python - 为什么在 Keras 中使用多处理时推理时间会变慢？

Question

我想要几个进程，每个进程一次加载一个不同的图像并执行推理（例如 VGG16）。

我正在使用带有 tensorFlow 后端的 Keras，一个 GPU（GTX 1070）。以下是代码：

import tensorflow as tf
import multiprocessing
from multiprocessing import Pool, Process, Queue
import os
from os.path import isfile, join
from PIL import Image
import time
from keras.applications.vgg16 import VGG16
import numpy as np
from keras.backend.tensorflow_backend import set_session



test_path = 'test path to images ...'
output = Queue()

def worker(file_names, output):
    config = tf.ConfigProto()
    config.gpu_options.per_process_gpu_memory_fraction = 0.25
    config.gpu_options.visible_device_list = "0"
    set_session(tf.Session(config=config))
    inference_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling='avg')
    model_image_size = (224,224)
    times = []

    for file_name in file_names:
        image = Image.open(os.path.join(test_path, file_name))
        im_width = image.size[0]
        im_height = image.size[1]
        m = (im_width - im_height) // 2
        image = image.crop((m, 0, im_width - m, im_height))
        image = image.resize((model_image_size), Image.BICUBIC)
        image = np.array(image, dtype='float32')
        image /= 255.
        image = np.expand_dims(image, 0)  # Add batch dimension.
        start = time.time()
        res = inference_model.predict(image)
        end = time.time()
        elapsed_time = end - start
        print("elapsed time", elapsed_time)
        times.append(elapsed_time)
    average_time = np.mean(times[2:])
    print("average time ", average_time)

if __name__ == '__main__':
    file_names = [f for f in os.listdir(test_path) if isfile(join(test_path, f))]
    file_names.sort()
    num_workers = 3
    processes = [Process(target=worker, args=(file_names[x::num_workers], output)) for x in range(num_workers)]

    for p in processes:
        p.start()
    for p in processes:
        p.join()

我注意到与单进程相比，多进程的每个图像的推理经过时间较慢。例如，对于单个图像，推理经过的时间是 0.012 秒。当运行 3 个进程时，我希望得到相同的结果，但是，每张图像的平均推理时间几乎是 0.02 秒。这可能是什么原因？（也许 CUDA 上下文 - 切换？）有没有办法解决这个问题？

标签： pythonkerasdeep-learningmultiprocessing