==0 else 1 for x in b]\n",
        "            epoch_loss = running_loss / dataset_sizes[phase]\n",
        "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
        "            if phase == \"train\":\n",
        "              train_loss.append(epoch_loss)\n",
        "            elif phase == \"test\":\n",
        "              test_loss.append(epoch_loss)\n",
        "            else:\n",
        "              val_loss.append(epoch_loss)\n",
        "\n",
        "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
        "                phase, epoch_loss, epoch_acc))\n",
        "\n",
        "            if phase == 'validation' and epoch_acc > best_acc:\n",
        "                best_acc = epoch_acc\n",
        "                model_weight_dir = './weights_'+model_name\n",
        "        print()\n",
        "    time_elapsed = time.time() - since\n",
        "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
        "        time_elapsed // 60, time_elapsed % 60))\n",
        "    print('Best val Acc: {:4f}'.format(best_acc))\n",
        "\n",
        "    return model, train_loss, val_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khyA0iJWazLt",
        "outputId": "2e5bed8b-5017-43d5-b4b6-56d7ae418f08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model_training = train_model(model, criterion, \n",
        "                              optimizer_fc, optimizer_conv, \n",
        "                              exp_lr_scheduler_fc, exp_lr_scheduler_conv,\n",
        "                              num_epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 0/29\n",
            "----------\n",
            "train Loss: 1.1303 Acc: 0.6223\n",
            "validation Loss: 0.5566 Acc: 0.8304\n",
            "\n",
            "Epoch 1/29\n",
            "----------\n",
            "train Loss: 0.5578 Acc: 0.8312\n",
            "validation Loss: 0.3676 Acc: 0.8750\n",
            "\n",
            "Epoch 2/29\n",
            "----------\n",
            "train Loss: 0.4028 Acc: 0.8643\n",
            "validation Loss: 0.2686 Acc: 0.9018\n",
            "\n",
            "Epoch 3/29\n",
            "----------\n",
            "train Loss: 0.3279 Acc: 0.8930\n",
            "validation Loss: 0.2181 Acc: 0.9286\n",
            "\n",
            "Epoch 4/29\n",
            "----------\n",
            "train Loss: 0.2707 Acc: 0.9114\n",
            "validation Loss: 0.2214 Acc: 0.9018\n",
            "\n",
            "Epoch 5/29\n",
            "----------\n",
            "train Loss: 0.2324 Acc: 0.9256\n",
            "validation Loss: 0.1814 Acc: 0.9196\n",
            "\n",
            "Epoch 6/29\n",
            "----------\n",
            "train Loss: 0.2052 Acc: 0.9362\n",
            "validation Loss: 0.1486 Acc: 0.9375\n",
            "\n",
            "Epoch 7/29\n",
            "----------\n",
            "train Loss: 0.1915 Acc: 0.9367\n",
            "validation Loss: 0.1420 Acc: 0.9464\n",
            "\n",
            "Epoch 8/29\n",
            "----------\n",
            "train Loss: 0.1626 Acc: 0.9494\n",
            "validation Loss: 0.1579 Acc: 0.9464\n",
            "\n",
            "Epoch 9/29\n",
            "----------\n",
            "train Loss: 0.1453 Acc: 0.9555\n",
            "validation Loss: 0.1166 Acc: 0.9643\n",
            "\n",
            "Epoch 10/29\n",
            "----------\n",
            "train Loss: 0.1297 Acc: 0.9645\n",
            "validation Loss: 0.1799 Acc: 0.9375\n",
            "\n",
            "Epoch 11/29\n",
            "----------\n",
            "train Loss: 0.1203 Acc: 0.9654\n",
            "validation Loss: 0.1743 Acc: 0.9286\n",
            "\n",
            "Epoch 12/29\n",
            "----------\n",
            "train Loss: 0.1125 Acc: 0.9656\n",
            "validation Loss: 0.0924 Acc: 0.9732\n",
            "\n",
            "Epoch 13/29\n",
            "----------\n",
            "train Loss: 0.0947 Acc: 0.9732\n",
            "validation Loss: 0.1270 Acc: 0.9554\n",
            "\n",
            "Epoch 14/29\n",
            "----------\n",
            "train Loss: 0.0924 Acc: 0.9756\n",
            "validation Loss: 0.1400 Acc: 0.9554\n",
            "\n",
            "Epoch 15/29\n",
            "----------\n",
            "train Loss: 0.0886 Acc: 0.9719\n",
            "validation Loss: 0.1192 Acc: 0.9643\n",
            "\n",
            "Epoch 16/29\n",
            "----------\n",
            "train Loss: 0.0823 Acc: 0.9784\n",
            "validation Loss: 0.1188 Acc: 0.9732\n",
            "\n",
            "Epoch 17/29\n",
            "----------\n",
