{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":62057,"databundleVersionId":6714922,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load Data","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms\nfrom PIL import Image\n\n# Load data\ntrain_df = pd.read_csv('/kaggle/input/applications-of-deep-learning-wustl-fall-2023/train.csv')\ntest_df = pd.read_csv('/kaggle/input/applications-of-deep-learning-wustl-fall-2023/test.csv')\n\nclass GlassesDataset(Dataset):\n    def __init__(self, dataframe, root_dir, transform=None, is_test=False):\n        self.dataframe = dataframe\n        self.root_dir = root_dir\n        self.transform = transform\n        self.is_test = is_test\n    \n    def __len__(self):\n        return len(self.dataframe)\n    \n    def __getitem__(self, idx):\n        img_name = self.root_dir + self.dataframe.iloc[idx, 1]\n        image = Image.open(img_name)\n        if self.transform:\n            image = self.transform(image)\n        \n        if self.is_test:\n            return image\n        else:\n            label = self.dataframe.iloc[idx, 2]\n            return image, label\n\ntransform = transforms.Compose([\n    transforms.Resize((128, 128)),\n    transforms.ToTensor(),\n])","metadata":{"execution":{"iopub.status.busy":"2024-05-09T16:57:07.670701Z","iopub.execute_input":"2024-05-09T16:57:07.671168Z","iopub.status.idle":"2024-05-09T16:57:07.862763Z","shell.execute_reply.started":"2024-05-09T16:57:07.671134Z","shell.execute_reply":"2024-05-09T16:57:07.860781Z"},"trusted":true},"execution_count":null,"outputs":[]}]}