{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MMcCFWZDcjGF",
"outputId": "e7787ead-b10c-4e53-bec5-3c61c063fe6b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: tensorflow in /usr/local/lib/python3.8/dist-packages (2.9.2)\n",
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"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow) (3.2.2)\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting keras-unet-collection==0.1.13\n",
" Downloading keras_unet_collection-0.1.13-py3-none-any.whl (67 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.9/67.9 KB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: keras-unet-collection\n",
"Successfully installed keras-unet-collection-0.1.13\n"
]
}
],
"source": [
"!pip install tensorflow\n",
"!pip install keras-unet-collection==0.1.13"
]
},
{
"cell_type": "code",
"source": [
"!pip install --upgrade --no-cache-dir gdown\n",
"!gdown --id 11hcgbRcs_9mV1QkiDq8Eb6FKENQClI2f\n",
"!pip install patool\n",
"import patoolib\n",
"patoolib.extract_archive('archive.zip')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 663
},
"id": "fSofbbw6cq9I",
"outputId": "9e7a0cef-eb4e-45a8-f051-ceec60ece5ed"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
"Collecting gdown\n",
" Downloading gdown-4.6.0-py3-none-any.whl (14 kB)\n",
"Requirement already satisfied: requests[socks] in /usr/local/lib/python3.8/dist-packages (from gdown) (2.25.1)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (2.10)\n",
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (4.0.0)\n",
"Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
"Installing collected packages: gdown\n",
" Attempting uninstall: gdown\n",
" Found existing installation: gdown 4.4.0\n",
" Uninstalling gdown-4.4.0:\n",
" Successfully uninstalled gdown-4.4.0\n",
"Successfully installed gdown-4.6.0\n",
"/usr/local/lib/python3.8/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
" warnings.warn(\n",
"Downloading...\n",
"From: https://drive.google.com/uc?id=11hcgbRcs_9mV1QkiDq8Eb6FKENQClI2f\n",
"To: /content/archive.zip\n",
"100% 327M/327M [00:06<00:00, 46.9MB/s]\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting patool\n",
" Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 KB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: patool\n",
"Successfully installed patool-1.12\n",
"patool: Extracting archive.zip ...\n",
"patool: running /usr/bin/7z x -o./Unpack_jrwmke3t -- archive.zip\n",
"patool: ... archive.zip extracted to `archive' (multiple files in root).\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'archive'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
"\n",
"import numpy as np\n",
"import cv2\n",
"from glob import glob\n",
"from sklearn.utils import shuffle\n",
"import tensorflow as tf\n",
"from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard\n",
"from tensorflow.keras.optimizers import Adam\n",
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras.metrics import Recall, Precision\n",
"\n",
"\n",
"\"\"\" Global parameters \"\"\"\n",
"H = 256\n",
"W = 256\n",
"\n",
"def create_dir(path):\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"\n",
"def load_dataset(path, split=0.2):\n",
" images = sorted(glob(os.path.join(path, \"images\", \"*.png\")))\n",
" masks = sorted(glob(os.path.join(path, \"masks\", \"*.png\")))\n",
"\n",
" split_size = int(len(images) * split)\n",
"\n",
" train_x, valid_x = train_test_split(images, test_size=split_size, random_state=42)\n",
" train_y, valid_y = train_test_split(masks, test_size=split_size, random_state=42)\n",
"\n",
" train_x, test_x = train_test_split(train_x, test_size=split_size, random_state=42)\n",
" train_y, test_y = train_test_split(train_y, test_size=split_size, random_state=42)\n",
"\n",
" return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)\n",
"\n",
"def read_image(path):\n",
" path = path.decode()\n",
" x = cv2.imread(path, cv2.IMREAD_COLOR)\n",
" x = cv2.resize(x, (W, H))\n",
" x = x / 255.0\n",
" x = x.astype(np.float32)\n",
