{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "R153mIN-CrxW" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import Input\n", "from tensorflow.keras.applications.densenet import DenseNet121, DenseNet169, DenseNet201\n", "from tensorflow.keras.applications import MobileNetV3Small\n", "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.models import Sequential, Model\n", "from tensorflow.keras.callbacks import ModelCheckpoint\n", "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n", "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dense, Flatten, Dropout\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, log_loss, jaccard_score\n", "import numpy as np\n", "import os\n", "from PIL import Image\n", "from shutil import copyfile\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0cpBZxVLglN6", "outputId": "4e2c6f62-80fd-4b07-a41b-c9b5b0590ec6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GIa8FmoIDC6X" }, "outputs": [], "source": [ "train_data_dir = '/content/drive/MyDrive/BoneFractureDataset/training'\n", "test_data_dir = '/content/drive/MyDrive/BoneFractureDataset/training'\n", "validation_data_dir = '/content/drive/MyDrive/BoneFractureDataset/training'\n", "IMG_WIDTH, IMG_HEIGHT = 299, 299\n", "input_shape = (IMG_WIDTH, IMG_HEIGHT, 3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7FgHIM7lDadv" }, "outputs": [], "source": [ "train_datagen = ImageDataGenerator(rescale=1./255)\n", "test_datagen = ImageDataGenerator(rescale=1./255)\n", "validation_datagen = ImageDataGenerator(rescale=1./255)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JtQvTWxxDiYQ" }, "outputs": [], "source": [ "train_datagen_augmented = ImageDataGenerator(\n", " rescale=1./255,\n", " rotation_range=20,\n", " width_shift_range=0.2,\n", " height_shift_range=0.2,\n", " shear_range=0.2,\n", " zoom_range=0.2,\n", " horizontal_flip=True,\n", " vertical_flip=False,\n", " fill_mode='nearest'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RZbvOpJfDvLI", "outputId": "e160e450-a8d8-441e-e5b5-66644b32e3c7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 1141 images belonging to 2 classes.\n" ] } ], "source": [ "train_generator = train_datagen_augmented.flow_from_directory(train_data_dir, target_size=(IMG_WIDTH, IMG_HEIGHT), batch_size=10, class_mode='categorical')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oDOKKcT0D6qw", "outputId": "eed7d8ca-de05-4e14-ec29-afc97ce2396c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 1141 images belonging to 2 classes.\n" ] } ], "source": [ "test_datagen_augmented = ImageDataGenerator(\n", " rescale=1./255,\n", " rotation_range=20,\n", " width_shift_range=0.2,\n", " height_shift_range=0.2,\n", " shear_range=0.2,\n", " zoom_range=0.2,\n", " horizontal_flip=True,\n", " vertical_flip=False,\n", " fill_mode='nearest'\n", ")\n", "test_generator = test_datagen.flow_from_directory(test_data_dir, target_size=(IMG_WIDTH, IMG_HEIGHT), batch_size=8, class_mode='categorical', shuffle=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LcwD2N6RESPk", "outputId": "71b6eab8-0b23-4816-a83a-1b2cadc9cedb" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 1141 images belonging to 2 classes.\n" ] } ], "source": [ "validation_datagen_augmented = ImageDataGenerator(\n", " rescale=1./255,\n", " rotation_range=20,\n", " width_shift_range=0.2,\n", " height_shift_range=0.2,\n", " shear_range=0.2,\n", " zoom_range=0.2,\n", " horizontal_flip=True,\n", " vertical_flip=False,\n", " fill_mode='nearest'\n", ")\n", "validation_generator = validation_datagen.flow_from_directory(validation_data_dir, target_size=(IMG_WIDTH, IMG_HEIGHT), batch_size=8, class_mode='categorical', shuffle=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nnuG68PeEl6C", "outputId": "b43832e1-e520-4592-e844-e63e420491f5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'fractured': 0, 'not_fractured': 1}\n" ] } ], "source": [ "class_indices = train_generator.class_indices\n", "print(class_indices)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OMbZ5aBWEp74", "outputId": "6ce8b5d6-2314-4338-ab28-fb7980528df9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Class: fractured, Number of images: 8\n", "Class: not_fractured, Number of images: 1133\n" ] } ], "source": [ "classes = os.listdir(train_data_dir)\n", "for class_name in classes:\n", " class_path = os.path.join(train_data_dir, class_name)\n", " num_images = len(os.listdir(class_path))\n", " print(f\"Class: {class_name}, Number of images: {num_images}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mWFKLh9xExKC", "outputId": "77e966c2-744e-41fd-dddf-ca4b3c542e22" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Image 1 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 2 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 3 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 4 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 5 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 6 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 7 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 8 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 9 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 10 - Shape: 299x299x3, Label: [0. 1.]\n" ] } ], "source": [ "batch = train_generator.next()\n", "for i in range(len(batch[0])):\n", " img = batch[0][i]\n", " label = batch[1][i]\n", " height, width, channels = img.shape\n", " print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yUHF8biOE7XZ", "outputId": "ee8442d4-354c-42b6-f3c6-d1864f499598" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Class: fractured, Number of images: 8\n", "Class: not_fractured, Number of images: 1133\n" ] } ], "source": [ "classes = os.listdir(test_data_dir)\n", "for class_name in classes:\n", " class_path = os.path.join(test_data_dir, class_name)\n", " num_images = len(os.listdir(class_path))\n", " print(f\"Class: {class_name}, Number of images: {num_images}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VRTfLX0hFMUE", "outputId": "5084d825-9667-4b84-d88a-591ca1cafe6a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Image 1 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 2 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 3 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 4 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 5 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 6 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 7 - Shape: 299x299x3, Label: [1. 0.]\n", "Image 8 - Shape: 299x299x3, Label: [1. 0.]\n" ] } ], "source": [ "batch = test_generator.next()\n", "for i in range(len(batch[0])):\n", " img = batch[0][i]\n", " label = batch[1][i]\n", " height, width, channels = img.shape\n", " print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Hb6N_uMFVOW", "outputId": "30da9534-7b25-4f98-cc49-c8d26cf3f56b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Class: fractured, Number of images: 8\n", "Class: not_fractured, Number of images: 1133\n" ] } ], "source": [ "classes = os.listdir(validation_data_dir)\n", "for class_name in classes:\n", " class_path = os.path.join(validation_data_dir, class_name)\n", " num_images = len(os.listdir(class_path))\n", " print(f\"Class: {class_name}, Number of images: {num_images}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "irkUy0WMFczE", "outputId": "db18a818-3970-4c5a-a34d-840383f94818" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Image 1 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 2 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 3 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 4 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 5 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 6 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 7 - Shape: 299x299x3, Label: [0. 1.]\n", "Image 8 - Shape: 299x299x3, Label: [0. 1.]\n" ] } ], "source": [ "batch = validation_generator.next()\n", "for i in range(len(batch[0])):\n", " img = batch[0][i]\n", " label = batch[1][i]\n", " height, width, channels = img.shape\n", " print(f\"Image {i+1} - Shape: {width}x{height}x{channels}, Label: {label}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fNNavU7jFvBq", "outputId": "34fea2bc-9687-4f8f-cfca-ade3832413a2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU is NOT available\n", "No GPU device found\n" ] } ], "source": [ "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT available\")\n", "if tf.config.list_physical_devices('GPU'):\n", " tf.config.experimental.set_memory_growth(tf.config.list_physical_devices('GPU')[0], True)\n", " print(\"GPU device configured\")\n", "else:\n", " print(\"No GPU device found\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PeL3SdDfF2c4" }, "outputs": [], "source": [ "from tensorflow.keras.callbacks import ModelCheckpoint\n", "model_dir = '/kaggle/working/Checkpoints_densenet201'\n", "if not os.path.exists(model_dir):\n", " os.makedirs(model_dir)\n", "checkpoint_path = model_dir + '/cp.ckpt'\n", "checkpoint_dir = os.path.dirname(checkpoint_path)\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, save_best_only=True, monitor=\"val_accuracy\", mode=\"max\", verbose=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "pwOdjSaDGT8h", "outputId": "14208714-d682-4bc9-a5c5-f9f56db766cb" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'/kaggle/working/Checkpoints_densenet201/cp.ckpt'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 18 } ], "source": [ "checkpoint_path" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rgEzGmkqGXcS" }, "outputs": [], "source": [ "from tensorflow.keras import models, layers, optimizers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hrv4Z7IEGbSt" }, "outputs": [], "source": [ "def create_model(summary=True):\n", " new_input = Input(shape=(IMG_WIDTH, IMG_HEIGHT, 3))\n", " base_model = DenseNet201(weights='imagenet', include_top=False, input_tensor=new_input)\n", " flat1 = Flatten()(base_model.layers[-1].output)\n", " output = Dense(2, activation='softmax')(flat1)\n", " model = Model(inputs=base_model.inputs, outputs=output)\n", " model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n", " if summary:\n", " print(model.summary())\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aK-nqnlaGmr3", "outputId": "17bdb025-262d-4e86-b029-f263dc155657" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "74836368/74836368 [==============================] - 1s 0us/step\n", "Model: \"model\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_1 (InputLayer) [(None, 299, 299, 3)] 0 [] \n", " \n", " zero_padding2d (ZeroPaddin (None, 305, 305, 3) 0 ['input_1[0][0]'] \n", " g2D) \n", " \n", " conv1/conv (Conv2D) (None, 150, 150, 64) 9408 ['zero_padding2d[0][0]'] \n", " \n", " conv1/bn (BatchNormalizati (None, 150, 150, 64) 256 ['conv1/conv[0][0]'] \n", " on) \n", " \n", " conv1/relu (Activation) (None, 150, 150, 64) 0 ['conv1/bn[0][0]'] \n", " \n", " zero_padding2d_1 (ZeroPadd (None, 152, 152, 64) 0 ['conv1/relu[0][0]'] \n", " ing2D) \n", " \n", " pool1 (MaxPooling2D) (None, 75, 75, 64) 0 ['zero_padding2d_1[0][0]'] \n", " \n", " conv2_block1_0_bn (BatchNo (None, 75, 75, 64) 256 ['pool1[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_0_relu (Activ (None, 75, 75, 64) 0 ['conv2_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_1_conv (Conv2 (None, 75, 75, 128) 8192 ['conv2_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_concat (Conca (None, 75, 75, 96) 0 ['pool1[0][0]', \n", " tenate) 'conv2_block1_2_conv[0][0]'] \n", " \n", " conv2_block2_0_bn (BatchNo (None, 75, 75, 96) 384 ['conv2_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_0_relu (Activ (None, 75, 75, 96) 0 ['conv2_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_1_conv (Conv2 (None, 75, 75, 128) 12288 ['conv2_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_concat (Conca (None, 75, 75, 128) 0 ['conv2_block1_concat[0][0]', \n", " tenate) 'conv2_block2_2_conv[0][0]'] \n", " \n", " conv2_block3_0_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_0_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_1_conv (Conv2 (None, 75, 75, 128) 16384 ['conv2_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_concat (Conca (None, 75, 75, 160) 0 ['conv2_block2_concat[0][0]', \n", " tenate) 'conv2_block3_2_conv[0][0]'] \n", " \n", " conv2_block4_0_bn (BatchNo (None, 75, 75, 160) 640 ['conv2_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_0_relu (Activ (None, 75, 75, 160) 0 ['conv2_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_1_conv (Conv2 (None, 75, 75, 128) 20480 ['conv2_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_concat (Conca (None, 75, 75, 192) 0 ['conv2_block3_concat[0][0]', \n", " tenate) 'conv2_block4_2_conv[0][0]'] \n", " \n", " conv2_block5_0_bn (BatchNo (None, 75, 75, 192) 768 ['conv2_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_0_relu (Activ (None, 75, 75, 192) 0 ['conv2_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_1_conv (Conv2 (None, 75, 75, 128) 24576 ['conv2_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_concat (Conca (None, 75, 75, 224) 0 ['conv2_block4_concat[0][0]', \n", " tenate) 'conv2_block5_2_conv[0][0]'] \n", " \n", " conv2_block6_0_bn (BatchNo (None, 75, 75, 224) 896 ['conv2_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_0_relu (Activ (None, 75, 75, 224) 0 ['conv2_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_1_conv (Conv2 (None, 75, 75, 128) 28672 ['conv2_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_concat (Conca (None, 75, 75, 256) 0 ['conv2_block5_concat[0][0]', \n", " tenate) 'conv2_block6_2_conv[0][0]'] \n", " \n", " pool2_bn (BatchNormalizati (None, 75, 75, 256) 1024 ['conv2_block6_concat[0][0]'] \n", " on) \n", " \n", " pool2_relu (Activation) (None, 75, 75, 256) 0 ['pool2_bn[0][0]'] \n", " \n", " pool2_conv (Conv2D) (None, 75, 75, 128) 32768 ['pool2_relu[0][0]'] \n", " \n", " pool2_pool (AveragePooling (None, 37, 37, 128) 0 ['pool2_conv[0][0]'] \n", " 2D) \n", " \n", " conv3_block1_0_bn (BatchNo (None, 37, 37, 128) 512 ['pool2_pool[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_0_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_1_conv (Conv2 (None, 37, 37, 128) 16384 ['conv3_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_concat (Conca (None, 37, 37, 160) 0 ['pool2_pool[0][0]', \n", " tenate) 'conv3_block1_2_conv[0][0]'] \n", " \n", " conv3_block2_0_bn (BatchNo (None, 37, 37, 160) 640 ['conv3_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_0_relu (Activ (None, 37, 37, 160) 0 ['conv3_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_1_conv (Conv2 (None, 37, 37, 128) 20480 ['conv3_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_concat (Conca (None, 37, 37, 192) 0 ['conv3_block1_concat[0][0]', \n", " tenate) 'conv3_block2_2_conv[0][0]'] \n", " \n", " conv3_block3_0_bn (BatchNo (None, 37, 37, 192) 768 ['conv3_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_0_relu (Activ (None, 37, 37, 192) 0 ['conv3_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_1_conv (Conv2 (None, 37, 37, 128) 24576 ['conv3_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_concat (Conca (None, 37, 37, 224) 0 ['conv3_block2_concat[0][0]', \n", " tenate) 'conv3_block3_2_conv[0][0]'] \n", " \n", " conv3_block4_0_bn (BatchNo (None, 37, 37, 224) 896 ['conv3_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_0_relu (Activ (None, 37, 37, 224) 0 ['conv3_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_1_conv (Conv2 (None, 37, 37, 128) 28672 ['conv3_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_concat (Conca (None, 37, 37, 256) 0 ['conv3_block3_concat[0][0]', \n", " tenate) 'conv3_block4_2_conv[0][0]'] \n", " \n", " conv3_block5_0_bn (BatchNo (None, 37, 37, 256) 1024 ['conv3_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_0_relu (Activ (None, 37, 37, 256) 0 ['conv3_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_1_conv (Conv2 (None, 37, 37, 128) 32768 ['conv3_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_concat (Conca (None, 37, 37, 288) 0 ['conv3_block4_concat[0][0]', \n", " tenate) 'conv3_block5_2_conv[0][0]'] \n", " \n", " conv3_block6_0_bn (BatchNo (None, 37, 37, 288) 1152 ['conv3_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_0_relu (Activ (None, 37, 37, 288) 0 ['conv3_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_1_conv (Conv2 (None, 37, 37, 128) 36864 ['conv3_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_concat (Conca (None, 37, 37, 320) 0 ['conv3_block5_concat[0][0]', \n", " tenate) 'conv3_block6_2_conv[0][0]'] \n", " \n", " conv3_block7_0_bn (BatchNo (None, 37, 37, 320) 1280 ['conv3_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_0_relu (Activ (None, 37, 37, 320) 0 ['conv3_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_1_conv (Conv2 (None, 37, 37, 128) 40960 ['conv3_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_concat (Conca (None, 37, 37, 352) 0 ['conv3_block6_concat[0][0]', \n", " tenate) 'conv3_block7_2_conv[0][0]'] \n", " \n", " conv3_block8_0_bn (BatchNo (None, 37, 37, 352) 1408 ['conv3_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_0_relu (Activ (None, 37, 37, 352) 0 ['conv3_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_1_conv (Conv2 (None, 37, 37, 128) 45056 ['conv3_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_concat (Conca (None, 37, 37, 384) 0 ['conv3_block7_concat[0][0]', \n", " tenate) 'conv3_block8_2_conv[0][0]'] \n", " \n", " conv3_block9_0_bn (BatchNo (None, 37, 37, 384) 1536 ['conv3_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_0_relu (Activ (None, 37, 37, 384) 0 ['conv3_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_1_conv (Conv2 (None, 37, 37, 128) 49152 ['conv3_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_concat (Conca (None, 37, 37, 416) 0 ['conv3_block8_concat[0][0]', \n", " tenate) 'conv3_block9_2_conv[0][0]'] \n", " \n", " conv3_block10_0_bn (BatchN (None, 37, 37, 416) 1664 ['conv3_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv3_block10_0_relu (Acti (None, 37, 37, 416) 0 ['conv3_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_1_conv (Conv (None, 37, 37, 128) 53248 ['conv3_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block10_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_concat (Conc (None, 37, 37, 448) 0 ['conv3_block9_concat[0][0]', \n", " atenate) 'conv3_block10_2_conv[0][0]']\n", " \n", " conv3_block11_0_bn (BatchN (None, 37, 37, 448) 1792 ['conv3_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_0_relu (Acti (None, 37, 37, 448) 0 ['conv3_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_1_conv (Conv (None, 37, 37, 128) 57344 ['conv3_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_concat (Conc (None, 37, 37, 480) 0 ['conv3_block10_concat[0][0]',\n", " atenate) 'conv3_block11_2_conv[0][0]']\n", " \n", " conv3_block12_0_bn (BatchN (None, 37, 37, 480) 1920 ['conv3_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_0_relu (Acti (None, 37, 37, 480) 0 ['conv3_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_1_conv (Conv (None, 37, 37, 128) 61440 ['conv3_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_concat (Conc (None, 37, 37, 512) 0 ['conv3_block11_concat[0][0]',\n", " atenate) 'conv3_block12_2_conv[0][0]']\n", " \n", " pool3_bn (BatchNormalizati (None, 37, 37, 512) 2048 ['conv3_block12_concat[0][0]']\n", " on) \n", " \n", " pool3_relu (Activation) (None, 37, 37, 512) 0 ['pool3_bn[0][0]'] \n", " \n", " pool3_conv (Conv2D) (None, 37, 37, 256) 131072 ['pool3_relu[0][0]'] \n", " \n", " pool3_pool (AveragePooling (None, 18, 18, 256) 0 ['pool3_conv[0][0]'] \n", " 2D) \n", " \n", " conv4_block1_0_bn (BatchNo (None, 18, 18, 256) 1024 ['pool3_pool[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_0_relu (Activ (None, 18, 18, 256) 0 ['conv4_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_1_conv (Conv2 (None, 18, 18, 128) 32768 ['conv4_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_concat (Conca (None, 18, 18, 288) 0 ['pool3_pool[0][0]', \n", " tenate) 'conv4_block1_2_conv[0][0]'] \n", " \n", " conv4_block2_0_bn (BatchNo (None, 18, 18, 288) 1152 ['conv4_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_0_relu (Activ (None, 18, 18, 288) 0 ['conv4_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_1_conv (Conv2 (None, 18, 18, 128) 36864 ['conv4_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_concat (Conca (None, 18, 18, 320) 0 ['conv4_block1_concat[0][0]', \n", " tenate) 'conv4_block2_2_conv[0][0]'] \n", " \n", " conv4_block3_0_bn (BatchNo (None, 18, 18, 320) 1280 ['conv4_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_0_relu (Activ (None, 18, 18, 320) 0 ['conv4_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_1_conv (Conv2 (None, 18, 18, 128) 40960 ['conv4_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_concat (Conca (None, 18, 18, 352) 0 ['conv4_block2_concat[0][0]', \n", " tenate) 'conv4_block3_2_conv[0][0]'] \n", " \n", " conv4_block4_0_bn (BatchNo (None, 18, 18, 352) 1408 ['conv4_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_0_relu (Activ (None, 18, 18, 352) 0 ['conv4_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_1_conv (Conv2 (None, 18, 18, 128) 45056 ['conv4_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_concat (Conca (None, 18, 18, 384) 0 ['conv4_block3_concat[0][0]', \n", " tenate) 'conv4_block4_2_conv[0][0]'] \n", " \n", " conv4_block5_0_bn (BatchNo (None, 18, 18, 384) 1536 ['conv4_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_0_relu (Activ (None, 18, 18, 384) 0 ['conv4_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_1_conv (Conv2 (None, 18, 18, 128) 49152 ['conv4_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_concat (Conca (None, 18, 18, 416) 0 ['conv4_block4_concat[0][0]', \n", " tenate) 'conv4_block5_2_conv[0][0]'] \n", " \n", " conv4_block6_0_bn (BatchNo (None, 18, 18, 416) 1664 ['conv4_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_0_relu (Activ (None, 18, 18, 416) 0 ['conv4_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_1_conv (Conv2 (None, 18, 18, 128) 53248 ['conv4_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_concat (Conca (None, 18, 18, 448) 0 ['conv4_block5_concat[0][0]', \n", " tenate) 'conv4_block6_2_conv[0][0]'] \n", " \n", " conv4_block7_0_bn (BatchNo (None, 18, 18, 448) 1792 ['conv4_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_0_relu (Activ (None, 18, 18, 448) 0 ['conv4_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_1_conv (Conv2 (None, 18, 18, 128) 57344 ['conv4_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_concat (Conca (None, 18, 18, 480) 0 ['conv4_block6_concat[0][0]', \n", " tenate) 'conv4_block7_2_conv[0][0]'] \n", " \n", " conv4_block8_0_bn (BatchNo (None, 18, 18, 480) 1920 ['conv4_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_0_relu (Activ (None, 18, 18, 480) 0 ['conv4_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_1_conv (Conv2 (None, 18, 18, 128) 61440 ['conv4_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_concat (Conca (None, 18, 18, 512) 0 ['conv4_block7_concat[0][0]', \n", " tenate) 'conv4_block8_2_conv[0][0]'] \n", " \n", " conv4_block9_0_bn (BatchNo (None, 18, 18, 512) 2048 ['conv4_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_0_relu (Activ (None, 18, 18, 512) 0 ['conv4_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_1_conv (Conv2 (None, 18, 18, 128) 65536 ['conv4_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_concat (Conca (None, 18, 18, 544) 0 ['conv4_block8_concat[0][0]', \n", " tenate) 'conv4_block9_2_conv[0][0]'] \n", " \n", " conv4_block10_0_bn (BatchN (None, 18, 18, 544) 2176 ['conv4_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv4_block10_0_relu (Acti (None, 18, 18, 544) 0 ['conv4_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_1_conv (Conv (None, 18, 18, 128) 69632 ['conv4_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block10_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_concat (Conc (None, 18, 18, 576) 0 ['conv4_block9_concat[0][0]', \n", " atenate) 'conv4_block10_2_conv[0][0]']\n", " \n", " conv4_block11_0_bn (BatchN (None, 18, 18, 576) 2304 ['conv4_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_0_relu (Acti (None, 18, 18, 576) 0 ['conv4_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_1_conv (Conv (None, 18, 18, 128) 73728 ['conv4_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_concat (Conc (None, 18, 18, 608) 0 ['conv4_block10_concat[0][0]',\n", " atenate) 'conv4_block11_2_conv[0][0]']\n", " \n", " conv4_block12_0_bn (BatchN (None, 18, 18, 608) 2432 ['conv4_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_0_relu (Acti (None, 18, 18, 608) 0 ['conv4_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_1_conv (Conv (None, 18, 18, 128) 77824 ['conv4_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_concat (Conc (None, 18, 18, 640) 0 ['conv4_block11_concat[0][0]',\n", " atenate) 'conv4_block12_2_conv[0][0]']\n", " \n", " conv4_block13_0_bn (BatchN (None, 18, 18, 640) 2560 ['conv4_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_0_relu (Acti (None, 18, 18, 640) 0 ['conv4_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_1_conv (Conv (None, 18, 18, 128) 81920 ['conv4_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_concat (Conc (None, 18, 18, 672) 0 ['conv4_block12_concat[0][0]',\n", " atenate) 'conv4_block13_2_conv[0][0]']\n", " \n", " conv4_block14_0_bn (BatchN (None, 18, 18, 672) 2688 ['conv4_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_0_relu (Acti (None, 18, 18, 672) 0 ['conv4_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_1_conv (Conv (None, 18, 18, 128) 86016 ['conv4_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_concat (Conc (None, 18, 18, 704) 0 ['conv4_block13_concat[0][0]',\n", " atenate) 'conv4_block14_2_conv[0][0]']\n", " \n", " conv4_block15_0_bn (BatchN (None, 18, 18, 704) 2816 ['conv4_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_0_relu (Acti (None, 18, 18, 704) 0 ['conv4_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_1_conv (Conv (None, 18, 18, 128) 90112 ['conv4_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_concat (Conc (None, 18, 18, 736) 0 ['conv4_block14_concat[0][0]',\n", " atenate) 'conv4_block15_2_conv[0][0]']\n", " \n", " conv4_block16_0_bn (BatchN (None, 18, 18, 736) 2944 ['conv4_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_0_relu (Acti (None, 18, 18, 736) 0 ['conv4_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_1_conv (Conv (None, 18, 18, 128) 94208 ['conv4_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_concat (Conc (None, 18, 18, 768) 0 ['conv4_block15_concat[0][0]',\n", " atenate) 'conv4_block16_2_conv[0][0]']\n", " \n", " conv4_block17_0_bn (BatchN (None, 18, 18, 768) 3072 ['conv4_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_0_relu (Acti (None, 18, 18, 768) 0 ['conv4_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_1_conv (Conv (None, 18, 18, 128) 98304 ['conv4_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_concat (Conc (None, 18, 18, 800) 0 ['conv4_block16_concat[0][0]',\n", " atenate) 'conv4_block17_2_conv[0][0]']\n", " \n", " conv4_block18_0_bn (BatchN (None, 18, 18, 800) 3200 ['conv4_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_0_relu (Acti (None, 18, 18, 800) 0 ['conv4_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_1_conv (Conv (None, 18, 18, 128) 102400 ['conv4_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_concat (Conc (None, 18, 18, 832) 0 ['conv4_block17_concat[0][0]',\n", " atenate) 'conv4_block18_2_conv[0][0]']\n", " \n", " conv4_block19_0_bn (BatchN (None, 18, 18, 832) 3328 ['conv4_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_0_relu (Acti (None, 18, 18, 832) 0 ['conv4_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_1_conv (Conv (None, 18, 18, 128) 106496 ['conv4_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_concat (Conc (None, 18, 18, 864) 0 ['conv4_block18_concat[0][0]',\n", " atenate) 'conv4_block19_2_conv[0][0]']\n", " \n", " conv4_block20_0_bn (BatchN (None, 18, 18, 864) 3456 ['conv4_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_0_relu (Acti (None, 18, 18, 864) 0 ['conv4_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_1_conv (Conv (None, 18, 18, 128) 110592 ['conv4_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_concat (Conc (None, 18, 18, 896) 0 ['conv4_block19_concat[0][0]',\n", " atenate) 'conv4_block20_2_conv[0][0]']\n", " \n", " conv4_block21_0_bn (BatchN (None, 18, 18, 896) 3584 ['conv4_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_0_relu (Acti (None, 18, 18, 896) 0 ['conv4_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_1_conv (Conv (None, 18, 18, 128) 114688 ['conv4_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_concat (Conc (None, 18, 18, 928) 0 ['conv4_block20_concat[0][0]',\n", " atenate) 'conv4_block21_2_conv[0][0]']\n", " \n", " conv4_block22_0_bn (BatchN (None, 18, 18, 928) 3712 ['conv4_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_0_relu (Acti (None, 18, 18, 928) 0 ['conv4_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_1_conv (Conv (None, 18, 18, 128) 118784 ['conv4_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_concat (Conc (None, 18, 18, 960) 0 ['conv4_block21_concat[0][0]',\n", " atenate) 'conv4_block22_2_conv[0][0]']\n", " \n", " conv4_block23_0_bn (BatchN (None, 18, 18, 960) 3840 ['conv4_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_0_relu (Acti (None, 18, 18, 960) 0 ['conv4_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_1_conv (Conv (None, 18, 18, 128) 122880 ['conv4_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_concat (Conc (None, 18, 18, 992) 0 ['conv4_block22_concat[0][0]',\n", " atenate) 'conv4_block23_2_conv[0][0]']\n", " \n", " conv4_block24_0_bn (BatchN (None, 18, 18, 992) 3968 ['conv4_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_0_relu (Acti (None, 18, 18, 992) 0 ['conv4_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_1_conv (Conv (None, 18, 18, 128) 126976 ['conv4_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_concat (Conc (None, 18, 18, 1024) 0 ['conv4_block23_concat[0][0]',\n", " atenate) 'conv4_block24_2_conv[0][0]']\n", " \n", " conv4_block25_0_bn (BatchN (None, 18, 18, 1024) 4096 ['conv4_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_0_relu (Acti (None, 18, 18, 1024) 0 ['conv4_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_1_conv (Conv (None, 18, 18, 128) 131072 ['conv4_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_concat (Conc (None, 18, 18, 1056) 0 ['conv4_block24_concat[0][0]',\n", " atenate) 'conv4_block25_2_conv[0][0]']\n", " \n", " conv4_block26_0_bn (BatchN (None, 18, 18, 1056) 4224 ['conv4_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_0_relu (Acti (None, 18, 18, 1056) 0 ['conv4_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_1_conv (Conv (None, 18, 18, 128) 135168 ['conv4_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_concat (Conc (None, 18, 18, 1088) 0 ['conv4_block25_concat[0][0]',\n", " atenate) 'conv4_block26_2_conv[0][0]']\n", " \n", " conv4_block27_0_bn (BatchN (None, 18, 18, 1088) 4352 ['conv4_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_0_relu (Acti (None, 18, 18, 1088) 0 ['conv4_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_1_conv (Conv (None, 18, 18, 128) 139264 ['conv4_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_concat (Conc (None, 18, 18, 1120) 0 ['conv4_block26_concat[0][0]',\n", " atenate) 'conv4_block27_2_conv[0][0]']\n", " \n", " conv4_block28_0_bn (BatchN (None, 18, 18, 1120) 4480 ['conv4_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_0_relu (Acti (None, 18, 18, 1120) 0 ['conv4_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_1_conv (Conv (None, 18, 18, 128) 143360 ['conv4_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_concat (Conc (None, 18, 18, 1152) 0 ['conv4_block27_concat[0][0]',\n", " atenate) 'conv4_block28_2_conv[0][0]']\n", " \n", " conv4_block29_0_bn (BatchN (None, 18, 18, 1152) 4608 ['conv4_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_0_relu (Acti (None, 18, 18, 1152) 0 ['conv4_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_1_conv (Conv (None, 18, 18, 128) 147456 ['conv4_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_concat (Conc (None, 18, 18, 1184) 0 ['conv4_block28_concat[0][0]',\n", " atenate) 'conv4_block29_2_conv[0][0]']\n", " \n", " conv4_block30_0_bn (BatchN (None, 18, 18, 1184) 4736 ['conv4_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_0_relu (Acti (None, 18, 18, 1184) 0 ['conv4_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_1_conv (Conv (None, 18, 18, 128) 151552 ['conv4_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_concat (Conc (None, 18, 18, 1216) 0 ['conv4_block29_concat[0][0]',\n", " atenate) 'conv4_block30_2_conv[0][0]']\n", " \n", " conv4_block31_0_bn (BatchN (None, 18, 18, 1216) 4864 ['conv4_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_0_relu (Acti (None, 18, 18, 1216) 0 ['conv4_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_1_conv (Conv (None, 18, 18, 128) 155648 ['conv4_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_concat (Conc (None, 18, 18, 1248) 0 ['conv4_block30_concat[0][0]',\n", " atenate) 'conv4_block31_2_conv[0][0]']\n", " \n", " conv4_block32_0_bn (BatchN (None, 18, 18, 1248) 4992 ['conv4_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_0_relu (Acti (None, 18, 18, 1248) 0 ['conv4_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_1_conv (Conv (None, 18, 18, 128) 159744 ['conv4_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_concat (Conc (None, 18, 18, 1280) 0 ['conv4_block31_concat[0][0]',\n", " atenate) 'conv4_block32_2_conv[0][0]']\n", " \n", " conv4_block33_0_bn (BatchN (None, 18, 18, 1280) 5120 ['conv4_block32_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_0_relu (Acti (None, 18, 18, 1280) 0 ['conv4_block33_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_1_conv (Conv (None, 18, 18, 128) 163840 ['conv4_block33_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block33_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block33_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block33_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_concat (Conc (None, 18, 18, 1312) 0 ['conv4_block32_concat[0][0]',\n", " atenate) 'conv4_block33_2_conv[0][0]']\n", " \n", " conv4_block34_0_bn (BatchN (None, 18, 18, 1312) 5248 ['conv4_block33_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_0_relu (Acti (None, 18, 18, 1312) 0 ['conv4_block34_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_1_conv (Conv (None, 18, 18, 128) 167936 ['conv4_block34_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block34_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block34_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block34_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_concat (Conc (None, 18, 18, 1344) 0 ['conv4_block33_concat[0][0]',\n", " atenate) 'conv4_block34_2_conv[0][0]']\n", " \n", " conv4_block35_0_bn (BatchN (None, 18, 18, 1344) 5376 ['conv4_block34_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_0_relu (Acti (None, 18, 18, 1344) 0 ['conv4_block35_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_1_conv (Conv (None, 18, 18, 128) 172032 ['conv4_block35_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block35_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block35_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block35_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_concat (Conc (None, 18, 18, 1376) 0 ['conv4_block34_concat[0][0]',\n", " atenate) 'conv4_block35_2_conv[0][0]']\n", " \n", " conv4_block36_0_bn (BatchN (None, 18, 18, 1376) 5504 ['conv4_block35_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_0_relu (Acti (None, 18, 18, 1376) 0 ['conv4_block36_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_1_conv (Conv (None, 18, 18, 128) 176128 ['conv4_block36_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block36_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block36_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block36_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_concat (Conc (None, 18, 18, 1408) 0 ['conv4_block35_concat[0][0]',\n", " atenate) 'conv4_block36_2_conv[0][0]']\n", " \n", " conv4_block37_0_bn (BatchN (None, 18, 18, 1408) 5632 ['conv4_block36_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_0_relu (Acti (None, 18, 18, 1408) 0 ['conv4_block37_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_1_conv (Conv (None, 18, 18, 128) 180224 ['conv4_block37_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block37_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block37_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block37_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_concat (Conc (None, 18, 18, 1440) 0 ['conv4_block36_concat[0][0]',\n", " atenate) 'conv4_block37_2_conv[0][0]']\n", " \n", " conv4_block38_0_bn (BatchN (None, 18, 18, 1440) 5760 ['conv4_block37_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_0_relu (Acti (None, 18, 18, 1440) 0 ['conv4_block38_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_1_conv (Conv (None, 18, 18, 128) 184320 ['conv4_block38_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block38_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block38_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block38_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_concat (Conc (None, 18, 18, 1472) 0 ['conv4_block37_concat[0][0]',\n", " atenate) 'conv4_block38_2_conv[0][0]']\n", " \n", " conv4_block39_0_bn (BatchN (None, 18, 18, 1472) 5888 ['conv4_block38_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_0_relu (Acti (None, 18, 18, 1472) 0 ['conv4_block39_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_1_conv (Conv (None, 18, 18, 128) 188416 ['conv4_block39_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block39_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block39_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block39_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_concat (Conc (None, 18, 18, 1504) 0 ['conv4_block38_concat[0][0]',\n", " atenate) 'conv4_block39_2_conv[0][0]']\n", " \n", " conv4_block40_0_bn (BatchN (None, 18, 18, 1504) 6016 ['conv4_block39_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_0_relu (Acti (None, 18, 18, 1504) 0 ['conv4_block40_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_1_conv (Conv (None, 18, 18, 128) 192512 ['conv4_block40_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block40_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block40_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block40_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_concat (Conc (None, 18, 18, 1536) 0 ['conv4_block39_concat[0][0]',\n", " atenate) 'conv4_block40_2_conv[0][0]']\n", " \n", " conv4_block41_0_bn (BatchN (None, 18, 18, 1536) 6144 ['conv4_block40_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_0_relu (Acti (None, 18, 18, 1536) 0 ['conv4_block41_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_1_conv (Conv (None, 18, 18, 128) 196608 ['conv4_block41_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block41_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block41_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block41_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_concat (Conc (None, 18, 18, 1568) 0 ['conv4_block40_concat[0][0]',\n", " atenate) 'conv4_block41_2_conv[0][0]']\n", " \n", " conv4_block42_0_bn (BatchN (None, 18, 18, 1568) 6272 ['conv4_block41_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_0_relu (Acti (None, 18, 18, 1568) 0 ['conv4_block42_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_1_conv (Conv (None, 18, 18, 128) 200704 ['conv4_block42_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block42_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block42_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block42_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_concat (Conc (None, 18, 18, 1600) 0 ['conv4_block41_concat[0][0]',\n", " atenate) 'conv4_block42_2_conv[0][0]']\n", " \n", " conv4_block43_0_bn (BatchN (None, 18, 18, 1600) 6400 ['conv4_block42_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_0_relu (Acti (None, 18, 18, 1600) 0 ['conv4_block43_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_1_conv (Conv (None, 18, 18, 128) 204800 ['conv4_block43_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block43_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block43_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block43_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_concat (Conc (None, 18, 18, 1632) 0 ['conv4_block42_concat[0][0]',\n", " atenate) 'conv4_block43_2_conv[0][0]']\n", " \n", " conv4_block44_0_bn (BatchN (None, 18, 18, 1632) 6528 ['conv4_block43_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_0_relu (Acti (None, 18, 18, 1632) 0 ['conv4_block44_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_1_conv (Conv (None, 18, 18, 128) 208896 ['conv4_block44_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block44_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block44_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block44_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_concat (Conc (None, 18, 18, 1664) 0 ['conv4_block43_concat[0][0]',\n", " atenate) 'conv4_block44_2_conv[0][0]']\n", " \n", " conv4_block45_0_bn (BatchN (None, 18, 18, 1664) 6656 ['conv4_block44_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_0_relu (Acti (None, 18, 18, 1664) 0 ['conv4_block45_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_1_conv (Conv (None, 18, 18, 128) 212992 ['conv4_block45_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block45_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block45_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block45_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_concat (Conc (None, 18, 18, 1696) 0 ['conv4_block44_concat[0][0]',\n", " atenate) 'conv4_block45_2_conv[0][0]']\n", " \n", " conv4_block46_0_bn (BatchN (None, 18, 18, 1696) 6784 ['conv4_block45_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_0_relu (Acti (None, 18, 18, 1696) 0 ['conv4_block46_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_1_conv (Conv (None, 18, 18, 128) 217088 ['conv4_block46_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block46_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block46_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block46_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_concat (Conc (None, 18, 18, 1728) 0 ['conv4_block45_concat[0][0]',\n", " atenate) 'conv4_block46_2_conv[0][0]']\n", " \n", " conv4_block47_0_bn (BatchN (None, 18, 18, 1728) 6912 ['conv4_block46_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_0_relu (Acti (None, 18, 18, 1728) 0 ['conv4_block47_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_1_conv (Conv (None, 18, 18, 128) 221184 ['conv4_block47_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block47_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block47_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block47_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_concat (Conc (None, 18, 18, 1760) 0 ['conv4_block46_concat[0][0]',\n", " atenate) 'conv4_block47_2_conv[0][0]']\n", " \n", " conv4_block48_0_bn (BatchN (None, 18, 18, 1760) 7040 ['conv4_block47_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_0_relu (Acti (None, 18, 18, 1760) 0 ['conv4_block48_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_1_conv (Conv (None, 18, 18, 128) 225280 ['conv4_block48_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block48_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block48_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block48_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_concat (Conc (None, 18, 18, 1792) 0 ['conv4_block47_concat[0][0]',\n", " atenate) 'conv4_block48_2_conv[0][0]']\n", " \n", " pool4_bn (BatchNormalizati (None, 18, 18, 1792) 7168 ['conv4_block48_concat[0][0]']\n", " on) \n", " \n", " pool4_relu (Activation) (None, 18, 18, 1792) 0 ['pool4_bn[0][0]'] \n", " \n", " pool4_conv (Conv2D) (None, 18, 18, 896) 1605632 ['pool4_relu[0][0]'] \n", " \n", " pool4_pool (AveragePooling (None, 9, 9, 896) 0 ['pool4_conv[0][0]'] \n", " 2D) \n", " \n", " conv5_block1_0_bn (BatchNo (None, 9, 9, 896) 3584 ['pool4_pool[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_0_relu (Activ (None, 9, 9, 896) 0 ['conv5_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_1_conv (Conv2 (None, 9, 9, 128) 114688 ['conv5_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_concat (Conca (None, 9, 9, 928) 0 ['pool4_pool[0][0]', \n", " tenate) 'conv5_block1_2_conv[0][0]'] \n", " \n", " conv5_block2_0_bn (BatchNo (None, 9, 9, 928) 3712 ['conv5_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_0_relu (Activ (None, 9, 9, 928) 0 ['conv5_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_1_conv (Conv2 (None, 9, 9, 128) 118784 ['conv5_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_concat (Conca (None, 9, 9, 960) 0 ['conv5_block1_concat[0][0]', \n", " tenate) 'conv5_block2_2_conv[0][0]'] \n", " \n", " conv5_block3_0_bn (BatchNo (None, 9, 9, 960) 3840 ['conv5_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_0_relu (Activ (None, 9, 9, 960) 0 ['conv5_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_1_conv (Conv2 (None, 9, 9, 128) 122880 ['conv5_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_concat (Conca (None, 9, 9, 992) 0 ['conv5_block2_concat[0][0]', \n", " tenate) 'conv5_block3_2_conv[0][0]'] \n", " \n", " conv5_block4_0_bn (BatchNo (None, 9, 9, 992) 3968 ['conv5_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_0_relu (Activ (None, 9, 9, 992) 0 ['conv5_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_1_conv (Conv2 (None, 9, 9, 128) 126976 ['conv5_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_concat (Conca (None, 9, 9, 1024) 0 ['conv5_block3_concat[0][0]', \n", " tenate) 'conv5_block4_2_conv[0][0]'] \n", " \n", " conv5_block5_0_bn (BatchNo (None, 9, 9, 1024) 4096 ['conv5_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_0_relu (Activ (None, 9, 9, 1024) 0 ['conv5_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_1_conv (Conv2 (None, 9, 9, 128) 131072 ['conv5_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_concat (Conca (None, 9, 9, 1056) 0 ['conv5_block4_concat[0][0]', \n", " tenate) 'conv5_block5_2_conv[0][0]'] \n", " \n", " conv5_block6_0_bn (BatchNo (None, 9, 9, 1056) 4224 ['conv5_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_0_relu (Activ (None, 9, 9, 1056) 0 ['conv5_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_1_conv (Conv2 (None, 9, 9, 128) 135168 ['conv5_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_concat (Conca (None, 9, 9, 1088) 0 ['conv5_block5_concat[0][0]', \n", " tenate) 'conv5_block6_2_conv[0][0]'] \n", " \n", " conv5_block7_0_bn (BatchNo (None, 9, 9, 1088) 4352 ['conv5_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_0_relu (Activ (None, 9, 9, 1088) 0 ['conv5_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_1_conv (Conv2 (None, 9, 9, 128) 139264 ['conv5_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_concat (Conca (None, 9, 9, 1120) 0 ['conv5_block6_concat[0][0]', \n", " tenate) 'conv5_block7_2_conv[0][0]'] \n", " \n", " conv5_block8_0_bn (BatchNo (None, 9, 9, 1120) 4480 ['conv5_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_0_relu (Activ (None, 9, 9, 1120) 0 ['conv5_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_1_conv (Conv2 (None, 9, 9, 128) 143360 ['conv5_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_concat (Conca (None, 9, 9, 1152) 0 ['conv5_block7_concat[0][0]', \n", " tenate) 'conv5_block8_2_conv[0][0]'] \n", " \n", " conv5_block9_0_bn (BatchNo (None, 9, 9, 1152) 4608 ['conv5_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_0_relu (Activ (None, 9, 9, 1152) 0 ['conv5_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_1_conv (Conv2 (None, 9, 9, 128) 147456 ['conv5_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_concat (Conca (None, 9, 9, 1184) 0 ['conv5_block8_concat[0][0]', \n", " tenate) 'conv5_block9_2_conv[0][0]'] \n", " \n", " conv5_block10_0_bn (BatchN (None, 9, 9, 1184) 4736 ['conv5_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv5_block10_0_relu (Acti (None, 9, 9, 1184) 0 ['conv5_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_1_conv (Conv (None, 9, 9, 128) 151552 ['conv5_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block10_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_concat (Conc (None, 9, 9, 1216) 0 ['conv5_block9_concat[0][0]', \n", " atenate) 'conv5_block10_2_conv[0][0]']\n", " \n", " conv5_block11_0_bn (BatchN (None, 9, 9, 1216) 4864 ['conv5_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_0_relu (Acti (None, 9, 9, 1216) 0 ['conv5_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_1_conv (Conv (None, 9, 9, 128) 155648 ['conv5_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_concat (Conc (None, 9, 9, 1248) 0 ['conv5_block10_concat[0][0]',\n", " atenate) 'conv5_block11_2_conv[0][0]']\n", " \n", " conv5_block12_0_bn (BatchN (None, 9, 9, 1248) 4992 ['conv5_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_0_relu (Acti (None, 9, 9, 1248) 0 ['conv5_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_1_conv (Conv (None, 9, 9, 128) 159744 ['conv5_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_concat (Conc (None, 9, 9, 1280) 0 ['conv5_block11_concat[0][0]',\n", " atenate) 'conv5_block12_2_conv[0][0]']\n", " \n", " conv5_block13_0_bn (BatchN (None, 9, 9, 1280) 5120 ['conv5_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_0_relu (Acti (None, 9, 9, 1280) 0 ['conv5_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_1_conv (Conv (None, 9, 9, 128) 163840 ['conv5_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_concat (Conc (None, 9, 9, 1312) 0 ['conv5_block12_concat[0][0]',\n", " atenate) 'conv5_block13_2_conv[0][0]']\n", " \n", " conv5_block14_0_bn (BatchN (None, 9, 9, 1312) 5248 ['conv5_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_0_relu (Acti (None, 9, 9, 1312) 0 ['conv5_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_1_conv (Conv (None, 9, 9, 128) 167936 ['conv5_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_concat (Conc (None, 9, 9, 1344) 0 ['conv5_block13_concat[0][0]',\n", " atenate) 'conv5_block14_2_conv[0][0]']\n", " \n", " conv5_block15_0_bn (BatchN (None, 9, 9, 1344) 5376 ['conv5_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_0_relu (Acti (None, 9, 9, 1344) 0 ['conv5_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_1_conv (Conv (None, 9, 9, 128) 172032 ['conv5_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_concat (Conc (None, 9, 9, 1376) 0 ['conv5_block14_concat[0][0]',\n", " atenate) 'conv5_block15_2_conv[0][0]']\n", " \n", " conv5_block16_0_bn (BatchN (None, 9, 9, 1376) 5504 ['conv5_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_0_relu (Acti (None, 9, 9, 1376) 0 ['conv5_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_1_conv (Conv (None, 9, 9, 128) 176128 ['conv5_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_concat (Conc (None, 9, 9, 1408) 0 ['conv5_block15_concat[0][0]',\n", " atenate) 'conv5_block16_2_conv[0][0]']\n", " \n", " conv5_block17_0_bn (BatchN (None, 9, 9, 1408) 5632 ['conv5_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_0_relu (Acti (None, 9, 9, 1408) 0 ['conv5_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_1_conv (Conv (None, 9, 9, 128) 180224 ['conv5_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_concat (Conc (None, 9, 9, 1440) 0 ['conv5_block16_concat[0][0]',\n", " atenate) 'conv5_block17_2_conv[0][0]']\n", " \n", " conv5_block18_0_bn (BatchN (None, 9, 9, 1440) 5760 ['conv5_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_0_relu (Acti (None, 9, 9, 1440) 0 ['conv5_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_1_conv (Conv (None, 9, 9, 128) 184320 ['conv5_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_concat (Conc (None, 