def decode_predictions(*args, **kwargs):
return resnet.decode_predictions(*args, **kwargs)
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)