train_resnet.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "colab": {
      "name": "DL_CS.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Y3Z4LCshWk0"
      },
      "source": [
        "## **Connect to drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IutWF1UAKyoO",
        "outputId": "4c174b6a-dd9c-43ca-e3e3-db7cca022ae2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhB9Jxx2_VZd"
      },
      "source": [
        "## **Change the following paths**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-VZo9pP_QM4"
      },
      "source": [
        "import os",
        "\n",
        "\n",
        "# path where train csv file stored\n",
        "csv_file_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/train.truth.csv'\n",
        "\n",
        "\n",
        "# path of the training data\n",
        "train_data_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/train'\n",
        "\n",
        "\n",
        "# path to save the trained model \n",
        "save_model = '/content/drive/My Drive/Assignments/Deeper_Systems/resnet'\n",
        "\n",
        "if not os.path.exists(save_model):\n",
        "    os.makedirs(save_model)\n",
        "\n",
        "\n",
        "# path of the testing data\n",
        "test_path = '/content/drive/My Drive/Assignments/Deeper_Systems/data/test'\n",
        "test_image_list = os.listdir(test_path)\n",
        "\n",
        "\n",
        "# path to save the testing images after correcting the orientation\n",
        "corrected_image_path = '/content/drive/My Drive/Assignments/Deeper_Systems/resnet/corrected_images'\n",
        "\n",
        "if not os.path.exists(corrected_image_path):\n",
        "    os.makedirs(corrected_image_path)\n",
        "\n",
        "\n",
        "# path to save the results\n",
        "path_to_save = '/content/drive/My Drive/Assignments/Deeper_Systems/resnet/result'\n",
        "\n",
        "if not os.path.exists(path_to_save):\n",
        "    os.makedirs(path_to_save)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1d1xw2t9hkbm"
      },
      "source": [
        "## **Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIpJWjDSNWdI"
      },
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "from skimage import io\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmxFWuawhxRZ"
      },
      "source": [
        "## **Check device**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dn2UEGaCNWe_",
        "outputId": "d6fd2259-a2d2-483f-c083-012d15d1e076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9AcDYi5h15Y"
      },
      "source": [
        "## **Map labels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-MuUpfHS79j"
      },
      "source": [
        "def map_label(l):\n",
        "  d = {'rotated_right':0, 'rotated_left':1, 'upright':2, 'upside_down':3}\n",
        "  return d[l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lSHAZUbg2Jc"
      },
      "source": [
        "def return_label(l):\n",
        "  d = {0:'rotated_right', 1:'rotated_left', 2:'upright', 3:'upside_down'}\n",
        "  return d[l]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJDGWG0Dh69B"
      },
      "source": [
        "## **DataLoader**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_wD7Hq7OV7i"
      },
      "source": [
        "class create_dataloader(Dataset):\n",
        "\n",
        "    def __init__(self, csv_file, root_dir, transform):\n",
        "        self.data = pd.read_csv(csv_file)\n",
        "        self.root_dir = root_dir\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        if torch.is_tensor(idx):\n",
        "            idx = idx.tolist()\n",
        "\n",
        "        img_name = os.path.join(self.root_dir, self.data.fn[idx])\n",
        "\n",
        "        try:\n",
        "          image = io.imread(img_name)\n",
        "        except:\n",
        "          image = np.empty([64, 64, 3])\n",
        "\n",
        "        label = map_label(self.data.label[idx])\n",
        "        image = self.transform(image)\n",
        "        sample = {'image': image, 'label': label}\n",
        "\n",
