{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install /kaggle/input/kerasapplications -q\n!pip install /kaggle/input/efficientnet-keras-source-code/ -q --no-deps","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os, gc\n\nimport efficientnet.tfkeras as efn\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Unhide below to see helper functions:"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true,"_kg_hide-output":false,"_kg_hide-input":true},"cell_type":"code","source":"def auto_select_accelerator():\n    try:\n        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n        tf.config.experimental_connect_to_cluster(tpu)\n        tf.tpu.experimental.initialize_tpu_system(tpu)\n        strategy = tf.distribute.experimental.TPUStrategy(tpu)\n        print(\"Running on TPU:\", tpu.master())\n    except ValueError:\n        strategy = tf.distribute.get_strategy()\n    print(f\"Running on {strategy.num_replicas_in_sync} replicas\")\n    \n    return strategy\n\n\ndef build_decoder(with_labels=True, target_size=(300, 300), ext='jpg'):\n    def decode(path):\n        file_bytes = tf.io.read_file(path)\n        if ext == 'png':\n            img = tf.image.decode_png(file_bytes, channels=3)\n        elif ext in ['jpg', 'jpeg']:\n            img = tf.image.decode_jpeg(file_bytes, channels=3)\n        else:\n            raise ValueError(\"Image extension not supported\")\n\n        img = tf.cast(img, tf.float32) / 255.0\n        img = tf.image.resize(img, target_size)\n\n        return img\n    \n    def decode_with_labels(path, label):\n        return decode(path), label\n    \n    return decode_with_labels if with_labels else decode\n\n\ndef build_augmenter(with_labels=True):\n    def augment(img):\n        img = tf.image.random_flip_left_right(img)\n        img = tf.image.random_flip_up_down(img)\n        return img\n    \n    def augment_with_labels(img, label):\n        return augment(img), label\n    \n    return augment_with_labels if with_labels else augment\n\n\ndef build_dataset(paths, labels=None, bsize=32, cache=True,\n                  decode_fn=None, augment_fn=None,\n                  augment=True, repeat=True, shuffle=1024, \n                  cache_dir=\"\"):\n    if cache_dir != \"\" and cache is True:\n        os.makedirs(cache_dir, exist_ok=True)\n    \n    if decode_fn is None:\n        decode_fn = build_decoder(labels is not None)\n    \n    if augment_fn is None:\n        augment_fn = build_augmenter(labels is not None)\n    \n    AUTO = tf.data.experimental.AUTOTUNE\n    slices = paths if labels is None else (paths, labels)\n    \n    dset = tf.data.Dataset.from_tensor_slices(slices)\n    dset = dset.map(decode_fn, num_parallel_calls=AUTO)\n    dset = dset.cache(cache_dir) if cache else dset\n    dset = dset.map(augment_fn, num_parallel_calls=AUTO) if augment else dset\n    dset = dset.repeat() if repeat else dset\n    dset = dset.shuffle(shuffle) if shuffle else dset\n    dset = dset.batch(bsize).prefetch(AUTO)\n    \n    return dset","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"COMPETITION_NAME = \"ranzcr-clip-catheter-line-classification\"\nstrategy = auto_select_accelerator()\nBATCH_SIZE = strategy.num_replicas_in_sync * 16","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_paths = [\n    '../input/ranzcr-last-models/0.952_model_640_47.h5',\n    '../input/ranzcr-last-models/0.953_model_616_51.h5',\n    '../input/ranzcr-last-models/0.953_model_640_43.h5',\n    '../input/ranzcr-last-models/0.954_model_640_42.h5',\n    '../input/ranzcr-last-models/0.954_model_632_48.h5',\n]\n\nsubs = []\nfor model_path in model_paths:\n    load_dir = f\"/kaggle/input/{COMPETITION_NAME}/\"\n    sub_df = pd.read_csv(load_dir + 'sample_submission.csv')\n    test_paths = load_dir + \"test/\" + sub_df['StudyInstanceUID'] + '.jpg'\n\n    label_cols = sub_df.columns[1:]\n\n    image_size = int(model_path.split('_')[2])\n    test_decoder = build_decoder(with_labels=False, target_size=(image_size, image_size))\n    dtest = build_dataset(\n        test_paths, bsize=BATCH_SIZE, repeat=False, \n        shuffle=False, augment=False, cache=False,\n        decode_fn=test_decoder\n    )\n    with strategy.scope():\n        model = tf.keras.models.load_model(model_path)\n\n    print('predict:', model_path)\n        \n    sub_df[label_cols] = model.predict(dtest, verbose=1)\n    subs.append(sub_df)\n    \n    del model\n    gc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.concat(subs)\nsubmission = submission.groupby('StudyInstanceUID').mean()\nsubmission.to_csv('submission.csv')\nsubmission","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}