arch_style_clasif.ipynb

arch_style_clasif.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/gist/MioOgbeni/550aa3a740b3fff98f55b63f3d808b82/arch_style_clasif.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "bIuNz9rR4lIK",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff8e53c3-1fc0-4b44-bbc4-98f14799c0e4"
      },
      "source": [
        "! rm -r cropDataset\n",
        "! rm -r origDataset\n",
        "\n",
        "! gdown https://drive.google.com/u/0/uc?id=1knoUofDhpHDBLkjGSb-1Fd68SnLCREye\n",
        "! gdown https://drive.google.com/u/0/uc?id=1hhdiCKEUgwWpmg3eVgk0Pppb7hcPWlsE\n",
        "\n",
        "! unzip -q cropDataset-20201128T173038Z-001.zip\n",
        "! unzip -q origDataset-20201128T174342Z-001.zip\n",
        "\n",
        "! rm cropDataset-20201128T173038Z-001.zip\n",
        "! rm origDataset-20201128T174342Z-001.zip\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'cropDataset': No such file or directory\n",
            "rm: cannot remove 'origDataset': No such file or directory\n",
            "Downloading...\n",
            "From: https://drive.google.com/u/0/uc?id=1knoUofDhpHDBLkjGSb-1Fd68SnLCREye\n",
            "To: /content/cropDataset-20201128T173038Z-001.zip\n",
            "464MB [00:07, 65.3MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/u/0/uc?id=1hhdiCKEUgwWpmg3eVgk0Pppb7hcPWlsE\n",
            "To: /content/origDataset-20201128T174342Z-001.zip\n",
            "775MB [00:07, 105MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfc3c93d-b10b-4505-c346-4b9a3c4275fa"
      },
      "source": [
        "# Setup missing modules\n",
        "! pip install -q neptune-client\n",
        "! pip install -q scikit-plot\n",
        "! pip install -q pytorch-lightning\n",
        "! pip install -q pytorch-lightning-bolts\n",
        "! pip install -q pretrainedmodels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 102kB 8.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 829kB 18.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 10.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 204kB 50.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 163kB 48.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 10.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 133kB 60.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 12.1MB/s \n",
            "\u001b[?25h  Building wheel for neptune-client (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for strict-rfc3339 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 675kB 13.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 92kB 12.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 276kB 48.8MB/s \n",
            "\u001b[?25h  Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 194kB 18.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 6.7MB/s \n",
            "\u001b[?25h  Building wheel for pretrainedmodels (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSmQlSobe8M7"
      },
      "source": [
        "# Imports\n",
        "\n",
        "import os\n",
        "\n",
        "import neptune\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pretrainedmodels\n",
        "import cv2\n",
        "import imutils\n",
        "import random\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import PIL \n",
        "from PIL import Image\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import DataLoader, random_split, Dataset\n",
        "from torch.optim.lr_scheduler import LambdaLR\n",
        "\n",
        "import torchvision\n",
        "from torchvision import transforms, datasets, models\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from pytorch_lightning.metrics.functional import accuracy\n",
        "from pytorch_lightning.loggers.neptune import NeptuneLogger\n",
        "from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint, EarlyStopping\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from scikitplot.metrics import plot_confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SpNqCJJWqfP5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37f64315-1c33-438b-aa66-ee43f4f17718"
      },
      "source": [
        "# Check available device\n",
        "\n",
        "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"
        }
      ]
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      "source": [
        "# Download additional models\n",
        "\n",
        "model_name = 'inceptionresnetv2'\n",
        "model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')\n",
        "print(model.std)\n",
        "print(model.mean)\n",
        "print(model.input_size)\n",
        "print(model.last_linear.in_features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth\" to /root/.cache/torch/hub/checkpoints/inceptionresnetv2-520b38e4.pth\n"
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            "\n",
            "[0.5, 0.5, 0.5]\n",
            "[0.5, 0.5, 0.5]\n",
            "[3, 299, 299]\n",
            "1536\n"
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    },
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      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "# Hyper Parameters\n",
        "\n",
        "LightningModule_Params = {'n_classes': 25,\n",
        "                          'learning_rate': 0.0023,\n",
        "                          'decay_factor': 0.95}\n",
        "\n",
        "LearningRateMonitor_Params = {'logging_interval': 'epoch'}\n",
        "\n",
        "ModelCheckpoint_Params = {'filepath': 'my_model/checkpoints/{epoch:02d}-{val_loss:.2f}',\n",
        "                          'save_weights_only': False,\n",
        "                          'save_top_k': 5,\n",
        "                          'monitor': 'val_loss',\n",
        "                          'period': 1}\n",
        "\n",
        "LightningDataModule_Params = {'batch_size': 4,\n",
        "                              'num_workers': 4,\n",
        "                              'resize': 320,\n",
        "                              #'resize': 256,\n",
        "                              'crop': 299,\n",
        "                              #'crop': 224,\n",
        "                              'normalization_vector': ((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),}\n",
        "                              #'normalization_vector': ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),}\n",