" return x\n",
"\n",
"def read_mask(path):\n",
" path = path.decode()\n",
" x = cv2.imread(path, cv2.IMREAD_GRAYSCALE) ## (h, w)\n",
" x = cv2.resize(x, (W, H)) ## (h, w)\n",
" x = x / 255.0 ## (h, w)\n",
" x = x.astype(np.float32) ## (h, w)\n",
" x = np.expand_dims(x, axis=-1)## (h, w, 1)\n",
" return x\n",
"\n",
"def tf_parse(x, y):\n",
" def _parse(x, y):\n",
" x = read_image(x)\n",
" y = read_mask(y)\n",
" return x, y\n",
"\n",
" x, y = tf.numpy_function(_parse, [x, y], [tf.float32, tf.float32])\n",
" x.set_shape([H, W, 3])\n",
" y.set_shape([H, W, 1])\n",
" return x, y\n",
"\n",
"def tf_dataset(X, Y, batch=2):\n",
" dataset = tf.data.Dataset.from_tensor_slices((X, Y))\n",
" dataset = dataset.map(tf_parse)\n",
" dataset = dataset.batch(batch)\n",
" dataset = dataset.prefetch(10)\n",
" return dataset"
],
"metadata": {
"id": "XlRfAwegcwgl"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras import backend as K\n",
"from tensorflow.keras.layers import Reshape,Input\n",
"from tensorflow import float32\n",
"\n",
"\n",
"def iou(y_true, y_pred):\n",
" def f(y_true, y_pred):\n",
" intersection = (y_true * y_pred).sum()\n",
" union = y_true.sum() + y_pred.sum() - intersection\n",
" x = (intersection + 1e-15) / (union + 1e-15)\n",
" x = x.astype(np.float32)\n",
" return x\n",
" return tf.numpy_function(f, [y_true, y_pred], tf.float32)\n",
"\n"
],
"metadata": {
"id": "4xRnuvgydqJa"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from keras_unet_collection import models, losses\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
" \"\"\" Directory for storing files \"\"\"\n",
" create_dir(\"files\")\n",
"\n",
" \"\"\" Hyperparameters \"\"\"\n",
" batch_size = 16\n",
" lr = 1e-4\n",
" num_epochs = 30\n",
" model_path = os.path.join(\"files\", \"attention_Unet_model.h5\")\n",
" csv_path = os.path.join(\"files\", \"attention_UNET_data.csv\")\n",
"\n",
" \"\"\" Dataset \"\"\"\n",
" dataset_path = \"/content/archive\"\n",
" (train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_dataset(dataset_path)\n",
"\n",
" print(f\"Train: {len(train_x)} - {len(train_y)}\")\n",
" print(f\"Valid: {len(valid_x)} - {len(valid_y)}\")\n",
" print(f\"Test : {len(test_x)} - {len(test_y)}\")\n",
" \n",
"\n",
" train_dataset = tf_dataset(train_x, train_y, batch=batch_size)\n",
" valid_dataset = tf_dataset(valid_x, valid_y, batch=batch_size)\n",
"\n",
" model_att_unet = models.att_unet_2d((256, 256, 3), filter_num=[64, 128, 256, 512, 1024], \n",
" n_labels=1, \n",
" stack_num_down=2, stack_num_up=2, \n",
" activation='ReLU', \n",
" atten_activation='ReLU', attention='add', \n",
" output_activation='Sigmoid', \n",
" batch_norm=True, pool=False, unpool=False, \n",
" backbone='VGG19', weights='imagenet', # VGG16,ResNet50,VGG19,ResNet101,ResNet152,ResNet50V2, ResNet101V2, ResNet152V2\n",
" freeze_backbone=True, freeze_batch_norm=True, #DenseNet121, DenseNet169, DenseNet201,EfficientNetB[0,7]\n",
" name='attunet')\n",
"\n",
" model_att_unet.compile(loss='binary_crossentropy', optimizer=Adam(lr = 1e-3), \n",
" metrics=['accuracy', losses.dice_coef,iou])\n",
"\n",
" print(model_att_unet.summary())\n",
" "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hFnBk3OkcyoF",
"outputId": "a63655f4-b29f-4c12-fc07-203d2a90ea2f"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train: 1840 - 1840\n",
"Valid: 612 - 612\n",
"Test : 612 - 612\n",
"Model: \"attunet_model\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_10 (InputLayer) [(None, 256, 256, 3 0 [] \n",
" )] \n",
" \n",
" VGG19_backbone (Functional) [(None, 256, 256, 6 20024384 ['input_10[0][0]'] \n",
" 4), \n",
" (None, 128, 128, 1 \n",
" 28), \n",
" (None, 64, 64, 256 \n",
" ), \n",
" (None, 32, 32, 512 \n",
" ), \n",
" (None, 16, 16, 512 \n",
" )] \n",
" \n",
" attunet_up0_decode_trans_conv (None, 32, 32, 512) 2359808 ['VGG19_backbone[0][4]'] \n",
" (Conv2DTranspose) \n",
" \n",
" attunet_up0_decode_bn (BatchNo (None, 32, 32, 512) 2048 ['attunet_up0_decode_trans_conv[0\n",
