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2e5f03d4ab4af1465a4f3177524":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":61446,"databundleVersionId":6962461,"sourceType":"competition"},{"sourceId":7370110,"sourceType":"datasetVersion","datasetId":4282007}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install git+https://github.com/qubvel/segmentation_models -q","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nimport os\nos.environ[\"SM_FRAMEWORK\"] = \"tf.keras\"\nimport segmentation_models as sm\nfrom tensorflow.keras import mixed_precision","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class LovaszLoss(tf.keras.losses.Loss):\n    def __init__(self, name='lovasz_loss', ignore_index=None):\n        super(LovaszLoss, self).__init__(name=name,reduction=tf.keras.losses.Reduction.NONE)\n        self.ignore_index = ignore_index\n        \n    @tf.function    \n    def lovasz_grad(self,gt_sorted):\n        \"\"\"\n        Computes gradient of the Lovasz extension w.r.t sorted errors\n        See Alg. 1 in paper\n        \"\"\"\n        p = tf.shape(gt_sorted)[0]\n        gts = tf.reduce_sum(gt_sorted)\n        intersection = gts - tf.cast(tf.cumsum(tf.cast(gt_sorted, dtype=tf.float32), axis=0), dtype=tf.float32)\n        union = gts + tf.cumsum(1 - tf.cast(gt_sorted, dtype=tf.float32), axis=0)\n        jaccard = 1 - intersection / union\n        if p > 1:  # cover 1-pixel case\n            jaccard = jaccard - tf.concat([[0.0], jaccard[:-1]], axis=0)\n        return jaccard\n    \n    @tf.function\n    def hinge(self,pred, label):\n        signs = 2 * label - 1\n        errors = 1 - pred * signs\n        return errors\n\n    @tf.function\n    def lovasz_hinge_flat(self, logits, labels, ignore_index=None):\n\n        logits = tf.reshape(logits, [-1])\n        labels = tf.reshape(labels, [-1])\n\n        if ignore_index is not None:\n            mask = tf.not_equal(labels, ignore_index)\n            logits = tf.boolean_mask(logits, mask)\n            labels = tf.boolean_mask(labels, mask)\n\n        errors = self.hinge(logits, labels)\n        errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], sorted=True)\n        gt_sorted = tf.gather(labels, perm)\n        grad = self.lovasz_grad(gt_sorted)\n        loss = tf.tensordot(tf.nn.elu(errors_sorted) + 1, grad, axes=1)\n        return loss\n    \n    @tf.function\n    def call(self, labels, logits):\n        return self.lovasz_hinge_flat(logits, labels, self.ignore_index)\n\n@tf.function\ndef dice_coef(y_true, y_pred, thr=0.5, dim=(-1, -2), epsilon=0.001):\n    y_pred = tf.sigmoid(y_pred)\n    y_true = tf.cast(y_true, tf.float32)\n    y_pred = tf.cast(tf.greater(y_pred, thr), tf.float32)\n    inter = tf.reduce_sum(y_true * y_pred, axis=dim)\n    den = tf.reduce_sum(y_true, axis=dim) + tf.reduce_sum(y_pred, axis=dim)\n    dice = tf.reduce_mean((2 * inter + epsilon) / (den + epsilon))\n    return dice\n\nclass DiceLoss(tf.keras.losses.Loss):\n    def __init__(self, name='dice_loss', smooth=1.0):\n        super(DiceLoss, self).__init__(name=name,reduction=tf.keras.losses.Reduction.NONE)\n        self.smooth = smooth\n        \n    @tf.function\n    def call(self, y_true, y_pred):\n        y_true = tf.cast(y_true,dtype=tf.float32)\n        y_pred = tf.sigmoid(y_pred)\n        y_true = tf.reshape(y_true, (-1,))\n        y_pred = tf.reshape(y_pred, (-1,))\n\n        intersection = tf.reduce_sum(y_true * y_pred)\n        dice = (2. * intersection + self.smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + self.smooth)\n\n        return 1.0 - dice\n\nclass FocalLoss(tf.keras.losses.Loss):\n    def __init__(self, name='focal_loss', gamma=2, alpha=0.25):\n        super(FocalLoss, self).