        "\n",
        "LightningDataModuleSaliency_Params = {'batch_size': 4,\n",
        "                              'num_workers': 4,\n",
        "                              #'resize': 299,\n",
        "                              'resize': 224,\n",
        "                              #'normalization_vector': ((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),}\n",
        "                              'normalization_vector': ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),}\n",
        "\n",
        "EarlyStopping_Params = {'monitor': 'val_acc',\n",
        "                        'min_delta': 0.00,\n",
        "                        'patience': 4,\n",
        "                        'verbose': True,\n",
        "                        'mode': 'max'}\n",
        "\n",
        "Trainer_Params = {'gpus': 1,\n",
        "                  'log_every_n_steps': 100,\n",
        "                  'min_epochs': 1,\n",
        "                  'max_epochs': 100,\n",
        "                  'auto_lr_find': False,\n",
        "                  'auto_scale_batch_size': False,\n",
        "                  'track_grad_norm': 2}\n",
        "\n",
        "ALL_PARAMS = {**LightningModule_Params,\n",
        "              #**LightningDataModule_Params,\n",
        "              **LightningDataModuleSaliency_Params,\n",
        "              **LearningRateMonitor_Params,\n",
        "              **ModelCheckpoint_Params,\n",
        "              **Trainer_Params}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "KS9QR8w1xE_H"
      },
      "source": [
        "# Classes\n",
        "\n",
        "# Data module class\n",
        "\n",
        "class ArchitectureDataModule(pl.LightningDataModule):\n",
        "\n",
        "    def __init__(self, batch_size, num_workers, resize, crop, normalization_vector):\n",
        "        super().__init__()\n",
        "        self.batch_size = batch_size\n",
        "        self.num_workers = num_workers\n",
        "        self.resize = resize\n",
        "        self.crop = crop\n",
        "        self.normalization_vector = normalization_vector\n",
        "\n",
        "    def setup(self, stage):\n",
        "        # transforms\n",
        "        transform=transforms.Compose([\n",
        "            transforms.Resize((self.resize, self.resize)),\n",
        "            transforms.CenterCrop(self.crop),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize(self.normalization_vector[0], self.normalization_vector[1])\n",
        "        ])\n",
        "\n",
        "        if stage == 'fit':\n",
        "            dataset =  datasets.ImageFolder('origDataset/', transform=transform)\n",
        "            self.classes = dataset.classes\n",
        "\n",
        "            dataset_len = len(dataset)\n",
        "            val_len = int(math.ceil((dataset_len * 0.2) / 100.0)) * 100\n",
        "            train_len = dataset_len - val_len\n",
        "\n",
        "            self.dataset_train, self.dataset_val = random_split(dataset, [train_len, val_len])\n",
        "\n",
        "        if stage == 'test':\n",
        "            self.dataset_test = datasets.ImageFolder('origDataset/', transform=transform)\n",
        "            self.classes = self.dataset_test.classes\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        self.dataloader_train = DataLoader(self.dataset_train, batch_size=self.batch_size, num_workers=self.num_workers,shuffle=True)\n",
        "        return self.dataloader_train\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        self.dataloader_val = DataLoader(self.dataset_val, batch_size=self.batch_size, num_workers=self.num_workers)\n",
        "        return self.dataloader_val\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        self.dataloader_test = DataLoader(self.dataset_test, batch_size=self.batch_size, num_workers=self.num_workers,shuffle=True)\n",
        "        return self.dataloader_test\n",
        "\n",
        "    def show_preview(self, stage):\n",
        "        self.setup(stage)\n",
        "\n",
        "        if stage == 'fit':\n",
        "            # train\n",
        "            print('TRAIN DATALOADER PREVIEW')\n",
        "            self.train_dataloader()\n",
        "            inputs, classes = next(iter(self.dataloader_train))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "          \n",
        "            # validation\n",
        "            print('VALIDATION DATALOADER PREVIEW')\n",
        "            self.val_dataloader()\n",
        "            inputs, classes = next(iter(self.dataloader_val))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "        if stage == 'test':\n",
        "            # test\n",
        "            print('TEST DATALOADER PREVIEW')\n",
        "            self.test_dataloader()\n",
        "            inputs, classes = next(iter(self.dataset_test))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "    def show_inp(self, inputs, title):\n",
        "        inputs = inputs.numpy().transpose((1, 2, 0))\n",
        "        mean = np.array(self.normalization_vector[0])\n",
        "        std = np.array(self.normalization_vector[1])\n",
        "\n",
        "        inputs = std * inputs + mean\n",
        "        inputs = np.clip(inputs, 0, 1)\n",
        "\n",
        "        plt.imshow(inputs)\n",
        "        if title is not None:\n",
        "            plt.title(title)\n",
        "        plt.pause(0.001)\n",
        "\n",
        "# Data module class\n",
        "\n",
        "class ArchitectureDataModuleSalienced(pl.LightningDataModule):\n",
        "\n",
        "    def __init__(self, batch_size, num_workers, resize, normalization_vector):\n",
        "        super().__init__()\n",
        "        self.batch_size = batch_size\n",
        "        self.num_workers = num_workers\n",
        "        self.resize = resize\n",
        "        self.normalization_vector = normalization_vector\n",
        "\n",
        "    def setup(self, stage):\n",
        "        # transforms\n",
        "        transform=transforms.Compose([\n",
        "            transforms.Resize((self.resize, self.resize)),\n",
        "            transforms.ToTensor(),\n",
