{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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","execution":{"iopub.status.busy":"2023-07-26T13:31:24.565028Z","iopub.execute_input":"2023-07-26T13:31:24.5654Z","iopub.status.idle":"2023-07-26T13:31:24.605233Z","shell.execute_reply.started":"2023-07-26T13:31:24.56537Z","shell.execute_reply":"2023-07-26T13:31:24.604406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import zipfile\nimport glob\nfrom torchvision.io import read_image","metadata":{"execution":{"iopub.status.busy":"2023-07-26T13:53:12.257343Z","iopub.execute_input":"2023-07-26T13:53:12.257971Z","iopub.status.idle":"2023-07-26T13:53:12.263115Z","shell.execute_reply.started":"2023-07-26T13:53:12.257933Z","shell.execute_reply":"2023-07-26T13:53:12.261954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path = \"/kaggle/input/dogs-vs-cats-redux-kernels-edition/\"\n\nwith zipfile.ZipFile(path + \"train.zip\") as train_zip:\n    train_zip.extractall(\"/kaggle/working\")\n    \nwith zipfile.ZipFile(path + \"test.zip\") as test_zip:\n    test_zip.extractall(\"/kaggle/working\")","metadata":{"execution":{"iopub.status.busy":"2023-07-26T13:36:53.956162Z","iopub.execute_input":"2023-07-26T13:36:53.956732Z","iopub.status.idle":"2023-07-26T13:37:12.845659Z","shell.execute_reply.started":"2023-07-26T13:36:53.956698Z","shell.execute_reply":"2023-07-26T13:37:12.844382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_list = glob.glob(os.path.join('/kaggle/working/train', '*.jpg'))\ntest_list = glob.glob(os.path.join('/kaggle/working/test', '*.jpg'))","metadata":{"execution":{"iopub.status.busy":"2023-07-26T13:53:14.204496Z","iopub.execute_input":"2023-07-26T13:53:14.204885Z","iopub.status.idle":"2023-07-26T13:53:14.365248Z","shell.execute_reply.started":"2023-07-26T13:53:14.204855Z","shell.execute_reply":"2023-07-26T13:53:14.364181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class loadDataset(Dataset):\n    \n    def __init__(self,file_lst, transform=None):    \n        self.file_lst = file_lst\n        self.transform = transform\n        \n    def __len__(self):\n        return len(self.file_lst)\n    \n    def __getitem__(self, idx):\n        img_path = self.file_lst[idx]\n        img = read_image(img_path)\n        label = self.transform(img) #function to label\n        # Get Label\n        if label == 'dog':\n            label = 1\n        elif label == 'cat':\n            label = 0\n        return img, label","metadata":{"execution":{"iopub.status.busy":"2023-07-26T13:54:34.17966Z","iopub.execute_input":"2023-07-26T13:54:34.180722Z","iopub.status.idle":"2023-07-26T13:54:34.228818Z","shell.execute_reply.started":"2023-07-26T13:54:34.180684Z","shell.execute_reply":"2023-07-26T13:54:34.227383Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataset = loadDataset(train_list, transform= None)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}