{"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":"import os\nimport cv2\nimport random\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_hub as hub\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.models import load_model\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.optimizers import *\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom sklearn.preprocessing import MultiLabelBinarizer","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-24T00:08:14.510252Z","iopub.execute_input":"2021-11-24T00:08:14.510807Z","iopub.status.idle":"2021-11-24T00:08:20.487065Z","shell.execute_reply.started":"2021-11-24T00:08:14.510713Z","shell.execute_reply":"2021-11-24T00:08:20.486339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"HEIGHT = 480\nWIDTH = 480\nCHANNELS = 3\nCLASSES = 6\ntop_dropout_rate = 0.2","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:10:13.371330Z","iopub.execute_input":"2021-11-24T00:10:13.371935Z","iopub.status.idle":"2021-11-24T00:10:13.376166Z","shell.execute_reply.started":"2021-11-24T00:10:13.371899Z","shell.execute_reply":"2021-11-24T00:10:13.375417Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"weights_path = \"../input/efnet-v2-geoaug-model/EfficientNet_V2_GeoAug.h5\"\nhub_url = '../input/efficientnetv2-tfhub-weight-files/tfhub_models/efficientnetv2-l-21k-ft1k/feature_vector'","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:10:17.959811Z","iopub.execute_input":"2021-11-24T00:10:17.960340Z","iopub.status.idle":"2021-11-24T00:10:17.963981Z","shell.execute_reply.started":"2021-11-24T00:10:17.960305Z","shell.execute_reply":"2021-11-24T00:10:17.963319Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_model():\n    model = tf.keras.Sequential([\n            tf.keras.layers.InputLayer(input_shape = [HEIGHT, WIDTH, CHANNELS]),\n            hub.KerasLayer(hub_url, trainable = True),\n            Dropout(top_dropout_rate, name = \"top_dropout\"),\n            Dense(CLASSES, activation = 'sigmoid')\n        ])\n    model.build((HEIGHT, WIDTH, CHANNELS))\n    model.compile(optimizer ='adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n    model.summary()\n    return model","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:10:20.154218Z","iopub.execute_input":"2021-11-24T00:10:20.154743Z","iopub.status.idle":"2021-11-24T00:10:20.160637Z","shell.execute_reply.started":"2021-11-24T00:10:20.154707Z","shell.execute_reply":"2021-11-24T00:10:20.159860Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = get_model()\nmodel.load_weights(weights_path)","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:10:23.158865Z","iopub.execute_input":"2021-11-24T00:10:23.159441Z","iopub.status.idle":"2021-11-24T00:11:05.718952Z","shell.execute_reply.started":"2021-11-24T00:10:23.159403Z","shell.execute_reply":"2021-11-24T00:11:05.718181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_img = '../input/plant-pathology-2021-fgvc8/test_images'\nsubmission = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')\nsubmission","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:11:21.654885Z","iopub.execute_input":"2021-11-24T00:11:21.655376Z","iopub.status.idle":"2021-11-24T00:11:21.688880Z","shell.execute_reply.started":"2021-11-24T00:11:21.655338Z","shell.execute_reply":"2021-11-24T00:11:21.688095Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def load_image(image_id):\n    file_path = str(image_id)\n    img = cv2.imread(test_img+'/'+file_path)\n    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:11:24.735719Z","iopub.execute_input":"2021-11-24T00:11:24.735971Z","iopub.status.idle":"2021-11-24T00:11:24.740241Z","shell.execute_reply.started":"2021-11-24T00:11:24.735943Z","shell.execute_reply":"2021-11-24T00:11:24.739516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def data_full_augment(image):\n    \n    p_spatial = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_rotate = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_crop = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_pixel_1 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_pixel_2 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    p_pixel_3 = tf.random.uniform([], 0, 1.0, dtype=tf.float32)\n    \n    flag = False\n\n\n    # Pixel-level transforms\n    if p_pixel_1 >= .4:\n        image = tf.image.random_saturation(image, lower=.7, upper=1.3)\n        flag = True\n    if p_pixel_2 >= .4:\n        image = tf.image.random_contrast(image, lower=.8, upper=1.2)\n        flag = True\n    if p_pixel_3 >= .4:\n        image = tf.image.random_brightness(image, max_delta=.1)\n        flag = True\n\n\n    # Flips\n    image = tf.image.random_flip_left_right(image)\n    image = tf.image.random_flip_up_down(image)\n    if p_spatial > .75:\n        image = tf.image.transpose(image)\n        flag = True\n        \n    # Rotates\n    if p_rotate > .75:\n        image = tf.image.rot90(image, k=3) # rotate 270º\n        flag = True\n    elif p_rotate > .5:\n        image = tf.image.rot90(image, k=2) # rotate 180º\n        flag = True\n    elif p_rotate > .25:\n        image = tf.image.rot90(image, k=1) # rotate 90\n        flag = True\n        \n    # Crops\n    if p_crop > .7:\n        if p_crop > .9:\n            image = tf.image.central_crop(image, central_fraction=.7)\n        elif p_crop > .8:\n            image = tf.image.central_crop(image, central_fraction=.8)\n        else:\n            image = tf.image.central_crop(image, central_fraction=.9)\n        flag = True\n    elif p_crop > .4:\n        HEIGHT1 = image.shape[0]\n        WIDTH1 = image.shape[1]\n        crop_size_h = tf.random.uniform([], int(HEIGHT1*.8), HEIGHT1, dtype=tf.float32)\n        crop_size_w = tf.random.uniform([], int(WIDTH1*.8), WIDTH1, dtype=tf.float32)\n        image = tf.image.random_crop(image, size=[crop_size_h, crop_size_w, 3])\n        flag = True\n    \n    return image","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:11:28.705692Z","iopub.execute_input":"2021-11-24T00:11:28.706213Z","iopub.status.idle":"2021-11-24T00:11:28.720183Z","shell.execute_reply.started":"2021-11-24T00:11:28.706175Z","shell.execute_reply":"2021-11-24T00:11:28.719278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def process(img):\n    img = data_full_augment(img)\n    img = tf.cast(img, tf.float32) / 255.0\n    img = np.array(img)\n    return cv2.resize(img , (480, 480)).reshape(-1, 480, 480, 3)\ndef predict(img):\n    img = load_image(img)\n    tta_steps = 5\n    predictions = []\n    for i in range(tta_steps):\n        pred = model.layers[2](model.layers[1](model.layers[0](process(img)))).numpy()[0]\n        predictions.append(pred)\n    result = np.median(predictions, axis=0)\n    \n    return result","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:11:31.827690Z","iopub.execute_input":"2021-11-24T00:11:31.828008Z","iopub.status.idle":"2021-11-24T00:11:31.836496Z","shell.execute_reply.started":"2021-11-24T00:11:31.827965Z","shell.execute_reply":"2021-11-24T00:11:31.835723Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_images = submission['image'][2]\ntest_images = load_image(test_images)\npreds = model.layers[2](model.layers[1](model.layers[0](process(test_images)))).numpy()[0]\nprint(preds)","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:12:22.492857Z","iopub.execute_input":"2021-11-24T00:12:22.493140Z","iopub.status.idle":"2021-11-24T00:12:22.882597Z","shell.execute_reply.started":"2021-11-24T00:12:22.493109Z","shell.execute_reply":"2021-11-24T00:12:22.881840Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"n_label = ['complex', 'frog_eye_leaf_spot', 'healthy', 'powdery_mildew', 'rust', 'scab']\nlabel = []\nfor i in range(len(submission['image'])):\n    test_images = submission['image'][i]\n    preds = predict(test_images)\n    answer = []\n    for j in range(len(preds)):\n        if preds[j] > 0.4:\n            answer.append(n_label[j])\n    answer = ' '.join(answer)\n    label.append(answer)\nsubmission['labels'] = label\nsubmission","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:13:54.913435Z","iopub.execute_input":"2021-11-24T00:13:54.913697Z","iopub.status.idle":"2021-11-24T00:13:59.342548Z","shell.execute_reply.started":"2021-11-24T00:13:54.913670Z","shell.execute_reply":"2021-11-24T00:13:59.341740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2021-11-24T00:14:06.369506Z","iopub.execute_input":"2021-11-24T00:14:06.369754Z","iopub.status.idle":"2021-11-24T00:14:06.377307Z","shell.execute_reply.started":"2021-11-24T00:14:06.369727Z","shell.execute_reply":"2021-11-24T00:14:06.376396Z"},"trusted":true},"execution_count":null,"outputs":[]}]}