        "            transforms.Normalize(self.normalization_vector[0], self.normalization_vector[1])\n",
        "        ])\n",
        "\n",
        "        if stage == 'fit':\n",
        "            dataset =  datasets.ImageFolder('cropDataset/', transform=transform)\n",
        "            self.classes = dataset.classes\n",
        "\n",
        "            dataset_len = len(dataset)\n",
        "            val_len = int(math.ceil((dataset_len * 0.2) / 100.0)) * 100\n",
        "            train_len = dataset_len - val_len\n",
        "\n",
        "            self.dataset_train, self.dataset_val = random_split(dataset, [train_len, val_len])\n",
        "\n",
        "        if stage == 'test':\n",
        "            self.dataset_test = datasets.ImageFolder('cropDataset/', transform=transform)\n",
        "            self.classes = self.dataset_test.classes\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        self.dataloader_train = DataLoader(self.dataset_train, batch_size=self.batch_size, num_workers=self.num_workers,shuffle=True)\n",
        "        return self.dataloader_train\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        self.dataloader_val = DataLoader(self.dataset_val, batch_size=self.batch_size, num_workers=self.num_workers)\n",
        "        return self.dataloader_val\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        self.dataloader_test = DataLoader(self.dataset_test, batch_size=self.batch_size, num_workers=self.num_workers,shuffle=True)\n",
        "        return self.dataloader_test\n",
        "\n",
        "    def show_preview(self, stage):\n",
        "        self.setup(stage)\n",
        "\n",
        "        if stage == 'fit':\n",
        "            # train\n",
        "            print('TRAIN DATALOADER PREVIEW')\n",
        "            self.train_dataloader()\n",
        "            inputs, classes = next(iter(self.dataloader_train))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "          \n",
        "            # validation\n",
        "            print('VALIDATION DATALOADER PREVIEW')\n",
        "            self.val_dataloader()\n",
        "            inputs, classes = next(iter(self.dataloader_val))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "        if stage == 'test':\n",
        "            # test\n",
        "            print('TEST DATALOADER PREVIEW')\n",
        "            self.test_dataloader()\n",
        "            inputs, classes = next(iter(self.dataset_test))\n",
        "            out = torchvision.utils.make_grid(inputs)\n",
        "\n",
        "            class_names = self.classes\n",
        "            self.show_inp(out, title=[class_names[x] for x in classes])\n",
        "\n",
        "    def show_inp(self, inputs, title):\n",
        "        inputs = inputs.numpy().transpose((1, 2, 0))\n",
        "        mean = np.array(self.normalization_vector[0])\n",
        "        std = np.array(self.normalization_vector[1])\n",
        "\n",
        "        inputs = std * inputs + mean\n",
        "        inputs = np.clip(inputs, 0, 1)\n",
        "\n",
        "        plt.imshow(inputs)\n",
        "        if title is not None:\n",
        "            plt.title(title)\n",
        "        plt.pause(0.001)\n",
        "\n",
        "# CNN module class\n",
        "\n",
        "class ChellesNet(pl.LightningModule):\n",
        "\n",
        "    def __init__(self, n_classes, learning_rate, decay_factor):\n",
        "        super().__init__()\n",
        "        self.n_classes = n_classes\n",
        "        self.learning_rate = learning_rate\n",
        "        self.decay_factor = decay_factor\n",
        "        model_name = 'inceptionresnetv2'\n",
        "        self.model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')\n",
        "        num_ftrs = self.model.last_linear.in_features\n",
        "        #self.model = torch.hub.load('pytorch/vision:v0.6.0', 'googlenet', pretrained=True)\n",
        "\n",
        "        \n",
        "        num_ftrs = self.model.fc.in_features\n",
        "        \n",
        "        self.model.fc = nn.Linear(num_ftrs, n_classes)\n",
        "        self.acc = pl.metrics.Accuracy()\n",
        "\n",
        "        self.save_hyperparameters()\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "    \n",
        "    def configure_optimizers(self):\n",
        "        optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n",
        "        scheduler = LambdaLR(optimizer, lambda epoch: self.decay_factor ** epoch)\n",
        "        return [optimizer], [scheduler]\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "\n",
        "        loss = F.cross_entropy(y_hat, y)\n",
        "        self.log('train_loss', loss, prog_bar=False)\n",
        "\n",
        "        y_true = y.cpu().detach().numpy()\n",
        "        y_pred = y_hat.argmax(axis=1).cpu().detach().numpy()\n",
        "\n",
        "        return {'loss': loss, 'y_true': y_true, 'y_pred': y_pred}\n",
        "\n",
        "    def training_epoch_end(self, outs):\n",
        "        y_true = np.array([])\n",
        "        y_pred = np.array([])\n",
        "\n",
        "        for results_dict in outs:\n",
        "            y_true = np.append(y_true, results_dict['y_true'])\n",
        "            y_pred = np.append(y_pred, results_dict['y_pred'])\n",
        "            loss = results_dict['loss']\n",
        "        acc = accuracy_score(y_true, y_pred)\n",
        "        self.log('train_acc', acc)\n",
        "        self.log('train_acc_loss_rate', loss/acc)\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        y_hat = self(x)\n",
        "        \n",
        "        loss = F.cross_entropy(y_hat, y)\n",
        "        self.log('val_loss', loss, prog_bar=False)\n",
        "\n",
        "        y_true = y.cpu().detach().numpy()\n",
        "        y_pred = y_hat.argmax(axis=1).cpu().detach().numpy()\n",
        "\n",
        "        return {'loss': loss, 'y_true': y_true, 'y_pred': y_pred}\n",
        "\n",
        "    def validation_epoch_end(self, outs):\n",
        "        y_true = np.array([])\n",
        "        y_pred = np.array([])\n",
        "\n",
        "        for results_dict in outs:\n",
        "            y_true = np.append(y_true, results_dict['y_true'])\n",