        "        return sample"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1O-7KzQpOpI9"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "                        [transforms.ToTensor()\n",
        "                        ])\n",
        "\n",
        "train_dataset = create_dataloader(csv_file=csv_file_path,\n",
        "                                  root_dir=train_data_path,\n",
        "                                  transform=transform)\n",
        "\n",
        "training_data = DataLoader(dataset=train_dataset, batch_size=4, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taJUcPT3iAC2"
      },
      "source": [
        "## **Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FIykcq-iw7k"
      },
      "source": [
        "# 3*3 convolution\n",
        "def conv3x3(in_channels, out_channels, stride=1):\n",
        "    return nn.Conv2d(in_channels, out_channels, kernel_size=3,\n",
        "                    stride=stride, padding=1, bias=False)\n",
        "\n",
        "# Residual block\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride=1, downsample=None):\n",
        "        super(ResidualBlock, self).__init__()\n",
        "        self.conv1 = conv3x3(in_channels, out_channels, stride)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.conv2 = conv3x3(out_channels, out_channels)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "        self.downsample = downsample\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "        if self.downsample:\n",
        "            residual = self.downsample(x)\n",
        "        out += residual\n",
        "        out = self.relu(out)\n",
        "        return out\n",
        "\n",
        "# ResNet\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, layers, num_classes=4):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_channels = 64\n",
        "        self.conv = nn.Conv2d(3, 64, 5)\n",
        "        self.bn = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "        self.max_pool = nn.MaxPool2d(3)\n",
        "        self.layer1 = self.make_layer(block, 64, layers[0])\n",
        "        self.layer2 = self.make_layer(block, 128, layers[0], 2)\n",
        "        self.layer3 = self.make_layer(block, 256, layers[1], 2)\n",
        "        self.layer4 = self.make_layer(block, 512, layers[1], 2)\n",
        "        self.avg_pool = nn.AvgPool2d(3)\n",
        "        self.fc = nn.Linear(512, num_classes)\n",
        "\n",
        "    def make_layer(self, block, out_channels, blocks, stride=1):\n",
        "        downsample = None\n",
        "        if (stride != 1) or (self.in_channels != out_channels):\n",
        "            downsample = nn.Sequential(\n",
        "                conv3x3(self.in_channels, out_channels, stride=stride),\n",
        "                nn.BatchNorm2d(out_channels))\n",
        "        layers = []\n",
        "        layers.append(block(self.in_channels, out_channels, stride, downsample))\n",
        "        self.in_channels = out_channels\n",
        "        for i in range(1, blocks):\n",
        "            layers.append(block(out_channels, out_channels))\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.conv(x)\n",
        "        out = self.bn(out)\n",
        "        # print(out.shape)\n",
        "        out = self.max_pool(out)\n",
        "        # print(out.shape)\n",
        "        out = self.layer1(out)\n",
        "        # print(out.shape)\n",
        "        out = self.layer2(out)\n",
        "        # print(out.shape)\n",
        "        out = self.layer3(out)\n",
        "        # print(out.shape)\n",
        "        out = self.layer4(out)\n",
        "        # print(out.shape)\n",
        "        out = self.avg_pool(out)\n",
        "        # print(out.shape)\n",
        "        out = out.view(out.size(0), -1)\n",
        "        out = self.fc(out)\n",
        "        out = torch.softmax(out, dim=1)\n",
        "        return out\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NG8ZboNjLR_"
      },
      "source": [
        "## **Initialize Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLyw2QsTjJ5I"
      },
      "source": [
        "''' Network '''\n",
        "\n",
        "net_args = {\n",
        "        \"block\": ResidualBlock,\n",
