{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.9","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":13836,"databundleVersionId":1718836,"sourceType":"competition"}],"dockerImageVersionId":30061,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Overview\n\nThis notebook implements Vision Transformer (ViT) model by Alexey Dosovitskiy et al for image classification, and demonstrates it on the Cassava Leaf Disease Classification dataset.\n\n# Model Architecture\n\n![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Libraries and Configurations","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport tensorflow.keras.layers as L\nimport tensorflow_addons as tfa\nimport glob, random, os, warnings\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport seaborn as sns\n\nprint('TensorFlow Version ' + tf.__version__)\n\ndef seed_everything(seed = 0):\n    random.seed(seed)\n    np.random.seed(seed)\n    tf.random.set_seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    os.environ['TF_DETERMINISTIC_OPS'] = '1'\n\nseed_everything()\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-02-04T12:56:03.945021Z","iopub.execute_input":"2024-02-04T12:56:03.945441Z","iopub.status.idle":"2024-02-04T12:56:09.872706Z","shell.execute_reply.started":"2024-02-04T12:56:03.945352Z","shell.execute_reply":"2024-02-04T12:56:09.871778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_size = 224\nbatch_size = 16\nn_classes = 5\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')\n\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":"2024-02-04T12:56:09.874689Z","iopub.execute_input":"2024-02-04T12:56:09.874966Z","iopub.status.idle":"2024-02-04T12:56:09.919304Z","shell.execute_reply.started":"2024-02-04T12:56:09.874939Z","shell.execute_reply":"2024-02-04T12:56:09.918525Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Augmentations","metadata":{}},{"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 \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    return image","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:56:09.920743Z","iopub.execute_input":"2024-02-04T12:56:09.921199Z","iopub.status.idle":"2024-02-04T12:56:09.928212Z","shell.execute_reply.started":"2024-02-04T12:56:09.921160Z","shell.execute_reply":"2024-02-04T12:56:09.927464Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Generator","metadata":{}},{"cell_type":"code","source":"datagen = tf.keras.preprocessing.image.ImageDataGenerator(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":"2024-02-04T12:56:09.929534Z","iopub.execute_input":"2024-02-04T12:56:09.929812Z","iopub.status.idle":"2024-02-04T12:57:35.565870Z","shell.execute_reply.started":"2024-02-04T12:56:09.929785Z","shell.execute_reply":"2024-02-04T12:57:35.565083Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Sample Images Visualization","metadata":{}},{"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":"2024-02-04T12:57:35.568799Z","iopub.execute_input":"2024-02-04T12:57:35.569092Z","iopub.status.idle":"2024-02-04T12:57:42.531279Z","shell.execute_reply.started":"2024-02-04T12:57:35.569064Z","shell.execute_reply":"2024-02-04T12:57:42.529927Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Hyperparameters","metadata":{}},{"cell_type":"code","source":"learning_rate = 0.001\nweight_decay = 0.0001\nnum_epochs = 1\n\npatch_size = 7  # Size of the patches to be extract from the input images\nnum_patches = (image_size // patch_size) ** 2\nprojection_dim = 64\nnum_heads = 4\ntransformer_units = [\n    projection_dim * 2,\n    projection_dim,\n]  # Size of the transformer layers\ntransformer_layers = 8\nmlp_head_units = [56, 28]  # Size of the dense layers of the final classifier","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:57:42.533120Z","iopub.execute_input":"2024-02-04T12:57:42.533562Z","iopub.status.idle":"2024-02-04T12:57:42.539453Z","shell.execute_reply.started":"2024-02-04T12:57:42.533506Z","shell.execute_reply":"2024-02-04T12:57:42.538501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Building the Model and it's Components","metadata":{}},{"cell_type":"markdown","source":"## 1. Multilayer Perceptron (MLP)","metadata":{}},{"cell_type":"code","source":"def mlp(x, hidden_units, dropout_rate):\n    for units in hidden_units:\n        x = L.Dense(units, activation = tf.nn.gelu)(x)\n        x = L.Dropout(dropout_rate)(x)\n    return x","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:57:42.540783Z","iopub.execute_input":"2024-02-04T12:57:42.541056Z","iopub.status.idle":"2024-02-04T12:57:42.551264Z","shell.execute_reply.started":"2024-02-04T12:57:42.541030Z","shell.execute_reply":"2024-02-04T12:57:42.550352Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Patch Creation Layer","metadata":{}},{"cell_type":"code","source":"class Patches(L.Layer):\n    def __init__(self, patch_size):\n        super(Patches, self).