" rmalization) ][0]'] \n",
" \n",
" attunet_up0_decode_activation (None, 32, 32, 512) 0 ['attunet_up0_decode_bn[0][0]'] \n",
" (ReLU) \n",
" \n",
" attunet_up0_att_theta_x (Conv2 (None, 32, 32, 256) 131328 ['VGG19_backbone[0][3]'] \n",
" D) \n",
" \n",
" attunet_up0_att_phi_g (Conv2D) (None, 32, 32, 256) 131328 ['attunet_up0_decode_activation[0\n",
" ][0]'] \n",
" \n",
" attunet_up0_att_add (Add) (None, 32, 32, 256) 0 ['attunet_up0_att_theta_x[0][0]',\n",
" 'attunet_up0_att_phi_g[0][0]'] \n",
" \n",
" attunet_up0_att_activation (Re (None, 32, 32, 256) 0 ['attunet_up0_att_add[0][0]'] \n",
" LU) \n",
" \n",
" attunet_up0_att_psi_f (Conv2D) (None, 32, 32, 1) 257 ['attunet_up0_att_activation[0][0\n",
" ]'] \n",
" \n",
" attunet_up0_att_sigmoid (Activ (None, 32, 32, 1) 0 ['attunet_up0_att_psi_f[0][0]'] \n",
" ation) \n",
" \n",
" attunet_up0_att_masking (Multi (None, 32, 32, 512) 0 ['VGG19_backbone[0][3]', \n",
" ply) 'attunet_up0_att_sigmoid[0][0]']\n",
" \n",
" attunet_up0_concat (Concatenat (None, 32, 32, 1024 0 ['attunet_up0_decode_activation[0\n",
" e) ) ][0]', \n",
" 'attunet_up0_att_masking[0][0]']\n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 4718592 ['attunet_up0_concat[0][0]'] \n",
" 0 (Conv2D) \n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 2048 ['attunet_up0_conv_after_concat_0\n",
" 0_bn (BatchNormalization) [0][0]'] \n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 0 ['attunet_up0_conv_after_concat_0\n",
" 0_activation (ReLU) _bn[0][0]'] \n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 2359296 ['attunet_up0_conv_after_concat_0\n",
" 1 (Conv2D) _activation[0][0]'] \n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 2048 ['attunet_up0_conv_after_concat_1\n",
" 1_bn (BatchNormalization) [0][0]'] \n",
" \n",
" attunet_up0_conv_after_concat_ (None, 32, 32, 512) 0 ['attunet_up0_conv_after_concat_1\n",
" 1_activation (ReLU) _bn[0][0]'] \n",
" \n",
" attunet_up1_decode_trans_conv (None, 64, 64, 256) 1179904 ['attunet_up0_conv_after_concat_1\n",
" (Conv2DTranspose) _activation[0][0]'] \n",
" \n",
" attunet_up1_decode_bn (BatchNo (None, 64, 64, 256) 1024 ['attunet_up1_decode_trans_conv[0\n",
" rmalization) ][0]'] \n",
" \n",
" attunet_up1_decode_activation (None, 64, 64, 256) 0 ['attunet_up1_decode_bn[0][0]'] \n",
" (ReLU) \n",
" \n",
" attunet_up1_att_theta_x (Conv2 (None, 64, 64, 128) 32896 ['VGG19_backbone[0][2]'] \n",
" D) \n",
" \n",
" attunet_up1_att_phi_g (Conv2D) (None, 64, 64, 128) 32896 ['attunet_up1_decode_activation[0\n",
" ][0]'] \n",
" \n",
" attunet_up1_att_add (Add) (None, 64, 64, 128) 0 ['attunet_up1_att_theta_x[0][0]',\n",
" 'attunet_up1_att_phi_g[0][0]'] \n",
" \n",
" attunet_up1_att_activation (Re (None, 64, 64, 128) 0 ['attunet_up1_att_add[0][0]'] \n",
" LU) \n",
" \n",
" attunet_up1_att_psi_f (Conv2D) (None, 64, 64, 1) 129 ['attunet_up1_att_activation[0][0\n",
" ]'] \n",
" \n",
" attunet_up1_att_sigmoid (Activ (None, 64, 64, 1) 0 ['attunet_up1_att_psi_f[0][0]'] \n",
" ation) \n",
" \n",
" attunet_up1_att_masking (Multi (None, 64, 64, 256) 0 ['VGG19_backbone[0][2]', \n",
" ply) 'attunet_up1_att_sigmoid[0][0]']\n",
" \n",
" attunet_up1_concat (Concatenat (None, 64, 64, 512) 0 ['attunet_up1_decode_activation[0\n",
" e) ][0]', \n",
" 'attunet_up1_att_masking[0][0]']\n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 1179648 ['attunet_up1_concat[0][0]'] \n",
" 0 (Conv2D) \n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 1024 ['attunet_up1_conv_after_concat_0\n",
" 0_bn (BatchNormalization) [0][0]'] \n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 0 ['attunet_up1_conv_after_concat_0\n",
" 0_activation (ReLU) _bn[0][0]'] \n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 589824 ['attunet_up1_conv_after_concat_0\n",
" 1 (Conv2D) _activation[0][0]'] \n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 1024 ['attunet_up1_conv_after_concat_1\n",
" 1_bn (BatchNormalization) [0][0]'] \n",
" \n",
" attunet_up1_conv_after_concat_ (None, 64, 64, 256) 0 ['attunet_up1_conv_after_concat_1\n",
