{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"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\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 5GB 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":"!git clone https://github.com/ahmadelsallab/MultiCheXNet.git","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from MultiCheXNet.data_loader.RSNA_dataloader import get_train_validation_generator\nfrom MultiCheXNet.MTL_model import MTL_model\nfrom MultiCheXNet.evaluation.evaluate import evaluate\nfrom tensorflow.keras.optimizers import Adam","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"csv_path = \"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv\"\nimages_path=\"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_images/\"\ntrain_gen , val_gen = get_train_validation_generator(csv_path,images_path,augmentation=True,hist_eq=True,normalize=True)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Build Model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"detector_clss = MTL_model(add_class_head=False,add_detector_head=True,add_segmenter_head=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"detector_clss.MTL_model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Train Model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"lr= 1e-4\nepochs=2\ndetector_clss.MTL_model.compile(loss=detector_clss.get_MTL_loss() , optimizer = Adam(lr))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = detector_clss.MTL_model.fit_generator(train_gen,  validation_data = val_gen , epochs=epochs)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Evaluate Model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"mAP = evaluate(val_gen, detector_clss.MTL_model , model_type=\"detector\" ,anchors=detector_clss.detector.anchors )","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}