__init__()\n        self.patch_size = patch_size\n\n    def call(self, images):\n        batch_size = tf.shape(images)[0]\n        patches = tf.image.extract_patches(\n            images = images,\n            sizes = [1, self.patch_size, self.patch_size, 1],\n            strides = [1, self.patch_size, self.patch_size, 1],\n            rates = [1, 1, 1, 1],\n            padding = 'VALID',\n        )\n        patch_dims = patches.shape[-1]\n        patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n        return patches","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:57:42.552385Z","iopub.execute_input":"2024-02-04T12:57:42.552704Z","iopub.status.idle":"2024-02-04T12:57:42.563309Z","shell.execute_reply.started":"2024-02-04T12:57:42.552676Z","shell.execute_reply":"2024-02-04T12:57:42.562462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Sample Image Patches Visualization","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(4, 4))\n\nx = train_gen.next()\nimage = x[0][0]\n\nplt.imshow(image.astype('uint8'))\nplt.axis('off')\n\nresized_image = tf.image.resize(\n    tf.convert_to_tensor([image]), size = (image_size, image_size)\n)\n\npatches = Patches(patch_size)(resized_image)\nprint(f'Image size: {image_size} X {image_size}')\nprint(f'Patch size: {patch_size} X {patch_size}')\nprint(f'Patches per image: {patches.shape[1]}')\nprint(f'Elements per patch: {patches.shape[-1]}')\n\nn = int(np.sqrt(patches.shape[1]))\nplt.figure(figsize=(4, 4))\n\nfor i, patch in enumerate(patches[0]):\n    ax = plt.subplot(n, n, i + 1)\n    patch_img = tf.reshape(patch, (patch_size, patch_size, 3))\n    plt.imshow(patch_img.numpy().astype('uint8'))\n    plt.axis('off')","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:57:42.564438Z","iopub.execute_input":"2024-02-04T12:57:42.564729Z","iopub.status.idle":"2024-02-04T12:58:30.567086Z","shell.execute_reply.started":"2024-02-04T12:57:42.564702Z","shell.execute_reply":"2024-02-04T12:58:30.566300Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3. Patch Encoding Layer\nThe `PatchEncoder` layer will linearly transform a patch by projecting it into a vector of size `projection_dim`. In addition, it adds a learnable position embedding to the projected vector.","metadata":{}},{"cell_type":"code","source":"class PatchEncoder(L.Layer):\n    def __init__(self, num_patches, projection_dim):\n        super(PatchEncoder, self).__init__()\n        self.num_patches = num_patches\n        self.projection = L.Dense(units = projection_dim)\n        self.position_embedding = L.Embedding(\n            input_dim = num_patches, output_dim = projection_dim\n        )\n\n    def call(self, patch):\n        positions = tf.range(start = 0, limit = self.num_patches, delta = 1)\n        encoded = self.projection(patch) + self.position_embedding(positions)\n        return encoded","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:58:30.568282Z","iopub.execute_input":"2024-02-04T12:58:30.568580Z","iopub.status.idle":"2024-02-04T12:58:30.575426Z","shell.execute_reply.started":"2024-02-04T12:58:30.568548Z","shell.execute_reply":"2024-02-04T12:58:30.574589Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Build the ViT model\nThe ViT model consists of multiple Transformer blocks, which use the `MultiHeadAttention` layer as a self-attention mechanism applied to the sequence of patches. The Transformer blocks produce a `[batch_size, num_patches, projection_dim]` tensor, which is processed via an classifier head with softmax to produce the final class probabilities output.\n\nUnlike the technique described in the paper, which prepends a learnable embedding to the sequence of encoded patches to serve as the image representation, all the outputs of the final Transformer block are reshaped with `Flatten()` and used as the image representation input to the classifier head. Note that the `GlobalAveragePooling1D` layer could also be used instead to aggregate the outputs of the Transformer block, especially when the number of patches and the projection dimensions are large.","metadata":{}},{"cell_type":"code","source":"def vision_transformer():\n    inputs = L.Input(shape = (image_size, image_size, 3))\n    \n    # Create patches.\n    patches = Patches(patch_size)(inputs)\n    \n    # Encode patches.\n    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)\n\n    # Create multiple layers of the Transformer block.\n    for _ in range(transformer_layers):\n        \n        # Layer normalization 1.\n        x1 = L.LayerNormalization(epsilon = 1e-6)(encoded_patches)\n        \n        # Create a multi-head attention layer.