        "        \"layers\": [2, 2, 2, 2]\n",
        "    }\n",
        "net = ResNet(**net_args).to(device)\n",
        "\n",
        "''' Loss Function '''\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "''' Optimizer '''\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.0001)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlBcFyPdiIo6"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiDuNJZ9NWeD",
        "outputId": "03f0d6c5-9481-435d-9029-95e1492c9901",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for epoch in range(2):  # loop over the dataset multiple times\n",
        "\n",
        "    running_loss = 0.0\n",
        "    for i, batch in enumerate(training_data):\n",
        "        # get the inputs; data is a list of [inputs, labels]\n",
        "        image= batch['image']\n",
        "        label= batch['label']\n",
        "        image = image.to(device)\n",
        "        label = label.to(device)\n",
        "\n",
        "        # zero the parameter gradients\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # forward + backward + optimize\n",
        "        logits = net(image.float())\n",
        "        loss = criterion(logits, label)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        print('Epoch Number -> {} Iteration Number: {} | Loss: {}'.format(epoch, i+1, loss))\n",
        "\n",
        "    PATH = os.path.join(save_model, 'train_net.pth')\n",
        "    torch.save(net.state_dict(), PATH)\n",
        "\n",
        "print('Finished Training')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "epoch number: 1 data_number: 7227 Loss: 0.7437856197357178\n",
            "epoch number: 1 data_number: 7228 Loss: 0.7437458038330078\n",
            "epoch number: 1 data_number: 7229 Loss: 0.7443171143531799\n",
            "epoch number: 1 data_number: 7230 Loss: 1.1500239372253418\n",
            "epoch number: 1 data_number: 7231 Loss: 0.8900158405303955\n",
            "epoch number: 1 data_number: 7232 Loss: 0.906859815120697\n",
            "epoch number: 1 data_number: 7233 Loss: 1.0818263292312622\n",
            "epoch number: 1 data_number: 7234 Loss: 0.9132832884788513\n",
            "epoch number: 1 data_number: 7235 Loss: 1.074206829071045\n",
            "epoch number: 1 data_number: 7236 Loss: 1.066899061203003\n",
            "epoch number: 1 data_number: 7237 Loss: 0.7439416646957397\n",
            "epoch number: 1 data_number: 7238 Loss: 0.9023317098617554\n",
            "epoch number: 1 data_number: 7239 Loss: 1.561038851737976\n",
            "epoch number: 1 data_number: 7240 Loss: 0.923812747001648\n",
            "epoch number: 1 data_number: 7241 Loss: 0.9084219932556152\n",
            "epoch number: 1 data_number: 7242 Loss: 0.9145650863647461\n",
            "epoch number: 1 data_number: 7243 Loss: 0.9093688130378723\n",
            "epoch number: 1 data_number: 7244 Loss: 0.7438052892684937\n",
            "epoch number: 1 data_number: 7245 Loss: 0.909253716468811\n",
            "epoch number: 1 data_number: 7246 Loss: 0.8106383681297302\n",
            "epoch number: 1 data_number: 7247 Loss: 0.9028953313827515\n",
            "epoch number: 1 data_number: 7248 Loss: 0.8991587162017822\n",
            "epoch number: 1 data_number: 7249 Loss: 1.086808681488037\n",
            "epoch number: 1 data_number: 7250 Loss: 0.8983038663864136\n",
            "epoch number: 1 data_number: 7251 Loss: 1.0570508241653442\n",
            "epoch number: 1 data_number: 7252 Loss: 1.0714528560638428\n",
            "epoch number: 1 data_number: 7253 Loss: 0.8976070880889893\n",
            "epoch number: 1 data_number: 7254 Loss: 0.7441450357437134\n",
            "epoch number: 1 data_number: 7255 Loss: 0.7448393702507019\n",
            "epoch number: 1 data_number: 7256 Loss: 1.0665788650512695\n",
            "epoch number: 1 data_number: 7257 Loss: 0.882056474685669\n",
            "epoch number: 1 data_number: 7258 Loss: 0.8962671756744385\n",
            "epoch number: 1 data_number: 7259 Loss: 0.9040507674217224\n",
            "epoch number: 1 data_number: 7260 Loss: 1.0599806308746338\n",