__init__(name=name,reduction=tf.keras.losses.Reduction.NONE)\n        self.gamma = gamma\n        self.alpha = alpha\n\n    @tf.function    \n    def call(self, y_true, y_pred):\n        bce_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n        modulating_factor = tf.exp(-self.gamma * y_true * y_pred - self.gamma * tf.math.log1p(tf.exp(-y_pred)))\n        focal_loss = modulating_factor * bce_loss\n        alpha_factor = self.alpha * y_true + (1 - self.alpha) * (1 - y_true)\n        balanced_focal_loss = alpha_factor * focal_loss\n\n        return tf.reduce_mean(balanced_focal_loss)\n    \nclass BCELoss(tf.keras.losses.Loss):\n    def __init__(self, name='bce_loss'):\n        super(BCELoss, self).__init__(name=name,reduction=tf.keras.losses.Reduction.NONE)\n        \n    @tf.function\n    def call(self, y_true, y_pred):\n        bce_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n        return tf.reduce_mean(bce_loss)\n    \nclass CombinedLoss(tf.keras.losses.Loss):\n    def __init__(self, name='combined_loss', weight1=1.0, weight2=1.0, weight3=1.0, weight4=1.0, gamma=2, alpha=0.5):\n        super(CombinedLoss, self).__init__(reduction=tf.keras.losses.Reduction.NONE)\n        self.weight1 = weight1\n        self.weight2 = weight2\n        self.weight3 = weight3\n        self.weight4 = weight4\n        self.dice_loss = DiceLoss()\n        self.focal_loss = FocalLoss(gamma=gamma, alpha=alpha)\n        self.bce_loss = BCELoss()\n        self.hinge_loss = LovaszLoss()\n        \n    @tf.function\n    def call(self, targets, outputs):\n        targets = tf.cast(targets,dtype=tf.float32)\n        dice_loss = self.weight1 * self.dice_loss(targets, outputs)\n        focal_loss = self.weight2 * self.focal_loss(targets, outputs)\n        bce_loss = self.weight3 * self.bce_loss(targets, outputs)\n        hinge_loss = self.weight4 * self.hinge_loss(targets, outputs)\n        \n        # Combine losses\n        print('lllllll')\n        print(dice_loss)\n        print(focal_loss)\n        print(bce_loss)\n        print(hinge_loss)\n        total_loss = dice_loss + focal_loss + bce_loss + hinge_loss\n\n        return total_loss","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def load_and_preprocess_image_label(image_path, label_path):\n    img = tf.io.decode_png(tf.io.read_file(image_path), channels=1)\n    label = tf.io.decode_png(tf.io.read_file(label_path), channels=1)\n    img = tf.image.convert_image_dtype(img, tf.float32)\n    label = tf.image.convert_image_dtype(label, tf.uint8)\n    return img, label\n\ndef random_crop(img, label):\n    combined = tf.concat([img, tf.cast(label,dtype = tf.float32)], axis=-1)\n    combined_cropped = tf.image.random_crop(combined, size=[512, 512, 2])\n    img_cropped, label_cropped = tf.split(combined_cropped, [1, 1], axis=-1)\n    return img_cropped, label_cropped #tf.cast(label_cropped,dtype = tf.uint8)\n\ndef expand_dim3(img,label):\n    return tf.tile(img,[1,1,3]), label \n\ndef chfirst(img,label):\n    return tf.transpose(img, perm=[2,0,1]), tf.transpose(label, perm=[2,0,1])\n\ndef normalize(input_image):\n    mean = tf.constant([0.3,0.3,0.3])\n    std = tf.constant([0.3, 0.3, 0.3])\n    input_image = tf.image.convert_image_dtype(input_image, tf.float32)\n    input_image = (input_image - mean) / tf.maximum(std, backend.epsilon())\n    return input_image","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_dataset(data_folder = '/kaggle/input/hoadataset/train/kidney_1_dense', batch_size = 4, ttv = 'train'):\n\n    image_dir = os.path.join(data_folder, 'images')\n    label_dir = os.path.join(data_folder, 'labels')\n\n    image_file_paths = [os.path.join(image_dir, filename) for filename in os.listdir(image_dir) if filename.endswith('.png')]\n    label_file_paths = [os.path.join(label_dir, filename) for filename in os.listdir(label_dir) if filename.endswith('.png')]\n\n    file_paths = list(zip(image_file_paths, label_file_paths))\n\n    dataset = tf.data.Dataset.from_tensor_slices(file_paths)\n    records = dataset.reduce(0, lambda x,_: x+1).numpy() \n    \n    dataset = dataset.map(lambda x: load_and_preprocess_image_label(x[0], x[1]))\n    dataset = dataset.map(random_crop)\n    \n    auto = tf.data.AUTOTUNE\n    \n    if ttv == 'train':\n        dataset = (dataset\n                    .cache()\n                    .shuffle(batch_size * 10)\n                    .batch(batch_size)\n                    .prefetch(auto))\n    else:\n        dataset = (dataset\n            .batch(batch_size)\n            .prefetch(auto))\n        \n    steps = records//batch_size\n\n    return dataset, steps\n\ntrain_data_folder = '/kaggle/input/hoadataset/train/kidney_1_dense'\nval_data_folder = '/kaggle/input/hoadataset/train/kidney_3_dense'\n\ntrain_ds, steps_per_epoch = get_dataset(train_data_folder)\nval_ds, validation_steps = get_dataset(val_data_folder, ttv = 'val')\n\nfor example in train_ds.take(2):\n    print(\"Image shape:\", example[0].shape)\n    print(\"Label shape:\", example[1].shape)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#sample_dataset = tf.data.Dataset.sample_from_datasets([dataset1, dataset2], weights=[0.5, 0.5])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print(\"Running on TPU\", tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import layers, models\n\n# Define the U-Net model\ndef unet_model(input_shape=(512, 512, 1)):\n    inputs = tf.keras.Input(shape=input_shape)\n\n    # Encoder\n    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(inputs)\n    conv1 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv1)\n    pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)\n\n    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(pool1)\n    conv2 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv2)\n    pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)\n\n    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(pool2)\n    conv3 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv3)\n    pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)\n\n    # Bottleneck\n    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(pool3)\n    conv4 = layers.Conv2D(512, 3, activation='relu', padding='same')(conv4)\n\n    # Decoder\n    up5 = layers.Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv4)\n    concat5 = layers.concatenate([conv3, up5], axis=-1)\n    conv5 = layers.Conv2D(256, 3, activation='relu', padding='same')(concat5)\n    conv5 = layers.Conv2D(256, 3, activation='relu', padding='same')(conv5)\n\n    up6 = layers.Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv5)\n    concat6 = layers.concatenate([conv2, up6], axis=-1)\n    conv6 = layers.Conv2D(128, 3, activation='relu', padding='same')(concat6)\n    conv6 = layers.Conv2D(128, 3, activation='relu', padding='same')(conv6)\n\n    up7 = layers.Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv6)\n    concat7 = layers.concatenate([conv1, up7], axis=-1)\n    conv7 = layers.Conv2D(64, 3, activation='relu', padding='same')(concat7)\n    conv7 = layers.Conv2D(64, 3, activation='relu', padding='same')(conv7)\n\n    # Output layer\n    output = layers.Conv2D(1, 1, activation='sigmoid')(conv7)\n\n    model = models.Model(inputs=inputs, outputs=output)\n\n    return model\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    # Instantiate the model\n    model = unet_model()\n\n    # Compile the model\n    model.compile(optimizer='adam', loss=DiceLoss(), metrics=['accuracy'])\n\n    # Print the model summary\n    model.summary()\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch = next(iter(train_ds))\n_ = model(batch[0])\nsummary = model.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"epochs = 2\n\nhistory = model.fit(\n    train_ds,\n    steps_per_epoch=steps_per_epoch,\n    validation_data=val_ds,\n    validation_steps=validation_steps,\n    epochs=epochs\n)","metadata":{"id":"o0yu60_iGWHi","outputId":"81f9a51d-86e3-4445-d7cd-687bf494e2ab","trusted":true},"execution_count":null,"outputs":[]}]}