" 1_activation (ReLU) _bn[0][0]'] \n",
" \n",
" attunet_up2_decode_trans_conv (None, 128, 128, 12 295040 ['attunet_up1_conv_after_concat_1\n",
" (Conv2DTranspose) 8) _activation[0][0]'] \n",
" \n",
" attunet_up2_decode_bn (BatchNo (None, 128, 128, 12 512 ['attunet_up2_decode_trans_conv[0\n",
" rmalization) 8) ][0]'] \n",
" \n",
" attunet_up2_decode_activation (None, 128, 128, 12 0 ['attunet_up2_decode_bn[0][0]'] \n",
" (ReLU) 8) \n",
" \n",
" attunet_up2_att_theta_x (Conv2 (None, 128, 128, 64 8256 ['VGG19_backbone[0][1]'] \n",
" D) ) \n",
" \n",
" attunet_up2_att_phi_g (Conv2D) (None, 128, 128, 64 8256 ['attunet_up2_decode_activation[0\n",
" ) ][0]'] \n",
" \n",
" attunet_up2_att_add (Add) (None, 128, 128, 64 0 ['attunet_up2_att_theta_x[0][0]',\n",
" ) 'attunet_up2_att_phi_g[0][0]'] \n",
" \n",
" attunet_up2_att_activation (Re (None, 128, 128, 64 0 ['attunet_up2_att_add[0][0]'] \n",
" LU) ) \n",
" \n",
" attunet_up2_att_psi_f (Conv2D) (None, 128, 128, 1) 65 ['attunet_up2_att_activation[0][0\n",
" ]'] \n",
" \n",
" attunet_up2_att_sigmoid (Activ (None, 128, 128, 1) 0 ['attunet_up2_att_psi_f[0][0]'] \n",
" ation) \n",
" \n",
" attunet_up2_att_masking (Multi (None, 128, 128, 12 0 ['VGG19_backbone[0][1]', \n",
" ply) 8) 'attunet_up2_att_sigmoid[0][0]']\n",
" \n",
" attunet_up2_concat (Concatenat (None, 128, 128, 25 0 ['attunet_up2_decode_activation[0\n",
" e) 6) ][0]', \n",
" 'attunet_up2_att_masking[0][0]']\n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 294912 ['attunet_up2_concat[0][0]'] \n",
" 0 (Conv2D) 8) \n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 512 ['attunet_up2_conv_after_concat_0\n",
" 0_bn (BatchNormalization) 8) [0][0]'] \n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 0 ['attunet_up2_conv_after_concat_0\n",
" 0_activation (ReLU) 8) _bn[0][0]'] \n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 147456 ['attunet_up2_conv_after_concat_0\n",
" 1 (Conv2D) 8) _activation[0][0]'] \n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 512 ['attunet_up2_conv_after_concat_1\n",
" 1_bn (BatchNormalization) 8) [0][0]'] \n",
" \n",
" attunet_up2_conv_after_concat_ (None, 128, 128, 12 0 ['attunet_up2_conv_after_concat_1\n",
" 1_activation (ReLU) 8) _bn[0][0]'] \n",
" \n",
" attunet_up3_decode_trans_conv (None, 256, 256, 64 73792 ['attunet_up2_conv_after_concat_1\n",
" (Conv2DTranspose) ) _activation[0][0]'] \n",
" \n",
" attunet_up3_decode_bn (BatchNo (None, 256, 256, 64 256 ['attunet_up3_decode_trans_conv[0\n",
" rmalization) ) ][0]'] \n",
" \n",
" attunet_up3_decode_activation (None, 256, 256, 64 0 ['attunet_up3_decode_bn[0][0]'] \n",
" (ReLU) ) \n",
" \n",
" attunet_up3_att_theta_x (Conv2 (None, 256, 256, 32 2080 ['VGG19_backbone[0][0]'] \n",
" D) ) \n",
" \n",
" attunet_up3_att_phi_g (Conv2D) (None, 256, 256, 32 2080 ['attunet_up3_decode_activation[0\n",
" ) ][0]'] \n",
" \n",
" attunet_up3_att_add (Add) (None, 256, 256, 32 0 ['attunet_up3_att_theta_x[0][0]',\n",
" ) 'attunet_up3_att_phi_g[0][0]'] \n",
" \n",
" attunet_up3_att_activation (Re (None, 256, 256, 32 0 ['attunet_up3_att_add[0][0]'] \n",
" LU) ) \n",
" \n",
" attunet_up3_att_psi_f (Conv2D) (None, 256, 256, 1) 33 ['attunet_up3_att_activation[0][0\n",
" ]'] \n",
" \n",
" attunet_up3_att_sigmoid (Activ (None, 256, 256, 1) 0 ['attunet_up3_att_psi_f[0][0]'] \n",
" ation) \n",
" \n",
" attunet_up3_att_masking (Multi (None, 256, 256, 64 0 ['VGG19_backbone[0][0]', \n",
" ply) ) 'attunet_up3_att_sigmoid[0][0]']\n",
" \n",
" attunet_up3_concat (Concatenat (None, 256, 256, 12 0 ['attunet_up3_decode_activation[0\n",
" e) 8) ][0]', \n",
" 'attunet_up3_att_masking[0][0]']\n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 73728 ['attunet_up3_concat[0][0]'] \n",
" 0 (Conv2D) ) \n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 256 ['attunet_up3_conv_after_concat_0\n",
" 0_bn (BatchNormalization) ) [0][0]'] \n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 0 ['attunet_up3_conv_after_concat_0\n",