9, 9, 1472) 0 ['conv5_block17_concat[0][0]',\n", " atenate) 'conv5_block18_2_conv[0][0]']\n", " \n", " conv5_block19_0_bn (BatchN (None, 9, 9, 1472) 5888 ['conv5_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_0_relu (Acti (None, 9, 9, 1472) 0 ['conv5_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_1_conv (Conv (None, 9, 9, 128) 188416 ['conv5_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_concat (Conc (None, 9, 9, 1504) 0 ['conv5_block18_concat[0][0]',\n", " atenate) 'conv5_block19_2_conv[0][0]']\n", " \n", " conv5_block20_0_bn (BatchN (None, 9, 9, 1504) 6016 ['conv5_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_0_relu (Acti (None, 9, 9, 1504) 0 ['conv5_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_1_conv (Conv (None, 9, 9, 128) 192512 ['conv5_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_concat (Conc (None, 9, 9, 1536) 0 ['conv5_block19_concat[0][0]',\n", " atenate) 'conv5_block20_2_conv[0][0]']\n", " \n", " conv5_block21_0_bn (BatchN (None, 9, 9, 1536) 6144 ['conv5_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_0_relu (Acti (None, 9, 9, 1536) 0 ['conv5_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_1_conv (Conv (None, 9, 9, 128) 196608 ['conv5_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_concat (Conc (None, 9, 9, 1568) 0 ['conv5_block20_concat[0][0]',\n", " atenate) 'conv5_block21_2_conv[0][0]']\n", " \n", " conv5_block22_0_bn (BatchN (None, 9, 9, 1568) 6272 ['conv5_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_0_relu (Acti (None, 9, 9, 1568) 0 ['conv5_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_1_conv (Conv (None, 9, 9, 128) 200704 ['conv5_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_concat (Conc (None, 9, 9, 1600) 0 ['conv5_block21_concat[0][0]',\n", " atenate) 'conv5_block22_2_conv[0][0]']\n", " \n", " conv5_block23_0_bn (BatchN (None, 9, 9, 1600) 6400 ['conv5_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_0_relu (Acti (None, 9, 9, 1600) 0 ['conv5_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_1_conv (Conv (None, 9, 9, 128) 204800 ['conv5_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_concat (Conc (None, 9, 9, 1632) 0 ['conv5_block22_concat[0][0]',\n", " atenate) 'conv5_block23_2_conv[0][0]']\n", " \n", " conv5_block24_0_bn (BatchN (None, 9, 9, 1632) 6528 ['conv5_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_0_relu (Acti (None, 9, 9, 1632) 0 ['conv5_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_1_conv (Conv (None, 9, 9, 128) 208896 ['conv5_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_concat (Conc (None, 9, 9, 1664) 0 ['conv5_block23_concat[0][0]',\n", " atenate) 'conv5_block24_2_conv[0][0]']\n", " \n", " conv5_block25_0_bn (BatchN (None, 9, 9, 1664) 6656 ['conv5_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_0_relu (Acti (None, 9, 9, 1664) 0 ['conv5_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_1_conv (Conv (None, 9, 9, 128) 212992 ['conv5_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_concat (Conc (None, 9, 9, 1696) 0 ['conv5_block24_concat[0][0]',\n", " atenate) 'conv5_block25_2_conv[0][0]']\n", " \n", " conv5_block26_0_bn (BatchN (None, 9, 9, 1696) 6784 ['conv5_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_0_relu (Acti (None, 9, 9, 1696) 0 ['conv5_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_1_conv (Conv (None, 9, 9, 128) 217088 ['conv5_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_concat (Conc (None, 9, 9, 1728) 0 ['conv5_block25_concat[0][0]',\n", " atenate) 'conv5_block26_2_conv[0][0]']\n", " \n", " conv5_block27_0_bn (BatchN (None, 9, 9, 1728) 6912 ['conv5_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_0_relu (Acti (None, 9, 9, 1728) 0 ['conv5_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_1_conv (Conv (None, 9, 9, 128) 221184 ['conv5_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_concat (Conc (None, 9, 9, 1760) 0 ['conv5_block26_concat[0][0]',\n", " atenate) 'conv5_block27_2_conv[0][0]']\n", " \n", " conv5_block28_0_bn (BatchN (None, 9, 9, 1760) 7040 ['conv5_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_0_relu (Acti (None, 9, 9, 1760) 0 ['conv5_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_1_conv (Conv (None, 9, 9, 128) 225280 ['conv5_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_concat (Conc (None, 9, 9, 1792) 0 ['conv5_block27_concat[0][0]',\n", " atenate) 'conv5_block28_2_conv[0][0]']\n", " \n", " conv5_block29_0_bn (BatchN (None, 9, 9, 1792) 7168 ['conv5_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_0_relu (Acti (None, 9, 9, 1792) 0 ['conv5_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_1_conv (Conv (None, 9, 9, 128) 229376 ['conv5_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_concat (Conc (None, 9, 9, 1824) 0 ['conv5_block28_concat[0][0]',\n", " atenate) 'conv5_block29_2_conv[0][0]']\n", " \n", " conv5_block30_0_bn (BatchN (None, 9, 9, 1824) 7296 ['conv5_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_0_relu (Acti (None, 9, 9, 1824) 0 ['conv5_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_1_conv (Conv (None, 9, 9, 128) 233472 ['conv5_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_concat (Conc (None, 9, 9, 1856) 0 ['conv5_block29_concat[0][0]',\n", " atenate) 'conv5_block30_2_conv[0][0]']\n", " \n", " conv5_block31_0_bn (BatchN (None, 9, 9, 1856) 7424 ['conv5_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_0_relu (Acti (None, 9, 9, 1856) 0 ['conv5_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_1_conv (Conv (None, 9, 9, 128) 237568 ['conv5_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_concat (Conc (None, 9, 9, 1888) 0 ['conv5_block30_concat[0][0]',\n", " atenate) 'conv5_block31_2_conv[0][0]']\n", " \n", " conv5_block32_0_bn (BatchN (None, 9, 9, 1888) 7552 ['conv5_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_0_relu (Acti (None, 9, 9, 1888) 0 ['conv5_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_1_conv (Conv (None, 9, 9, 128) 241664 ['conv5_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_concat (Conc (None, 9, 9, 1920) 0 ['conv5_block31_concat[0][0]',\n", " atenate) 'conv5_block32_2_conv[0][0]']\n", " \n", " bn (BatchNormalization) (None, 9, 9, 1920) 7680 ['conv5_block32_concat[0][0]']\n", " \n", " relu (Activation) (None, 9, 9, 1920) 0 ['bn[0][0]'] \n", " \n", " flatten (Flatten) (None, 155520) 0 ['relu[0][0]'] \n", " \n", " dense (Dense) (None, 2) 311042 ['flatten[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 18633026 (71.08 MB)\n", "Trainable params: 18403970 (70.21 MB)\n", "Non-trainable params: 229056 (894.75 KB)\n", "__________________________________________________________________________________________________\n", "None\n" ] } ], "source": [ "model = create_model()" ] }, { "cell_type": "code", "source": [ "history = model.fit(train_generator, steps_per_epoch=20, epochs=20, validation_data=validation_generator, validation_steps=25, callbacks=[cp_callback])\n", "evaluation = model.evaluate(train_generator)\n", "print(f\"Test Accuracy: {evaluation[1] * 100:.2f}%\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MTI59ef-h5GC", "outputId": "f612db5c-c2d3-4701-8d68-e89955b04bbe" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.9500 - accuracy: 0.9900 \n", "Epoch 1: val_accuracy improved from -inf to 1.00000, saving model to /kaggle/working/Checkpoints_densenet201/cp.ckpt\n", "20/20 [==============================] - 454s 23s/step - loss: 0.9500 - accuracy: 0.9900 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n", "Epoch 2/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9850 \n", "Epoch 2: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 435s 22s/step - loss: 0.0617 - accuracy: 0.9850 - val_loss: 11822.7178 - val_accuracy: 0.9850\n", "Epoch 3/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0677 - accuracy: 0.9900 \n", "Epoch 3: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 431s 22s/step - loss: 0.0677 - accuracy: 0.9900 - val_loss: 133.2602 - val_accuracy: 0.9850\n", "Epoch 4/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0375 - accuracy: 0.9900 \n", "Epoch 4: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 430s 22s/step - loss: 0.0375 - accuracy: 0.9900 - val_loss: 20160.7246 - val_accuracy: 0.9950\n", "Epoch 5/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0244 - accuracy: 0.9950 \n", "Epoch 5: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 436s 22s/step - loss: 0.0244 - accuracy: 0.9950 - val_loss: 36.8947 - val_accuracy: 0.9900\n", "Epoch 6/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0757 - accuracy: 0.9950 \n", "Epoch 6: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 437s 22s/step - loss: 0.0757 - accuracy: 0.9950 - val_loss: 43882.2109 - val_accuracy: 0.9900\n", "Epoch 7/20\n", "20/20 [==============================] - ETA: 0s - loss: 1.1539 - accuracy: 0.9850 \n", "Epoch 7: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 438s 22s/step - loss: 1.1539 - accuracy: 0.9850 - val_loss: 26.1904 - val_accuracy: 0.9950\n", "Epoch 8/20\n", "20/20 [==============================] - ETA: 0s - loss: 9.9906e-07 - accuracy: 1.0000 \n", "Epoch 8: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 438s 22s/step - loss: 9.9906e-07 - accuracy: 1.0000 - val_loss: 111.5942 - val_accuracy: 0.9900\n", "Epoch 9/20\n", "20/20 [==============================] - ETA: 0s - loss: 2.6019 - accuracy: 0.9800 \n", "Epoch 9: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 438s 22s/step - loss: 2.6019 - accuracy: 0.9800 - val_loss: 0.0000e+00 - val_accuracy: 1.0000\n", "Epoch 10/20\n", "20/20 [==============================] - ETA: 0s - loss: 1.9539 - accuracy: 0.9686 \n", "Epoch 10: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 421s 21s/step - loss: 1.9539 - accuracy: 0.9686 - val_loss: 2144.8069 - val_accuracy: 0.9950\n", "Epoch 11/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.1941 - accuracy: 0.9950 \n", "Epoch 11: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 438s 22s/step - loss: 0.1941 - accuracy: 0.9950 - val_loss: 400.3810 - val_accuracy: 0.9900\n", "Epoch 12/20\n", "20/20 [==============================] - ETA: 0s - loss: 1.5106e-07 - accuracy: 1.0000 \n", "Epoch 12: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 431s 22s/step - loss: 1.5106e-07 - accuracy: 1.0000 - val_loss: 6.5265 - val_accuracy: 0.9950\n", "Epoch 13/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0676 - accuracy: 0.9950 \n", "Epoch 13: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 437s 22s/step - loss: 0.0676 - accuracy: 0.9950 - val_loss: 0.3767 - val_accuracy: 0.9900\n", "Epoch 14/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0534 - accuracy: 0.9850 \n", "Epoch 14: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 440s 22s/step - loss: 0.0534 - accuracy: 0.9850 - val_loss: 0.2921 - val_accuracy: 0.9850\n", "Epoch 15/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0106 - accuracy: 0.9950 \n", "Epoch 15: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 436s 22s/step - loss: 0.0106 - accuracy: 0.9950 - val_loss: 0.2687 - val_accuracy: 0.9900\n", "Epoch 16/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.1836 - accuracy: 0.9950 \n", "Epoch 16: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 437s 22s/step - loss: 0.1836 - accuracy: 0.9950 - val_loss: 0.1264 - val_accuracy: 0.9950\n", "Epoch 17/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.1612 - accuracy: 0.9900 \n", "Epoch 17: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 430s 22s/step - loss: 0.1612 - accuracy: 0.9900 - val_loss: 0.0228 - val_accuracy: 0.9950\n", "Epoch 18/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0363 - accuracy: 0.9850 \n", "Epoch 18: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 436s 22s/step - loss: 0.0363 - accuracy: 0.9850 - val_loss: 0.0376 - val_accuracy: 0.9950\n", "Epoch 19/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0392 - accuracy: 0.9900 \n", "Epoch 19: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 435s 22s/step - loss: 0.0392 - accuracy: 0.9900 - val_loss: 0.0561 - val_accuracy: 0.9850\n", "Epoch 20/20\n", "20/20 [==============================] - ETA: 0s - loss: 0.0386 - accuracy: 0.9950 \n", "Epoch 20: val_accuracy did not improve from 1.00000\n", "20/20 [==============================] - 434s 22s/step - loss: 0.0386 - accuracy: 0.9950 - val_loss: 0.0515 - val_accuracy: 0.9900\n", "115/115 [==============================] - 457s 4s/step - loss: 0.0380 - accuracy: 0.9930\n", "Test Accuracy: 99.30%\n" ] } ] }, { "cell_type": "code", "source": [ "initial_epoch = 0\n", "saved_history = {\n", " 'loss': history.history['loss'],\n", " 'accuracy': history.history['accuracy'],\n", " 'val_loss': history.history['val_loss'],\n", " 'val_accuracy': history.history['val_accuracy'],\n", "}" ], "metadata": { "id": "9EC_PA6WjTtF" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "np.save(\"/kaggle/working/saved_D201history.npy\", saved_history)" ], "metadata": { "id": "u-2P1eOOHInk" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\n", "print(latest_checkpoint)\n", "if latest_checkpoint is not None:\n", " loaded_model = create_model(summary=True)\n", " status = loaded_model.load_weights(latest_checkpoint)\n", " status.expect_partial()\n", "else:\n", " print(\"No checkpoint file found in the specified directory.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_1szNokTHLyY", "outputId": "e09e6090-4d0a-4a3d-e97e-e7fe25ca6765" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/kaggle/working/Checkpoints_densenet201/cp.ckpt\n", "Model: \"model_1\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_2 (InputLayer) [(None, 299, 299, 3)] 0 [] \n", " \n", " zero_padding2d_2 (ZeroPadd (None, 305, 305, 3) 0 ['input_2[0][0]'] \n", " ing2D) \n", " \n", " conv1/conv (Conv2D) (None, 150, 150, 64) 9408 ['zero_padding2d_2[0][0]'] \n", " \n", " conv1/bn (BatchNormalizati (None, 150, 150, 64) 256 ['conv1/conv[0][0]'] \n", " on) \n", " \n", " conv1/relu (Activation) (None, 150, 150, 64) 0 ['conv1/bn[0][0]'] \n", " \n", " zero_padding2d_3 (ZeroPadd (None, 152, 152, 64) 0 ['conv1/relu[0][0]'] \n", " ing2D) \n", " \n", " pool1 (MaxPooling2D) (None, 75, 75, 64) 0 ['zero_padding2d_3[0][0]'] \n", " \n", " conv2_block1_0_bn (BatchNo (None, 75, 75, 64) 256 ['pool1[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_0_relu (Activ (None, 75, 75, 64) 0 ['conv2_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_1_conv (Conv2 (None, 75, 75, 128) 8192 ['conv2_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_concat (Conca (None, 75, 75, 96) 0 ['pool1[0][0]', \n", " tenate) 'conv2_block1_2_conv[0][0]'] \n", " \n", " conv2_block2_0_bn (BatchNo (None, 75, 75, 96) 384 ['conv2_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_0_relu (Activ (None, 75, 75, 96) 0 ['conv2_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_1_conv (Conv2 (None, 75, 75, 128) 12288 ['conv2_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_concat (Conca (None, 75, 75, 128) 0 ['conv2_block1_concat[0][0]', \n", " tenate) 'conv2_block2_2_conv[0][0]'] \n", " \n", " conv2_block3_0_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_0_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_1_conv (Conv2 (None, 75, 75, 128) 16384 ['conv2_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_concat (Conca (None, 75, 75, 160) 0 ['conv2_block2_concat[0][0]', \n", " tenate) 'conv2_block3_2_conv[0][0]'] \n", " \n", " conv2_block4_0_bn (BatchNo (None, 75, 75, 160) 640 ['conv2_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_0_relu (Activ (None, 75, 75, 160) 0 ['conv2_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_1_conv (Conv2 (None, 75, 75, 128) 20480 ['conv2_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_concat (Conca (None, 75, 75, 192) 0 ['conv2_block3_concat[0][0]', \n", " tenate) 'conv2_block4_2_conv[0][0]'] \n", " \n", " conv2_block5_0_bn (BatchNo (None, 75, 75, 192) 768 ['conv2_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_0_relu (Activ (None, 75, 75, 192) 0 ['conv2_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_1_conv (Conv2 (None, 75, 75, 128) 24576 ['conv2_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_concat (Conca (None, 75, 75, 224) 0 ['conv2_block4_concat[0][0]', \n", " tenate) 'conv2_block5_2_conv[0][0]'] \n", " \n", " conv2_block6_0_bn (BatchNo (None, 75, 75, 224) 896 ['conv2_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_0_relu (Activ (None, 75, 75, 224) 0 ['conv2_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_1_conv (Conv2 (None, 75, 75, 128) 28672 ['conv2_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_concat (Conca (None, 75, 75, 256) 0 ['conv2_block5_concat[0][0]', \n", " tenate) 'conv2_block6_2_conv[0][0]'] \n", " \n", " pool2_bn (BatchNormalizati (None, 75, 75, 256) 1024 ['conv2_block6_concat[0][0]'] \n", " on) \n", " \n", " pool2_relu (Activation) (None, 75, 75, 256) 0 ['pool2_bn[0][0]'] \n", " \n", " pool2_conv (Conv2D) (None, 75, 75, 128) 32768 ['pool2_relu[0][0]'] \n", " \n", " pool2_pool (AveragePooling (None, 37, 37, 128) 0 ['pool2_conv[0][0]'] \n", " 2D) \n", " \n", " conv3_block1_0_bn (BatchNo (None, 37, 37, 128) 512 ['pool2_pool[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_0_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_1_conv (Conv2 (None, 37, 37, 128) 16384 ['conv3_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_concat (Conca (None, 37, 37, 160) 0 ['pool2_pool[0][0]', \n", " tenate) 'conv3_block1_2_conv[0][0]'] \n", " \n", " conv3_block2_0_bn (BatchNo (None, 37, 37, 160) 640 ['conv3_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_0_relu (Activ (None, 37, 37, 160) 0 ['conv3_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_1_conv (Conv2 (None, 37, 37, 128) 20480 ['conv3_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_concat (Conca (None, 37, 37, 192) 0 ['conv3_block1_concat[0][0]', \n", " tenate) 'conv3_block2_2_conv[0][0]'] \n", " \n", " conv3_block3_0_bn (BatchNo (None, 37, 37, 192) 768 ['conv3_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_0_relu (Activ (None, 37, 37, 192) 0 ['conv3_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_1_conv (Conv2 (None, 37, 37, 128) 24576 ['conv3_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_concat (Conca (None, 37, 37, 224) 0 ['conv3_block2_concat[0][0]', \n", " tenate) 'conv3_block3_2_conv[0][0]'] \n", " \n", " conv3_block4_0_bn (BatchNo (None, 37, 37, 224) 896 ['conv3_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_0_relu (Activ (None, 37, 37, 224) 0 ['conv3_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_1_conv (Conv2 (None, 37, 37, 128) 28672 ['conv3_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_concat (Conca (None, 37, 37, 256) 0 ['conv3_block3_concat[0][0]', \n", " tenate) 'conv3_block4_2_conv[0][0]'] \n", " \n", " conv3_block5_0_bn (BatchNo (None, 37, 37, 256) 1024 ['conv3_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_0_relu (Activ (None, 37, 37, 256) 0 ['conv3_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_1_conv (Conv2 (None, 37, 37, 128) 32768 ['conv3_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_concat (Conca (None, 37, 37, 288) 0 ['conv3_block4_concat[0][0]', \n", " tenate) 'conv3_block5_2_conv[0][0]'] \n", " \n", " conv3_block6_0_bn (BatchNo (None, 37, 37, 288) 1152 ['conv3_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_0_relu (Activ (None, 37, 37, 288) 0 ['conv3_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_1_conv (Conv2 (None, 37, 37, 128) 36864 ['conv3_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_concat (Conca (None, 37, 37, 320) 0 ['conv3_block5_concat[0][0]', \n", " tenate) 'conv3_block6_2_conv[0][0]'] \n", " \n", " conv3_block7_0_bn (BatchNo (None, 37, 37, 320) 1280 ['conv3_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_0_relu (Activ (None, 37, 37, 320) 0 ['conv3_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_1_conv (Conv2 (None, 37, 37, 128) 40960 ['conv3_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_concat (Conca (None, 37, 37, 352) 0 ['conv3_block6_concat[0][0]', \n", " tenate) 'conv3_block7_2_conv[0][0]'] \n", " \n", " conv3_block8_0_bn (BatchNo (None, 37, 37, 352) 1408 ['conv3_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_0_relu (Activ (None, 37, 37, 352) 0 ['conv3_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_1_conv (Conv2 (None, 37, 37, 128) 45056 ['conv3_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_concat (Conca (None, 37, 37, 384) 0 ['conv3_block7_concat[0][0]', \n", " tenate) 'conv3_block8_2_conv[0][0]'] \n", " \n", " conv3_block9_0_bn (BatchNo (None, 37, 37, 384) 1536 ['conv3_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_0_relu (Activ (None, 37, 37, 384) 0 ['conv3_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_1_conv (Conv2 (None, 37, 37, 128) 49152 ['conv3_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_concat (Conca (None, 37, 37, 416) 0 ['conv3_block8_concat[0][0]', \n", " tenate) 'conv3_block9_2_conv[0][0]'] \n", " \n", " conv3_block10_0_bn (BatchN (None, 37, 37, 416) 1664 ['conv3_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv3_block10_0_relu (Acti (None, 37, 37, 416) 0 ['conv3_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_1_conv (Conv (None, 37, 37, 128) 53248 ['conv3_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block10_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_concat (Conc (None, 37, 37, 448) 0 ['conv3_block9_concat[0][0]', \n", " atenate) 'conv3_block10_2_conv[0][0]']\n", " \n", " conv3_block11_0_bn (BatchN (None, 37, 37, 448) 1792 ['conv3_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_0_relu (Acti (None, 37, 37, 448) 0 ['conv3_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_1_conv (Conv (None, 37, 37, 128) 57344 ['conv3_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_concat (Conc (None, 37, 37, 480) 0 ['conv3_block10_concat[0][0]',\n", " atenate) 'conv3_block11_2_conv[0][0]']\n", " \n", " conv3_block12_0_bn (BatchN (None, 37, 37, 480) 1920 ['conv3_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_0_relu (Acti (None, 37, 37, 480) 0 ['conv3_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_1_conv (Conv (None, 37, 37, 128) 61440 ['conv3_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_concat (Conc (None, 37, 37, 512) 0 ['conv3_block11_concat[0][0]',\n", " atenate) 'conv3_block12_2_conv[0][0]']\n", " \n", " pool3_bn (BatchNormalizati (None, 37, 37, 512) 2048 ['conv3_block12_concat[0][0]']\n", " on) \n", " \n", " pool3_relu (Activation) (None, 37, 37, 512) 0 ['pool3_bn[0][0]'] \n", " \n", " pool3_conv (Conv2D) (None, 37, 37, 256) 131072 ['pool3_relu[0][0]'] \n", " \n", " pool3_pool (AveragePooling (None, 18, 18, 256) 0 ['pool3_conv[0][0]'] \n", " 2D) \n", " \n", " conv4_block1_0_bn (BatchNo (None, 18, 18, 256) 1024 ['pool3_pool[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_0_relu (Activ (None, 18, 18, 256) 0 ['conv4_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_1_conv (Conv2 (None, 18, 18, 128) 32768 ['conv4_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_concat (Conca (None, 18, 18, 288) 0 ['pool3_pool[0][0]', \n", " tenate) 'conv4_block1_2_conv[0][0]'] \n", " \n", " conv4_block2_0_bn (BatchNo (None, 18, 18, 288) 1152 ['conv4_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_0_relu (Activ (None, 18, 18, 288) 0 ['conv4_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_1_conv (Conv2 (None, 18, 18, 128) 36864 ['conv4_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_concat (Conca (None, 18, 18, 320) 0 ['conv4_block1_concat[0][0]', \n", " tenate) 'conv4_block2_2_conv[0][0]'] \n", " \n", " conv4_block3_0_bn (BatchNo (None, 18, 18, 320) 1280 ['conv4_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_0_relu (Activ (None, 18, 18, 320) 0 ['conv4_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_1_conv (Conv2 (None, 18, 18, 128) 40960 ['conv4_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_concat (Conca (None, 18, 18, 352) 0 ['conv4_block2_concat[0][0]', \n", " tenate) 'conv4_block3_2_conv[0][0]'] \n", " \n", " conv4_block4_0_bn (BatchNo (None, 18, 18, 352) 1408 ['conv4_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_0_relu (Activ (None, 18, 18, 352) 0 ['conv4_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_1_conv (Conv2 (None, 18, 18, 128) 45056 ['conv4_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_concat (Conca (None, 18, 18, 384) 0 ['conv4_block3_concat[0][0]', \n", " tenate) 'conv4_block4_2_conv[0][0]'] \n", " \n", " conv4_block5_0_bn (BatchNo (None, 18, 18, 384) 1536 ['conv4_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_0_relu (Activ (None, 18, 18, 384) 0 ['conv4_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_1_conv (Conv2 (None, 18, 18, 128) 49152 ['conv4_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_concat (Conca (None, 18, 18, 416) 0 ['conv4_block4_concat[0][0]', \n", " tenate) 'conv4_block5_2_conv[0][0]'] \n", " \n", " conv4_block6_0_bn (BatchNo (None, 18, 18, 416) 1664 ['conv4_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_0_relu (Activ (None, 18, 18, 416) 0 ['conv4_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_1_conv (Conv2 (None, 18, 18, 128) 53248 ['conv4_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_concat (Conca (None, 18, 18, 448) 0 ['conv4_block5_concat[0][0]', \n", " tenate) 'conv4_block6_2_conv[0][0]'] \n", " \n", " conv4_block7_0_bn (BatchNo (None, 18, 18, 448) 1792 ['conv4_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_0_relu (Activ (None, 18, 18, 448) 0 ['conv4_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_1_conv (Conv2 (None, 18, 18, 128) 57344 ['conv4_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_concat (Conca (None, 18, 18, 480) 0 ['conv4_block6_concat[0][0]', \n", " tenate) 'conv4_block7_2_conv[0][0]'] \n", " \n", " conv4_block8_0_bn (BatchNo (None, 18, 18, 480) 1920 ['conv4_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_0_relu (Activ (None, 18, 18, 480) 0 ['conv4_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_1_conv (Conv2 (None, 18, 18, 128) 61440 ['conv4_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_concat (Conca (None, 18, 18, 512) 0 ['conv4_block7_concat[0][0]', \n", " tenate) 'conv4_block8_2_conv[0][0]'] \n", " \n", " conv4_block9_0_bn (BatchNo (None, 18, 18, 512) 2048 ['conv4_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_0_relu (Activ (None, 18, 18, 512) 0 ['conv4_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_1_conv (Conv2 (None, 18, 18, 128) 65536 ['conv4_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_concat (Conca (None, 18, 18, 544) 0 ['conv4_block8_concat[0][0]', \n", " tenate) 'conv4_block9_2_conv[0][0]'] \n", " \n", " conv4_block10_0_bn (BatchN (None, 18, 18, 544) 2176 ['conv4_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv4_block10_0_relu (Acti (None, 18, 18, 544) 0 ['conv4_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_1_conv (Conv (None, 18, 18, 128) 69632 ['conv4_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block10_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_concat (Conc (None, 18, 18, 576) 0 ['conv4_block9_concat[0][0]', \n", " atenate) 'conv4_block10_2_conv[0][0]']\n", " \n", " conv4_block11_0_bn (BatchN (None, 18, 18, 576) 2304 ['conv4_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_0_relu (Acti (None, 18, 18, 576) 0 ['conv4_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_1_conv (Conv (None, 18, 18, 128) 73728 ['conv4_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_concat (Conc (None, 18, 18, 608) 0 ['conv4_block10_concat[0][0]',\n", " atenate) 'conv4_block11_2_conv[0][0]']\n", " \n", " conv4_block12_0_bn (BatchN (None, 18, 18, 608) 2432 ['conv4_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_0_relu (Acti (None, 18, 18, 608) 0 ['conv4_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_1_conv (Conv (None, 18, 18, 128) 77824 ['conv4_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_concat (Conc (None, 18, 18, 640) 0 ['conv4_block11_concat[0][0]',\n", " atenate) 'conv4_block12_2_conv[0][0]']\n", " \n", " conv4_block13_0_bn (BatchN (None, 18, 18, 640) 2560 ['conv4_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_0_relu (Acti (None, 18, 18, 640) 0 ['conv4_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_1_conv (Conv (None, 18, 18, 128) 81920 ['conv4_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_concat (Conc (None, 18, 18, 672) 0 ['conv4_block12_concat[0][0]',\n", " atenate) 'conv4_block13_2_conv[0][0]']\n", " \n", " conv4_block14_0_bn (BatchN (None, 18, 18, 672) 2688 ['conv4_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_0_relu (Acti (None, 18, 18, 672) 0 ['conv4_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_1_conv (Conv (None, 18, 18, 128) 86016 ['conv4_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_concat (Conc (None, 18, 18, 704) 0 ['conv4_block13_concat[0][0]',\n", " atenate) 'conv4_block14_2_conv[0][0]']\n", " \n", " conv4_block15_0_bn (BatchN (None, 18, 18, 704) 2816 ['conv4_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_0_relu (Acti (None, 18, 18, 704) 0 ['conv4_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_1_conv (Conv (None, 18, 18, 128) 90112 ['conv4_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_concat (Conc (None, 18, 18, 736) 0 ['conv4_block14_concat[0][0]',\n", " atenate) 'conv4_block15_2_conv[0][0]']\n", " \n", " conv4_block16_0_bn (BatchN (None, 18, 18, 736) 2944 ['conv4_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_0_relu (Acti (None, 18, 18, 736) 0 ['conv4_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_1_conv (Conv (None, 18, 18, 128) 94208 ['conv4_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_concat (Conc (None, 18, 18, 768) 0 ['conv4_block15_concat[0][0]',\n", " atenate) 'conv4_block16_2_conv[0][0]']\n", " \n", " conv4_block17_0_bn (BatchN (None, 18, 18, 768) 3072 ['conv4_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_0_relu (Acti (None, 18, 18, 768) 0 ['conv4_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_1_conv (Conv (None, 18, 18, 128) 98304 ['conv4_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_concat (Conc (None, 18, 18, 800) 0 ['conv4_block16_concat[0][0]',\n", " atenate) 'conv4_block17_2_conv[0][0]']\n", " \n", " conv4_block18_0_bn (BatchN (None, 18, 18, 800) 3200 ['conv4_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_0_relu (Acti (None, 18, 18, 800) 0 ['conv4_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_1_conv (Conv (None, 18, 18, 128) 102400 ['conv4_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_concat (Conc (None, 18, 18, 832) 0 ['conv4_block17_concat[0][0]',\n", " atenate) 'conv4_block18_2_conv[0][0]']\n", " \n", " conv4_block19_0_bn (BatchN (None, 18, 18, 832) 3328 ['conv4_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_0_relu (Acti (None, 18, 18, 832) 0 ['conv4_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_1_conv (Conv (None, 18, 18, 128) 106496 ['conv4_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_concat (Conc (None, 18, 18, 864) 0 ['conv4_block18_concat[0][0]',\n", " atenate) 'conv4_block19_2_conv[0][0]']\n", " \n", " conv4_block20_0_bn (BatchN (None, 18, 18, 864) 3456 ['conv4_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_0_relu (Acti (None, 18, 18, 864) 0 ['conv4_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_1_conv (Conv (None, 18, 18, 128) 110592 ['conv4_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_concat (Conc (None, 18, 18, 896) 0 ['conv4_block19_concat[0][0]',\n", " atenate) 'conv4_block20_2_conv[0][0]']\n", " \n", " conv4_block21_0_bn (BatchN (None, 18, 18, 896) 3584 ['conv4_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_0_relu (Acti (None, 18, 18, 896) 0 ['conv4_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_1_conv (Conv (None, 18, 18, 128) 114688 ['conv4_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_concat (Conc (None, 18, 18, 928) 0 ['conv4_block20_concat[0][0]',\n", " atenate) 'conv4_block21_2_conv[0][0]']\n", " \n", " conv4_block22_0_bn (BatchN (None, 18, 18, 928) 3712 ['conv4_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_0_relu (Acti (None, 18, 18, 928) 0 ['conv4_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_1_conv (Conv (None, 18, 18, 128) 118784 ['conv4_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_concat (Conc (None, 18, 18, 960) 0 ['conv4_block21_concat[0][0]',\n", " atenate) 'conv4_block22_2_conv[0][0]']\n", " \n", " conv4_block23_0_bn (BatchN (None, 18, 18, 960) 3840 ['conv4_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_0_relu (Acti (None, 18, 18, 960) 0 ['conv4_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_1_conv (Conv (None, 18, 18, 128) 122880 ['conv4_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_concat (Conc (None, 18, 18, 992) 0 ['conv4_block22_concat[0][0]',\n", " atenate) 'conv4_block23_2_conv[0][0]']\n", " \n", " conv4_block24_0_bn (BatchN (None, 18, 18, 992) 3968 ['conv4_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_0_relu (Acti (None, 18, 18, 992) 0 ['conv4_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_1_conv (Conv (None, 18, 18, 128) 126976 ['conv4_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_concat (Conc (None, 18, 18, 1024) 0 ['conv4_block23_concat[0][0]',\n", " atenate) 'conv4_block24_2_conv[0][0]']\n", " \n", " conv4_block25_0_bn (BatchN (None, 18, 18, 1024) 4096 ['conv4_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_0_relu (Acti (None, 18, 18, 1024) 0 ['conv4_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_1_conv (Conv (None, 18, 18, 128) 131072 ['conv4_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_concat (Conc (None, 18, 18, 1056) 0 ['conv4_block24_concat[0][0]',\n", " atenate) 'conv4_block25_2_conv[0][0]']\n", " \n", " conv4_block26_0_bn (BatchN (None, 18, 18, 1056) 4224 ['conv4_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_0_relu (Acti (None, 18, 18, 1056) 0 ['conv4_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_1_conv (Conv (None, 18, 18, 128) 135168 ['conv4_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_concat (Conc (None, 18, 18, 1088) 0 ['conv4_block25_concat[0][0]',\n", " atenate) 'conv4_block26_2_conv[0][0]']\n", " \n", " conv4_block27_0_bn (BatchN (None, 18, 18, 1088) 4352 ['conv4_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_0_relu (Acti (None, 18, 18, 1088) 0 ['conv4_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_1_conv (Conv (None, 18, 18, 128) 139264 ['conv4_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_concat (Conc (None, 18, 18, 1120) 0 ['conv4_block26_concat[0][0]',\n", " atenate) 'conv4_block27_2_conv[0][0]']\n", " \n", " conv4_block28_0_bn (BatchN (None, 18, 18, 1120) 4480 ['conv4_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_0_relu (Acti (None, 18, 18, 1120) 0 ['conv4_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_1_conv (Conv (None, 18, 18, 128) 143360 ['conv4_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_concat (Conc (None, 18, 18, 1152) 0 ['conv4_block27_concat[0][0]',\n", " atenate) 'conv4_block28_2_conv[0][0]']\n", " \n", " conv4_block29_0_bn (BatchN (None, 18, 18, 1152) 4608 ['conv4_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_0_relu (Acti (None, 18, 18, 1152) 0 ['conv4_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_1_conv (Conv (None, 18, 18, 128) 147456 ['conv4_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_concat (Conc (None, 18, 18, 1184) 0 ['conv4_block28_concat[0][0]',\n", " atenate) 'conv4_block29_2_conv[0][0]']\n", " \n", " conv4_block30_0_bn (BatchN (None, 18, 18, 1184) 4736 ['conv4_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_0_relu (Acti (None, 18, 18, 1184) 0 ['conv4_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_1_conv (Conv (None, 18, 18, 128) 151552 ['conv4_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_concat (Conc (None, 18, 18, 1216) 0 ['conv4_block29_concat[0][0]',\n", " atenate) 'conv4_block30_2_conv[0][0]']\n", " \n", " conv4_block31_0_bn (BatchN (None, 18, 18, 1216) 4864 ['conv4_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_0_relu (Acti (None, 18, 18, 1216) 0 ['conv4_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_1_conv (Conv (None, 18, 18, 128) 155648 ['conv4_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_concat (Conc (None, 18, 18, 1248) 0 ['conv4_block30_concat[0][0]',\n", " atenate) 'conv4_block31_2_conv[0][0]']\n", " \n", " conv4_block32_0_bn (BatchN (None, 18, 18, 1248) 4992 ['conv4_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_0_relu (Acti (None, 18, 18, 1248) 0 ['conv4_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_1_conv (Conv (None, 18, 18, 128) 159744 ['conv4_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_concat (Conc (None, 18, 18, 1280) 0 ['conv4_block31_concat[0][0]',\n", " atenate) 'conv4_block32_2_conv[0][0]']\n", " \n", " conv4_block33_0_bn (BatchN (None, 18, 18, 1280) 5120 ['conv4_block32_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_0_relu (Acti (None, 18, 18, 1280) 0 ['conv4_block33_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_1_conv (Conv (None, 18, 18, 128) 163840 ['conv4_block33_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block33_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block33_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block33_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_concat (Conc (None, 18, 18, 1312) 0 ['conv4_block32_concat[0][0]',\n", " atenate) 'conv4_block33_2_conv[0][0]']\n", " \n", " conv4_block34_0_bn (BatchN (None, 18, 18, 1312) 5248 ['conv4_block33_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_0_relu (Acti (None, 18, 18, 1312) 0 ['conv4_block34_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_1_conv (Conv (None, 18, 18, 128) 167936 ['conv4_block34_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block34_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block34_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block34_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_concat (Conc (None, 18, 18, 1344) 0 ['conv4_block33_concat[0][0]',\n", " atenate) 'conv4_block34_2_conv[0][0]']\n", " \n", " conv4_block35_0_bn (BatchN (None, 18, 18, 1344) 5376 ['conv4_block34_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_0_relu (Acti (None, 18, 18, 1344) 0 ['conv4_block35_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_1_conv (Conv (None, 18, 18, 128) 172032 ['conv4_block35_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block35_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block35_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block35_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_concat (Conc (None, 18, 18, 1376) 0 ['conv4_block34_concat[0][0]',\n", " atenate) 'conv4_block35_2_conv[0][0]']\n", " \n", " conv4_block36_0_bn (BatchN (None, 18, 18, 1376) 5504 ['conv4_block35_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_0_relu (Acti (None, 18, 18, 1376) 0 ['conv4_block36_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_1_conv (Conv (None, 18, 18, 128) 176128 ['conv4_block36_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block36_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block36_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block36_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_concat (Conc (None, 18, 18, 1408) 0 ['conv4_block35_concat[0][0]',\n", " atenate) 'conv4_block36_2_conv[0][0]']\n", " \n", " conv4_block37_0_bn (BatchN (None, 18, 18, 1408) 5632 ['conv4_block36_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_0_relu (Acti (None, 18, 18, 1408) 0 ['conv4_block37_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_1_conv (Conv (None, 18, 18, 128) 180224 ['conv4_block37_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block37_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block37_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block37_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_concat (Conc (None, 18, 18, 1440) 0 ['conv4_block36_concat[0][0]',\n", " atenate) 'conv4_block37_2_conv[0][0]']\n", " \n", " conv4_block38_0_bn (BatchN (None, 18, 18, 1440) 5760 ['conv4_block37_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_0_relu (Acti (None, 18, 18, 1440) 0 ['conv4_block38_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_1_conv (Conv (None, 18, 18, 128) 184320 ['conv4_block38_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block38_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block38_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block38_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_concat (Conc (None, 18, 18, 1472) 0 ['conv4_block37_concat[0][0]',\n", " atenate) 'conv4_block38_2_conv[0][0]']\n", " \n", " conv4_block39_0_bn (BatchN (None, 18, 18, 1472) 5888 ['conv4_block38_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_0_relu (Acti (None, 18, 18, 1472) 0 ['conv4_block39_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_1_conv (Conv (None, 18, 18, 128) 188416 ['conv4_block39_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block39_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block39_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block39_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_concat (Conc (None, 18, 18, 1504) 0 ['conv4_block38_concat[0][0]',\n", " atenate) 'conv4_block39_2_conv[0][0]']\n", " \n", " conv4_block40_0_bn (BatchN (None, 18, 18, 1504) 6016 ['conv4_block39_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_0_relu (Acti (None, 18, 18, 1504) 0 ['conv4_block40_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_1_conv (Conv (None, 18, 18, 128) 192512 ['conv4_block40_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block40_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block40_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block40_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_concat (Conc (None, 18, 18, 1536) 0 ['conv4_block39_concat[0][0]',\n", " atenate) 'conv4_block40_2_conv[0][0]']\n", " \n", " conv4_block41_0_bn (BatchN (None, 18, 18, 1536) 6144 ['conv4_block40_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_0_relu (Acti (None, 18, 18, 1536) 0 ['conv4_block41_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_1_conv (Conv (None, 18, 18, 128) 196608 ['conv4_block41_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block41_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block41_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block41_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_concat (Conc (None, 18, 18, 1568) 0 ['conv4_block40_concat[0][0]',\n", " atenate) 'conv4_block41_2_conv[0][0]']\n", " \n", " conv4_block42_0_bn (BatchN (None, 18, 18, 1568) 6272 ['conv4_block41_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_0_relu (Acti (None, 18, 18, 1568) 0 ['conv4_block42_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_1_conv (Conv (None, 18, 18, 128) 200704 ['conv4_block42_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block42_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block42_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block42_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_concat (Conc (None, 18, 18, 1600) 0 ['conv4_block41_concat[0][0]',\n", " atenate) 'conv4_block42_2_conv[0][0]']\n", " \n", " conv4_block43_0_bn (BatchN (None, 18, 18, 1600) 6400 ['conv4_block42_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_0_relu (Acti (None, 18, 18, 1600) 0 ['conv4_block43_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_1_conv (Conv (None, 18, 18, 128) 204800 ['conv4_block43_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block43_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block43_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block43_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_concat (Conc (None, 18, 18, 1632) 0 ['conv4_block42_concat[0][0]',\n", " atenate) 'conv4_block43_2_conv[0][0]']\n", " \n", " conv4_block44_0_bn (BatchN (None, 18, 18, 1632) 6528 ['conv4_block43_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_0_relu (Acti (None, 18, 18, 1632) 0 ['conv4_block44_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_1_conv (Conv (None, 18, 18, 128) 208896 ['conv4_block44_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block44_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block44_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block44_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_concat (Conc (None, 18, 18, 1664) 0 ['conv4_block43_concat[0][0]',\n", " atenate) 'conv4_block44_2_conv[0][0]']\n", " \n", " conv4_block45_0_bn (BatchN (None, 18, 18, 1664) 6656 ['conv4_block44_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_0_relu (Acti (None, 18, 18, 1664) 0 ['conv4_block45_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_1_conv (Conv (None, 18, 18, 128) 212992 ['conv4_block45_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block45_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block45_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block45_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_concat (Conc (None, 18, 18, 1696) 0 ['conv4_block44_concat[0][0]',\n", " atenate) 'conv4_block45_2_conv[0][0]']\n", " \n", " conv4_block46_0_bn (BatchN (None, 18, 18, 1696) 6784 ['conv4_block45_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_0_relu (Acti (None, 18, 18, 1696) 0 ['conv4_block46_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_1_conv (Conv (None, 18, 18, 128) 217088 ['conv4_block46_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block46_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block46_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block46_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_concat (Conc (None, 18, 18, 1728) 0 ['conv4_block45_concat[0][0]',\n", " atenate) 'conv4_block46_2_conv[0][0]']\n", " \n", " conv4_block47_0_bn (BatchN (None, 18, 18, 1728) 6912 ['conv4_block46_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_0_relu (Acti (None, 18, 18, 1728) 0 ['conv4_block47_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_1_conv (Conv (None, 18, 18, 128) 221184 ['conv4_block47_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block47_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block47_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block47_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_concat (Conc (None, 18, 18, 1760) 0 ['conv4_block46_concat[0][0]',\n", " atenate) 'conv4_block47_2_conv[0][0]']\n", " \n", " conv4_block48_0_bn (BatchN (None, 18, 18, 1760) 7040 ['conv4_block47_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_0_relu (Acti (None, 18, 18, 1760) 0 ['conv4_block48_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_1_conv (Conv (None, 18, 18, 128) 225280 ['conv4_block48_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block48_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block48_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block48_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_concat (Conc (None, 18, 18, 1792) 0 ['conv4_block47_concat[0][0]',\n", " atenate) 'conv4_block48_2_conv[0][0]']\n", " \n", " pool4_bn (BatchNormalizati (None, 18, 18, 1792) 7168 ['conv4_block48_concat[0][0]']\n", " on) \n", " \n", " pool4_relu (Activation) (None, 18, 18, 1792) 0 ['pool4_bn[0][0]'] \n", " \n", " pool4_conv (Conv2D) (None, 18, 18, 896) 1605632 ['pool4_relu[0][0]'] \n", " \n", " pool4_pool (AveragePooling (None, 9, 9, 896) 0 ['pool4_conv[0][0]'] \n", " 2D) \n", " \n", " conv5_block1_0_bn (BatchNo (None, 9, 9, 896) 3584 ['pool4_pool[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_0_relu (Activ (None, 9, 9, 896) 0 ['conv5_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_1_conv (Conv2 (None, 9, 9, 128) 114688 ['conv5_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_concat (Conca (None, 9, 9, 928) 0 ['pool4_pool[0][0]', \n", " tenate) 'conv5_block1_2_conv[0][0]'] \n", " \n", " conv5_block2_0_bn (BatchNo (None, 9, 9, 928) 3712 ['conv5_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_0_relu (Activ (None, 9, 9, 928) 0 ['conv5_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_1_conv (Conv2 (None, 9, 9, 128) 118784 ['conv5_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_concat (Conca (None, 9, 9, 960) 0 ['conv5_block1_concat[0][0]', \n", " tenate) 'conv5_block2_2_conv[0][0]'] \n", " \n", " conv5_block3_0_bn (BatchNo (None, 9, 9, 960) 3840 ['conv5_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_0_relu (Activ (None, 9, 9, 960) 0 ['conv5_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_1_conv (Conv2 (None, 9, 9, 128) 122880 ['conv5_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_concat (Conca (None, 9, 9, 992) 0 ['conv5_block2_concat[0][0]', \n", " tenate) 'conv5_block3_2_conv[0][0]'] \n", " \n", " conv5_block4_0_bn (BatchNo (None, 9, 9, 992) 3968 ['conv5_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_0_relu (Activ (None, 9, 9, 992) 0 ['conv5_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_1_conv (Conv2 (None, 9, 9, 128) 126976 ['conv5_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_concat (Conca (None, 9, 9, 1024) 0 ['conv5_block3_concat[0][0]', \n", " tenate) 'conv5_block4_2_conv[0][0]'] \n", " \n", " conv5_block5_0_bn (BatchNo (None, 9, 9, 1024) 4096 ['conv5_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_0_relu (Activ (None, 9, 9, 1024) 0 ['conv5_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_1_conv (Conv2 (None, 9, 9, 128) 131072 ['conv5_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_concat (Conca (None, 9, 9, 1056) 0 ['conv5_block4_concat[0][0]', \n", " tenate) 'conv5_block5_2_conv[0][0]'] \n", " \n", " conv5_block6_0_bn (BatchNo (None, 9, 9, 1056) 4224 ['conv5_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_0_relu (Activ (None, 9, 9, 1056) 0 ['conv5_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_1_conv (Conv2 (None, 9, 9, 128) 135168 ['conv5_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_concat (Conca (None, 9, 9, 1088) 0 ['conv5_block5_concat[0][0]', \n", " tenate) 'conv5_block6_2_conv[0][0]'] \n", " \n", " conv5_block7_0_bn (BatchNo (None, 9, 9, 1088) 4352 ['conv5_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_0_relu (Activ (None, 9, 9, 1088) 0 ['conv5_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_1_conv (Conv2 (None, 9, 9, 128) 139264 ['conv5_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_concat (Conca (None, 9, 9, 1120) 0 ['conv5_block6_concat[0][0]', \n", " tenate) 'conv5_block7_2_conv[0][0]'] \n", " \n", " conv5_block8_0_bn (BatchNo (None, 9, 9, 1120) 4480 ['conv5_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_0_relu (Activ (None, 9, 9, 1120) 0 ['conv5_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_1_conv (Conv2 (None, 9, 9, 128) 143360 ['conv5_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_concat (Conca (None, 9, 9, 1152) 0 ['conv5_block7_concat[0][0]', \n", " tenate) 'conv5_block8_2_conv[0][0]'] \n", " \n", " conv5_block9_0_bn (BatchNo (None, 9, 9, 1152) 4608 ['conv5_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_0_relu (Activ (None, 9, 9, 1152) 0 ['conv5_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_1_conv (Conv2 (None, 9, 9, 128) 147456 ['conv5_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_concat (Conca (None, 9, 9, 1184) 0 ['conv5_block8_concat[0][0]', \n", " tenate) 'conv5_block9_2_conv[0][0]'] \n", " \n", " conv5_block10_0_bn (BatchN (None, 9, 9, 1184) 4736 ['conv5_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv5_block10_0_relu (Acti (None, 9, 9, 1184) 0 ['conv5_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_1_conv (Conv (None, 9, 9, 128) 151552 ['conv5_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block10_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_concat (Conc (None, 9, 9, 1216) 0 ['conv5_block9_concat[0][0]', \n", " atenate) 'conv5_block10_2_conv[0][0]']\n", " \n", " conv5_block11_0_bn (BatchN (None, 9, 9, 1216) 4864 ['conv5_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_0_relu (Acti (None, 9, 9, 1216) 0 ['conv5_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_1_conv (Conv (None, 9, 9, 128) 155648 ['conv5_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_concat (Conc (None, 9, 9, 1248) 0 ['conv5_block10_concat[0][0]',\n", " atenate) 'conv5_block11_2_conv[0][0]']\n", " \n", " conv5_block12_0_bn (BatchN (None, 9, 9, 1248) 4992 ['conv5_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_0_relu (Acti (None, 9, 9, 1248) 0 ['conv5_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_1_conv (Conv (None, 9, 9, 128) 159744 ['conv5_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_concat (Conc (None, 9, 9, 1280) 0 ['conv5_block11_concat[0][0]',\n", " atenate) 'conv5_block12_2_conv[0][0]']\n", " \n", " conv5_block13_0_bn (BatchN (None, 9, 9, 1280) 5120 ['conv5_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_0_relu (Acti (None, 9, 9, 1280) 0 ['conv5_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_1_conv (Conv (None, 9, 9, 128) 163840 ['conv5_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_concat (Conc (None, 9, 9, 1312) 0 ['conv5_block12_concat[0][0]',\n", " atenate) 'conv5_block13_2_conv[0][0]']\n", " \n", " conv5_block14_0_bn (BatchN (None, 9, 9, 1312) 5248 ['conv5_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_0_relu (Acti (None, 9, 9, 1312) 0 ['conv5_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_1_conv (Conv (None, 9, 9, 128) 167936 ['conv5_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_concat (Conc (None, 9, 9, 1344) 0 ['conv5_block13_concat[0][0]',\n", " atenate) 'conv5_block14_2_conv[0][0]']\n", " \n", " conv5_block15_0_bn (BatchN (None, 9, 9, 1344) 5376 ['conv5_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_0_relu (Acti (None, 9, 9, 1344) 0 ['conv5_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_1_conv (Conv (None, 9, 9, 128) 172032 ['conv5_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_concat (Conc (None, 9, 9, 1376) 0 ['conv5_block14_concat[0][0]',\n", " atenate) 'conv5_block15_2_conv[0][0]']\n", " \n", " conv5_block16_0_bn (BatchN (None, 9, 9, 1376) 5504 ['conv5_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_0_relu (Acti (None, 9, 9, 1376) 0 ['conv5_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_1_conv (Conv (None, 9, 9, 128) 176128 ['conv5_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_concat (Conc (None, 9, 9, 1408) 0 ['conv5_block15_concat[0][0]',\n", " atenate) 'conv5_block16_2_conv[0][0]']\n", " \n", " conv5_block17_0_bn (BatchN (None, 9, 9, 1408) 5632 ['conv5_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_0_relu (Acti (None, 9, 9, 1408) 0 ['conv5_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_1_conv (Conv (None, 9, 9, 128) 180224 ['conv5_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_concat (Conc (None, 9, 9, 1440) 0 ['conv5_block16_concat[0][0]',\n", " atenate) 'conv5_block17_2_conv[0][0]']\n", " \n", " conv5_block18_0_bn (BatchN (None, 9, 9, 1440) 5760 ['conv5_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_0_relu (Acti (None, 9, 9, 1440) 0 ['conv5_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_1_conv (Conv (None, 9, 9, 128) 184320 ['conv5_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_concat (Conc (None, 9, 9, 1472) 0 ['conv5_block17_concat[0][0]',\n", " atenate) 'conv5_block18_2_conv[0][0]']\n", " \n", " conv5_block19_0_bn (BatchN (None, 9, 9, 1472) 5888 ['conv5_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_0_relu (Acti (None, 9, 9, 1472) 0 ['conv5_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_1_conv (Conv (None, 9, 9, 128) 188416 ['conv5_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_concat (Conc (None, 9, 9, 1504) 0 ['conv5_block18_concat[0][0]',\n", " atenate) 'conv5_block19_2_conv[0][0]']\n", " \n", " conv5_block20_0_bn (BatchN (None, 9, 9, 1504) 6016 ['conv5_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_0_relu (Acti (None, 9, 9, 1504) 0 ['conv5_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_1_conv (Conv (None, 9, 9, 128) 192512 ['conv5_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_concat (Conc (None, 9, 9, 1536) 0 ['conv5_block19_concat[0][0]',\n", " atenate) 'conv5_block20_2_conv[0][0]']\n", " \n", " conv5_block21_0_bn (BatchN (None, 9, 9, 1536) 6144 ['conv5_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_0_relu (Acti (None, 9, 9, 1536) 0 ['conv5_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_1_conv (Conv (None, 9, 9, 128) 196608 ['conv5_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_concat (Conc (None, 9, 9, 1568) 0 ['conv5_block20_concat[0][0]',\n", " atenate) 'conv5_block21_2_conv[0][0]']\n", " \n", " conv5_block22_0_bn (BatchN (None, 9, 9, 1568) 6272 ['conv5_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_0_relu (Acti (None, 9, 9, 1568) 0 ['conv5_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_1_conv (Conv (None, 9, 9, 128) 200704 ['conv5_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_concat (Conc (None, 9, 9, 1600) 0 ['conv5_block21_concat[0][0]',\n", " atenate) 'conv5_block22_2_conv[0][0]']\n", " \n", " conv5_block23_0_bn (BatchN (None, 9, 9, 1600) 6400 ['conv5_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_0_relu (Acti (None, 9, 9, 1600) 0 ['conv5_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_1_conv (Conv (None, 9, 9, 128) 204800 ['conv5_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_concat (Conc (None, 9, 9, 1632) 0 ['conv5_block22_concat[0][0]',\n", " atenate) 'conv5_block23_2_conv[0][0]']\n", " \n", " conv5_block24_0_bn (BatchN (None, 9, 9, 1632) 6528 ['conv5_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_0_relu (Acti (None, 9, 9, 1632) 0 ['conv5_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_1_conv (Conv (None, 9, 9, 128) 208896 ['conv5_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_concat (Conc (None, 9, 9, 1664) 0 ['conv5_block23_concat[0][0]',\n", " atenate) 'conv5_block24_2_conv[0][0]']\n", " \n", " conv5_block25_0_bn (BatchN (None, 9, 9, 1664) 6656 ['conv5_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_0_relu (Acti (None, 9, 9, 1664) 0 ['conv5_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_1_conv (Conv (None, 9, 9, 128) 212992 ['conv5_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_concat (Conc (None, 9, 9, 1696) 0 ['conv5_block24_concat[0][0]',\n", " atenate) 'conv5_block25_2_conv[0][0]']\n", " \n", " conv5_block26_0_bn (BatchN (None, 9, 9, 1696) 6784 ['conv5_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_0_relu (Acti (None, 9, 9, 1696) 0 ['conv5_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_1_conv (Conv (None, 9, 9, 128) 217088 ['conv5_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_concat (Conc (None, 9, 9, 1728) 0 ['conv5_block25_concat[0][0]',\n", " atenate) 'conv5_block26_2_conv[0][0]']\n", " \n", " conv5_block27_0_bn (BatchN (None, 9, 9, 1728) 6912 ['conv5_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_0_relu (Acti (None, 9, 9, 1728) 0 ['conv5_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_1_conv (Conv (None, 9, 9, 128) 221184 ['conv5_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_concat (Conc (None, 9, 9, 1760) 0 ['conv5_block26_concat[0][0]',\n", " atenate) 'conv5_block27_2_conv[0][0]']\n", " \n", " conv5_block28_0_bn (BatchN (None, 9, 9, 1760) 7040 ['conv5_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_0_relu (Acti (None, 9, 9, 1760) 0 ['conv5_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_1_conv (Conv (None, 9, 9, 128) 225280 ['conv5_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_concat (Conc (None, 9, 9, 1792) 0 ['conv5_block27_concat[0][0]',\n", " atenate) 'conv5_block28_2_conv[0][0]']\n", " \n", " conv5_block29_0_bn (BatchN (None, 9, 9, 1792) 7168 ['conv5_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_0_relu (Acti (None, 9, 9, 1792) 0 ['conv5_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_1_conv (Conv (None, 9, 9, 128) 229376 ['conv5_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_concat (Conc (None, 9, 9, 1824) 0 ['conv5_block28_concat[0][0]',\n", " atenate) 'conv5_block29_2_conv[0][0]']\n", " \n", " conv5_block30_0_bn (BatchN (None, 9, 9, 1824) 7296 ['conv5_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_0_relu (Acti (None, 9, 9, 1824) 0 ['conv5_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_1_conv (Conv (None, 9, 9, 128) 233472 ['conv5_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_concat (Conc (None, 9, 9, 1856) 0 ['conv5_block29_concat[0][0]',\n", " atenate) 'conv5_block30_2_conv[0][0]']\n", " \n", " conv5_block31_0_bn (BatchN (None, 9, 9, 1856) 7424 ['conv5_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_0_relu (Acti (None, 9, 9, 1856) 0 ['conv5_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_1_conv (Conv (None, 9, 9, 128) 237568 ['conv5_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_concat (Conc (None, 9, 9, 1888) 0 ['conv5_block30_concat[0][0]',\n", " atenate) 'conv5_block31_2_conv[0][0]']\n", " \n", " conv5_block32_0_bn (BatchN (None, 9, 9, 1888) 7552 ['conv5_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_0_relu (Acti (None, 9, 9, 1888) 0 ['conv5_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_1_conv (Conv (None, 9, 9, 128) 241664 ['conv5_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_concat (Conc (None, 9, 9, 1920) 0 ['conv5_block31_concat[0][0]',\n", " atenate) 'conv5_block32_2_conv[0][0]']\n", " \n", " bn (BatchNormalization) (None, 9, 9, 1920) 7680 ['conv5_block32_concat[0][0]']\n", " \n", " relu (Activation) (None, 9, 9, 1920) 0 ['bn[0][0]'] \n", " \n", " flatten_1 (Flatten) (None, 155520) 0 ['relu[0][0]'] \n", " \n", " dense_1 (Dense) (None, 2) 311042 ['flatten_1[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 18633026 (71.08 MB)\n", "Trainable params: 18403970 (70.21 MB)\n", "Non-trainable params: 229056 (894.75 KB)\n", "__________________________________________________________________________________________________\n", "None\n" ] } ] }, { "cell_type": "code", "source": [ "previous_history = np.load(\"/kaggle/working/saved_D201history.npy\", allow_pickle=True).item()\n", "initial_epoch = len(previous_history['loss'])\n", "print(initial_epoch)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QpmXz84qHgdf", "outputId": "255140f0-7113-4b15-b594-21523537ffc6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "20\n" ] } ] }, { "cell_type": "code", "source": [ "loaded_model.compile(optimizer=Adam(learning_rate=1e-5), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])\n", "new_history = loaded_model.fit(\n", " train_generator,\n", " steps_per_epoch=20,\n", " epochs=20,\n", " initial_epoch=initial_epoch,\n", " validation_data=validation_generator,\n", " validation_steps=30,\n", " callbacks=[cp_callback]\n", ")" ], "metadata": { "id": "j0kTz2a0IR7r" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib.lines import Line2D\n", "from matplotlib.legend_handler import HandlerLine2D\n", "import numpy as np" ], "metadata": { "id": "L8UKQgpEIsDt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(10, 6))\n", "train_loss, = plt.plot(previous_history['loss'], label='Train Loss', color='blue')\n", "val_loss, = plt.plot(previous_history['val_loss'], label='Validation Loss', color='orange')\n", "train_accuracy, = plt.plot(previous_history['accuracy'], label='Train Accuracy', color='green')\n", "val_accuracy, = plt.plot(previous_history['val_accuracy'], label='Validation Accuracy', color='red')\n", "plt.title('Model Performance during Training', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12},pad=10)\n", "plt.xlabel('No. of Epochs', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", "plt.xticks(np.linspace(0, 150, num=16), fontname='Serif', weight='bold')\n", "plt.yticks(np.linspace(0, 5, num=11), fontname='Serif', weight='bold')\n", "plt.xlim(0, 150)\n", "plt.ylim(0, 5)\n", "legend_lines = [\n", " Line2D([0], [0], color='blue', lw=3),\n", " Line2D([0], [0], color='orange', lw=3),\n", " Line2D([0], [0], color='green', lw=3),\n", " Line2D([0], [0], color='red', lw=3)\n", "]\n", "plt.legend(legend_lines, ['Train Loss', 'Validation Loss', 'Train Accuracy', 'Validation Accuracy'],\n", " loc='lower center', bbox_to_anchor=(0.5, 1.1), ncol=5,\n", " prop={'family': 'Serif', 'weight': 'bold', 'size': 8}, frameon=False,\n", " handler_map={Line2D: HandlerLine2D(numpoints=5)})\n", "plt.gca().xaxis.labelpad = 10\n", "plt.gca().spines['top'].set_visible(False)\n", "plt.gca().spines['right'].set_visible(False)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 526 }, "id": "6FKdqtPrI9pU", "outputId": "a8c72558-2cb7-43cf-ec62-a89ce5984c2d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\n", "print(checkpoint_dir)\n", "if latest_checkpoint is not None:\n", " loaded_model = create_model(summary=True)\n", " status = loaded_model.load_weights(latest_checkpoint)\n", " status.expect_partial()\n", "else:\n", " print(\"No checkpoint file found in the specified directory.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YuvXs46FJVqG", "outputId": "f3ba91ce-07e7-47d6-ff23-ac04630f4828" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/kaggle/working/Checkpoints_densenet201\n", "Model: \"model_2\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_3 (InputLayer) [(None, 299, 299, 3)] 0 [] \n", " \n", " zero_padding2d_4 (ZeroPadd (None, 305, 305, 3) 0 ['input_3[0][0]'] \n", " ing2D) \n", " \n", " conv1/conv (Conv2D) (None, 150, 150, 64) 9408 ['zero_padding2d_4[0][0]'] \n", " \n", " conv1/bn (BatchNormalizati (None, 150, 150, 64) 256 ['conv1/conv[0][0]'] \n", " on) \n", " \n", " conv1/relu (Activation) (None, 150, 150, 64) 0 ['conv1/bn[0][0]'] \n", " \n", " zero_padding2d_5 (ZeroPadd (None, 152, 152, 64) 0 ['conv1/relu[0][0]'] \n", " ing2D) \n", " \n", " pool1 (MaxPooling2D) (None, 75, 75, 64) 0 ['zero_padding2d_5[0][0]'] \n", " \n", " conv2_block1_0_bn (BatchNo (None, 75, 75, 64) 256 ['pool1[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_0_relu (Activ (None, 75, 75, 64) 0 ['conv2_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_1_conv (Conv2 (None, 75, 75, 128) 8192 ['conv2_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block1_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block1_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block1_concat (Conca (None, 75, 75, 96) 0 ['pool1[0][0]', \n", " tenate) 'conv2_block1_2_conv[0][0]'] \n", " \n", " conv2_block2_0_bn (BatchNo (None, 75, 75, 96) 384 ['conv2_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_0_relu (Activ (None, 75, 75, 96) 0 ['conv2_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_1_conv (Conv2 (None, 75, 75, 128) 12288 ['conv2_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block2_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block2_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block2_concat (Conca (None, 75, 75, 128) 0 ['conv2_block1_concat[0][0]', \n", " tenate) 'conv2_block2_2_conv[0][0]'] \n", " \n", " conv2_block3_0_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_0_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_1_conv (Conv2 (None, 75, 75, 128) 16384 ['conv2_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block3_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block3_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block3_concat (Conca (None, 75, 75, 160) 0 ['conv2_block2_concat[0][0]', \n", " tenate) 'conv2_block3_2_conv[0][0]'] \n", " \n", " conv2_block4_0_bn (BatchNo (None, 75, 75, 160) 640 ['conv2_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_0_relu (Activ (None, 75, 75, 160) 0 ['conv2_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_1_conv (Conv2 (None, 75, 75, 128) 20480 ['conv2_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block4_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block4_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block4_concat (Conca (None, 75, 75, 192) 0 ['conv2_block3_concat[0][0]', \n", " tenate) 'conv2_block4_2_conv[0][0]'] \n", " \n", " conv2_block5_0_bn (BatchNo (None, 75, 75, 192) 768 ['conv2_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_0_relu (Activ (None, 75, 75, 192) 0 ['conv2_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_1_conv (Conv2 (None, 75, 75, 128) 24576 ['conv2_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block5_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block5_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block5_concat (Conca (None, 75, 75, 224) 0 ['conv2_block4_concat[0][0]', \n", " tenate) 'conv2_block5_2_conv[0][0]'] \n", " \n", " conv2_block6_0_bn (BatchNo (None, 75, 75, 224) 896 ['conv2_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_0_relu (Activ (None, 75, 75, 224) 0 ['conv2_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_1_conv (Conv2 (None, 75, 75, 128) 28672 ['conv2_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_1_bn (BatchNo (None, 75, 75, 128) 512 ['conv2_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv2_block6_1_relu (Activ (None, 75, 75, 128) 0 ['conv2_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv2_block6_2_conv (Conv2 (None, 75, 75, 32) 36864 ['conv2_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv2_block6_concat (Conca (None, 75, 75, 256) 0 ['conv2_block5_concat[0][0]', \n", " tenate) 'conv2_block6_2_conv[0][0]'] \n", " \n", " pool2_bn (BatchNormalizati (None, 75, 75, 256) 1024 ['conv2_block6_concat[0][0]'] \n", " on) \n", " \n", " pool2_relu (Activation) (None, 75, 75, 256) 0 ['pool2_bn[0][0]'] \n", " \n", " pool2_conv (Conv2D) (None, 75, 75, 128) 32768 ['pool2_relu[0][0]'] \n", " \n", " pool2_pool (AveragePooling (None, 37, 37, 128) 0 ['pool2_conv[0][0]'] \n", " 2D) \n", " \n", " conv3_block1_0_bn (BatchNo (None, 37, 37, 128) 512 ['pool2_pool[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_0_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_1_conv (Conv2 (None, 37, 37, 128) 16384 ['conv3_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block1_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block1_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block1_concat (Conca (None, 37, 37, 160) 0 ['pool2_pool[0][0]', \n", " tenate) 'conv3_block1_2_conv[0][0]'] \n", " \n", " conv3_block2_0_bn (BatchNo (None, 37, 37, 160) 640 ['conv3_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_0_relu (Activ (None, 37, 37, 160) 0 ['conv3_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_1_conv (Conv2 (None, 37, 37, 128) 20480 ['conv3_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block2_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block2_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block2_concat (Conca (None, 37, 37, 192) 0 ['conv3_block1_concat[0][0]', \n", " tenate) 'conv3_block2_2_conv[0][0]'] \n", " \n", " conv3_block3_0_bn (BatchNo (None, 37, 37, 192) 768 ['conv3_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_0_relu (Activ (None, 37, 37, 192) 0 ['conv3_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_1_conv (Conv2 (None, 37, 37, 128) 24576 ['conv3_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block3_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block3_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block3_concat (Conca (None, 37, 37, 224) 0 ['conv3_block2_concat[0][0]', \n", " tenate) 'conv3_block3_2_conv[0][0]'] \n", " \n", " conv3_block4_0_bn (BatchNo (None, 37, 37, 224) 896 ['conv3_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_0_relu (Activ (None, 37, 37, 224) 0 ['conv3_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_1_conv (Conv2 (None, 37, 37, 128) 28672 ['conv3_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block4_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block4_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block4_concat (Conca (None, 37, 37, 256) 0 ['conv3_block3_concat[0][0]', \n", " tenate) 'conv3_block4_2_conv[0][0]'] \n", " \n", " conv3_block5_0_bn (BatchNo (None, 37, 37, 256) 1024 ['conv3_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_0_relu (Activ (None, 37, 37, 256) 0 ['conv3_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_1_conv (Conv2 (None, 37, 37, 128) 32768 ['conv3_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block5_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block5_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block5_concat (Conca (None, 37, 37, 288) 0 ['conv3_block4_concat[0][0]', \n", " tenate) 'conv3_block5_2_conv[0][0]'] \n", " \n", " conv3_block6_0_bn (BatchNo (None, 37, 37, 288) 1152 ['conv3_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_0_relu (Activ (None, 37, 37, 288) 0 ['conv3_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_1_conv (Conv2 (None, 37, 37, 128) 36864 ['conv3_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block6_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block6_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block6_concat (Conca (None, 37, 37, 320) 0 ['conv3_block5_concat[0][0]', \n", " tenate) 'conv3_block6_2_conv[0][0]'] \n", " \n", " conv3_block7_0_bn (BatchNo (None, 37, 37, 320) 1280 ['conv3_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_0_relu (Activ (None, 37, 37, 320) 0 ['conv3_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_1_conv (Conv2 (None, 37, 37, 128) 40960 ['conv3_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block7_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block7_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block7_concat (Conca (None, 37, 37, 352) 0 ['conv3_block6_concat[0][0]', \n", " tenate) 'conv3_block7_2_conv[0][0]'] \n", " \n", " conv3_block8_0_bn (BatchNo (None, 37, 37, 352) 1408 ['conv3_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_0_relu (Activ (None, 37, 37, 352) 0 ['conv3_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_1_conv (Conv2 (None, 37, 37, 128) 45056 ['conv3_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block8_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block8_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block8_concat (Conca (None, 37, 37, 384) 0 ['conv3_block7_concat[0][0]', \n", " tenate) 'conv3_block8_2_conv[0][0]'] \n", " \n", " conv3_block9_0_bn (BatchNo (None, 37, 37, 384) 1536 ['conv3_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_0_relu (Activ (None, 37, 37, 384) 0 ['conv3_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_1_conv (Conv2 (None, 37, 37, 128) 49152 ['conv3_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_1_bn (BatchNo (None, 37, 37, 128) 512 ['conv3_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv3_block9_1_relu (Activ (None, 37, 37, 128) 0 ['conv3_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv3_block9_2_conv (Conv2 (None, 37, 37, 32) 36864 ['conv3_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv3_block9_concat (Conca (None, 37, 37, 416) 0 ['conv3_block8_concat[0][0]', \n", " tenate) 'conv3_block9_2_conv[0][0]'] \n", " \n", " conv3_block10_0_bn (BatchN (None, 37, 37, 416) 1664 ['conv3_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv3_block10_0_relu (Acti (None, 37, 37, 416) 0 ['conv3_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_1_conv (Conv (None, 37, 37, 128) 53248 ['conv3_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block10_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block10_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block10_concat (Conc (None, 37, 37, 448) 0 ['conv3_block9_concat[0][0]', \n", " atenate) 'conv3_block10_2_conv[0][0]']\n", " \n", " conv3_block11_0_bn (BatchN (None, 37, 37, 448) 1792 ['conv3_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_0_relu (Acti (None, 37, 37, 448) 0 ['conv3_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_1_conv (Conv (None, 37, 37, 128) 57344 ['conv3_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block11_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block11_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block11_concat (Conc (None, 37, 37, 480) 0 ['conv3_block10_concat[0][0]',\n", " atenate) 'conv3_block11_2_conv[0][0]']\n", " \n", " conv3_block12_0_bn (BatchN (None, 37, 37, 480) 1920 ['conv3_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_0_relu (Acti (None, 37, 37, 480) 0 ['conv3_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_1_conv (Conv (None, 37, 37, 128) 61440 ['conv3_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_1_bn (BatchN (None, 37, 37, 128) 512 ['conv3_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv3_block12_1_relu (Acti (None, 37, 37, 128) 0 ['conv3_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv3_block12_2_conv (Conv (None, 37, 37, 32) 36864 ['conv3_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv3_block12_concat (Conc (None, 37, 37, 512) 0 ['conv3_block11_concat[0][0]',\n", " atenate) 'conv3_block12_2_conv[0][0]']\n", " \n", " pool3_bn (BatchNormalizati (None, 37, 37, 512) 2048 ['conv3_block12_concat[0][0]']\n", " on) \n", " \n", " pool3_relu (Activation) (None, 37, 37, 512) 0 ['pool3_bn[0][0]'] \n", " \n", " pool3_conv (Conv2D) (None, 37, 37, 256) 131072 ['pool3_relu[0][0]'] \n", " \n", " pool3_pool (AveragePooling (None, 18, 18, 256) 0 ['pool3_conv[0][0]'] \n", " 2D) \n", " \n", " conv4_block1_0_bn (BatchNo (None, 18, 18, 256) 1024 ['pool3_pool[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_0_relu (Activ (None, 18, 18, 256) 0 ['conv4_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_1_conv (Conv2 (None, 18, 18, 128) 32768 ['conv4_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block1_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block1_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block1_concat (Conca (None, 18, 18, 288) 0 ['pool3_pool[0][0]', \n", " tenate) 'conv4_block1_2_conv[0][0]'] \n", " \n", " conv4_block2_0_bn (BatchNo (None, 18, 18, 288) 1152 ['conv4_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_0_relu (Activ (None, 18, 18, 288) 0 ['conv4_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_1_conv (Conv2 (None, 18, 18, 128) 36864 ['conv4_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block2_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block2_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block2_concat (Conca (None, 18, 18, 320) 0 ['conv4_block1_concat[0][0]', \n", " tenate) 'conv4_block2_2_conv[0][0]'] \n", " \n", " conv4_block3_0_bn (BatchNo (None, 18, 18, 320) 1280 ['conv4_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_0_relu (Activ (None, 18, 18, 320) 0 ['conv4_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_1_conv (Conv2 (None, 18, 18, 128) 40960 ['conv4_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block3_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block3_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block3_concat (Conca (None, 18, 18, 352) 0 ['conv4_block2_concat[0][0]', \n", " tenate) 'conv4_block3_2_conv[0][0]'] \n", " \n", " conv4_block4_0_bn (BatchNo (None, 18, 18, 352) 1408 ['conv4_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_0_relu (Activ (None, 18, 18, 352) 0 ['conv4_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_1_conv (Conv2 (None, 18, 18, 128) 45056 ['conv4_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block4_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block4_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block4_concat (Conca (None, 18, 18, 384) 0 ['conv4_block3_concat[0][0]', \n", " tenate) 'conv4_block4_2_conv[0][0]'] \n", " \n", " conv4_block5_0_bn (BatchNo (None, 18, 18, 384) 1536 ['conv4_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_0_relu (Activ (None, 18, 18, 384) 0 ['conv4_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_1_conv (Conv2 (None, 18, 18, 128) 49152 ['conv4_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block5_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block5_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block5_concat (Conca (None, 18, 18, 416) 0 ['conv4_block4_concat[0][0]', \n", " tenate) 'conv4_block5_2_conv[0][0]'] \n", " \n", " conv4_block6_0_bn (BatchNo (None, 18, 18, 416) 1664 ['conv4_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_0_relu (Activ (None, 18, 18, 416) 0 ['conv4_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_1_conv (Conv2 (None, 18, 18, 128) 53248 ['conv4_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block6_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block6_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block6_concat (Conca (None, 18, 18, 448) 0 ['conv4_block5_concat[0][0]', \n", " tenate) 'conv4_block6_2_conv[0][0]'] \n", " \n", " conv4_block7_0_bn (BatchNo (None, 18, 18, 448) 1792 ['conv4_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_0_relu (Activ (None, 18, 18, 448) 0 ['conv4_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_1_conv (Conv2 (None, 18, 18, 128) 57344 ['conv4_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block7_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block7_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block7_concat (Conca (None, 18, 18, 480) 0 ['conv4_block6_concat[0][0]', \n", " tenate) 'conv4_block7_2_conv[0][0]'] \n", " \n", " conv4_block8_0_bn (BatchNo (None, 18, 18, 480) 1920 ['conv4_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_0_relu (Activ (None, 18, 18, 480) 0 ['conv4_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_1_conv (Conv2 (None, 18, 18, 128) 61440 ['conv4_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block8_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block8_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block8_concat (Conca (None, 18, 18, 512) 0 ['conv4_block7_concat[0][0]', \n", " tenate) 'conv4_block8_2_conv[0][0]'] \n", " \n", " conv4_block9_0_bn (BatchNo (None, 18, 18, 512) 2048 ['conv4_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_0_relu (Activ (None, 18, 18, 512) 0 ['conv4_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_1_conv (Conv2 (None, 18, 18, 128) 65536 ['conv4_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_1_bn (BatchNo (None, 18, 18, 128) 512 ['conv4_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv4_block9_1_relu (Activ (None, 18, 18, 128) 0 ['conv4_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv4_block9_2_conv (Conv2 (None, 18, 18, 32) 36864 ['conv4_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv4_block9_concat (Conca (None, 18, 18, 544) 0 ['conv4_block8_concat[0][0]', \n", " tenate) 'conv4_block9_2_conv[0][0]'] \n", " \n", " conv4_block10_0_bn (BatchN (None, 18, 18, 544) 2176 ['conv4_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv4_block10_0_relu (Acti (None, 18, 18, 544) 0 ['conv4_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_1_conv (Conv (None, 18, 18, 128) 69632 ['conv4_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block10_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block10_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block10_concat (Conc (None, 18, 18, 576) 0 ['conv4_block9_concat[0][0]', \n", " atenate) 'conv4_block10_2_conv[0][0]']\n", " \n", " conv4_block11_0_bn (BatchN (None, 18, 18, 576) 2304 ['conv4_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_0_relu (Acti (None, 18, 18, 576) 0 ['conv4_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_1_conv (Conv (None, 18, 18, 128) 73728 ['conv4_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block11_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block11_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block11_concat (Conc (None, 18, 18, 608) 0 ['conv4_block10_concat[0][0]',\n", " atenate) 'conv4_block11_2_conv[0][0]']\n", " \n", " conv4_block12_0_bn (BatchN (None, 18, 18, 608) 2432 ['conv4_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_0_relu (Acti (None, 18, 18, 608) 0 ['conv4_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_1_conv (Conv (None, 18, 18, 128) 77824 ['conv4_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block12_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block12_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block12_concat (Conc (None, 18, 18, 640) 0 ['conv4_block11_concat[0][0]',\n", " atenate) 'conv4_block12_2_conv[0][0]']\n", " \n", " conv4_block13_0_bn (BatchN (None, 18, 18, 640) 2560 ['conv4_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_0_relu (Acti (None, 18, 18, 640) 0 ['conv4_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_1_conv (Conv (None, 18, 18, 128) 81920 ['conv4_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block13_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block13_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block13_concat (Conc (None, 18, 18, 672) 0 ['conv4_block12_concat[0][0]',\n", " atenate) 'conv4_block13_2_conv[0][0]']\n", " \n", " conv4_block14_0_bn (BatchN (None, 18, 18, 672) 2688 ['conv4_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_0_relu (Acti (None, 18, 18, 672) 0 ['conv4_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_1_conv (Conv (None, 18, 18, 128) 86016 ['conv4_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block14_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block14_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block14_concat (Conc (None, 18, 18, 704) 0 ['conv4_block13_concat[0][0]',\n", " atenate) 'conv4_block14_2_conv[0][0]']\n", " \n", " conv4_block15_0_bn (BatchN (None, 18, 18, 704) 2816 ['conv4_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_0_relu (Acti (None, 18, 18, 704) 0 ['conv4_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_1_conv (Conv (None, 18, 18, 128) 90112 ['conv4_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block15_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block15_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block15_concat (Conc (None, 18, 18, 736) 0 ['conv4_block14_concat[0][0]',\n", " atenate) 'conv4_block15_2_conv[0][0]']\n", " \n", " conv4_block16_0_bn (BatchN (None, 18, 18, 736) 2944 ['conv4_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_0_relu (Acti (None, 18, 18, 736) 0 ['conv4_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_1_conv (Conv (None, 18, 18, 128) 94208 ['conv4_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block16_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block16_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block16_concat (Conc (None, 18, 18, 768) 0 ['conv4_block15_concat[0][0]',\n", " atenate) 'conv4_block16_2_conv[0][0]']\n", " \n", " conv4_block17_0_bn (BatchN (None, 18, 18, 768) 3072 ['conv4_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_0_relu (Acti (None, 18, 18, 768) 0 ['conv4_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_1_conv (Conv (None, 18, 18, 128) 98304 ['conv4_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block17_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block17_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block17_concat (Conc (None, 18, 18, 800) 0 ['conv4_block16_concat[0][0]',\n", " atenate) 'conv4_block17_2_conv[0][0]']\n", " \n", " conv4_block18_0_bn (BatchN (None, 18, 18, 800) 3200 ['conv4_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_0_relu (Acti (None, 18, 18, 800) 0 ['conv4_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_1_conv (Conv (None, 18, 18, 128) 102400 ['conv4_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block18_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block18_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block18_concat (Conc (None, 18, 18, 832) 0 ['conv4_block17_concat[0][0]',\n", " atenate) 'conv4_block18_2_conv[0][0]']\n", " \n", " conv4_block19_0_bn (BatchN (None, 18, 18, 832) 3328 ['conv4_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_0_relu (Acti (None, 18, 18, 832) 0 ['conv4_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_1_conv (Conv (None, 18, 18, 128) 106496 ['conv4_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block19_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block19_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block19_concat (Conc (None, 18, 18, 864) 0 ['conv4_block18_concat[0][0]',\n", " atenate) 'conv4_block19_2_conv[0][0]']\n", " \n", " conv4_block20_0_bn (BatchN (None, 18, 18, 864) 3456 ['conv4_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_0_relu (Acti (None, 18, 18, 864) 0 ['conv4_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_1_conv (Conv (None, 18, 18, 128) 110592 ['conv4_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block20_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block20_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block20_concat (Conc (None, 18, 18, 896) 0 ['conv4_block19_concat[0][0]',\n", " atenate) 'conv4_block20_2_conv[0][0]']\n", " \n", " conv4_block21_0_bn (BatchN (None, 18, 18, 896) 3584 ['conv4_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_0_relu (Acti (None, 18, 18, 896) 0 ['conv4_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_1_conv (Conv (None, 18, 18, 128) 114688 ['conv4_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block21_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block21_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block21_concat (Conc (None, 18, 18, 928) 0 ['conv4_block20_concat[0][0]',\n", " atenate) 'conv4_block21_2_conv[0][0]']\n", " \n", " conv4_block22_0_bn (BatchN (None, 18, 18, 928) 3712 ['conv4_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_0_relu (Acti (None, 18, 18, 928) 0 ['conv4_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_1_conv (Conv (None, 18, 18, 128) 118784 ['conv4_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block22_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block22_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block22_concat (Conc (None, 18, 18, 960) 0 ['conv4_block21_concat[0][0]',\n", " atenate) 'conv4_block22_2_conv[0][0]']\n", " \n", " conv4_block23_0_bn (BatchN (None, 18, 18, 960) 3840 ['conv4_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_0_relu (Acti (None, 18, 18, 960) 0 ['conv4_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_1_conv (Conv (None, 18, 18, 128) 122880 ['conv4_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block23_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block23_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block23_concat (Conc (None, 18, 18, 992) 0 ['conv4_block22_concat[0][0]',\n", " atenate) 'conv4_block23_2_conv[0][0]']\n", " \n", " conv4_block24_0_bn (BatchN (None, 18, 18, 992) 3968 ['conv4_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_0_relu (Acti (None, 18, 18, 992) 0 ['conv4_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_1_conv (Conv (None, 18, 18, 128) 126976 ['conv4_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block24_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block24_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block24_concat (Conc (None, 18, 18, 1024) 0 ['conv4_block23_concat[0][0]',\n", " atenate) 'conv4_block24_2_conv[0][0]']\n", " \n", " conv4_block25_0_bn (BatchN (None, 18, 18, 1024) 4096 ['conv4_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_0_relu (Acti (None, 18, 18, 1024) 0 ['conv4_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_1_conv (Conv (None, 18, 18, 128) 131072 ['conv4_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block25_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block25_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block25_concat (Conc (None, 18, 18, 1056) 0 ['conv4_block24_concat[0][0]',\n", " atenate) 'conv4_block25_2_conv[0][0]']\n", " \n", " conv4_block26_0_bn (BatchN (None, 18, 18, 1056) 4224 ['conv4_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_0_relu (Acti (None, 18, 18, 1056) 0 ['conv4_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_1_conv (Conv (None, 18, 18, 128) 135168 ['conv4_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block26_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block26_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block26_concat (Conc (None, 18, 18, 1088) 0 ['conv4_block25_concat[0][0]',\n", " atenate) 'conv4_block26_2_conv[0][0]']\n", " \n", " conv4_block27_0_bn (BatchN (None, 18, 18, 1088) 4352 ['conv4_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_0_relu (Acti (None, 18, 18, 1088) 0 ['conv4_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_1_conv (Conv (None, 18, 18, 128) 139264 ['conv4_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block27_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block27_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block27_concat (Conc (None, 18, 18, 1120) 0 ['conv4_block26_concat[0][0]',\n", " atenate) 'conv4_block27_2_conv[0][0]']\n", " \n", " conv4_block28_0_bn (BatchN (None, 18, 18, 1120) 4480 ['conv4_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_0_relu (Acti (None, 18, 18, 1120) 0 ['conv4_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_1_conv (Conv (None, 18, 18, 128) 143360 ['conv4_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block28_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block28_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block28_concat (Conc (None, 18, 18, 1152) 0 ['conv4_block27_concat[0][0]',\n", " atenate) 'conv4_block28_2_conv[0][0]']\n", " \n", " conv4_block29_0_bn (BatchN (None, 18, 18, 1152) 4608 ['conv4_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_0_relu (Acti (None, 18, 18, 1152) 0 ['conv4_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_1_conv (Conv (None, 18, 18, 128) 147456 ['conv4_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block29_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block29_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block29_concat (Conc (None, 18, 18, 1184) 0 ['conv4_block28_concat[0][0]',\n", " atenate) 'conv4_block29_2_conv[0][0]']\n", " \n", " conv4_block30_0_bn (BatchN (None, 18, 18, 1184) 4736 ['conv4_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_0_relu (Acti (None, 18, 18, 1184) 0 ['conv4_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_1_conv (Conv (None, 18, 18, 128) 151552 ['conv4_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block30_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block30_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block30_concat (Conc (None, 18, 18, 1216) 0 ['conv4_block29_concat[0][0]',\n", " atenate) 'conv4_block30_2_conv[0][0]']\n", " \n", " conv4_block31_0_bn (BatchN (None, 18, 18, 1216) 4864 ['conv4_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_0_relu (Acti (None, 18, 18, 1216) 0 ['conv4_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_1_conv (Conv (None, 18, 18, 128) 155648 ['conv4_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block31_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block31_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block31_concat (Conc (None, 18, 18, 1248) 0 ['conv4_block30_concat[0][0]',\n", " atenate) 'conv4_block31_2_conv[0][0]']\n", " \n", " conv4_block32_0_bn (BatchN (None, 18, 18, 1248) 4992 ['conv4_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_0_relu (Acti (None, 18, 18, 1248) 0 ['conv4_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_1_conv (Conv (None, 18, 18, 128) 159744 ['conv4_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block32_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block32_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block32_concat (Conc (None, 18, 18, 1280) 0 ['conv4_block31_concat[0][0]',\n", " atenate) 'conv4_block32_2_conv[0][0]']\n", " \n", " conv4_block33_0_bn (BatchN (None, 18, 18, 1280) 5120 ['conv4_block32_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_0_relu (Acti (None, 18, 18, 1280) 0 ['conv4_block33_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_1_conv (Conv (None, 18, 18, 128) 163840 ['conv4_block33_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block33_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block33_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block33_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block33_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block33_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block33_concat (Conc (None, 18, 18, 1312) 0 ['conv4_block32_concat[0][0]',\n", " atenate) 'conv4_block33_2_conv[0][0]']\n", " \n", " conv4_block34_0_bn (BatchN (None, 18, 18, 1312) 5248 ['conv4_block33_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_0_relu (Acti (None, 18, 18, 1312) 0 ['conv4_block34_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_1_conv (Conv (None, 18, 18, 128) 167936 ['conv4_block34_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block34_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block34_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block34_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block34_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block34_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block34_concat (Conc (None, 18, 18, 1344) 0 ['conv4_block33_concat[0][0]',\n", " atenate) 'conv4_block34_2_conv[0][0]']\n", " \n", " conv4_block35_0_bn (BatchN (None, 18, 18, 1344) 5376 ['conv4_block34_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_0_relu (Acti (None, 18, 18, 1344) 0 ['conv4_block35_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_1_conv (Conv (None, 18, 18, 128) 172032 ['conv4_block35_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block35_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block35_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block35_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block35_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block35_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block35_concat (Conc (None, 18, 18, 1376) 0 ['conv4_block34_concat[0][0]',\n", " atenate) 'conv4_block35_2_conv[0][0]']\n", " \n", " conv4_block36_0_bn (BatchN (None, 18, 18, 1376) 5504 ['conv4_block35_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_0_relu (Acti (None, 18, 18, 1376) 0 ['conv4_block36_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_1_conv (Conv (None, 18, 18, 128) 176128 ['conv4_block36_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block36_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block36_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block36_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block36_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block36_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block36_concat (Conc (None, 18, 18, 1408) 0 ['conv4_block35_concat[0][0]',\n", " atenate) 'conv4_block36_2_conv[0][0]']\n", " \n", " conv4_block37_0_bn (BatchN (None, 18, 18, 1408) 5632 ['conv4_block36_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_0_relu (Acti (None, 18, 18, 1408) 0 ['conv4_block37_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_1_conv (Conv (None, 18, 18, 128) 180224 ['conv4_block37_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block37_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block37_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block37_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block37_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block37_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block37_concat (Conc (None, 18, 18, 1440) 0 ['conv4_block36_concat[0][0]',\n", " atenate) 'conv4_block37_2_conv[0][0]']\n", " \n", " conv4_block38_0_bn (BatchN (None, 18, 18, 1440) 5760 ['conv4_block37_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_0_relu (Acti (None, 18, 18, 1440) 0 ['conv4_block38_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_1_conv (Conv (None, 18, 18, 128) 184320 ['conv4_block38_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block38_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block38_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block38_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block38_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block38_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block38_concat (Conc (None, 18, 18, 1472) 0 ['conv4_block37_concat[0][0]',\n", " atenate) 'conv4_block38_2_conv[0][0]']\n", " \n", " conv4_block39_0_bn (BatchN (None, 18, 18, 1472) 5888 ['conv4_block38_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_0_relu (Acti (None, 18, 18, 1472) 0 ['conv4_block39_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_1_conv (Conv (None, 18, 18, 128) 188416 ['conv4_block39_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block39_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block39_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block39_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block39_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block39_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block39_concat (Conc (None, 18, 18, 1504) 0 ['conv4_block38_concat[0][0]',\n", " atenate) 'conv4_block39_2_conv[0][0]']\n", " \n", " conv4_block40_0_bn (BatchN (None, 18, 18, 1504) 6016 ['conv4_block39_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_0_relu (Acti (None, 18, 18, 1504) 0 ['conv4_block40_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_1_conv (Conv (None, 18, 18, 128) 192512 ['conv4_block40_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block40_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block40_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block40_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block40_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block40_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block40_concat (Conc (None, 18, 18, 1536) 0 ['conv4_block39_concat[0][0]',\n", " atenate) 'conv4_block40_2_conv[0][0]']\n", " \n", " conv4_block41_0_bn (BatchN (None, 18, 18, 1536) 6144 ['conv4_block40_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_0_relu (Acti (None, 18, 18, 1536) 0 ['conv4_block41_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_1_conv (Conv (None, 18, 18, 128) 196608 ['conv4_block41_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block41_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block41_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block41_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block41_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block41_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block41_concat (Conc (None, 18, 18, 1568) 0 ['conv4_block40_concat[0][0]',\n", " atenate) 'conv4_block41_2_conv[0][0]']\n", " \n", " conv4_block42_0_bn (BatchN (None, 18, 18, 1568) 6272 ['conv4_block41_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_0_relu (Acti (None, 18, 18, 1568) 0 ['conv4_block42_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_1_conv (Conv (None, 18, 18, 128) 200704 ['conv4_block42_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block42_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block42_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block42_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block42_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block42_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block42_concat (Conc (None, 18, 18, 1600) 0 ['conv4_block41_concat[0][0]',\n", " atenate) 'conv4_block42_2_conv[0][0]']\n", " \n", " conv4_block43_0_bn (BatchN (None, 18, 18, 1600) 6400 ['conv4_block42_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_0_relu (Acti (None, 18, 18, 1600) 0 ['conv4_block43_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_1_conv (Conv (None, 18, 18, 128) 204800 ['conv4_block43_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block43_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block43_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block43_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block43_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block43_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block43_concat (Conc (None, 18, 18, 1632) 0 ['conv4_block42_concat[0][0]',\n", " atenate) 'conv4_block43_2_conv[0][0]']\n", " \n", " conv4_block44_0_bn (BatchN (None, 18, 18, 1632) 6528 ['conv4_block43_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_0_relu (Acti (None, 18, 18, 1632) 0 ['conv4_block44_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_1_conv (Conv (None, 18, 