def decode_predictions(*args, **kwargs): return resnet.decode_predictions(*args, **kwargs)
from keras_applications.resnet import ResNet152, preprocess_input, decode_predictions import keras from keras.preprocessing import image import numpy as np import os from datetime import datetime imgs = [] basepath = 'input/Inference/images' img_paths = os.listdir(basepath) for path in img_paths: img_path = basepath + '/' + path img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) imgs.append(x) x = np.stack(imgs) x = preprocess_input(x) model = ResNet50(weights='imagenet') start = datetime.now() preds = model.predict(x) end = datetime.now() decoded = decode_predictions(preds, top=1) print('Inference start time:', str(start)) print('Inference end time:', str(end)) print('Inference delta time:', str(end - start)) for pred in decoded: print('Predicted:', pred)