{"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_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install efficientnet -q","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:25:49.226371Z","iopub.execute_input":"2021-07-13T01:25:49.226914Z","iopub.status.idle":"2021-07-13T01:25:58.793223Z","shell.execute_reply.started":"2021-07-13T01:25:49.226805Z","shell.execute_reply":"2021-07-13T01:25:58.792304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\n\nimport efficientnet.tfkeras as efn\nimport numpy as np\nimport pandas as pd\nfrom kaggle_datasets import KaggleDatasets\nfrom sklearn.model_selection import train_test_split\nimport tensorflow as tf\nfrom sklearn.model_selection import GroupKFold\n# from functools import partial\n# from albumentations import (\n#     Compose, RandomBrightness, JpegCompression, HueSaturationValue, RandomContrast, HorizontalFlip,\n#     Rotate, ShiftScaleRotate, RandomBrightnessContrast\n# )","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:25:58.794494Z","iopub.execute_input":"2021-07-13T01:25:58.794791Z","iopub.status.idle":"2021-07-13T01:26:05.664833Z","shell.execute_reply.started":"2021-07-13T01:25:58.794736Z","shell.execute_reply":"2021-07-13T01:26:05.663536Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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=(256, 256), ext='jpg'):\n    def decode(path):\n        file_bytes = tf.io.read_file(path)\n\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        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        #img = tf.image.random_brightness(img, 0.2)\n        #img = tf.image.random_contrast(img, 0.0, 0.2)\n        \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# transforms = Compose([\n# #             Rotate(limit=40),\n# #             RandomBrightness(limit=0.1),\n# #             JpegCompression(quality_lower=85, quality_upper=100, p=0.5),\n# #             HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),\n# #             RandomContrast(limit=0.2, p=0.5),\n#             HorizontalFlip(),\n#             ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.0, rotate_limit=15, interpolation=1, p=0.5),\n#             RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),\n#         ])\n\n# def aug_fn(img):\n#     data = {\"img\":img}\n#     aug_data = transforms(**data)\n#     aug_img = aug_data[\"img\"]\n#     aug_img = tf.cast(aug_img/255.0, tf.float32)\n#     aug_img = tf.image.resize(aug_img, size=[512, 512])\n#     return aug_img\n\n# def build_augmenter(with_labels=True):\n#     def augment(img):\n#         aug_img = tf.numpy_function(func=aug_fn, inp=[img], Tout=tf.float32)\n#         return aug_img\n    \n#     def augment_with_labels(img, label):\n#         return augment(img), label\n#     return augment_with_labels if with_labels else augment\n\n\ndef build_dataset(paths, labels=None, bsize=128, 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.map(partial(augment_fn),num_parallel_calls=AUTO).prefetch(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","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:26:05.667088Z","iopub.execute_input":"2021-07-13T01:26:05.667507Z","iopub.status.idle":"2021-07-13T01:26:05.689964Z","shell.execute_reply.started":"2021-07-13T01:26:05.667463Z","shell.execute_reply":"2021-07-13T01:26:05.688684Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"COMPETITION_NAME = \"covid19-train-512px-png-data\"\nstrategy = auto_select_accelerator()\nBATCH_SIZE = strategy.num_replicas_in_sync * 16 #\nGCS_DS_PATH = KaggleDatasets().get_gcs_path(COMPETITION_NAME)","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:34:48.779155Z","iopub.execute_input":"2021-07-13T01:34:48.779594Z","iopub.status.idle":"2021-07-13T01:37:40.592861Z","shell.execute_reply.started":"2021-07-13T01:34:48.779557Z","shell.execute_reply":"2021-07-13T01:37:40.592024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"load_dir = f\"/kaggle/input/{COMPETITION_NAME}/\"\ndf = pd.read_csv('../input/covid19-train-csv-640/train.csv')\nlabel_cols = df.columns[4]","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:38:59.343975Z","iopub.execute_input":"2021-07-13T01:38:59.344308Z","iopub.status.idle":"2021-07-13T01:38:59.412931Z","shell.execute_reply.started":"2021-07-13T01:38:59.344280Z","shell.execute_reply":"2021-07-13T01:38:59.411893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gkf  = GroupKFold(n_splits = 5)\ndf['fold'] = -1\nfor fold, (train_idx, val_idx) in enumerate(gkf.split(df, groups = df.StudyInstanceUID.tolist())):\n    df.loc[val_idx, 'fold'] = fold\n","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:39:03.101425Z","iopub.execute_input":"2021-07-13T01:39:03.101808Z","iopub.status.idle":"2021-07-13T01:39:03.158333Z","shell.execute_reply.started":"2021-07-13T01:39:03.101761Z","shell.execute_reply":"2021-07-13T01:39:03.157273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(5):\n    \n    valid_paths = GCS_DS_PATH + '/image/' + df[df['fold'] == i]['id'] + '.png' #\"/train/\"\n    train_paths = GCS_DS_PATH + '/image/' + df[df['fold'] != i]['id'] + '.png' #\"/train/\" \n    valid_labels = df[df['fold'] == i][label_cols].values\n    train_labels = df[df['fold'] != i][label_cols].values\n\n    IMSIZE = (224, 240, 260, 300, 380, 456, 528, 600, 512)\n    IMS = 8\n\n    decoder = build_decoder(with_labels=True, target_size=(IMSIZE[IMS], IMSIZE[IMS]), ext='png')\n    test_decoder = build_decoder(with_labels=False, target_size=(IMSIZE[IMS], IMSIZE[IMS]),ext='png')\n\n    train_dataset = build_dataset(\n        train_paths, train_labels, bsize=BATCH_SIZE, decode_fn=decoder\n    )\n\n    valid_dataset = build_dataset(\n        valid_paths, valid_labels, bsize=BATCH_SIZE, decode_fn=decoder,\n        repeat=False, shuffle=False, augment=False\n    )\n    print('train:', train_dataset)\n    print('val:', valid_dataset)\n\n    try:\n        n_labels = train_labels.shape[1]\n    except:\n        n_labels = 1\n\n    with strategy.scope():\n        model = tf.keras.Sequential([\n            efn.EfficientNetB7(\n                input_shape=(IMSIZE[IMS], IMSIZE[IMS], 3),\n                weights='imagenet',\n                include_top=False),\n            tf.keras.layers.GlobalAveragePooling2D(),\n            tf.keras.layers.Dense(n_labels, activation='sigmoid')\n        ])\n        model.compile(\n            optimizer=tf.keras.optimizers.Adam(),\n            loss='binary_crossentropy',\n            metrics=[tf.keras.metrics.AUC(multi_label=True)])\n\n        model.summary()\n\n\n    steps_per_epoch = train_paths.shape[0] // BATCH_SIZE\n    checkpoint = tf.keras.callbacks.ModelCheckpoint(\n        f'model{i}.h5', save_best_only=True, monitor='val_loss', mode='min')\n    lr_reducer = tf.keras.callbacks.ReduceLROnPlateau(\n        monitor=\"val_loss\", patience=3, min_lr=1e-6, mode='min')\n\n    history = model.fit(\n        train_dataset, \n        epochs=25,\n        verbose=1,\n        callbacks=[checkpoint, lr_reducer],\n        steps_per_epoch=steps_per_epoch,\n        validation_data=valid_dataset)\n\n    hist_df = pd.DataFrame(history.history)\n    hist_df.to_csv(f'history{i}.csv')","metadata":{"execution":{"iopub.status.busy":"2021-07-13T01:39:44.998759Z","iopub.execute_input":"2021-07-13T01:39:44.999135Z","iopub.status.idle":"2021-07-13T03:25:03.344311Z","shell.execute_reply.started":"2021-07-13T01:39:44.999104Z","shell.execute_reply":"2021-07-13T03:25:03.343311Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}