            "train Loss: 0.0755 Acc: 0.9797\n",
            "validation Loss: 0.1161 Acc: 0.9643\n",
            "\n",
            "Epoch 18/29\n",
            "----------\n",
            "train Loss: 0.0785 Acc: 0.9782\n",
            "validation Loss: 0.0801 Acc: 0.9911\n",
            "\n",
            "Epoch 19/29\n",
            "----------\n",
            "train Loss: 0.0696 Acc: 0.9823\n",
            "validation Loss: 0.1333 Acc: 0.9464\n",
            "\n",
            "Epoch 20/29\n",
            "----------\n",
            "train Loss: 0.0616 Acc: 0.9847\n",
            "validation Loss: 0.0725 Acc: 0.9821\n",
            "\n",
            "Epoch 21/29\n",
            "----------\n",
            "train Loss: 0.0634 Acc: 0.9836\n",
            "validation Loss: 0.0984 Acc: 0.9554\n",
            "\n",
            "Epoch 22/29\n",
            "----------\n",
            "train Loss: 0.0590 Acc: 0.9855\n",
            "validation Loss: 0.0701 Acc: 0.9821\n",
            "\n",
            "Epoch 23/29\n",
            "----------\n",
            "train Loss: 0.0540 Acc: 0.9872\n",
            "validation Loss: 0.1118 Acc: 0.9554\n",
            "\n",
            "Epoch 24/29\n",
            "----------\n",
            "train Loss: 0.0490 Acc: 0.9900\n",
            "validation Loss: 0.0631 Acc: 0.9911\n",
            "\n",
            "Epoch 25/29\n",
            "----------\n",
            "train Loss: 0.0514 Acc: 0.9868\n",
            "validation Loss: 0.0909 Acc: 0.9643\n",
            "\n",
            "Epoch 26/29\n",
            "----------\n",
            "train Loss: 0.0557 Acc: 0.9866\n",
            "validation Loss: 0.1178 Acc: 0.9554\n",
            "\n",
            "Epoch 27/29\n",
            "----------\n",
            "train Loss: 0.0475 Acc: 0.9883\n",
            "validation Loss: 0.0605 Acc: 0.9732\n",
            "\n",
            "Epoch 28/29\n",
            "----------\n",
            "train Loss: 0.0443 Acc: 0.9924\n",
            "validation Loss: 0.0827 Acc: 0.9732\n",
            "\n",
            "Epoch 29/29\n",
            "----------\n",
            "train Loss: 0.0433 Acc: 0.9900\n",
            "validation Loss: 0.0569 Acc: 0.9732\n",
            "\n",
            "Training complete in 24m 18s\n",
            "Best val Acc: 0.991071\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMjroxvFfXKz",
        "outputId": "4ec5acac-95ef-4933-aa2f-890cab550ca4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "import matplotlib.patches as mpatches\n",
        "plt.plot(model_training[1], label=\"Training\")\n",
        "plt.plot(model_training[2], label=\"Validation\")\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3e9070d198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAjiF4t4qVZC"
      },
      "source": [
        "torch.save(model.state_dict(), \"/content/drive/My Drive/model.pt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaUF4kPet9If",
        "outputId": "24f38f67-734e-433a-d057-e2cfde1180ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from PIL import Image\n",
        "\n",
        "data = pd.DataFrame(columns=[\"ImageID\", \"Label\"])\n",
        "for filename in tqdm(os.listdir(\"/content/drive/My Drive/test_set\")):\n",
        "  img = Image.open(\"/content/drive/My Drive/test_set/\"+filename)\n",
        "  img = data_transforms['validation'](img)\n",
        "  img = torch.reshape(img, (1, 3, 224, 224))\n",
        "  input_img = img.to(device)\n",
        "  outputs = model(input_img)\n",
        "  outputs = outputs.cpu()\n",
        "  outputs = list(outputs[0].detach().numpy())\n",
        "  pred_class = classes[outputs.index(max(outputs))]\n",
        "  if pred_class =='basketball_court':\n",
        "    pred_label = 1\n",
        "  elif pred_class == 'beach':\n",
        "    pred_label = 2\n",
        "  elif pred_class == 'forest':\n",
        "    pred_label = 3\n",
        "  elif pred_class == 'railway':\n",
        "    pred_label = 4\n",
        "  elif pred_class == 'tennis_court':\n",
        "    pred_label = 5\n",
        "  elif pred_class == 'swimming_pool':\n",
        "    pred_label = 6\n",
        "  elif pred_class == 'others':\n",
        "    pred_label = 7\n",
        "  \n",
        "  data = data.append({\"ImageID\": int(filename[:4]),\n",
        "                      \"Label\": pred_label}, ignore_index=True)\n",
        "\n",
        "data = data.sort_values(by='ImageID', ascending=1)\n",
        "data.to_csv(\"submission.csv\", index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 95/95 [00:27<00:00,  3.39it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}