        "            y_pred = np.append(y_pred, results_dict['y_pred'])\n",
        "            loss = results_dict['loss']\n",
        "        acc = accuracy_score(y_true, y_pred)\n",
        "        self.log('val_acc', acc)\n",
        "        self.log('val_acc_loss_rate', loss/acc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itbqiLNv6Jdi"
      },
      "source": [
        "# Methods\n",
        "\n",
        "# Saliency Crop\n",
        "\n",
        "def saliency_crop(image_path):\n",
        "    image = cv2.imread(image_path) # Read picture\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    saliency = cv2.saliency.StaticSaliencySpectralResidual_create()\n",
        "    saliencyMap = saliency.computeSaliency(image)[1]\n",
        "    saliencyMap = (saliencyMap * 255).astype(\"uint8\")\n",
        "\n",
        "    blur = cv2.GaussianBlur(saliencyMap,(5,5),0)\n",
        "    threshMap = cv2.threshold(blur.astype(\"uint8\"), 0, 255, cv2.THRESH_TOZERO | cv2.THRESH_OTSU)[1]\n",
        "\n",
        "    contours = cv2.findContours(threshMap,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]  # Find all contours, each contour information is stored in the contours array\n",
        "\n",
        "    cont = np.vstack(contours)\n",
        "    hull = cv2.convexHull(cont)\n",
        "    x,y,w,h = cv.boundingRect(cont)\n",
        "    image = image[y:y+h, x:x+w]\n",
        "\n",
        "    image = Image.open(io.BytesIO(image_data))\n",
        "    image = image.crop((x,y,x+w,y+h))\n",
        "\n",
        "    return image\n",
        "\n",
        "\n",
        "# Image show\n",
        "\n",
        "def imgshow(inp, title=None):\n",
        "    inp = inp.numpy().transpose((1, 2, 0))\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "\n",
        "    inp = std * inp + mean\n",
        "    inp = np.clip(inp, 0, 1)\n",
        "\n",
        "    plt.imshow(inp)\n",
        "    if title is not None:\n",
        "        plt.title(title)\n",
        "    plt.pause(0.001)\n",
        "\n",
        "\n",
        "# Vizualize model\n",
        "\n",
        "def vizualize_model(model, data, num_images=6):\n",
        "  model.eval()\n",
        "  images_so_far = 0\n",
        "  fig = plt.figure()\n",
        "\n",
        "  for i, (inputs, labels) in enumerate(data.test_dataloader()):\n",
        "    if bool(random.getrandbits(1)):\n",
        "      continue\n",
        "\n",
        "    print(labels)\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "\n",
        "    outputs = model(inputs)\n",
        "    _, preds = torch.max(outputs, 1)\n",
        "\n",
        "    for j in range(inputs.size()[0]):\n",
        "      images_so_far = images_so_far+1\n",
        "      ax = plt.subplot(num_images//2, 2, images_so_far)\n",
        "      ax.axis(\"off\")\n",
        "      ax.set_title(\"Prediccted: {}, label: {}\".format(getattr(data, 'classes')[preds[j]], getattr(data, 'classes')[labels[j]]))\n",
        "\n",
        "      imgshow(inputs.cpu().data[j])\n",
        "\n",
        "      if images_so_far == num_images:\n",
        "        return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBy_y2sR3MHJ"
      },
      "source": [
        "lr_logger = LearningRateMonitor(**LearningRateMonitor_Params)\n",
        "\n",
        "model_checkpoint = ModelCheckpoint(**ModelCheckpoint_Params)\n",
        "\n",
        "neptune_logger = NeptuneLogger(\n",
        "    api_key='eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5haSIsImFwaV91cmwiOiJodHRwczovL3VpLm5lcHR1bmUuYWkiLCJhcGlfa2V5IjoiNjVhMTI2NWQtMGViMy00Zjk2LWFlMjAtMjQ5MmExMWJjYzk1In0=',\n",
        "    project_name='mioogbeni/chelles-net',\n",
        "    close_after_fit=False,\n",
        "    experiment_name=\"CP_GGLNET\",\n",
        "    params=ALL_PARAMS,\n",
        "    tags=['1.0', 'advanced', 'googlenet', 'saliency_crop'],)\n",
        "\n",
        "early_stop_callback = EarlyStopping(**EarlyStopping_Params)\n",
        "    \n",
        "trainer = pl.Trainer(logger=neptune_logger,\n",
        "                     checkpoint_callback=model_checkpoint,\n",
        "                     callbacks=[lr_logger,early_stop_callback],\n",
        "                     **Trainer_Params)\n",
        "\n",
        "model = ChellesNet(**LightningModule_Params)\n",
        "\n",
        "# dm = ArchitectureDataModule(**LightningDataModule_Params)\n",
        "dm = ArchitectureDataModuleSalienced(**LightningDataModuleSaliency_Params)\n",
        "dm.show_preview('fit')\n",
        "\n",
        "# Run Lr tuning\n",
        "lr_finder = trainer.tuner.lr_find(model, dm)\n",
        "\n",
        "lr_finder.results\n",
        "\n",
        "fig = lr_finder.plot(suggest=True)\n",
        "neptune_logger.experiment.log_image('learning_rate', fig)\n",
        "\n",
        "fig.show()\n",
        "\n",
        "new_lr = lr_finder.suggestion()\n",
        "neptune_logger.experiment.log_metric('learning_rate_suggested', new_lr)\n",
        "\n",
        "# LightningModule_Params['learning_rate'] = new_lr\n",
        "model = ChellesNet(**LightningModule_Params)\n",
        "\n",
        "# Run training\n",
        "\n",
        "trainer.fit(model, dm)\n",
        "\n",
        "# Run testing\n",
        "\n",
        "trainer.test(datamodule=dm)\n",
        "\n",
        "# Log confusion matrix\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from scikitplot.metrics import plot_confusion_matrix\n",
        "\n",
        "model.freeze()\n",
        "test_data = dm.test_dataloader()\n",
        "y_true = np.array([])\n",
        "y_pred = np.array([])\n",
        "\n",
        "for i, (x, y) in enumerate(test_data):\n",
        "    x, y = x.to(device), y.to(device)\n",
        "    y = y.cpu().detach().numpy()\n",
        "    y_hat = model.forward(x).argmax(axis=1).cpu().detach().numpy()\n",
        "\n",
        "    y_true = np.append(y_true, y)\n",
        "    y_pred = np.append(y_pred, y_hat)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16, 12))\n",
        "plot_confusion_matrix(y_true, y_pred, ax=ax)\n",
        "neptune_logger.experiment.log_image('confusion_matrix', fig)\n",
        "\n",
        "# Log model checkpoints to Neptune\n",
        "\n",
        "for k in model_checkpoint.best_k_models.keys():\n",