\n        attention_output = L.MultiHeadAttention(\n            num_heads = num_heads, key_dim = projection_dim, dropout = 0.1\n        )(x1, x1)\n        \n        # Skip connection 1.\n        x2 = L.Add()([attention_output, encoded_patches])\n        \n        # Layer normalization 2.\n        x3 = L.LayerNormalization(epsilon = 1e-6)(x2)\n        \n        # MLP.\n        x3 = mlp(x3, hidden_units = transformer_units, dropout_rate = 0.1)\n        \n        # Skip connection 2.\n        encoded_patches = L.Add()([x3, x2])\n\n    # Create a [batch_size, projection_dim] tensor.\n    representation = L.LayerNormalization(epsilon = 1e-6)(encoded_patches)\n    representation = L.Flatten()(representation)\n    representation = L.Dropout(0.5)(representation)\n    \n    # Add MLP.\n    features = mlp(representation, hidden_units = mlp_head_units, dropout_rate = 0.5)\n    \n    # Classify outputs.\n    logits = L.Dense(n_classes)(features)\n    \n    # Create the model.\n    model = tf.keras.Model(inputs = inputs, outputs = logits)\n    \n    return model","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:58:30.577005Z","iopub.execute_input":"2024-02-04T12:58:30.577328Z","iopub.status.idle":"2024-02-04T12:58:30.588209Z","shell.execute_reply.started":"2024-02-04T12:58:30.577268Z","shell.execute_reply":"2024-02-04T12:58:30.587543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"decay_steps = train_gen.n // train_gen.batch_size\ninitial_learning_rate = learning_rate\n\nlr_decayed_fn = tf.keras.experimental.CosineDecay(initial_learning_rate, decay_steps)\n\nlr_scheduler = tf.keras.callbacks.LearningRateScheduler(lr_decayed_fn)","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:58:30.589345Z","iopub.execute_input":"2024-02-04T12:58:30.589651Z","iopub.status.idle":"2024-02-04T12:58:30.601886Z","shell.execute_reply.started":"2024-02-04T12:58:30.589623Z","shell.execute_reply":"2024-02-04T12:58:30.601171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"optimizer = tf.keras.optimizers.Adam(learning_rate = learning_rate)\n\nmodel = vision_transformer()\n    \nmodel.compile(optimizer = optimizer, \n              loss = tf.keras.losses.CategoricalCrossentropy(label_smoothing = 0.1), \n              metrics = ['accuracy'])\n\n\nSTEP_SIZE_TRAIN = train_gen.n // train_gen.batch_size\nSTEP_SIZE_VALID = valid_gen.n // valid_gen.batch_size\n\nearlystopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_accuracy',\n                                                 min_delta = 1e-4,\n                                                 patience = 5,\n                                                 mode = 'max',\n                                                 restore_best_weights = True,\n                                                 verbose = 1)\n\ncheckpointer = tf.keras.callbacks.ModelCheckpoint(filepath = './model.hdf5',\n                                                  monitor = 'val_accuracy', \n                                                  verbose = 1, \n                                                  save_best_only = True,\n                                                  save_weights_only = True,\n                                                  mode = 'max')\n\ncallbacks = [earlystopping, lr_scheduler, checkpointer]\n\nmodel.fit(x = train_gen,\n          steps_per_epoch = STEP_SIZE_TRAIN,\n          validation_data = valid_gen,\n          validation_steps = STEP_SIZE_VALID,\n          epochs = num_epochs,\n          callbacks = callbacks)","metadata":{"execution":{"iopub.status.busy":"2024-02-04T12:58:30.603003Z","iopub.execute_input":"2024-02-04T12:58:30.603297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Results","metadata":{}},{"cell_type":"code","source":"print('Training results')\nmodel.evaluate(train_gen)\n\nprint('Validation results')\nmodel.evaluate(valid_gen)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Summary\n\nNote that the state of the art results reported in the paper are achieved by pre-training the ViT model using the JFT-300M dataset, then fine-tuning it on the target dataset. To improve the model quality without pre-training, you can try to train the model for more epochs, use a larger number of Transformer layers, resize the input images, change the patch size, or increase the projection dimensions. Besides, as mentioned in the paper, the quality of the model is affected not only by architecture choices, but also by parameters such as the learning rate schedule, optimizer, weight decay, etc. In practice, it's recommended to fine-tune a ViT model that was pre-trained using a large, high-resolution dataset. <br>\n\n**References:** <br>\nKeras Docs: https://keras.io/api/ <br>\nResearch Paper: https://arxiv.org/pdf/2010.11929.pdf","metadata":{}}]}