            "epoch number: 1 data_number: 7261 Loss: 1.4787089824676514\n",
            "epoch number: 1 data_number: 7262 Loss: 0.7983567118644714\n",
            "epoch number: 1 data_number: 7263 Loss: 0.743821382522583\n",
            "epoch number: 1 data_number: 7264 Loss: 0.8947744369506836\n",
            "epoch number: 1 data_number: 7265 Loss: 1.0574753284454346\n",
            "epoch number: 1 data_number: 7266 Loss: 0.8914120197296143\n",
            "epoch number: 1 data_number: 7267 Loss: 1.0697500705718994\n",
            "epoch number: 1 data_number: 7268 Loss: 0.8936664462089539\n",
            "epoch number: 1 data_number: 7269 Loss: 0.7479528188705444\n",
            "epoch number: 1 data_number: 7270 Loss: 1.1293723583221436\n",
            "epoch number: 1 data_number: 7271 Loss: 0.9061720371246338\n",
            "epoch number: 1 data_number: 7272 Loss: 0.9103485345840454\n",
            "epoch number: 1 data_number: 7273 Loss: 0.9199738502502441\n",
            "epoch number: 1 data_number: 7274 Loss: 1.1086379289627075\n",
            "epoch number: 1 data_number: 7275 Loss: 0.9125127792358398\n",
            "epoch number: 1 data_number: 7276 Loss: 0.9154689311981201\n",
            "epoch number: 1 data_number: 7277 Loss: 0.914467453956604\n",
            "epoch number: 1 data_number: 7278 Loss: 0.9014012813568115\n",
            "epoch number: 1 data_number: 7279 Loss: 0.8862293362617493\n",
            "epoch number: 1 data_number: 7280 Loss: 0.9169617295265198\n",
            "epoch number: 1 data_number: 7281 Loss: 0.8935424089431763\n",
            "epoch number: 1 data_number: 7282 Loss: 0.9066706895828247\n",
            "epoch number: 1 data_number: 7283 Loss: 1.0511971712112427\n",
            "epoch number: 1 data_number: 7284 Loss: 0.9885304570198059\n",
            "epoch number: 1 data_number: 7285 Loss: 0.7438820600509644\n",
            "epoch number: 1 data_number: 7286 Loss: 0.9790983200073242\n",
            "epoch number: 1 data_number: 7287 Loss: 0.7436909675598145\n",
            "epoch number: 1 data_number: 7288 Loss: 0.9310375452041626\n",
            "epoch number: 1 data_number: 7289 Loss: 1.058379888534546\n",
            "epoch number: 1 data_number: 7290 Loss: 0.7481554746627808\n",
            "epoch number: 1 data_number: 7291 Loss: 0.8987526297569275\n",
            "epoch number: 1 data_number: 7292 Loss: 0.9254705309867859\n",
            "epoch number: 1 data_number: 7293 Loss: 1.0413607358932495\n",
            "epoch number: 1 data_number: 7294 Loss: 1.3044854402542114\n",
            "epoch number: 1 data_number: 7295 Loss: 0.7437553405761719\n",
            "epoch number: 1 data_number: 7296 Loss: 0.91689532995224\n",
            "epoch number: 1 data_number: 7297 Loss: 0.917600691318512\n",
            "epoch number: 1 data_number: 7298 Loss: 0.9396679997444153\n",
            "epoch number: 1 data_number: 7299 Loss: 0.8702085018157959\n",
            "epoch number: 1 data_number: 7300 Loss: 0.936840295791626\n",
            "epoch number: 1 data_number: 7301 Loss: 1.306188941001892\n",
            "epoch number: 1 data_number: 7302 Loss: 0.7438281774520874\n",
            "epoch number: 1 data_number: 7303 Loss: 0.7437710762023926\n",
            "epoch number: 1 data_number: 7304 Loss: 0.8582342863082886\n",
            "epoch number: 1 data_number: 7305 Loss: 1.053844690322876\n",
            "epoch number: 1 data_number: 7306 Loss: 1.0561227798461914\n",
            "epoch number: 1 data_number: 7307 Loss: 1.3392338752746582\n",
            "epoch number: 1 data_number: 7308 Loss: 0.743790328502655\n",
            "epoch number: 1 data_number: 7309 Loss: 1.0702192783355713\n",
            "epoch number: 1 data_number: 7310 Loss: 0.9150736331939697\n",
            "epoch number: 1 data_number: 7311 Loss: 0.7440768480300903\n",
            "epoch number: 1 data_number: 7312 Loss: 0.868300199508667\n",
            "epoch number: 1 data_number: 7313 Loss: 0.955794632434845\n",
            "epoch number: 1 data_number: 7314 Loss: 1.1113054752349854\n",
            "epoch number: 1 data_number: 7315 Loss: 0.918293833732605\n",
            "epoch number: 1 data_number: 7316 Loss: 0.9011822938919067\n",
