{"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 pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_addons as tfa\nimport glob, warnings\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport seaborn as sns\n\nwarnings.filterwarnings('ignore')\nprint('TensorFlow Version ' + tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:09.844862Z","iopub.execute_input":"2023-10-22T10:30:09.845269Z","iopub.status.idle":"2023-10-22T10:30:09.851452Z","shell.execute_reply.started":"2023-10-22T10:30:09.845239Z","shell.execute_reply":"2023-10-22T10:30:09.850467Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = 224\nBATCH_SIZE = 16\nEPOCHS = 7\n\nTRAIN_PATH = '/kaggle/input/cassava-leaf-disease-classification/train_images'\nTEST_PATH = '/kaggle/input/cassava-leaf-disease-classification/test_images'\n\nDF_TRAIN = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv', dtype='str')\nTEST_IMAGES = glob.glob(TEST_PATH + '/*.jpg')\nDF_TEST = pd.DataFrame(TEST_IMAGES, columns = ['image_path'])\n\nclasses = {0 : \"Cassava Bacterial Blight (CBB)\",\n           1 : \"Cassava Brown Streak Disease (CBSD)\",\n           2 : \"Cassava Green Mottle (CGM)\",\n           3 : \"Cassava Mosaic Disease (CMD)\",\n           4 : \"Healthy\"}","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:09.853298Z","iopub.execute_input":"2023-10-22T10:30:09.853669Z","iopub.status.idle":"2023-10-22T10:30:09.882931Z","shell.execute_reply.started":"2023-10-22T10:30:09.853636Z","shell.execute_reply":"2023-10-22T10:30:09.882199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def data_augment(image):\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_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    \n# Flips\n    image = tf.image.random_flip_left_right(image)\n    image = tf.image.random_flip_up_down(image)\n    \n    if p_spatial > .75:\n        image = tf.image.transpose(image)\n        \n    # Rotates\n    if p_rotate > .75:\n        image = tf.image.rot90(image, k = 3) # rotate 270º\n    elif p_rotate > .5:\n        image = tf.image.rot90(image, k = 2) # rotate 180º\n    elif p_rotate > .25:\n        image = tf.image.rot90(image, k = 1) # rotate 90º\n        \n# Pixel-level transforms\n    if p_pixel_1 >= .4:\n        image = tf.image.random_saturation(image, lower = .7, upper = 1.3)\n    if p_pixel_2 >= .4:\n        image = tf.image.random_contrast(image, lower = .8, upper = 1.2)\n    if p_pixel_3 >= .4:\n        image = tf.image.random_brightness(image, max_delta = .1)\n        \n    return image","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:09.884005Z","iopub.execute_input":"2023-10-22T10:30:09.884283Z","iopub.status.idle":"2023-10-22T10:30:09.893570Z","shell.execute_reply.started":"2023-10-22T10:30:09.884259Z","shell.execute_reply":"2023-10-22T10:30:09.892768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255,\n                                                          samplewise_center = True,\n                                                          samplewise_std_normalization = True,\n                                                          validation_split = 0.2,\n                                                          preprocessing_function = data_augment)\n\ntrain_gen = datagen.flow_from_dataframe(dataframe = DF_TRAIN,\n                                        directory = TRAIN_PATH,\n                                        x_col = 'image_id',\n                                        y_col = 'label',\n                                        subset = 'training',\n                                        batch_size = BATCH_SIZE,\n                                        seed = 1,\n                                        color_mode = 'rgb',\n                                        shuffle = True,\n                                        class_mode = 'categorical',\n                                        target_size = (IMAGE_SIZE, IMAGE_SIZE))\n\nvalid_gen = datagen.flow_from_dataframe(dataframe = DF_TRAIN,\n                                        directory = TRAIN_PATH,\n                                        x_col = 'image_id',\n                                        y_col = 'label',\n                                        subset = 'validation',\n                                        batch_size = BATCH_SIZE,\n                                        seed = 1,\n                                        color_mode = 'rgb',\n                                        shuffle = False,\n                                        class_mode = 'categorical',\n                                        target_size = (IMAGE_SIZE, IMAGE_SIZE))\n\ntest_gen = datagen.flow_from_dataframe(dataframe = DF_TEST,\n                                       x_col = 'image_path',\n                                       y_col = None,\n                                       batch_size = BATCH_SIZE,\n                                       seed = 1,\n                                       color_mode = 'rgb',\n                                       shuffle = False,\n                                       class_mode = None,\n                                       target_size = (IMAGE_SIZE, IMAGE_SIZE))","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:09.895953Z","iopub.execute_input":"2023-10-22T10:30:09.896243Z","iopub.status.idle":"2023-10-22T10:30:19.539799Z","shell.execute_reply.started":"2023-10-22T10:30:09.896218Z","shell.execute_reply":"2023-10-22T10:30:19.538935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images = [train_gen[0][0][i] for i in range(16)]\nfig, axes = plt.subplots(3, 5, figsize = (10, 10))\n\naxes = axes.flatten()\n\nfor img, ax in zip(images, axes):\n    ax.imshow(img.reshape(IMAGE_SIZE, IMAGE_SIZE, 3))\n    ax.axis('off')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:19.540807Z","iopub.execute_input":"2023-10-22T10:30:19.541074Z","iopub.status.idle":"2023-10-22T10:30:24.147568Z","shell.execute_reply.started":"2023-10-22T10:30:19.541052Z","shell.execute_reply":"2023-10-22T10:30:24.146565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install --quiet vit-keras\n\nfrom vit_keras import vit","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:24.148775Z","iopub.execute_input":"2023-10-22T10:30:24.149151Z","iopub.status.idle":"2023-10-22T10:30:35.646082Z","shell.execute_reply.started":"2023-10-22T10:30:24.149123Z","shell.execute_reply":"2023-10-22T10:30:35.644671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"vit_model = vit.vit_b32(\n        image_size = IMAGE_SIZE,\n        activation = 'softmax',\n        pretrained = True,\n        include_top = False,\n        pretrained_top = False,\n        classes = 5)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:35.648974Z","iopub.execute_input":"2023-10-22T10:30:35.649303Z","iopub.status.idle":"2023-10-22T10:30:38.669112Z","shell.execute_reply.started":"2023-10-22T10:30:35.649274Z","shell.execute_reply":"2023-10-22T10:30:38.667615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tf.keras.Sequential([\n        vit_model,\n        tf.keras.layers.Flatten(),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dense(11, activation = tfa.activations.gelu),\n        tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dense(5, 'softmax')\n    ],\n    name = 'vision_transformer')\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:38.669786Z","iopub.status.idle":"2023-10-22T10:30:38.670118Z","shell.execute_reply.started":"2023-10-22T10:30:38.669958Z","shell.execute_reply":"2023-10-22T10:30:38.669973Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from vit_keras import visualize\n\nx = test_gen.next()\nimage = x[0]\n\nattention_map = visualize.attention_map(model = vit_model, image = image)\n\n# Plot results\nfig, (ax1, ax2) = plt.subplots(ncols = 2)\nax1.axis('off')\nax2.axis('off')\nax1.set_title('Original')\nax2.set_title('Attention Map')\n_ = ax1.imshow(image)\n_ = ax2.imshow(attention_map)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:30:38.671265Z","iopub.status.idle":"2023-10-22T10:30:38.671562Z","shell.execute_reply.started":"2023-10-22T10:30:38.671412Z","shell.execute_reply":"2023-10-22T10:30:38.671426Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"raw","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}}]}