NameError: name 'get_prepocessing' is not defined

I can’t run the sample code from https://segmentation-models.readthedocs.io/en/latest/tutorial.html, the error information is just like the title. I only alter the path of input like this,

x_train, y_train, x_val, y_val = load_data("/Volumes/NT/code/data/dataset/train/src",
                                            "/Volumes/NT/code/data/dataset/train/label",
                                            "/Volumes/NT/code/data/dataset/test/src",
                                            "/Volumes/NT/code/data/dataset/test/label")

the whole error is this: UserWarning: Current ResNext models are deprecated, use keras.applications ResNeXt models warnings.warn('Current ResNext models are deprecated, ’ Traceback (most recent call last): File “/Users/zhangsanhuo/Desktop/code/test.py”, line 7, in <module> preprocess_input = get_prepocessing(BACKBONE) NameError: name ‘get_prepocessing’ is not defined waiting for your reply,thanks.

[Diyago]

You can start with smth like this:

import os
import numpy as np
from skimage.io import imread
from skimage import transform
from cv2 import resize
from keras.preprocessing.image import load_img

data_dir = './data_objects'

def load_data(root_dir):
    data = []

    for stage in ['train']:
        directory = os.path.join(root_dir,  'images')
        file_names = [filename.replace('jpg' , 'png') for filename in os.listdir(directory)]
        fps = [os.path.join(directory, filename) for filename in os.listdir(directory)]
        for content in ['images', 'segmentation']:
            # construct path to each image
            directory = os.path.join(root_dir,  content)
            
            if content != 'images':
                fps = [os.path.join(directory, filename) for filename in file_names]
                
            # read images
            images = [imread(filepath)/255 for filepath in fps]
 
            # if images have different sizes you have to resize them before:
            resized_images = [resize(image, (256, 256)) for image in images]
            
            # stack to one np.array 
            np_images = np.stack(resized_images, axis=0)
            
            data.append(np_images)
            
    return data
x_train, y_train  = load_data(data_dir)

[zthreefires]

You can start with smth like this:

import os
import numpy as np
from skimage.io import imread
from skimage import transform
from cv2 import resize
from keras.preprocessing.image import load_img

data_dir = './data_objects'

def load_data(root_dir):
    data = []

    for stage in ['train']:
        directory = os.path.join(root_dir,  'images')
        file_names = [filename.replace('jpg' , 'png') for filename in os.listdir(directory)]
        fps = [os.path.join(directory, filename) for filename in os.listdir(directory)]
        for content in ['images', 'segmentation']:
            # construct path to each image
            directory = os.path.join(root_dir,  content)
            
            if content != 'images':
                fps = [os.path.join(directory, filename) for filename in file_names]
                
            # read images
            images = [imread(filepath)/255 for filepath in fps]
 
            # if images have different sizes you have to resize them before:
            resized_images = [resize(image, (256, 256)) for image in images]
            
            # stack to one np.array 
            np_images = np.stack(resized_images, axis=0)
            
            data.append(np_images)
            
    return data
x_train, y_train  = load_data(data_dir)

thank you very much!!!