            "epoch number: 1 data_number: 7317 Loss: 0.9188414812088013\n",
            "epoch number: 1 data_number: 7318 Loss: 0.8991521000862122\n",
            "epoch number: 1 data_number: 7319 Loss: 1.0856029987335205\n",
            "epoch number: 1 data_number: 7320 Loss: 0.9054670333862305\n",
            "epoch number: 1 data_number: 7321 Loss: 1.0384466648101807\n",
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            "epoch number: 1 data_number: 12109 Loss: 0.9189544320106506\n",
            "epoch number: 1 data_number: 12110 Loss: 1.0706212520599365\n",
            "epoch number: 1 data_number: 12111 Loss: 0.7453718185424805\n",
            "epoch number: 1 data_number: 12112 Loss: 1.0467121601104736\n",
            "epoch number: 1 data_number: 12113 Loss: 1.070244312286377\n",
            "epoch number: 1 data_number: 12114 Loss: 0.8568964600563049\n",
            "epoch number: 1 data_number: 12115 Loss: 1.0151710510253906\n",
            "epoch number: 1 data_number: 12116 Loss: 0.7507046461105347\n",
            "epoch number: 1 data_number: 12117 Loss: 1.0755829811096191\n",
            "epoch number: 1 data_number: 12118 Loss: 0.7436889410018921\n",
            "epoch number: 1 data_number: 12119 Loss: 1.064774751663208\n",
            "epoch number: 1 data_number: 12120 Loss: 0.9255578517913818\n",
            "epoch number: 1 data_number: 12121 Loss: 0.8921500444412231\n",
            "epoch number: 1 data_number: 12122 Loss: 1.0531641244888306\n",
            "epoch number: 1 data_number: 12123 Loss: 0.7737336754798889\n",
            "epoch number: 1 data_number: 12124 Loss: 0.925687849521637\n",
            "epoch number: 1 data_number: 12125 Loss: 0.882334291934967\n",
            "epoch number: 1 data_number: 12126 Loss: 1.0068376064300537\n",
            "epoch number: 1 data_number: 12127 Loss: 0.8966552019119263\n",
            "epoch number: 1 data_number: 12128 Loss: 0.7437843680381775\n",
            "epoch number: 1 data_number: 12129 Loss: 0.7437793016433716\n",
            "epoch number: 1 data_number: 12130 Loss: 0.8875824809074402\n",
            "epoch number: 1 data_number: 12131 Loss: 0.9102554321289062\n",
            "epoch number: 1 data_number: 12132 Loss: 0.9346818327903748\n",
            "epoch number: 1 data_number: 12133 Loss: 0.9235638976097107\n",
            "epoch number: 1 data_number: 12134 Loss: 1.019931435585022\n",
            "epoch number: 1 data_number: 12135 Loss: 0.8791648149490356\n",
            "epoch number: 1 data_number: 12136 Loss: 0.8941728472709656\n",
            "epoch number: 1 data_number: 12137 Loss: 1.1031923294067383\n",
            "epoch number: 1 data_number: 12138 Loss: 1.1102378368377686\n",
            "epoch number: 1 data_number: 12139 Loss: 0.9628212451934814\n",
            "epoch number: 1 data_number: 12140 Loss: 0.8758190274238586\n",
            "epoch number: 1 data_number: 12141 Loss: 0.7436990737915039\n",
            "epoch number: 1 data_number: 12142 Loss: 1.3365854024887085\n",
            "epoch number: 1 data_number: 12143 Loss: 1.0107342004776\n",
            "epoch number: 1 data_number: 12144 Loss: 0.8790963888168335\n",
            "epoch number: 1 data_number: 12145 Loss: 1.056770920753479\n",
            "epoch number: 1 data_number: 12146 Loss: 0.7459965944290161\n",
            "epoch number: 1 data_number: 12147 Loss: 1.0167944431304932\n",
            "epoch number: 1 data_number: 12148 Loss: 1.121018886566162\n",
            "epoch number: 1 data_number: 12149 Loss: 0.8524967432022095\n",
            "epoch number: 1 data_number: 12150 Loss: 0.87174391746521\n",
            "epoch number: 1 data_number: 12151 Loss: 1.0528132915496826\n",
            "epoch number: 1 data_number: 12152 Loss: 0.8656052350997925\n",
            "epoch number: 1 data_number: 12153 Loss: 0.7437117695808411\n",
            "epoch number: 1 data_number: 12154 Loss: 0.7436872124671936\n",
            "epoch number: 1 data_number: 12155 Loss: 0.8071378469467163\n",
            "epoch number: 1 data_number: 12156 Loss: 0.743777334690094\n",
            "epoch number: 1 data_number: 12157 Loss: 0.7437483668327332\n",