        "    model_name = 'checkpoints/' + k.split('/')[-1]\n",
        "    neptune_logger.experiment.log_artifact(k, model_name)\n",
        "\n",
        "# Log best model checkpoint score to Neptune\n",
        "\n",
        "neptune_logger.experiment.log_metric('best_model_score', model_checkpoint.best_model_score.tolist())\n",
        "\n",
        "# Log model summary\n",
        "\n",
        "for chunk in [x for x in str(model).split('\\n')]:\n",
        "    neptune_logger.experiment.log_text('model_summary', str(chunk))\n",
        "\n",
        "# Log number of GPU units used\n",
        "\n",
        "neptune_logger.experiment.log_metric('num_gpus', trainer.num_gpus)\n",
        "\n",
        "# Stop Neptune logger at the end\n",
        "\n",
        "neptune_logger.experiment.stop()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "NrWoTWYF9GTk",
        "outputId": "d8a93f26-d884-4fae-ca03-a3376cd76ce6"
      },
      "source": [
        "project = neptune.init(\r\n",
        "    'mioogbeni/chelles-net', \r\n",
        "    'eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5haSIsImFwaV91cmwiOiJodHRwczovL3VpLm5lcHR1bmUuYWkiLCJhcGlfa2V5IjoiNjVhMTI2NWQtMGViMy00Zjk2LWFlMjAtMjQ5MmExMWJjYzk1In0='\r\n",
        "    )\r\n",
        "\r\n",
        "experiments = project.get_experiments(tag='final')\r\n",
        "\r\n",
        "metrics_df = pd.DataFrame(columns=['id', 'train_acc', 'name'])\r\n",
        "for experiment in experiments:\r\n",
        "    df = experiment.get_numeric_channels_values('train_acc')\r\n",
        "    df.insert(loc=0, column='id', value=experiment.id)\r\n",
        "    df.insert(loc=1, column='name', value=experiment.name)\r\n",
        "    metrics_df = metrics_df.append(df, sort=True)\r\n",
        "\r\n",
        "# Prepare dataframe\r\n",
        "metrics_df.sort_values(by='train_acc', ascending=False, inplace=True)\r\n",
        "\r\n",
        "# Make seaborn plot\r\n",
        "sns.set_style(\"darkgrid\")\r\n",
        "g = sns.relplot(x='x', y='train_acc', data=metrics_df, hue='id', kind=\"line\")\r\n",
        "g.set(ylabel='train_acc')\r\n",
        "g.fig.set_size_inches(10, 4.5)\r\n",
        "\r\n",
        "g.savefig(\"output.png\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x324 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md6xf48X_zct",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "3d2626f5-efde-45cd-a74c-ca015fc9d6f6"
      },
      "source": [
        "project = neptune.init(\n",
        "    'mioogbeni/chelles-net', \n",
        "    'eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5haSIsImFwaV91cmwiOiJodHRwczovL3VpLm5lcHR1bmUuYWkiLCJhcGlfa2V5IjoiNjVhMTI2NWQtMGViMy00Zjk2LWFlMjAtMjQ5MmExMWJjYzk1In0='\n",
        "    )\n",
        "\n",
        "finalDashboard= project.get_leaderboard(tag='final')\n",
        "\n",
        "finalExperiment = project.get_experiments(id='CHEL-84')[0]\n",
        "finalExperiment.download_artifacts('checkpoints/')\n",
        "\n",
        "! unzip -q checkpoints.zip\n",
        "! dir checkpoints"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>created</th>\n",
              "      <th>finished</th>\n",
              "      <th>owner</th>\n",
              "      <th>notes</th>\n",
              "      <th>running_time</th>\n",
              "      <th>size</th>\n",
              "      <th>tags</th>\n",
              "      <th>channel_best_model_score</th>\n",
              "      <th>channel_epoch</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch0.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch0.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch0.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.0.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.0.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.0.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.1.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.1.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.1.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.2.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.2.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.branch1.2.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.conv2d.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.block8.conv2d.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv1.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv1.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv1.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_1a.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_1a.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_1a.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2a.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2a.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2a.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2b.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2b.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.conv2d_2b.conv.weight</th>\n",
              "      <th>...</th>\n",
              "      <th>channel_grad_2.0_norm_model.repeat_2.8.branch1.2.bn.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.repeat_2.8.branch1.2.bn.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.repeat_2.8.branch1.2.conv.weight</th>\n",
              "      <th>channel_grad_2.0_norm_model.repeat_2.8.conv2d.bias</th>\n",
              "      <th>channel_grad_2.0_norm_model.repeat_2.8.conv2d.weight</th>\n",
              "      <th>channel_grad_2.0_norm_total</th>\n",
              "      <th>channel_learning_rate_suggested</th>\n",
              "      <th>channel_lr-SGD</th>\n",
              "      <th>channel_model_summary</th>\n",
              "      <th>channel_num_gpus</th>\n",
              "      <th>channel_train_acc</th>\n",
              "      <th>channel_train_acc_loss_rate</th>\n",
              "      <th>channel_train_loss</th>\n",
              "      <th>channel_val_acc</th>\n",
              "      <th>channel_val_acc_loss_rate</th>\n",
              "      <th>channel_val_loss</th>\n",
              "      <th>parameter_auto_lr_find</th>\n",
              "      <th>parameter_auto_scale_batch_size</th>\n",
              "      <th>parameter_batch_size</th>\n",
              "      <th>parameter_crop</th>\n",
              "      <th>parameter_decay_factor</th>\n",
              "      <th>parameter_filepath</th>\n",
