{"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\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\n# for 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","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from fastai.basics import *\nfrom fastai.callback.all import *\nfrom fastai.vision.all import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_x(df):\n    return '../input/cassava-leaf-disease-classification/train_images/' + str(df[\"image_id\"]) \ndef get_y(df):\n    s = df['label'] \n    return s","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df.info()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['label'].unique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df['label'] = df['label'].apply(lambda x: str(x))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_dls(size):\n    dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n                   get_x=get_x,\n                   get_y=get_y,\n                   item_tfms=Resize(460),\n                   batch_tfms=[*aug_transforms(size=size, min_scale=0.75),\n                               Normalize.from_stats(*imagenet_stats)])\n    return dblock.dataloaders(df, bs=32)\n\ndls = get_dls(128)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dls.show_batch(nrows=1, ncols=3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if not os.path.exists('/root/.cache/torch/hub/checkpoints/'):\n        os.makedirs('/root/.cache/torch/hub/checkpoints/')\n!cp '../input/resnet50/resnet50.pth' '/root/.cache/torch/hub/checkpoints/resnet50-19c8e357.pth'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nlearner = cnn_learner(dls, resnet50, loss_func=LabelSmoothingCrossEntropy(), metrics=accuracy\n                      , cbs=MixUp()\n                     )\n#loss_func=LabelSmoothingCrossEntropy()\n#learner = cnn_learner(dls, resnet18, pretrained=False, metrics=partial(accuracy_multi, thresh=0.2)).to_fp16() ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.lr_find()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.fine_tune(8, base_lr=2e-2, freeze_epochs=5) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#learner.save('presize')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#learner = learner.load('presize')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Now for some progressive resizing"},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.dls = get_dls(360) # progressive resizing\nlearner.lr_find()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.fine_tune(7, base_lr=2e-4, freeze_epochs=2) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learner.dls = get_dls(400) # progressive resizing\n# learner.lr_find()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learner.fine_tune(7, base_lr=1e-4, freeze_epochs=2) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.recorder.plot_loss()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Lets apply some test time augmentation"},{"metadata":{"trusted":true},"cell_type":"code","source":"learner.to_native_fp32()\npreds, targs = learner.tta()\nprint(accuracy(preds, targs))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path = Path('../input/cassava-leaf-disease-classification')\nsubmission_df = pd.read_csv(path/'sample_submission.csv')\nsubmission_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data_path = submission_df['image_id'].apply(lambda x: path/'test_images'/x)\ntst_dl = learner.dls.test_dl(test_data_path)\npredictions = learner.tta(dl = tst_dl, n=10, beta=0)\n\nsubmission_df['label'] = np.argmax(predictions[0],axis=1)\nsubmission_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df.to_csv('submission.csv',index=False)","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}