            "epoch number: 1 data_number: 12158 Loss: 0.8605476021766663\n",
            "epoch number: 1 data_number: 12159 Loss: 1.0483652353286743\n",
            "epoch number: 1 data_number: 12160 Loss: 1.2410212755203247\n",
            "epoch number: 1 data_number: 12161 Loss: 1.0868045091629028\n",
            "epoch number: 1 data_number: 12162 Loss: 1.0856086015701294\n",
            "epoch number: 1 data_number: 12163 Loss: 1.3937050104141235\n",
            "epoch number: 1 data_number: 12164 Loss: 0.9312720894813538\n",
            "epoch number: 1 data_number: 12165 Loss: 0.7449034452438354\n",
            "epoch number: 1 data_number: 12166 Loss: 0.9319400191307068\n",
            "epoch number: 1 data_number: 12167 Loss: 0.9350579380989075\n",
            "epoch number: 1 data_number: 12168 Loss: 0.7437918186187744\n",
            "epoch number: 1 data_number: 12169 Loss: 0.7448014616966248\n",
            "epoch number: 1 data_number: 12170 Loss: 0.7436848878860474\n",
            "epoch number: 1 data_number: 12171 Loss: 0.7437174916267395\n",
            "epoch number: 1 data_number: 12172 Loss: 0.7436896562576294\n",
            "epoch number: 1 data_number: 12173 Loss: 0.9370994567871094\n",
            "epoch number: 1 data_number: 12174 Loss: 0.9377148151397705\n",
            "epoch number: 1 data_number: 12175 Loss: 0.860622763633728\n",
            "epoch number: 1 data_number: 12176 Loss: 0.9252155423164368\n",
            "epoch number: 1 data_number: 12177 Loss: 1.3687331676483154\n",
            "epoch number: 1 data_number: 12178 Loss: 1.0572810173034668\n",
            "epoch number: 1 data_number: 12179 Loss: 0.8780530691146851\n",
            "epoch number: 1 data_number: 12180 Loss: 0.8971539735794067\n",
            "epoch number: 1 data_number: 12181 Loss: 0.7474768161773682\n",
            "epoch number: 1 data_number: 12182 Loss: 0.8986001014709473\n",
            "epoch number: 1 data_number: 12183 Loss: 0.8954418301582336\n",
            "epoch number: 1 data_number: 12184 Loss: 0.941939115524292\n",
            "epoch number: 1 data_number: 12185 Loss: 0.7440906167030334\n",
            "epoch number: 1 data_number: 12186 Loss: 1.0280728340148926\n",
            "epoch number: 1 data_number: 12187 Loss: 1.327409029006958\n",
            "epoch number: 1 data_number: 12188 Loss: 1.0475685596466064\n",
            "epoch number: 1 data_number: 12189 Loss: 0.743751049041748\n",
            "epoch number: 1 data_number: 12190 Loss: 0.7437452077865601\n",
            "epoch number: 1 data_number: 12191 Loss: 0.8845892548561096\n",
            "epoch number: 1 data_number: 12192 Loss: 1.0490878820419312\n",
            "epoch number: 1 data_number: 12193 Loss: 0.7751008868217468\n",
            "epoch number: 1 data_number: 12194 Loss: 0.7436795830726624\n",
            "epoch number: 1 data_number: 12195 Loss: 0.9295722246170044\n",
            "epoch number: 1 data_number: 12196 Loss: 0.7436807751655579\n",
            "epoch number: 1 data_number: 12197 Loss: 0.7437155842781067\n",
            "epoch number: 1 data_number: 12198 Loss: 0.9369335174560547\n",
            "epoch number: 1 data_number: 12199 Loss: 1.1759326457977295\n",
            "epoch number: 1 data_number: 12200 Loss: 0.743718683719635\n",
            "epoch number: 1 data_number: 12201 Loss: 1.082908034324646\n",
            "epoch number: 1 data_number: 12202 Loss: 1.0818034410476685\n",
            "epoch number: 1 data_number: 12203 Loss: 0.937382161617279\n",
            "epoch number: 1 data_number: 12204 Loss: 0.8749958276748657\n",
            "epoch number: 1 data_number: 12205 Loss: 0.7436907291412354\n",
            "epoch number: 1 data_number: 12206 Loss: 0.9249840378761292\n",
            "epoch number: 1 data_number: 12207 Loss: 1.086379885673523\n",
            "epoch number: 1 data_number: 12208 Loss: 0.7467659711837769\n",
            "epoch number: 1 data_number: 12209 Loss: 0.7464614510536194\n",
            "epoch number: 1 data_number: 12210 Loss: 1.0545109510421753\n",
            "epoch number: 1 data_number: 12211 Loss: 1.1110678911209106\n",
            "epoch number: 1 data_number: 12212 Loss: 1.1398894786834717\n",
