{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport sys\nfrom tensorflow.keras.models import load_model\nimport cv2\nimport skimage.io\n\nsys.path.insert(0, '/kaggle/input/efficientnet-keras-source-code/')\nimport efficientnet.tfkeras as efn\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"img_size = 512\ntest_dir = '/kaggle/input/prostate-cancer-grade-assessment/test_images'\nmodel = load_model('/kaggle/input/panda-efficientnetb7-on-tpu/model.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_image(img_name):\n    data_dir = test_dir\n    img_path = os.path.join(data_dir, f'{img_name}.tiff')    \n    img = skimage.io.MultiImage(img_path)        \n    img = cv2.resize(img[-1], (img_size, img_size))    \n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    img = img / 255\n    return img","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub = pd.read_csv('/kaggle/input/prostate-cancer-grade-assessment/sample_submission.csv')\ntest_images = sub['image_id'].values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = []\ntry:\n    for image in test_images:                \n        img = get_image(image)    \n        im1 = img.reshape((1, img_size, img_size, 3))    \n        preds = model.predict(im1, batch_size=1)    \n        result = np.argmax(preds ,axis = 1)               \n        labels.append(result)\n    sub['isup_grade'] = labels\nexcept:\n    print('exception')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub['isup_grade'] = sub['isup_grade'].astype(int)\nsub.to_csv('submission.csv', index=False)\nsub.head()","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}