{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Setup Environment"},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install ../input/fastai2-offline/timm-0.2.1-py3-none-any.whl ../input/fastai2-offline/wwf-0.0.3-py3-none-any.whl -q","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom fastai.vision.all import *\nfrom wwf.vision.timm import *\n\nimport os","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"set_seed(42, reproducible=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Import Train Data"},{"metadata":{"trusted":true},"cell_type":"code","source":"path = Path('../input/ranzcr-clip-catheter-line-classification')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train_df = pd.read_csv(path/'train.csv')\ntrain_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Image.open(path/'train'/(train_df['StudyInstanceUID'][0]+'.jpg'))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create Dataloaders"},{"metadata":{"trusted":true},"cell_type":"code","source":"columns = list(train_df.columns[1:12])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_x(r):\n    return path/'train'/(r['StudyInstanceUID']+'.jpg')\n\ndef get_y(r):\n    return r[columns].values.tolist()\n\ndef get_data(size=224,bs=128,data_df=train_df):\n    dblock = DataBlock(blocks=(ImageBlock, MultiCategoryBlock(encoded=True,vocab=columns)),\n                       splitter=RandomSplitter(seed=42),\n                       get_x=get_x, \n                       get_y=get_y,\n                       item_tfms = RandomResizedCrop(460, min_scale=0.75, ratio=(1.,1.)),\n                       batch_tfms = [*aug_transforms(size=size, max_warp=0),\n                                     Normalize.from_stats(*imagenet_stats)]\n                      )\n    return dblock.dataloaders(data_df,bs=bs)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dls = get_data()\ndls.show_batch()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Create learner"},{"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'\n!cp '../input/timm-pretrained-efficientnet/efficientnet/efficientnet_b0_ra-3dd342df.pth' '/root/.cache/torch/hub/checkpoints/efficientnet_b0_ra-3dd342df.pth'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learn = cnn_learner(dls, resnet50, metrics=accuracy_multi).to_native_fp16()\nlearn = timm_learner(dls, 'efficientnet_b0',metrics=accuracy_multi).to_native_fp16()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learn.lr_find()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fine_tune(5, base_lr=3e-2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = learn.to_native_fp32()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Make Submission file"},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df = pd.read_csv(path/'sample_submission.csv')\nsubmission_df.iloc[:,1:] = submission_df.iloc[:,1:].astype(float)\nsubmission_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_data_path = submission_df['StudyInstanceUID'].apply(lambda x: path/'test'/(x + '.jpg'))\ntst_dl = learn.dls.test_dl(test_data_path)\npreds,targs = learn.tta(dl = tst_dl,n=10, beta=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df[columns] = pd.DataFrame(preds,columns=columns)\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}