test.py

import os
import numpy as np
from skimage.io import imread
from skimage.transform import resize
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from model import ResNet152

os.environ['CUDA_VISIBLE_DEVICES'] = "-1"

def preprocess(x):
    x = resize(x, (224,224), mode='constant') * 255
    x = preprocess_input(x)
    if x.ndim == 3:
        x = np.expand_dims(x, 0)
    return x

if __name__ == '__main__':

    print('loading ResNet-152 model...', end='')
    model = ResNet152()
    print('done!')

    print('Predicting image with "cat" ...')
    cat = imread('./imgs/cat.jpg')
    x = preprocess(cat)
    y = model.predict(x)
    pred_title = decode_predictions(y, top=1)[0][0][1]
    print('Model prediction: {}\n'.format(pred_title))
    assert pred_title == 'tiger_cat'

    print('Predicting image with "dog" ...')
    dog = imread('./imgs/dog.jpg')
    x = preprocess(dog)
    y = model.predict(x)
    pred_title = decode_predictions(y, top=1)[0][0][1]
    print('Model prediction: {}\n'.format(pred_title))
    assert pred_title == 'Eskimo_dog'
    print ('Success!')