            "epoch number: 1 data_number: 12213 Loss: 1.0541002750396729\n",
            "epoch number: 1 data_number: 12214 Loss: 0.930640697479248\n",
            "epoch number: 1 data_number: 12215 Loss: 0.8894003629684448\n",
            "epoch number: 1 data_number: 12216 Loss: 0.9238890409469604\n",
            "epoch number: 1 data_number: 12217 Loss: 0.9239541292190552\n",
            "epoch number: 1 data_number: 12218 Loss: 1.0095237493515015\n",
            "epoch number: 1 data_number: 12219 Loss: 0.8704304099082947\n",
            "epoch number: 1 data_number: 12220 Loss: 0.8759565353393555\n",
            "epoch number: 1 data_number: 12221 Loss: 0.7437450885772705\n",
            "epoch number: 1 data_number: 12222 Loss: 0.927413821220398\n",
            "epoch number: 1 data_number: 12223 Loss: 0.9349186420440674\n",
            "epoch number: 1 data_number: 12224 Loss: 0.9297528266906738\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HFbJLAJiLQ6"
      },
      "source": [
        "## **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbNRP9sBcE1X",
        "outputId": "9014097b-1f80-4247-b8d2-d86330e2860c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "PATH = os.path.join(save_model, 'train_net.pth')\n",
        "\n",
        "''' Network '''\n",
        "# net = Net().to(device)\n",
        "\n",
        "net_args = {\n",
        "        \"block\": ResidualBlock,\n",
        "        \"layers\": [2, 2, 2, 2]\n",
        "    }\n",
        "net = ResNet(**net_args).to(device)\n",
        "\n",
        "net.load_state_dict(torch.load(PATH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yOP8J3Sf7Ct"
      },
      "source": [
        "transform = transforms.Compose(\n",
        "                        [transforms.ToTensor()\n",
        "                        ])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7nry2SINWev",
        "outputId": "10126217-cfa9-4e7d-851c-8fe9546459fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "test_image_array = list()\n",
        "\n",
        "predicted_label = list()\n",
        "\n",
        "for image_name in test_image_list:\n",
        "  img = os.path.join(test_path, image_name)\n",
        "  img = io.imread(img)\n",
        "  image = transform(img)\n",
        "  image = image.to(device)\n",
        "\n",
        "  image = torch.reshape(image, (1, image.shape[0], image.shape[1], image.shape[2]))\n",
        "\n",
        "  outputs = net(image)\n",
        "  _, predicted = torch.max(outputs, 1)\n",
        "  pred_label = predicted.item()\n",
        "  label = return_label(pred_label)\n",
        "  predicted_label.append(label)\n",
        "\n",
        "  # rotated_right\n",
        "  if pred_label==0:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)\n",
        "  # rotated_left\n",
        "  if pred_label==1:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n",
        "  # upside_down\n",
        "  if pred_label==3:\n",
        "    corrected_img = cv2.rotate(img, cv2.ROTATE_180)\n",
        "  # upright\n",
        "  if pred_label==2:\n",
        "    corrected_img = img\n",
        "\n",
        "  cv2.imwrite(os.path.join(corrected_image_path, image_name), corrected_img)\n",
        "  test_image_array.append(corrected_img)\n",
        "\n",
        "  print(image_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "91-7464891_1984-09-30_2013.jpg\n",
            "91-7194491_1893-05-08_1954.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M20gvaDLgJ8B",
        "outputId": "94e28756-42c6-4889-a01d-39c35f63babd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Create a dataframe from 2 lists\n",
        "\n",
        "data_df = {'fn': image_name, 'label': predicted_label}\n",
        "df = pd.DataFrame(data_df)\n",
        "\n",
        "df.to_csv(os.path.join(path_to_save, 'test.preds.csv'), index=False)\n",
        "\n",
        "print('File generated...')\n",
        "\n",
        "# save numpy array of corrected test image\n",
        "np.save(os.path.join(path_to_save, 'corrected_test_image_array'), test_image_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File generated...\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}