" 0_activation (ReLU) ) _bn[0][0]'] \n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 36864 ['attunet_up3_conv_after_concat_0\n",
" 1 (Conv2D) ) _activation[0][0]'] \n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 256 ['attunet_up3_conv_after_concat_1\n",
" 1_bn (BatchNormalization) ) [0][0]'] \n",
" \n",
" attunet_up3_conv_after_concat_ (None, 256, 256, 64 0 ['attunet_up3_conv_after_concat_1\n",
" 1_activation (ReLU) ) _bn[0][0]'] \n",
" \n",
" attunet_output (Conv2D) (None, 256, 256, 1) 65 ['attunet_up3_conv_after_concat_1\n",
" _activation[0][0]'] \n",
" \n",
" attunet_output_activation (Act (None, 256, 256, 1) 0 ['attunet_output[0][0]'] \n",
" ivation) \n",
" \n",
"==================================================================================================\n",
"Total params: 33,694,437\n",
"Trainable params: 13,664,293\n",
"Non-trainable params: 20,030,144\n",
"__________________________________________________________________________________________________\n",
"None\n"
]
}
]
},
{
"cell_type": "code",
"source": [
" callbacks = [\n",
" ModelCheckpoint(model_path, verbose=1, save_best_only=True),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=1e-7, verbose=1),\n",
" CSVLogger(csv_path),\n",
" EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=False),\n",
" ]\n",
"\n",
" model_att_unet.fit(\n",
" train_dataset,\n",
" epochs=num_epochs,\n",
" validation_data=valid_dataset,\n",
" callbacks=callbacks\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sLvBStPSc_eg",
"outputId": "670e17ba-ef4e-40dc-e57f-c6dcc967ed2b"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.1747 - accuracy: 0.9559 - dice_coef: 0.0694 - iou: 0.0365\n",
"Epoch 1: val_loss improved from inf to 0.26010, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 122s 957ms/step - loss: 0.1747 - accuracy: 0.9559 - dice_coef: 0.0694 - iou: 0.0365 - val_loss: 0.2601 - val_accuracy: 0.9817 - val_dice_coef: 0.0562 - val_iou: 0.0289 - lr: 0.0010\n",
"Epoch 2/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0549 - accuracy: 0.9871 - dice_coef: 0.2623 - iou: 0.1530\n",
"Epoch 2: val_loss improved from 0.26010 to 0.07722, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 926ms/step - loss: 0.0549 - accuracy: 0.9871 - dice_coef: 0.2623 - iou: 0.1530 - val_loss: 0.0772 - val_accuracy: 0.9827 - val_dice_coef: 0.2532 - val_iou: 0.1455 - lr: 0.0010\n",
"Epoch 3/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0386 - accuracy: 0.9888 - dice_coef: 0.4083 - iou: 0.2587\n",
"Epoch 3: val_loss improved from 0.07722 to 0.03906, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 924ms/step - loss: 0.0386 - accuracy: 0.9888 - dice_coef: 0.4083 - iou: 0.2587 - val_loss: 0.0391 - val_accuracy: 0.9889 - val_dice_coef: 0.4419 - val_iou: 0.2850 - lr: 0.0010\n",
"Epoch 4/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0304 - accuracy: 0.9903 - dice_coef: 0.5100 - iou: 0.3441\n",
"Epoch 4: val_loss improved from 0.03906 to 0.03536, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 925ms/step - loss: 0.0304 - accuracy: 0.9903 - dice_coef: 0.5100 - iou: 0.3441 - val_loss: 0.0354 - val_accuracy: 0.9896 - val_dice_coef: 0.5236 - val_iou: 0.3566 - lr: 0.0010\n",
"Epoch 5/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0257 - accuracy: 0.9913 - dice_coef: 0.5771 - iou: 0.4077\n",
"Epoch 5: val_loss did not improve from 0.03536\n",
"115/115 [==============================] - 106s 921ms/step - loss: 0.0257 - accuracy: 0.9913 - dice_coef: 0.5771 - iou: 0.4077 - val_loss: 0.0363 - val_accuracy: 0.9898 - val_dice_coef: 0.5519 - val_iou: 0.3829 - lr: 0.0010\n",
"Epoch 6/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0220 - accuracy: 0.9922 - dice_coef: 0.6315 - iou: 0.4632\n",
"Epoch 6: val_loss improved from 0.03536 to 0.03408, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 925ms/step - loss: 0.0220 - accuracy: 0.9922 - dice_coef: 0.6315 - iou: 0.4632 - val_loss: 0.0341 - val_accuracy: 0.9900 - val_dice_coef: 0.5711 - val_iou: 0.4015 - lr: 0.0010\n",
"Epoch 7/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0197 - accuracy: 0.9928 - dice_coef: 0.6685 - iou: 0.5036\n",
"Epoch 7: val_loss improved from 0.03408 to 0.02819, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 107s 928ms/step - loss: 0.0197 - accuracy: 0.9928 - dice_coef: 0.6685 - iou: 0.5036 - val_loss: 0.0282 - val_accuracy: 0.9910 - val_dice_coef: 0.6524 - val_iou: 0.4858 - lr: 0.0010\n",
"Epoch 8/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0179 - accuracy: 0.9933 - dice_coef: 0.6989 - iou: 0.5385\n",
"Epoch 8: val_loss did not improve from 0.02819\n",
"115/115 [==============================] - 106s 921ms/step - loss: 0.0179 - accuracy: 0.9933 - dice_coef: 0.6989 - iou: 0.5385 - val_loss: 0.0319 - val_accuracy: 0.9907 - val_dice_coef: 0.6474 - val_iou: 0.4807 - lr: 0.0010\n",
"Epoch 9/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0161 - accuracy: 0.9939 - dice_coef: 0.7291 - iou: 0.5753\n",
"Epoch 9: val_loss improved from 0.02819 to 0.02699, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 926ms/step - loss: 0.0161 - accuracy: 0.9939 - dice_coef: 0.7291 - iou: 0.5753 - val_loss: 0.0270 - val_accuracy: 0.9910 - val_dice_coef: 0.6601 - val_iou: 0.4942 - lr: 0.0010\n",
"Epoch 10/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0148 - accuracy: 0.9942 - dice_coef: 0.7507 - iou: 0.6025\n",
"Epoch 10: val_loss did not improve from 0.02699\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0148 - accuracy: 0.9942 - dice_coef: 0.7507 - iou: 0.6025 - val_loss: 0.0295 - val_accuracy: 0.9911 - val_dice_coef: 0.6581 - val_iou: 0.4924 - lr: 0.0010\n",
"Epoch 11/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0138 - accuracy: 0.9945 - dice_coef: 0.7672 - iou: 0.6238\n",
"Epoch 11: val_loss did not improve from 0.02699\n",
"115/115 [==============================] - 105s 918ms/step - loss: 0.0138 - accuracy: 0.9945 - dice_coef: 0.7672 - iou: 0.6238 - val_loss: 0.0393 - val_accuracy: 0.9893 - val_dice_coef: 0.5682 - val_iou: 0.3994 - lr: 0.0010\n",
"Epoch 12/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0126 - accuracy: 0.9948 - dice_coef: 0.7861 - iou: 0.6488\n",
"Epoch 12: val_loss did not improve from 0.02699\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0126 - accuracy: 0.9948 - dice_coef: 0.7861 - iou: 0.6488 - val_loss: 0.0377 - val_accuracy: 0.9901 - val_dice_coef: 0.6221 - val_iou: 0.4542 - lr: 0.0010\n",
"Epoch 13/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0120 - accuracy: 0.9950 - dice_coef: 0.7940 - iou: 0.6595\n",
"Epoch 13: val_loss did not improve from 0.02699\n",
"115/115 [==============================] - 106s 921ms/step - loss: 0.0120 - accuracy: 0.9950 - dice_coef: 0.7940 - iou: 0.6595 - val_loss: 0.0399 - val_accuracy: 0.9907 - val_dice_coef: 0.6638 - val_iou: 0.4993 - lr: 0.0010\n",
"Epoch 14/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9951 - dice_coef: 0.8034 - iou: 0.6728\n",
"Epoch 14: val_loss did not improve from 0.02699\n",
"\n",
"Epoch 14: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0113 - accuracy: 0.9951 - dice_coef: 0.8034 - iou: 0.6728 - val_loss: 0.0319 - val_accuracy: 0.9907 - val_dice_coef: 0.6689 - val_iou: 0.5042 - lr: 0.0010\n",
"Epoch 15/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0098 - accuracy: 0.9957 - dice_coef: 0.8283 - iou: 0.7084\n",
"Epoch 15: val_loss improved from 0.02699 to 0.02384, saving model to files/attention_Unet_model.h5\n",
"115/115 [==============================] - 106s 926ms/step - loss: 0.0098 - accuracy: 0.9957 - dice_coef: 0.8283 - iou: 0.7084 - val_loss: 0.0238 - val_accuracy: 0.9924 - val_dice_coef: 0.7303 - val_iou: 0.5770 - lr: 1.0000e-04\n",
"Epoch 16/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0082 - accuracy: 0.9963 - dice_coef: 0.8527 - iou: 0.7437\n",
"Epoch 16: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0082 - accuracy: 0.9963 - dice_coef: 0.8527 - iou: 0.7437 - val_loss: 0.0253 - val_accuracy: 0.9924 - val_dice_coef: 0.7302 - val_iou: 0.5771 - lr: 1.0000e-04\n",
"Epoch 17/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0077 - accuracy: 0.9964 - dice_coef: 0.8603 - iou: 0.7554\n",
"Epoch 17: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0077 - accuracy: 0.9964 - dice_coef: 0.8603 - iou: 0.7554 - val_loss: 0.0260 - val_accuracy: 0.9924 - val_dice_coef: 0.7322 - val_iou: 0.5797 - lr: 1.0000e-04\n",
"Epoch 18/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0074 - accuracy: 0.9966 - dice_coef: 0.8658 - iou: 0.7638\n",
"Epoch 18: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0074 - accuracy: 0.9966 - dice_coef: 0.8658 - iou: 0.7638 - val_loss: 0.0267 - val_accuracy: 0.9924 - val_dice_coef: 0.7341 - val_iou: 0.5821 - lr: 1.0000e-04\n",
"Epoch 19/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0071 - accuracy: 0.9967 - dice_coef: 0.8705 - iou: 0.7711\n",
"Epoch 19: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0071 - accuracy: 0.9967 - dice_coef: 0.8705 - iou: 0.7711 - val_loss: 0.0273 - val_accuracy: 0.9924 - val_dice_coef: 0.7355 - val_iou: 0.5839 - lr: 1.0000e-04\n",
"Epoch 20/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0069 - accuracy: 0.9968 - dice_coef: 0.8747 - iou: 0.7777\n",
"Epoch 20: val_loss did not improve from 0.02384\n",
"\n",
"Epoch 20: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0069 - accuracy: 0.9968 - dice_coef: 0.8747 - iou: 0.7777 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7367 - val_iou: 0.5855 - lr: 1.0000e-04\n",
"Epoch 21/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0066 - accuracy: 0.9969 - dice_coef: 0.8804 - iou: 0.7866\n",
"Epoch 21: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0066 - accuracy: 0.9969 - dice_coef: 0.8804 - iou: 0.7866 - val_loss: 0.0274 - val_accuracy: 0.9924 - val_dice_coef: 0.7412 - val_iou: 0.5911 - lr: 1.0000e-05\n",
"Epoch 22/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8809 - iou: 0.7874\n",
"Epoch 22: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8809 - iou: 0.7874 - val_loss: 0.0276 - val_accuracy: 0.9924 - val_dice_coef: 0.7417 - val_iou: 0.5917 - lr: 1.0000e-05\n",
"Epoch 23/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8813 - iou: 0.7880\n",
"Epoch 23: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8813 - iou: 0.7880 - val_loss: 0.0278 - val_accuracy: 0.9924 - val_dice_coef: 0.7417 - val_iou: 0.5917 - lr: 1.0000e-05\n",
"Epoch 24/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8817 - iou: 0.7887\n",
"Epoch 24: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0065 - accuracy: 0.9969 - dice_coef: 0.8817 - iou: 0.7887 - val_loss: 0.0279 - val_accuracy: 0.9924 - val_dice_coef: 0.7417 - val_iou: 0.5917 - lr: 1.0000e-05\n",
"Epoch 25/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8822 - iou: 0.7895\n",
"Epoch 25: val_loss did not improve from 0.02384\n",
"\n",
"Epoch 25: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8822 - iou: 0.7895 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7417 - val_iou: 0.5917 - lr: 1.0000e-05\n",
"Epoch 26/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7909\n",
"Epoch 26: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 920ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7909 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7420 - val_iou: 0.5921 - lr: 1.0000e-06\n",
"Epoch 27/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7909\n",
"Epoch 27: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 105s 918ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7909 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7421 - val_iou: 0.5922 - lr: 1.0000e-06\n",
"Epoch 28/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7910\n",
"Epoch 28: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8831 - iou: 0.7910 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7421 - val_iou: 0.5923 - lr: 1.0000e-06\n",
"Epoch 29/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8832 - iou: 0.7910\n",
"Epoch 29: val_loss did not improve from 0.02384\n",
