{"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":"markdown","source":"Thanks to https://www.kaggle.com/xhlulu/ranzcr-efficientnet-tpu-training","metadata":{}},{"cell_type":"code","source":"!pip install efficientnet -q","metadata":{"execution":{"iopub.status.busy":"2021-06-09T09:47:51.123113Z","iopub.execute_input":"2021-06-09T09:47:51.123448Z","iopub.status.idle":"2021-06-09T09:47:57.811731Z","shell.execute_reply.started":"2021-06-09T09:47:51.123415Z","shell.execute_reply":"2021-06-09T09:47:57.810696Z"},"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","metadata":{"execution":{"iopub.status.busy":"2021-06-09T09:47:57.815210Z","iopub.execute_input":"2021-06-09T09:47:57.815490Z","iopub.status.idle":"2021-06-09T09:48:03.761036Z","shell.execute_reply.started":"2021-06-09T09:47:57.815460Z","shell.execute_reply":"2021-06-09T09:48:03.760100Z"},"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.keras.preprocessing.image.random_zoom(img, zoom_range=(256,256))\n        img = tf.image.random_brightness(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=128, cache=True,\n                  decode_fn=None, augment_fn=None,\n                  augment=False, 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","metadata":{"execution":{"iopub.status.busy":"2021-06-09T09:48:03.762920Z","iopub.execute_input":"2021-06-09T09:48:03.763334Z","iopub.status.idle":"2021-06-09T09:48:03.779692Z","shell.execute_reply.started":"2021-06-09T09:48:03.763297Z","shell.execute_reply":"2021-06-09T09:48:03.777683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"COMPETITION_NAME = \"siimcovid19-512-img-png-600-study-png\"\nstrategy = auto_select_accelerator()\nBATCH_SIZE = strategy.num_replicas_in_sync * 5\nGCS_DS_PATH = KaggleDatasets().get_gcs_path(COMPETITION_NAME)","metadata":{"execution":{"iopub.status.busy":"2021-06-09T10:11:00.191537Z","iopub.execute_input":"2021-06-09T10:11:00.191945Z","iopub.status.idle":"2021-06-09T10:11:00.662099Z","shell.execute_reply.started":"2021-06-09T10:11:00.191898Z","shell.execute_reply":"2021-06-09T10:11:00.661174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"load_dir = f\"/kaggle/input/{COMPETITION_NAME}/\"\ndf = pd.read_csv('../input/siim-covid19-detection/train_study_level.csv')\nlabel_cols = df.columns[1:5]\n","metadata":{"execution":{"iopub.status.busy":"2021-06-09T10:11:04.762275Z","iopub.execute_input":"2021-06-09T10:11:04.762621Z","iopub.status.idle":"2021-06-09T10:11:04.779746Z","shell.execute_reply.started":"2021-06-09T10:11:04.762575Z","shell.execute_reply":"2021-06-09T10:11:04.778803Z"},"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.id.tolist())):\n    df.loc[val_idx, 'fold'] = fold","metadata":{"execution":{"iopub.status.busy":"2021-06-09T10:11:08.042030Z","iopub.execute_input":"2021-06-09T10:11:08.042409Z","iopub.status.idle":"2021-06-09T10:11:08.086197Z","shell.execute_reply.started":"2021-06-09T10:11:08.042375Z","shell.execute_reply":"2021-06-09T10:11:08.085370Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(5):\n    \n    valid_paths = GCS_DS_PATH + '/study/' + df[df['fold'] == i]['id'] + '.png' #\"/train/\"\n    train_paths = GCS_DS_PATH + '/study/' + 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)\n    IMS = 4\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   \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='softmax')\n        ])\n        model.compile(\n            optimizer=tf.keras.optimizers.Adam(),\n            loss='categorical_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    print(IMSIZE[IMS], IMSIZE[IMS], 3)\n    print(train_dataset)\n    history = model.fit(\n        train_dataset, \n        epochs=20,\n        verbose=1,\n        \n        steps_per_epoch=steps_per_epoch)\n    #,callbacks=[checkpoint, lr_reducer],\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":"2021-06-09T10:11:13.308195Z","iopub.execute_input":"2021-06-09T10:11:13.308568Z"},"trusted":true},"execution_count":null,"outputs":[]}]}