              "      <th>parameter_gpus</th>\n",
              "      <th>parameter_learning_rate</th>\n",
              "      <th>parameter_log_every_n_steps</th>\n",
              "      <th>parameter_logging_interval</th>\n",
              "      <th>parameter_max_epochs</th>\n",
              "      <th>parameter_min_epochs</th>\n",
              "      <th>parameter_monitor</th>\n",
              "      <th>parameter_n_classes</th>\n",
              "      <th>parameter_normalization_vector</th>\n",
              "      <th>parameter_num_workers</th>\n",
              "      <th>parameter_period</th>\n",
              "      <th>parameter_resize</th>\n",
              "      <th>parameter_save_top_k</th>\n",
              "      <th>parameter_save_weights_only</th>\n",
              "      <th>parameter_track_grad_norm</th>\n",
              "      <th>property_param__decay_factor</th>\n",
              "      <th>property_param__learning_rate</th>\n",
              "      <th>property_param__n_classes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEL-74</td>\n",
              "      <td>CP_GGLNET</td>\n",
              "      <td>2020-12-03 09:17:06.635000+00:00</td>\n",
              "      <td>2020-12-03 09:54:41.337000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>2254</td>\n",
              "      <td>116712139</td>\n",
              "      <td>[1.0, advanced, googlenet, central_crop, final]</td>\n",
              "      <td>0.9072269797325134</td>\n",
              "      <td>23.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0447</td>\n",
              "      <td>0.1192</td>\n",
              "      <td>0.9058</td>\n",
              "      <td>0.0843</td>\n",
              "      <td>0.0864</td>\n",
              "      <td>0.6692</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.6388</td>\n",
              "      <td>1.3182567385564074E-7</td>\n",
              "      <td>7.441271534395307E-4</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8993147074327886</td>\n",
              "      <td>1.665102243423462</td>\n",
              "      <td>0.11259587854146957</td>\n",
              "      <td>0.737</td>\n",
              "      <td>0.3190460205078125</td>\n",
              "      <td>0.9072269797325134</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEL-75</td>\n",
              "      <td>CP_GGLNET</td>\n",
              "      <td>2020-12-03 10:00:40.578000+00:00</td>\n",
              "      <td>2020-12-03 10:34:25.391000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>2024</td>\n",
              "      <td>116434229</td>\n",
              "      <td>[1.0, advanced, googlenet, central_crop, final]</td>\n",
              "      <td>0.9464539289474487</td>\n",
              "      <td>21.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0451</td>\n",
              "      <td>0.1912</td>\n",
              "      <td>1.9233</td>\n",
              "      <td>0.1616</td>\n",
              "      <td>0.1464</td>\n",
              "      <td>0.9746</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0748</td>\n",
              "      <td>2.2908676527677735E-7</td>\n",
              "      <td>8.245176215396463E-4</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8755930416447022</td>\n",
              "      <td>0.4725327491760254</td>\n",
              "      <td>0.25583016872406006</td>\n",
              "      <td>0.721</td>\n",
              "      <td>1.6605457067489624</td>\n",
              "      <td>0.9464539289474487</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEL-76</td>\n",
              "      <td>CP_INCPRSNET2</td>\n",
              "      <td>2020-12-03 11:19:14.304000+00:00</td>\n",
              "      <td>2020-12-03 12:40:00.760000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>4846</td>\n",
              "      <td>1130545460</td>\n",
              "      <td>[1.0, advanced, inceptionresnetv2, central_cro...</td>\n",
              "      <td>1.0595057010650635</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0054</td>\n",
              "      <td>0.0055</td>\n",
              "      <td>0.1082</td>\n",
              "      <td>0.0247</td>\n",
              "      <td>0.0346</td>\n",
              "      <td>0.9519</td>\n",
              "      <td>0.0124</td>\n",
              "      <td>0.017</td>\n",
              "      <td>0.4537</td>\n",
              "      <td>0.0245</td>\n",
              "      <td>0.0377</td>\n",
              "      <td>0.3684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3764</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.018</td>\n",
              "      <td>0.0235</td>\n",
              "      <td>0.1036</td>\n",
              "      <td>0.0194</td>\n",
              "      <td>0.0334</td>\n",
              "      <td>0.1435</td>\n",
              "      <td>0.0085</td>\n",
              "      <td>0.0245</td>\n",
              "      <td>0.1939</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0052</td>\n",
              "      <td>0.0055</td>\n",
              "      <td>0.158</td>\n",
              "      <td>0.0188</td>\n",
              "      <td>0.1967</td>\n",
              "      <td>2.9857</td>\n",
              "      <td>0.05248074602497723</td>\n",
              "      <td>0.0010122913378990603</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9604638903531892</td>\n",
              "      <td>0.5816752314567566</td>\n",
              "      <td>0.03915056958794594</td>\n",
              "      <td>0.745</td>\n",
              "      <td>0.0429389551281929</td>\n",
              "      <td>1.2311694622039795</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEL-77</td>\n",
              "      <td>CP_INCPRSNET2</td>\n",
              "      <td>2020-12-03 12:48:25.443000+00:00</td>\n",
              "      <td>2020-12-03 14:17:08.908000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>5323</td>\n",
              "      <td>1131600148</td>\n",
              "      <td>[1.0, advanced, inceptionresnetv2, central_cro...</td>\n",
              "      <td>1.2169110774993896</td>\n",
              "      <td>19.0</td>\n",
              "      <td>8.0E-4</td>\n",
              "      <td>7.0E-4</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.0056</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0029</td>\n",
