{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 in \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 \"../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\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport cv2\nimport os\nfrom fastai.vision import *\nfrom fastai.metrics import KappaScore\nfrom pathlib import Path","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_path = Path(\"/kaggle/input/aptos2019-blindness-detection\")\nout_path = Path(\"/kaggle/working\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv(base_path/\"train.csv\")\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test = pd.read_csv(base_path/\"test.csv\")\ntest.head()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['diagnosis'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"kappa = KappaScore(weights=\"quadratic\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = (ImageList.from_df(train, base_path, folder='train_images', suffix=\".png\")\n                   .split_by_rand_pct()\n                   .label_from_df()\n                   .transform(get_transforms(), size=256)\n                   .databunch(bs=64)\n     ).normalize(imagenet_stats)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.show_batch(rows=3, figsize=(7,6))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(\"Using device:\", device)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = cnn_learner(data, models.resnet50, metrics=[kappa], pretrained=True, model_dir=out_path)\nlearn.model.cuda();\nlearn.lr_find()\nlearn.recorder.plot()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(5, max_lr=3e-3)\nlearn.save(out_path/\"stage-1\")\nlearn.recorder.plot_losses()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.unfreeze()\nlearn.lr_find()\nlearn.recorder.plot()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(5, max_lr=3e-4)\nlearn.save(out_path/\"stage-2\")\nlearn.recorder.plot_losses()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.read_csv(base_path/\"sample_submission.csv\")\nlearn.data.add_test(ImageList.from_df(submission,Path(base_path),folder='test_images',suffix='.png'))\n\npreds = []\nfrom tqdm import tqdm_notebook as tqdm\nfor i in tqdm(learn.data.test_ds):\n    preds.append(int(learn.predict(i[0])[1]))\nprint(len(preds))\n\nsubmission[\"diagnosis\"] = np.asarray(preds).reshape(-1, 1)\nsubmission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}