tfserve_vision22.py

# create serving function
INPUT_SHAPE_RN = (32, 32, 3)
model2 = create_cnn_architecture_model2(input_shape=INPUT_SHAPE_RN)
model2.load_weights('./model_weights/cnn_model2_wt.h5')

def predict_apparel_model2_regular(img, img_dims=(32,32), label_map=class_names):
    
    sample_img_processed = (np.array([resize_image_array(img, 
                                                         img_size_dims=img_dims) 
                                                for img in np.stack([[img]]*3, 
                                                                    axis=-1)])) / 255.
    prediction = model2.predict(sample_img_processed)
    prediction = np.argmax(np.array(prediction), axis=1)[0]
    return label_map[prediction]
    
# benchmark 10K requests
%%time

pred_labels = []
for img in tqdm(test_images):
    pred_label = predict_apparel_model2_regular(img)
    pred_labels.append(img)
len(pred_labels)