              "      <td>0.003</td>\n",
              "      <td>0.0858</td>\n",
              "      <td>0.0047</td>\n",
              "      <td>0.0078</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0034</td>\n",
              "      <td>0.005</td>\n",
              "      <td>0.0174</td>\n",
              "      <td>0.0036</td>\n",
              "      <td>0.0052</td>\n",
              "      <td>0.0303</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>0.0056</td>\n",
              "      <td>0.0333</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>0.0017</td>\n",
              "      <td>0.0378</td>\n",
              "      <td>0.0057</td>\n",
              "      <td>0.0427</td>\n",
              "      <td>0.4134</td>\n",
              "      <td>0.04365158322401657</td>\n",
              "      <td>9.135929324539018E-4</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9757511860832894</td>\n",
              "      <td>0.07880431413650513</td>\n",
              "      <td>0.004632722120732069</td>\n",
              "      <td>0.708</td>\n",
              "      <td>1.840059757232666</td>\n",
              "      <td>1.3916211128234863</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEL-80</td>\n",
              "      <td>CP_INCPRSNET2</td>\n",
              "      <td>2020-12-03 14:26:16.958000+00:00</td>\n",
              "      <td>2020-12-03 15:34:16.150000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>4079</td>\n",
              "      <td>1128930798</td>\n",
              "      <td>[1.0, advanced, inceptionresnetv2, saliency_cr...</td>\n",
              "      <td>1.0880546569824219</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0095</td>\n",
              "      <td>0.0128</td>\n",
              "      <td>0.1811</td>\n",
              "      <td>0.166</td>\n",
              "      <td>0.243</td>\n",
              "      <td>7.2996</td>\n",
              "      <td>0.0746</td>\n",
              "      <td>0.0817</td>\n",
              "      <td>2.9742</td>\n",
              "      <td>0.1832</td>\n",
              "      <td>0.2266</td>\n",
              "      <td>2.1228</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.7278</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2291</td>\n",
              "      <td>0.9383</td>\n",
              "      <td>0.1658</td>\n",
              "      <td>0.2214</td>\n",
              "      <td>1.2334</td>\n",
              "      <td>0.0662</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>1.7317</td>\n",
              "      <td>...</td>\n",
              "      <td>0.042</td>\n",
              "      <td>0.0414</td>\n",
              "      <td>1.3903</td>\n",
              "      <td>0.2582</td>\n",
              "      <td>2.2522</td>\n",
              "      <td>23.0887</td>\n",
              "      <td>0.05248074602497723</td>\n",
              "      <td>0.001180686791542861</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9681075382182394</td>\n",
              "      <td>0.2669737935066223</td>\n",
              "      <td>0.12437985092401505</td>\n",
              "      <td>0.727</td>\n",
              "      <td>0.7939651012420654</td>\n",
              "      <td>1.4071052074432373</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>CHEL-81</td>\n",
              "      <td>CP_INCPRSNET2</td>\n",
              "      <td>2020-12-03 15:38:25.782000+00:00</td>\n",
              "      <td>2020-12-03 16:39:09.987000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>3644</td>\n",
              "      <td>1127881883</td>\n",
              "      <td>[1.0, advanced, inceptionresnetv2, saliency_cr...</td>\n",
              "      <td>1.0347622632980347</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.0329</td>\n",
              "      <td>0.0615</td>\n",
              "      <td>0.7894</td>\n",
              "      <td>0.2473</td>\n",
              "      <td>0.3618</td>\n",
              "      <td>11.0825</td>\n",
              "      <td>0.0991</td>\n",
              "      <td>0.1447</td>\n",
              "      <td>5.0778</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.2593</td>\n",
              "      <td>3.8908</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.7657</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.3729</td>\n",
              "      <td>0.4664</td>\n",
              "      <td>1.7669</td>\n",
              "      <td>0.3958</td>\n",
              "      <td>0.3161</td>\n",
              "      <td>2.1624</td>\n",
              "      <td>0.1358</td>\n",
              "      <td>0.5211</td>\n",
              "      <td>2.8642</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0675</td>\n",
              "      <td>0.152</td>\n",
              "      <td>2.5513</td>\n",
              "      <td>0.2168</td>\n",
              "      <td>2.5729</td>\n",
              "      <td>41.3295</td>\n",
              "      <td>0.036307805477010104</td>\n",
              "      <td>0.0013082402122358572</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9148655772272009</td>\n",
              "      <td>0.5936670303344727</td>\n",
              "      <td>0.7617248296737671</td>\n",
              "      <td>0.704</td>\n",
              "      <td>0.08358468860387802</td>\n",
              "      <td>1.1933084726333618</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>CHEL-82</td>\n",
              "      <td>CP_GGLNET</td>\n",
              "      <td>2020-12-03 16:49:45.867000+00:00</td>\n",
              "      <td>2020-12-03 17:21:29.039000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>1903</td>\n",
              "      <td>116291064</td>\n",
              "      <td>[1.0, advanced, googlenet, saliency_crop, final]</td>\n",
              "      <td>0.9267977476119995</td>\n",
              "      <td>20.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>0.236</td>\n",
              "      <td>1.8474</td>\n",
              "      <td>0.2056</td>\n",
              "      <td>0.2453</td>\n",
              "      <td>1.3847</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.1371</td>\n",
              "      <td>1.9054607179632475E-7</td>\n",
              "      <td>8.679132858312067E-4</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8695308381655246</td>\n",