18, 128) 208896 ['conv4_block44_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block44_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block44_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block44_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block44_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block44_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block44_concat (Conc (None, 18, 18, 1664) 0 ['conv4_block43_concat[0][0]',\n", " atenate) 'conv4_block44_2_conv[0][0]']\n", " \n", " conv4_block45_0_bn (BatchN (None, 18, 18, 1664) 6656 ['conv4_block44_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_0_relu (Acti (None, 18, 18, 1664) 0 ['conv4_block45_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_1_conv (Conv (None, 18, 18, 128) 212992 ['conv4_block45_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block45_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block45_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block45_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block45_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block45_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block45_concat (Conc (None, 18, 18, 1696) 0 ['conv4_block44_concat[0][0]',\n", " atenate) 'conv4_block45_2_conv[0][0]']\n", " \n", " conv4_block46_0_bn (BatchN (None, 18, 18, 1696) 6784 ['conv4_block45_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_0_relu (Acti (None, 18, 18, 1696) 0 ['conv4_block46_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_1_conv (Conv (None, 18, 18, 128) 217088 ['conv4_block46_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block46_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block46_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block46_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block46_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block46_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block46_concat (Conc (None, 18, 18, 1728) 0 ['conv4_block45_concat[0][0]',\n", " atenate) 'conv4_block46_2_conv[0][0]']\n", " \n", " conv4_block47_0_bn (BatchN (None, 18, 18, 1728) 6912 ['conv4_block46_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_0_relu (Acti (None, 18, 18, 1728) 0 ['conv4_block47_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_1_conv (Conv (None, 18, 18, 128) 221184 ['conv4_block47_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block47_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block47_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block47_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block47_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block47_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block47_concat (Conc (None, 18, 18, 1760) 0 ['conv4_block46_concat[0][0]',\n", " atenate) 'conv4_block47_2_conv[0][0]']\n", " \n", " conv4_block48_0_bn (BatchN (None, 18, 18, 1760) 7040 ['conv4_block47_concat[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_0_relu (Acti (None, 18, 18, 1760) 0 ['conv4_block48_0_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_1_conv (Conv (None, 18, 18, 128) 225280 ['conv4_block48_0_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_1_bn (BatchN (None, 18, 18, 128) 512 ['conv4_block48_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv4_block48_1_relu (Acti (None, 18, 18, 128) 0 ['conv4_block48_1_bn[0][0]'] \n", " vation) \n", " \n", " conv4_block48_2_conv (Conv (None, 18, 18, 32) 36864 ['conv4_block48_1_relu[0][0]']\n", " 2D) \n", " \n", " conv4_block48_concat (Conc (None, 18, 18, 1792) 0 ['conv4_block47_concat[0][0]',\n", " atenate) 'conv4_block48_2_conv[0][0]']\n", " \n", " pool4_bn (BatchNormalizati (None, 18, 18, 1792) 7168 ['conv4_block48_concat[0][0]']\n", " on) \n", " \n", " pool4_relu (Activation) (None, 18, 18, 1792) 0 ['pool4_bn[0][0]'] \n", " \n", " pool4_conv (Conv2D) (None, 18, 18, 896) 1605632 ['pool4_relu[0][0]'] \n", " \n", " pool4_pool (AveragePooling (None, 9, 9, 896) 0 ['pool4_conv[0][0]'] \n", " 2D) \n", " \n", " conv5_block1_0_bn (BatchNo (None, 9, 9, 896) 3584 ['pool4_pool[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_0_relu (Activ (None, 9, 9, 896) 0 ['conv5_block1_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_1_conv (Conv2 (None, 9, 9, 128) 114688 ['conv5_block1_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block1_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block1_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block1_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block1_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block1_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block1_concat (Conca (None, 9, 9, 928) 0 ['pool4_pool[0][0]', \n", " tenate) 'conv5_block1_2_conv[0][0]'] \n", " \n", " conv5_block2_0_bn (BatchNo (None, 9, 9, 928) 3712 ['conv5_block1_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_0_relu (Activ (None, 9, 9, 928) 0 ['conv5_block2_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_1_conv (Conv2 (None, 9, 9, 128) 118784 ['conv5_block2_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block2_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block2_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block2_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block2_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block2_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block2_concat (Conca (None, 9, 9, 960) 0 ['conv5_block1_concat[0][0]', \n", " tenate) 'conv5_block2_2_conv[0][0]'] \n", " \n", " conv5_block3_0_bn (BatchNo (None, 9, 9, 960) 3840 ['conv5_block2_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_0_relu (Activ (None, 9, 9, 960) 0 ['conv5_block3_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_1_conv (Conv2 (None, 9, 9, 128) 122880 ['conv5_block3_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block3_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block3_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block3_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block3_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block3_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block3_concat (Conca (None, 9, 9, 992) 0 ['conv5_block2_concat[0][0]', \n", " tenate) 'conv5_block3_2_conv[0][0]'] \n", " \n", " conv5_block4_0_bn (BatchNo (None, 9, 9, 992) 3968 ['conv5_block3_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_0_relu (Activ (None, 9, 9, 992) 0 ['conv5_block4_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_1_conv (Conv2 (None, 9, 9, 128) 126976 ['conv5_block4_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block4_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block4_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block4_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block4_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block4_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block4_concat (Conca (None, 9, 9, 1024) 0 ['conv5_block3_concat[0][0]', \n", " tenate) 'conv5_block4_2_conv[0][0]'] \n", " \n", " conv5_block5_0_bn (BatchNo (None, 9, 9, 1024) 4096 ['conv5_block4_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_0_relu (Activ (None, 9, 9, 1024) 0 ['conv5_block5_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_1_conv (Conv2 (None, 9, 9, 128) 131072 ['conv5_block5_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block5_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block5_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block5_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block5_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block5_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block5_concat (Conca (None, 9, 9, 1056) 0 ['conv5_block4_concat[0][0]', \n", " tenate) 'conv5_block5_2_conv[0][0]'] \n", " \n", " conv5_block6_0_bn (BatchNo (None, 9, 9, 1056) 4224 ['conv5_block5_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_0_relu (Activ (None, 9, 9, 1056) 0 ['conv5_block6_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_1_conv (Conv2 (None, 9, 9, 128) 135168 ['conv5_block6_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block6_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block6_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block6_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block6_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block6_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block6_concat (Conca (None, 9, 9, 1088) 0 ['conv5_block5_concat[0][0]', \n", " tenate) 'conv5_block6_2_conv[0][0]'] \n", " \n", " conv5_block7_0_bn (BatchNo (None, 9, 9, 1088) 4352 ['conv5_block6_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_0_relu (Activ (None, 9, 9, 1088) 0 ['conv5_block7_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_1_conv (Conv2 (None, 9, 9, 128) 139264 ['conv5_block7_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block7_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block7_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block7_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block7_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block7_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block7_concat (Conca (None, 9, 9, 1120) 0 ['conv5_block6_concat[0][0]', \n", " tenate) 'conv5_block7_2_conv[0][0]'] \n", " \n", " conv5_block8_0_bn (BatchNo (None, 9, 9, 1120) 4480 ['conv5_block7_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_0_relu (Activ (None, 9, 9, 1120) 0 ['conv5_block8_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_1_conv (Conv2 (None, 9, 9, 128) 143360 ['conv5_block8_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block8_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block8_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block8_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block8_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block8_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block8_concat (Conca (None, 9, 9, 1152) 0 ['conv5_block7_concat[0][0]', \n", " tenate) 'conv5_block8_2_conv[0][0]'] \n", " \n", " conv5_block9_0_bn (BatchNo (None, 9, 9, 1152) 4608 ['conv5_block8_concat[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_0_relu (Activ (None, 9, 9, 1152) 0 ['conv5_block9_0_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_1_conv (Conv2 (None, 9, 9, 128) 147456 ['conv5_block9_0_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_1_bn (BatchNo (None, 9, 9, 128) 512 ['conv5_block9_1_conv[0][0]'] \n", " rmalization) \n", " \n", " conv5_block9_1_relu (Activ (None, 9, 9, 128) 0 ['conv5_block9_1_bn[0][0]'] \n", " ation) \n", " \n", " conv5_block9_2_conv (Conv2 (None, 9, 9, 32) 36864 ['conv5_block9_1_relu[0][0]'] \n", " D) \n", " \n", " conv5_block9_concat (Conca (None, 9, 9, 1184) 0 ['conv5_block8_concat[0][0]', \n", " tenate) 'conv5_block9_2_conv[0][0]'] \n", " \n", " conv5_block10_0_bn (BatchN (None, 9, 9, 1184) 4736 ['conv5_block9_concat[0][0]'] \n", " ormalization) \n", " \n", " conv5_block10_0_relu (Acti (None, 9, 9, 1184) 0 ['conv5_block10_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_1_conv (Conv (None, 9, 9, 128) 151552 ['conv5_block10_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block10_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block10_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block10_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block10_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block10_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block10_concat (Conc (None, 9, 9, 1216) 0 ['conv5_block9_concat[0][0]', \n", " atenate) 'conv5_block10_2_conv[0][0]']\n", " \n", " conv5_block11_0_bn (BatchN (None, 9, 9, 1216) 4864 ['conv5_block10_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_0_relu (Acti (None, 9, 9, 1216) 0 ['conv5_block11_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_1_conv (Conv (None, 9, 9, 128) 155648 ['conv5_block11_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block11_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block11_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block11_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block11_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block11_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block11_concat (Conc (None, 9, 9, 1248) 0 ['conv5_block10_concat[0][0]',\n", " atenate) 'conv5_block11_2_conv[0][0]']\n", " \n", " conv5_block12_0_bn (BatchN (None, 9, 9, 1248) 4992 ['conv5_block11_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_0_relu (Acti (None, 9, 9, 1248) 0 ['conv5_block12_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_1_conv (Conv (None, 9, 9, 128) 159744 ['conv5_block12_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block12_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block12_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block12_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block12_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block12_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block12_concat (Conc (None, 9, 9, 1280) 0 ['conv5_block11_concat[0][0]',\n", " atenate) 'conv5_block12_2_conv[0][0]']\n", " \n", " conv5_block13_0_bn (BatchN (None, 9, 9, 1280) 5120 ['conv5_block12_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_0_relu (Acti (None, 9, 9, 1280) 0 ['conv5_block13_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_1_conv (Conv (None, 9, 9, 128) 163840 ['conv5_block13_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block13_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block13_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block13_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block13_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block13_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block13_concat (Conc (None, 9, 9, 1312) 0 ['conv5_block12_concat[0][0]',\n", " atenate) 'conv5_block13_2_conv[0][0]']\n", " \n", " conv5_block14_0_bn (BatchN (None, 9, 9, 1312) 5248 ['conv5_block13_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_0_relu (Acti (None, 9, 9, 1312) 0 ['conv5_block14_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_1_conv (Conv (None, 9, 9, 128) 167936 ['conv5_block14_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block14_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block14_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block14_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block14_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block14_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block14_concat (Conc (None, 9, 9, 1344) 0 ['conv5_block13_concat[0][0]',\n", " atenate) 'conv5_block14_2_conv[0][0]']\n", " \n", " conv5_block15_0_bn (BatchN (None, 9, 9, 1344) 5376 ['conv5_block14_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_0_relu (Acti (None, 9, 9, 1344) 0 ['conv5_block15_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_1_conv (Conv (None, 9, 9, 128) 172032 ['conv5_block15_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block15_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block15_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block15_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block15_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block15_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block15_concat (Conc (None, 9, 9, 1376) 0 ['conv5_block14_concat[0][0]',\n", " atenate) 'conv5_block15_2_conv[0][0]']\n", " \n", " conv5_block16_0_bn (BatchN (None, 9, 9, 1376) 5504 ['conv5_block15_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_0_relu (Acti (None, 9, 9, 1376) 0 ['conv5_block16_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_1_conv (Conv (None, 9, 9, 128) 176128 ['conv5_block16_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block16_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block16_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block16_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block16_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block16_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block16_concat (Conc (None, 9, 9, 1408) 0 ['conv5_block15_concat[0][0]',\n", " atenate) 'conv5_block16_2_conv[0][0]']\n", " \n", " conv5_block17_0_bn (BatchN (None, 9, 9, 1408) 5632 ['conv5_block16_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_0_relu (Acti (None, 9, 9, 1408) 0 ['conv5_block17_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_1_conv (Conv (None, 9, 9, 128) 180224 ['conv5_block17_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block17_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block17_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block17_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block17_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block17_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block17_concat (Conc (None, 9, 9, 1440) 0 ['conv5_block16_concat[0][0]',\n", " atenate) 'conv5_block17_2_conv[0][0]']\n", " \n", " conv5_block18_0_bn (BatchN (None, 9, 9, 1440) 5760 ['conv5_block17_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_0_relu (Acti (None, 9, 9, 1440) 0 ['conv5_block18_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_1_conv (Conv (None, 9, 9, 128) 184320 ['conv5_block18_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block18_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block18_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block18_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block18_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block18_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block18_concat (Conc (None, 9, 9, 1472) 0 ['conv5_block17_concat[0][0]',\n", " atenate) 'conv5_block18_2_conv[0][0]']\n", " \n", " conv5_block19_0_bn (BatchN (None, 9, 9, 1472) 5888 ['conv5_block18_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_0_relu (Acti (None, 9, 9, 1472) 0 ['conv5_block19_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_1_conv (Conv (None, 9, 9, 128) 188416 ['conv5_block19_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block19_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block19_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block19_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block19_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block19_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block19_concat (Conc (None, 9, 9, 1504) 0 ['conv5_block18_concat[0][0]',\n", " atenate) 'conv5_block19_2_conv[0][0]']\n", " \n", " conv5_block20_0_bn (BatchN (None, 9, 9, 1504) 6016 ['conv5_block19_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_0_relu (Acti (None, 9, 9, 1504) 0 ['conv5_block20_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_1_conv (Conv (None, 9, 9, 128) 192512 ['conv5_block20_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block20_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block20_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block20_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block20_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block20_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block20_concat (Conc (None, 9, 9, 1536) 0 ['conv5_block19_concat[0][0]',\n", " atenate) 'conv5_block20_2_conv[0][0]']\n", " \n", " conv5_block21_0_bn (BatchN (None, 9, 9, 1536) 6144 ['conv5_block20_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_0_relu (Acti (None, 9, 9, 1536) 0 ['conv5_block21_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_1_conv (Conv (None, 9, 9, 128) 196608 ['conv5_block21_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block21_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block21_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block21_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block21_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block21_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block21_concat (Conc (None, 9, 9, 1568) 0 ['conv5_block20_concat[0][0]',\n", " atenate) 'conv5_block21_2_conv[0][0]']\n", " \n", " conv5_block22_0_bn (BatchN (None, 9, 9, 1568) 6272 ['conv5_block21_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_0_relu (Acti (None, 9, 9, 1568) 0 ['conv5_block22_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_1_conv (Conv (None, 9, 9, 128) 200704 ['conv5_block22_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block22_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block22_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block22_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block22_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block22_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block22_concat (Conc (None, 9, 9, 1600) 0 ['conv5_block21_concat[0][0]',\n", " atenate) 'conv5_block22_2_conv[0][0]']\n", " \n", " conv5_block23_0_bn (BatchN (None, 9, 9, 1600) 6400 ['conv5_block22_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_0_relu (Acti (None, 9, 9, 1600) 0 ['conv5_block23_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_1_conv (Conv (None, 9, 9, 128) 204800 ['conv5_block23_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block23_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block23_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block23_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block23_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block23_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block23_concat (Conc (None, 9, 9, 1632) 0 ['conv5_block22_concat[0][0]',\n", " atenate) 'conv5_block23_2_conv[0][0]']\n", " \n", " conv5_block24_0_bn (BatchN (None, 9, 9, 1632) 6528 ['conv5_block23_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_0_relu (Acti (None, 9, 9, 1632) 0 ['conv5_block24_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_1_conv (Conv (None, 9, 9, 128) 208896 ['conv5_block24_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block24_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block24_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block24_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block24_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block24_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block24_concat (Conc (None, 9, 9, 1664) 0 ['conv5_block23_concat[0][0]',\n", " atenate) 'conv5_block24_2_conv[0][0]']\n", " \n", " conv5_block25_0_bn (BatchN (None, 9, 9, 1664) 6656 ['conv5_block24_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_0_relu (Acti (None, 9, 9, 1664) 0 ['conv5_block25_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_1_conv (Conv (None, 9, 9, 128) 212992 ['conv5_block25_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block25_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block25_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block25_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block25_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block25_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block25_concat (Conc (None, 9, 9, 1696) 0 ['conv5_block24_concat[0][0]',\n", " atenate) 'conv5_block25_2_conv[0][0]']\n", " \n", " conv5_block26_0_bn (BatchN (None, 9, 9, 1696) 6784 ['conv5_block25_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_0_relu (Acti (None, 9, 9, 1696) 0 ['conv5_block26_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_1_conv (Conv (None, 9, 9, 128) 217088 ['conv5_block26_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block26_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block26_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block26_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block26_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block26_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block26_concat (Conc (None, 9, 9, 1728) 0 ['conv5_block25_concat[0][0]',\n", " atenate) 'conv5_block26_2_conv[0][0]']\n", " \n", " conv5_block27_0_bn (BatchN (None, 9, 9, 1728) 6912 ['conv5_block26_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_0_relu (Acti (None, 9, 9, 1728) 0 ['conv5_block27_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_1_conv (Conv (None, 9, 9, 128) 221184 ['conv5_block27_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block27_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block27_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block27_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block27_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block27_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block27_concat (Conc (None, 9, 9, 1760) 0 ['conv5_block26_concat[0][0]',\n", " atenate) 'conv5_block27_2_conv[0][0]']\n", " \n", " conv5_block28_0_bn (BatchN (None, 9, 9, 1760) 7040 ['conv5_block27_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_0_relu (Acti (None, 9, 9, 1760) 0 ['conv5_block28_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_1_conv (Conv (None, 9, 9, 128) 225280 ['conv5_block28_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block28_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block28_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block28_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block28_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block28_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block28_concat (Conc (None, 9, 9, 1792) 0 ['conv5_block27_concat[0][0]',\n", " atenate) 'conv5_block28_2_conv[0][0]']\n", " \n", " conv5_block29_0_bn (BatchN (None, 9, 9, 1792) 7168 ['conv5_block28_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_0_relu (Acti (None, 9, 9, 1792) 0 ['conv5_block29_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_1_conv (Conv (None, 9, 9, 128) 229376 ['conv5_block29_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block29_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block29_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block29_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block29_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block29_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block29_concat (Conc (None, 9, 9, 1824) 0 ['conv5_block28_concat[0][0]',\n", " atenate) 'conv5_block29_2_conv[0][0]']\n", " \n", " conv5_block30_0_bn (BatchN (None, 9, 9, 1824) 7296 ['conv5_block29_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_0_relu (Acti (None, 9, 9, 1824) 0 ['conv5_block30_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_1_conv (Conv (None, 9, 9, 128) 233472 ['conv5_block30_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block30_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block30_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block30_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block30_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block30_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block30_concat (Conc (None, 9, 9, 1856) 0 ['conv5_block29_concat[0][0]',\n", " atenate) 'conv5_block30_2_conv[0][0]']\n", " \n", " conv5_block31_0_bn (BatchN (None, 9, 9, 1856) 7424 ['conv5_block30_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_0_relu (Acti (None, 9, 9, 1856) 0 ['conv5_block31_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_1_conv (Conv (None, 9, 9, 128) 237568 ['conv5_block31_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block31_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block31_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block31_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block31_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block31_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block31_concat (Conc (None, 9, 9, 1888) 0 ['conv5_block30_concat[0][0]',\n", " atenate) 'conv5_block31_2_conv[0][0]']\n", " \n", " conv5_block32_0_bn (BatchN (None, 9, 9, 1888) 7552 ['conv5_block31_concat[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_0_relu (Acti (None, 9, 9, 1888) 0 ['conv5_block32_0_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_1_conv (Conv (None, 9, 9, 128) 241664 ['conv5_block32_0_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_1_bn (BatchN (None, 9, 9, 128) 512 ['conv5_block32_1_conv[0][0]']\n", " ormalization) \n", " \n", " conv5_block32_1_relu (Acti (None, 9, 9, 128) 0 ['conv5_block32_1_bn[0][0]'] \n", " vation) \n", " \n", " conv5_block32_2_conv (Conv (None, 9, 9, 32) 36864 ['conv5_block32_1_relu[0][0]']\n", " 2D) \n", " \n", " conv5_block32_concat (Conc (None, 9, 9, 1920) 0 ['conv5_block31_concat[0][0]',\n", " atenate) 'conv5_block32_2_conv[0][0]']\n", " \n", " bn (BatchNormalization) (None, 9, 9, 1920) 7680 ['conv5_block32_concat[0][0]']\n", " \n", " relu (Activation) (None, 9, 9, 1920) 0 ['bn[0][0]'] \n", " \n", " flatten_2 (Flatten) (None, 155520) 0 ['relu[0][0]'] \n", " \n", " dense_2 (Dense) (None, 2) 311042 ['flatten_2[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 18633026 (71.08 MB)\n", "Trainable params: 18403970 (70.21 MB)\n", "Non-trainable params: 229056 (894.75 KB)\n", "__________________________________________________________________________________________________\n", "None\n" ] } ] }, { "cell_type": "code", "source": [ "loaded_model.compile(optimizer=Adam(learning_rate=1e-3), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])" ], "metadata": { "id": "d8tXgpyKJ8T2" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "test_loss, test_acc = loaded_model.evaluate(test_generator)\n", "print(f\"Test Accuracy: {test_acc}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9nI2bu32KmMe", "outputId": "17548549-58c4-45e1-d3b0-f0a0dc4b2919" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "143/143 [==============================] - 452s 3s/step - loss: 129141.1016 - accuracy: 0.9930\n", "Test Accuracy: 0.9929885864257812\n" ] } ] }, { "cell_type": "code", "source": [ "%whos" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kHgFK8qIKo53", "outputId": "f77fca61-4912-47f7-c28d-095b1a8536c6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Variable Type Data/Info\n", "----------------------------------------------------------------\n", "Adam type \n", "Conv2D type olutional.conv2d.Conv2D'>\n", "Dense type \n", "DenseNet121 function \n", "DenseNet169 function \n", "DenseNet201 function \n", "Dropout type ization.dropout.Dropout'>\n", "Flatten type shaping.flatten.Flatten'>\n", "GlobalAveragePooling2D type .GlobalAveragePooling2D'>\n", "HandlerLine2D type \n", "IMG_HEIGHT int 299\n", "IMG_WIDTH int 299\n", "Image module t-packages/PIL/Image.py'>\n", "ImageDataGenerator type mage.ImageDataGenerator'>\n", "Input function \n", "Line2D type \n", "MaxPooling2D type _pooling2d.MaxPooling2D'>\n", "MobileNetV3Small function \n", "Model type \n", "ModelCheckpoint type \n", "Sequential type \n", "accuracy_score function \n", "batch tuple n=2\n", "channels int 3\n", "checkpoint_dir str /kaggle/working/Checkpoints_densenet201\n", "checkpoint_path str /kaggle/working/Checkpoints_densenet201/cp.ckpt\n", "class_indices dict n=2\n", "class_name str not_fractured\n", "class_path str /content/drive/MyDrive/Bo<...>et/training/not_fractured\n", "classes list n=2\n", "classification_report function report at 0x7d6dbecd5ea0>\n", "confusion_matrix function \n", "copyfile function \n", "cp_callback ModelCheckpoint object at 0x7d6e46342e60>\n", "create_model function \n", "drive module s/google/colab/drive.py'>\n", "evaluation list n=2\n", "f1_score function \n", "height int 299\n", "history History object at 0x7d6da4e0da20>\n", "i int 7\n", "img ndarray 299x299x3: 268203 elems, type `float32`, 1072812 bytes (1.0231132507324219 Mb)\n", "initial_epoch int 20\n", "input_shape tuple n=3\n", "jaccard_score function \n", "label ndarray 2: 2 elems, type `float32`, 8 bytes\n", "latest_checkpoint str /kaggle/working/Checkpoints_densenet201/cp.ckpt\n", "layers module eras/layers/__init__.py'>\n", "legend_lines list n=4\n", "loaded_model Functional object at 0x7d6d8f966470>\n", "log_loss function \n", "model Functional object at 0x7d6dbc028940>\n", "model_dir str /kaggle/working/Checkpoints_densenet201\n", "models module eras/models/__init__.py'>\n", "new_history History object at 0x7d6d999cb160>\n", "np module kages/numpy/__init__.py'>\n", "num_images int 1133\n", "optimizers module /optimizers/__init__.py'>\n", "os module \n", "pd module ages/pandas/__init__.py'>\n", "plt module es/matplotlib/pyplot.py'>\n", "precision_score function \n", "previous_history dict n=4\n", "recall_score function \n", "saved_history dict n=4\n", "sns module ges/seaborn/__init__.py'>\n", "status CheckpointLoadStatus object at 0x7d6d8fa21ff0>\n", "test_acc float 0.9929885864257812\n", "test_data_dir str /content/drive/MyDrive/Bo<...>eFractureDataset/training\n", "test_datagen ImageDataGenerator object at 0x7d6e463431f0>\n", "test_datagen_augmented ImageDataGenerator object at 0x7d6e46342fe0>\n", "test_generator DirectoryIterator object at 0x7d6e46342d40>\n", "test_loss float 129141.1015625\n", "tf module /tensorflow/__init__.py'>\n", "train_accuracy Line2D Line2D(Train Accuracy)\n", "train_data_dir str /content/drive/MyDrive/Bo<...>eFractureDataset/training\n", "train_datagen ImageDataGenerator object at 0x7d6e46341c00>\n", "train_datagen_augmented ImageDataGenerator object at 0x7d6e463424a0>\n", "train_generator DirectoryIterator object at 0x7d6e463412d0>\n", "train_loss Line2D Line2D(Train Loss)\n", "train_test_split function \n", "val_accuracy Line2D Line2D(Validation Accuracy)\n", "val_loss Line2D Line2D(Validation Loss)\n", "validation_data_dir str /content/drive/MyDrive/Bo<...>eFractureDataset/training\n", "validation_datagen ImageDataGenerator object at 0x7d6e46341b40>\n", "validation_datagen_augmented ImageDataGenerator object at 0x7d6e46342440>\n", "validation_generator DirectoryIterator object at 0x7d6e463422f0>\n", "width int 299\n" ] } ] }, { "cell_type": "code", "source": [ "true_classes = [1, 0, 1, 1, 0]\n", "predicted_classes = [1, 1, 0, 1, 0]\n", "print(f\"Accuracy: {accuracy_score(true_classes, predicted_classes)}\")\n", "print(f\"Precision: {precision_score(true_classes, predicted_classes)}\")\n", "print(f\"Recall: {recall_score(true_classes, predicted_classes)}\")\n", "print(f\"F1 Score: {f1_score(true_classes, predicted_classes)}\")\n", "print(f\"Log Loss: {log_loss(true_classes, predicted_classes)}\")\n", "print(f\"Jaccard Score: {jaccard_score(true_classes, predicted_classes)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9S0-c1vzMnFz", "outputId": "e527a537-8315-488b-e8bf-39b23f0e9401" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.6\n", "Precision: 0.6666666666666666\n", "Recall: 0.6666666666666666\n", "F1 Score: 0.6666666666666666\n", "Log Loss: 14.41746135564686\n", "Jaccard Score: 0.5\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"\\nClassification Report:\")\n", "print(classification_report(true_classes, predicted_classes,digits=4))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rTRY-XaMM7iu", "outputId": "65697f4e-785d-4437-82a5-c75059494709" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " 0 0.5000 0.5000 0.5000 2\n", " 1 0.6667 0.6667 0.6667 3\n", "\n", " accuracy 0.6000 5\n", " macro avg 0.5833 0.5833 0.5833 5\n", "weighted avg 0.6000 0.6000 0.6000 5\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "conf_matrix = confusion_matrix(true_classes, predicted_classes)\n", "plt.figure(figsize=(6, 4.5))\n", "custom_palette = sns.color_palette(palette='blend:#7AB,#EDA')\n", "font = {'family': 'Serif', 'weight': 'bold', 'size': 12}\n", "heatmap = sns.heatmap(conf_matrix, annot=True, fmt='d', cmap=custom_palette,vmin=0,vmax=350,\n", " xticklabels=['Fractured', 'Non_fractured'], yticklabels=['Fractured', 'Non_fractured'],annot_kws={\"family\": \"Serif\",'weight': 'bold', 'size': 12})\n", "heatmap.set_xlabel('Predicted Labels', fontdict=font)\n", "heatmap.set_ylabel('True Labels', fontdict=font)\n", "heatmap.set_title('Fracture Classification', fontdict=font, pad=12)\n", "heatmap.set_xticklabels(heatmap.get_xticklabels(), fontname='Serif', fontsize=12)\n", "heatmap.set_yticklabels(heatmap.get_yticklabels(), fontname='Serif', fontsize=12)\n", "cbar = heatmap.collections[0].colorbar\n", "cbar.set_label('Count', fontdict=font)\n", "cbar.ax.tick_params(labelsize=10)\n", "plt.gca().xaxis.labelpad = 10\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 457 }, "id": "fHxryw87NIQm", "outputId": "e8d7a8b7-1185-4b26-bfd4-7ab032a11fae" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import numpy as np" ], "metadata": { "id": "grhsas-tPyQZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(type(true_classes))\n", "print(type(predictions))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Jpr1ilG0P9gA", "outputId": "df7f857b-34b9-4fff-ed8a-66825f14a572" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "!pip install scikit-learn\n", "!pip install matplotlib" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B7ZpgnWSP_My", "outputId": "57d0e5b2-851b-4143-acf0-19fad182e4e0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n", "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.25.2)\n", "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n", "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.3.0)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.49.0)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n", "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.25.2)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n", "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.metrics import roc_curve, roc_auc_score\n", "import matplotlib.pyplot as plt\n", "from matplotlib.patches import Patch" ], "metadata": { "id": "5HEAMNKSQCov" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(type(predictions))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QzazKdSQQWHN", "outputId": "755db31e-4f5c-4e1c-c1c7-fb34a3d01909" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "predictions = np.array(predictions)" ], "metadata": { "id": "PBRqlJSzQgnf" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def save_and_display_gradcam(img_path, heatmap, alpha=0.7):\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n", " superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n", " plt.title('GradCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "SN0veq2EQqxG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n", " model.layers[-1].activation = None\n", " grad_model = tf.keras.models.Model(\n", " [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n", " )\n", " with tf.GradientTape() as tape:\n", " last_conv_layer_output, preds = grad_model(img_array)\n", " if pred_index is None:\n", " pred_index = tf.argmax(preds[0])\n", " class_channel = preds[:, pred_index]\n", " grads = tape.gradient(class_channel, last_conv_layer_output)\n", " pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n", " last_conv_layer_output = last_conv_layer_output[0]\n", " heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]\n", " heatmap = tf.squeeze(heatmap)\n", " heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n", " return heatmap.numpy()" ], "metadata": { "id": "l1xTDmdVQtkh" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " rescaled_img = img/255.0\n", " batch_pred = np.expand_dims(rescaled_img, 0)\n", " last_conv_layer_name = 'conv5_block32_concat'\n", " heatmap = make_gradcam_heatmap(batch_pred, loaded_model, last_conv_layer_name)\n", " save_and_display_gradcam(img_path, heatmap)\n", "make_prediction_and_visualize_()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "tuypvKjBSIQk", "outputId": "567c5231-f460-42f7-d6ee-42ff73139061" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_gradcam_plusplus(img_path, heatmap, alpha=0.7):\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n", " superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n", " plt.title('GradCAM++', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "-PcHSYilScYH" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_gradcam_plusplus_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n", " model.layers[-1].activation = None\n", " grad_model = tf.keras.models.Model(\n", " [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n", " )\n", " with tf.GradientTape() as tape:\n", " last_conv_layer_output, preds = grad_model(img_array)\n", " if pred_index is None:\n", " pred_index = tf.argmax(preds[0])\n", " class_output = preds[:, pred_index]\n", " conv_output = last_conv_layer_output[0]\n", " grads = tape.gradient(class_output, last_conv_layer_output)\n", " pooled_grads = tf.reduce_mean(grads[0], axis=(0, 1, 2))\n", " last_conv_layer_output = last_conv_layer_output[0]\n", " guided_grads = tf.cast(last_conv_layer_output > 0, 'float32') * grads[0]\n", " weights = tf.reduce_mean(guided_grads, axis=(0, 1))\n", " heatmap = tf.reduce_sum(tf.multiply(weights, last_conv_layer_output), axis=-1)\n", " heatmap = tf.maximum(heatmap, 0) / tf.reduce_max(heatmap)\n", " return heatmap.numpy()" ], "metadata": { "id": "2l_CPfNMSvm-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_gradcam_plusplus():\n", " img_path = '/content/drive/MyDrive/testing/not_fractured/1-rotated1-rotated1-rotated1-rotated1.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " rescaled_img = img / 255.0\n", " batch_pred = np.expand_dims(rescaled_img, 0)\n", " last_conv_layer_name = 'conv5_block32_concat'\n", " heatmap = make_gradcam_plusplus_heatmap(batch_pred, loaded_model, last_conv_layer_name)\n", " save_and_display_gradcam_plusplus(img_path, heatmap)\n", "make_prediction_and_visualize_gradcam_plusplus()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "WrRs3bvQS709", "outputId": "b15ba014-3898-4287-b4a0-2974aebe1681" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_scorecam(img_path, heatmap, alpha=0.7):\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n", " superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n", " plt.title('ScoreCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "P5ABq7J5Thoy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "def make_scorecam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n", " model.layers[-1].activation = None\n", " grad_model = tf.keras.models.Model(\n", " [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n", " )\n", " with tf.GradientTape() as tape:\n", " last_conv_layer_output, preds = grad_model(img_array)\n", " if pred_index is None:\n", " pred_index = tf.argmax(preds[0])\n", " class_output = preds[:, pred_index]\n", " conv_output = last_conv_layer_output[0]\n", " grads = tape.gradient(class_output, last_conv_layer_output)\n", " guided_grads = tf.cast(grads[0] > 0, 'float32') * grads[0]\n", " weights = tf.reduce_mean(guided_grads, axis=(0, 1))\n", " cam = tf.reduce_sum(tf.multiply(weights, conv_output), axis=-1)\n", " cam = tf.maximum(cam, 0)\n", " cam /= tf.reduce_max(cam)\n", " return cam.numpy()" ], "metadata": { "id": "iy_FbmnrUGbl" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_scorecam():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/training/fractured/10.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " rescaled_img = img/255.0\n", " batch_pred = np.expand_dims(rescaled_img, 0)\n", " last_conv_layer_name = 'conv5_block32_concat'\n", " heatmap = make_scorecam_heatmap(batch_pred, loaded_model, last_conv_layer_name)\n", " save_and_display_scorecam(img_path, heatmap)\n", "make_prediction_and_visualize_scorecam()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "K0-7EHgaUSlc", "outputId": "c2456fc0-ae1e-4421-d6ad-59e643edeea7" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_faster_scorecam(img_path, heatmap, alpha=0.7):\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n", " superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n", " plt.title('Faster ScoreCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "wiWuVvFVUh-q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def faster_scorecam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n", " model.layers[-1].activation = None\n", " grad_model = tf.keras.models.Model(\n", " [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n", " )\n", " with tf.GradientTape() as tape:\n", " last_conv_layer_output, preds = grad_model(img_array)\n", " if pred_index is None:\n", " pred_index = tf.argmax(preds[0])\n", " class_output = preds[:, pred_index]\n", " conv_output = last_conv_layer_output[0]\n", " grads = tape.gradient(class_output, last_conv_layer_output)[0]\n", " weights = tf.reduce_mean(grads, axis=(0, 1))\n", " weights = tf.reshape(weights, (1, 1, -1))\n", " conv_output = tf.expand_dims(conv_output, axis=0)\n", " conv_output = tf.expand_dims(conv_output, axis=-1)\n", " cam = tf.matmul(weights, conv_output)\n", " cam = tf.squeeze(cam)\n", " cam = tf.maximum(cam, 0)\n", " cam /= tf.reduce_max(cam)\n", " return cam.numpy()" ], "metadata": { "id": "4oHVJswMU2KU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_faster_scorecam():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " rescaled_img = img/255.0\n", " batch_pred = np.expand_dims(rescaled_img, 0)\n", " last_conv_layer_name = 'conv5_block32_concat'\n", " heatmap = faster_scorecam_heatmap(batch_pred, loaded_model, last_conv_layer_name)\n", " save_and_display_faster_scorecam(img_path, heatmap)\n", "make_prediction_and_visualize_faster_scorecam()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "P0psMWkhVH03", "outputId": "f8326af8-1f44-43ca-f4c1-086e0e833972" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_layercam(img_path, heatmap, alpha=0.7):\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n", " heatmap = np.uint8(255 * heatmap)\n", " heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n", " superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n", " plt.title('LayerCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "tKtXmxwcVX1Z" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "def generate_layercam_heatmap(img_array, model, last_conv_layer_name, target_class_index=None):\n", " model.layers[-1].activation = None\n", " grad_model = tf.keras.models.Model(\n", " [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n", " )\n", " with tf.GradientTape() as tape:\n", " last_conv_layer_output, preds = grad_model(img_array)\n", " if target_class_index is None:\n", " target_class_index = tf.argmax(preds[0])\n", " class_output = preds[:, target_class_index]\n", " conv_output = last_conv_layer_output[0]\n", " grads = tape.gradient(class_output, last_conv_layer_output)[0]\n", " weights = tf.reduce_mean(grads, axis=(0, 1))\n", " weights = tf.reshape(weights, (1, 1, -1))\n", " conv_output = tf.expand_dims(conv_output, axis=0)\n", " conv_output = tf.expand_dims(conv_output, axis=-1)\n", " cam = tf.matmul(weights, conv_output)\n", " cam = tf.squeeze(cam)\n", " cam = tf.maximum(cam, 0)\n", " cam /= tf.reduce_max(cam)\n", " return cam.numpy()" ], "metadata": { "id": "Hrsxn2PcVn1t" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_layercam():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.resize(img, (299, 299))\n", " rescaled_img = img/255.0\n", " batch_pred = np.expand_dims(rescaled_img, 0)\n", " last_conv_layer_name = 'conv5_block32_concat'\n", " heatmap = generate_layercam_heatmap(batch_pred, loaded_model, last_conv_layer_name)\n", " save_and_display_layercam(img_path, heatmap)\n", "make_prediction_and_visualize_layercam()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "ceAzgwVzV-Zm", "outputId": "89f84076-1d69-4d49-fcda-06a39eeb4a21" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_saliency_map(img_path, saliency_map):\n", " img = cv2.imread(img_path)\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " saliency_map = cv2.resize(saliency_map, (img.shape[1], img.shape[0]))\n", " saliency_map = (saliency_map - saliency_map.min()) / (saliency_map.max() - saliency_map.min())\n", " heatmap = cv2.applyColorMap(np.uint8(255 * saliency_map), cv2.COLORMAP_JET)\n", " heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)\n", " alpha = 0.4\n", " blended = cv2.addWeighted(img, alpha, heatmap, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(blended)\n", " plt.title('Vanilla Saliency', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "idl4zvx9WOIM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def generate_vanilla_saliency_map(img_array, model):\n", " img_tensor = tf.convert_to_tensor(img_array)\n", " img_tensor = tf.expand_dims(img_tensor, axis=0)\n", " with tf.GradientTape() as tape:\n", " tape.watch(img_tensor)\n", " preds = model(img_tensor)\n", " top_pred_index = tf.argmax(preds[0])\n", " top_class_score = preds[:, top_pred_index]\n", " grads = tape.gradient(top_class_score, img_tensor)\n", " saliency_map = tf.abs(grads)\n", " saliency_map = tf.reduce_max(saliency_map, axis=-1)\n", " return saliency_map[0].numpy()" ], "metadata": { "id": "a2r0W2p3Wa4C" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_vanilla_saliency():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " img = cv2.resize(img, (299, 299))\n", " img = img / 255.0\n", " saliency_map = generate_vanilla_saliency_map(img, loaded_model)\n", " save_and_display_saliency_map(img_path, saliency_map)\n", "make_prediction_and_visualize_vanilla_saliency()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 234 }, "id": "XIh2l2TnWkmu", "outputId": "60d8fc47-6f87-4283-f21e-ce66607873d0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "def save_and_display_SmoothGrad(img_path, saliency_map):\n", " img = cv2.imread(img_path)\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " saliency_map = cv2.resize(saliency_map, (img.shape[1], img.shape[0]))\n", " saliency_map = (saliency_map - saliency_map.min()) / (saliency_map.max() - saliency_map.min())\n", " heatmap = cv2.applyColorMap(np.uint8(255 * saliency_map), cv2.COLORMAP_JET)\n", " heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)\n", " alpha = 0.4\n", " blended = cv2.addWeighted(img, alpha, heatmap, 1 - alpha, 0)\n", " plt.figure(figsize=(4, 4))\n", " plt.imshow(blended)\n", " plt.title('Smooth Grad', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n", " plt.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "rXRMZaNyWy7Q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def generate_smoothgrad_saliency_map(img_array, model, n=50, sigma=1.0):\n", " img_tensor = tf.convert_to_tensor(img_array)\n", " img_tensor = tf.expand_dims(img_tensor, axis=0)\n", " img_tensor = tf.cast(img_tensor, dtype=tf.float32)\n", " with tf.GradientTape() as tape:\n", " tape.watch(img_tensor)\n", " preds = model(img_tensor)\n", " top_pred_index = tf.argmax(preds[0])\n", " top_class_score = preds[:, top_pred_index]\n", " total_gradients = tf.zeros_like(img_tensor)\n", " for _ in range(n):\n", " noise = tf.random.normal(shape=img_tensor.shape, mean=0.0, stddev=sigma)\n", " perturbed_img = img_tensor + noise\n", " with tf.GradientTape() as perturbed_tape:\n", " perturbed_tape.watch(perturbed_img)\n", " perturbed_preds = model(perturbed_img)\n", " perturbed_top_class_score = perturbed_preds[:, top_pred_index]\n", " perturbed_grads = perturbed_tape.gradient(perturbed_top_class_score, perturbed_img)\n", " total_gradients += perturbed_grads\n", " averaged_gradients = total_gradients / n\n", " saliency_map = tf.abs(averaged_gradients)\n", " saliency_map = tf.reduce_max(saliency_map, axis=-1)\n", " return saliency_map[0].numpy()" ], "metadata": { "id": "gzZHPEGXW9jD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def make_prediction_and_visualize_smoothgrad_saliency():\n", " img_path = '/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg'\n", " img = cv2.imread(img_path)\n", " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", " img = cv2.resize(img, (299, 299))\n", " img = img / 255.0\n", " heatmap = generate_smoothgrad_saliency_map(img, loaded_model)\n", " save_and_display_SmoothGrad(img_path, heatmap)\n", "make_prediction_and_visualize_smoothgrad_saliency()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 234 }, "id": "sYdm9B3WXUJo", "outputId": "f39e6a9e-5f27-42a6-dd95-ecd4484b7604" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "WLJHDB5AXjTl" }, "execution_count": null, "outputs": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }