A week ago, my Notebook in Google Colaboratory was working fine after installing the following libraries:
!pip install te !pip install tensorflow==2.1 !pip install keras==2.3.1 !pip install -U segmentation-models !pip install -U --pre segmentation-models and
import tensorflow as tf import segmentation_models as sm import glob import cv2 import numpy as np from matplotlib import pyplot as plt import keras from keras import normalize from keras.metrics import MeanIoU It worked:
# set class weights for dice_loss (car: 1.; pedestrian: 2.; background: 0.5;) dice_loss = sm.losses.DiceLoss(class_weights=np.array([0.25, 0.25, 0.25, 0.25])) focal_loss = sm.losses.CategoricalFocalLoss() total_loss = dice_loss + (1 * focal_loss) metrics = [sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5)] BACKBONE1 = 'resnet34' preprocess_input1 = sm.get_preprocessing(BACKBONE1) # preprocess input X_train1 = preprocess_input1(X_train) X_test1 = preprocess_input1(X_test) # define model model1 = sm.Unet(BACKBONE1, encoder_weights='imagenet', classes=n_classes, activation=activation) Then, due to an error, I made changes:
!pip install -q tensorflow==2.1 !pip install -q keras==2.3.1 !pip install -q tensorflow-estimator==2.1 import os os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ["SM_FRAMEWORK"] = "tf.keras" from tensorflow import keras from tensorflow.keras.utils import normalize from tensorflow.keras.metrics import MeanIoU After that, this part does not work:
# define model model1 = sm.Unet(BACKBONE1, encoder_weights='imagenet', classes=n_classes, activation=activation) Error:
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/saving/hdf5_format.py in load_weights_from_hdf5_group(f, layers) 649 """ 650 if 'keras_version' in f.attrs: --> 651 original_keras_version = f.attrs['keras_version'].decode('utf8') 652 else: 653 original_keras_version = '1'
AttributeError: 'str' object has no attribute 'decode'
Problem with loading scale values. But I don't know how to fix it