              "      <td>4.518149375915527</td>\n",
              "      <td>0.2879756689071655</td>\n",
              "      <td>0.712</td>\n",
              "      <td>3.2194178104400635</td>\n",
              "      <td>0.9455347061157227</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>CHEL-84</td>\n",
              "      <td>CP_GGLNET</td>\n",
              "      <td>2020-12-03 18:48:09.144000+00:00</td>\n",
              "      <td>2020-12-03 19:20:31.318000+00:00</td>\n",
              "      <td>mioogbeni</td>\n",
              "      <td></td>\n",
              "      <td>1942</td>\n",
              "      <td>116936495</td>\n",
              "      <td>[1.0, advanced, googlenet, saliency_crop, final]</td>\n",
              "      <td>0.8739921450614929</td>\n",
              "      <td>25.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.057</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>1.4629</td>\n",
              "      <td>0.1563</td>\n",
              "      <td>0.1765</td>\n",
              "      <td>1.0801</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.5219</td>\n",
              "      <td>7.585775750291837E-8</td>\n",
              "      <td>6.715747559791765E-4</td>\n",
              "      <td>)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.9175013178703215</td>\n",
              "      <td>0.5827581286430359</td>\n",
              "      <td>0.40393757820129395</td>\n",
              "      <td>0.743</td>\n",
              "      <td>2.0625903606414795</td>\n",
              "      <td>0.8818655014038086</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.95</td>\n",
              "      <td>my_model/checkpoints/{epoch:02d}-{val_loss:.2f}</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>100.0</td>\n",
              "      <td>epoch</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>val_loss</td>\n",
              "      <td>25.0</td>\n",
              "      <td>((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>224.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>False</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.0023</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 913 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  ... property_param__n_classes\n",
              "0  CHEL-74  ...                        25\n",
              "1  CHEL-75  ...                        25\n",
              "2  CHEL-76  ...                        25\n",
              "3  CHEL-77  ...                        25\n",
              "4  CHEL-80  ...                        25\n",
              "5  CHEL-81  ...                        25\n",
              "6  CHEL-82  ...                        25\n",
              "7  CHEL-84  ...                        25\n",
              "\n",
              "[8 rows x 913 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P73rC6b07DSM"
      },
      "source": [
        "model = ChellesNet.load_from_checkpoint('checkpoints/checkpoint.ckpt')\n",
        "\n",
        "torch.save(model, 'chel-model.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ovujd42ks-m8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "cd6d5faf-c146-44d5-a3c0-d4ae7f831bbc"
      },
      "source": [
        "dir=\"./origDataset/\"\n",
        "subdir = os.listdir(dir)\n",
        "\n",
        "subdir_path = []\n",
        "for subdir in subdir:\n",
        "    subdir_path.append(os.path.join(dir, subdir))\n",
        "\n",
        "\n",
        "subdir_path = subdir_path[random.randint(0, len(subdir_path))]\n",
        "choises = random.sample(os.listdir(subdir_path), 1)\n",
        "\n",
        "images = []\n",
        "for choise in choises:\n",
        "    images.append(os.path.join(subdir_path, choise))\n",
        "\n",
        "columns = 3\n",
        "rows = len(images)\n",
        "fig = plt.figure(figsize=(20,10))\n",
        "for i in range(1, rows +1):\n",
        "    image_path=images[i-1]\n",
        "    image = cv2.imread(image_path) # Read picture\n",
        "\n",
        "    fig.add_subplot(rows, columns, ((i*columns)-2))\n",
        "\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) \n",
        "    plt.imshow(image)\n",
        "\n",
        "    saliency = cv2.saliency.StaticSaliencySpectralResidual_create()\n",
        "    saliencyMap = saliency.computeSaliency(image)[1]\n",
        "    saliencyMap = (saliencyMap * 255).astype(\"uint8\")\n",
        "\n",
        "    blur = cv2.GaussianBlur(saliencyMap,(5,5),0)\n",
        "    threshMap = cv2.threshold(blur.astype(\"uint8\"), 0, 255, cv2.THRESH_TOZERO | cv2.THRESH_OTSU)[1]\n",
        "\n",
        "    fig.add_subplot(rows, columns, ((i*columns)-1))\n",
        "    plt.imshow(threshMap)\n",
        "\n",
        "    contours = cv2.findContours(threshMap,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]  # Find all contours, each contour information is stored in the contours array\n",
        "\n",
        "    cont = np.vstack(contours)\n",
        "    hull = cv2.convexHull(cont)\n",
        "    x,y,w,h = cv2.boundingRect(cont)\n",
        "    image = image[y:y+h, x:x+w]\n",
        "\n",
        "    image = Image.open(image_path)\n",
        "    image = image.crop((x,y,x+w,y+h))\n",
        "\n",
        "    fig.add_subplot(rows, columns, (i*columns))\n",
        "    plt.imshow(image, cmap='gray')\n",
        "\n",
        "plt.savefig('saliency.png')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmCYRGB5zmjw"
      },
      "source": [
        "orig_dir=\"./origDataset/\"\n",
        "crop_dir=\"./cropDataset/\"\n",
        "\n",
        "\n",
        "sub_dir = os.listdir(orig_dir)\n",
        "\n",
        "for orig_sub_dir in sub_dir:\n",
        "    os.makedirs(os.path.join(crop_dir, orig_sub_dir))\n",
        "    sub_dir_pth = os.path.join(orig_dir, orig_sub_dir)\n",
        "\n",
        "    images_nms = os.listdir(sub_dir_pth)\n",
        "\n",
        "    for name in images_nms:\n",
        "        image_path = os.path.join(sub_dir_pth,name)\n",
        "\n",
        "        image = saliency_crop(image_path)\n",
        "\n",
        "        crop_pth = os.path.join(crop_dir, os.path.join(orig_sub_dir, name))\n",
        "\n",
        "        image.save(crop_pth)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}