"115/115 [==============================] - 114s 988ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8832 - iou: 0.7910 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7421 - val_iou: 0.5922 - lr: 1.0000e-06\n",
"Epoch 30/30\n",
"115/115 [==============================] - ETA: 0s - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8832 - iou: 0.7911\n",
"Epoch 30: val_loss did not improve from 0.02384\n",
"\n",
"Epoch 30: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.\n",
"115/115 [==============================] - 106s 919ms/step - loss: 0.0064 - accuracy: 0.9969 - dice_coef: 0.8832 - iou: 0.7911 - val_loss: 0.0280 - val_accuracy: 0.9924 - val_dice_coef: 0.7421 - val_iou: 0.5922 - lr: 1.0000e-06\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fe1c65261c0>"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
"import numpy as np\n",
"import cv2\n",
"from glob import glob\n",
"from tqdm import tqdm\n",
"import tensorflow as tf\n",
"from tensorflow.keras.utils import CustomObjectScope\n",
" \n",
"if __name__ == \"__main__\":\n",
" \n",
"\n",
" model = tf.keras.models.load_model(\"/content/files/attention_Unet_model.h5\",compile=False)\n",
" create_dir(\"output\")\n",
" i=0\n",
" for x, y in tqdm(zip(test_x,test_y), total=len(test_x)):\n",
"\n",
" \"\"\" Reading the image \"\"\"\n",
" img = cv2.imread(x, cv2.IMREAD_COLOR)\n",
" img = cv2.resize(img, (256, 256))\n",
" x = img/255.0\n",
" x = x.astype(np.float32)\n",
" x = np.expand_dims(x, axis=0)\n",
"\n",
" \"\"\" Reading the mask \"\"\"\n",
" img_y = cv2.imread(y, cv2.IMREAD_GRAYSCALE)\n",
" img_y = cv2.resize(img_y, (256, 256))\n",
" img_y = np.expand_dims(img_y, axis=-1) \n",
" img_y = np.concatenate([img_y, img_y, img_y], axis=-1) \n",
"\n",
" \"\"\" Predicting the mask. \"\"\"\n",
" y_pred = model.predict(x)[0] > 0.5\n",
" y_pred = y_pred.astype(np.int32)\n",
"\n",
" \"\"\" Saving the predicted mask along with the image and GT \"\"\"\n",
" y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)\n",
"\n",
" sep_line = np.ones((256, 10, 3)) * 255\n",
" output_image = np.concatenate([img, sep_line, img_y, sep_line, y_pred*255], axis=1)\n",
" cv2.imwrite(f\"/content/output/{i}.png\", output_image)\n",
" i=i+1\n",
" "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FYqyD78NMcfq",
"outputId": "2660b122-ae01-482e-dd4c-2b976c6121fd"
},
"execution_count": 21,
"outputs": [
{
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{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 97%|█████████▋| 594/612 [01:11<00:01, 9.10it/s]"
]
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"output_type": "stream",
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"text": [
"1/1 [==============================] - 0s 26ms/step\n",
"1/1 [==============================] - 0s 21ms/step\n"
]
},
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"1/1 [==============================] - 0s 22ms/step\n"
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]
},
{
"cell_type": "code",
"source": [
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"\n",
"x=cv2.imread(\"/content/output/1.png\")\n",
"x=cv2.cvtColor(x, cv2.COLOR_RGB2BGR)\n",
"plt.imshow(x)\n",
" "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177
},
"id": "Ee-lWjxLhT2O",
"outputId": "89606a9c-3f85-4031-b6ea-15d3e96a36df"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fe225480a00>"
]
},
"metadata": {},
"execution_count": 22
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"x=pd.read_csv(\"/content/files/attention_UNET_data.csv\")\n",
"epoch=x.iloc[:,0:1].values\n",
"regular_iou=x.iloc[:,3:4].values\n",
"val_iou=x.iloc[:,8:9].values \n",
"plt.plot(epoch,regular_iou,label='regular iou')\n",
"plt.plot(epoch,val_iou,label='validation iou')\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"id": "WmkuXegChyn0",
"outputId": "fac36683-21a1-4c25-8f93-d200166ec3ee"
},
"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "lSgs-co6iS2z"
},
"execution_count": null,
"outputs": []
}
]
}