{"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\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.MTL_dataloader import get_train_validation_generator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"det_csv_path = \"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_labels.csv\"\nseg_csv_path = \"/kaggle/input/siim-acr-pneumothorax-segmentation-data/train-rle.csv\"\ndet_images_path = \"/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_train_images/\"\nseg_images_path = \"/kaggle/input/siim-acr-pneumothorax-segmentation-data/dicom-images-train/\"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Prepare data generators","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"train_gen,val_gen = get_train_validation_generator(det_csv_path,seg_csv_path , det_images_path, seg_images_path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Build MTL model","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"from MultiCheXNet.utils.ModelBlock import ModelBlock\nfrom MultiCheXNet.utils.Encoder import Encoder\nfrom MultiCheXNet.utils.Detector import Detector\nfrom MultiCheXNet.utils.Segmenter import Segmenter\nfrom MultiCheXNet.utils.Classifier import Classifier\n\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nimport numpy as np\nfrom MultiCheXNet.utils.loss.MTL_loss import MTL_loss\nfrom tensorflow.keras.optimizers import Adam","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"encoder = Encoder( ) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classifier = Classifier(encoder)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img_size = 256\nn_classes=1\ndetector=Detector(encoder,img_size, n_classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"segmenter = Segmenter(encoder)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"MTL_model = ModelBlock.add_heads(encoder,[classifier,detector,segmenter ])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#from tensorflow.keras.utils import plot_model\n#plot_model(MTL_model)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#MTL_model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nclassification_loss= \"categorical_crossentropy\"\ndetection_loss= detector.loss\nsegmentation_loss= segmenter.loss\n\nmtl_loss= MTL_loss(classification_loss , detection_loss ,segmentation_loss)\nlossWeights = [1.0,1.0,1.0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"INIT_LR = 1e-4\nEPOCHS =20","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nopt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n\nMTL_model.compile(optimizer=opt, loss=mtl_loss, metrics=[])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"MTL_model.fit_generator(train_gen)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}