Question

我有很多图像要并行处理。

默认情况下，Tensorflow可以使用多个内核，这是一些有关此的信息 https://stackoverflow.com/a/41233901/1179925

”当前，这意味着每个线程池每个线程将有一个线程您计算机中的CPU内核。”

通过查看htop，我可以看到并非所有内核在此默认设置下都以100％的利用率使用，因此我想设置intra_op_parallelism_threads=1和inter_op_parallelism_threads=1并并行运行n_cpu模型，但是效果却更差。

在我的8核笔记本电脑上：

单核顺序处理：

Model init time: 0.77 sec
Processing time: 37.58 sec

多CPU默认Tensorflow设置：

Model init time: 0.76 sec
Processing time: 20.16 sec

此代码使用多重处理：

Model init time: 0.78 sec
Processing time: 39.14 sec

这是我使用multiprocessing的代码，我缺少什么了吗？：

import os
import glob
import time
import argparse
from multiprocessing.pool import ThreadPool
import multiprocessing
import itertools

import tensorflow as tf
import numpy as np
from tqdm import tqdm
import cv2

MODEL_FILEPATH = './tensorflow_example/inception_v3_2016_08_28_frozen.pb'

def get_image_filepaths(dataset_dir):
    if not os.path.isdir(dataset_dir):
        raise Exception(dataset_dir, 'not dir!')

    img_filepaths = []
    extensions = ['**/*.jpg', '**/*.png', '**/*.JPG', '**/*.PNG']
    for ext in extensions:
        img_filepaths.extend(glob.iglob(os.path.join(dataset_dir, ext), recursive=True))

    return img_filepaths


class ModelWrapper():
    def __init__(self, model_filepath):
        # TODO: estimate this from graph itself
        # Hardcoded for inception_v3_2016_08_28_frozen.pb
        self.input_node_names = ['input']
        self.output_node_names = ['InceptionV3/Predictions/Reshape_1']
        self.input_img_w = 299
        self.input_img_h = 299

        input_tensor_names = [name + ":0" for name in self.input_node_names]
        output_tensor_names = [name + ":0" for name in self.output_node_names]

        self.graph = self.load_graph(model_filepath)

        self.inputs = []
        for input_tensor_name in input_tensor_names:
            self.inputs.append(self.graph.get_tensor_by_name(input_tensor_name))

        self.outputs = []
        for output_tensor_name in output_tensor_names:
            self.outputs.append(self.graph.get_tensor_by_name(output_tensor_name))

        config_proto = tf.ConfigProto(device_count={'GPU': 0},
                                      intra_op_parallelism_threads=1,
                                      inter_op_parallelism_threads=1)
        self.sess = tf.Session(graph=self.graph, config=config_proto)

    def load_graph(self, model_filepath):
        # Expects frozen graph in .pb format
        with tf.gfile.GFile(model_filepath, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
        with tf.Graph().as_default() as graph:
            tf.import_graph_def(graph_def, name="")
        return graph

    def predict(self, img):
        h, w, c = img.shape
        if h != self.input_img_h or w != self.input_img_w:
            img = cv2.resize(img, (self.input_img_w, self.input_img_h))

        batch = img[np.newaxis, ...]
        feed_dict = {self.inputs[0] : batch}
        outputs = self.sess.run(self.outputs, feed_dict=feed_dict) # (1, 1001)

        return outputs


def process_single_file(args):
    model, img_filepath = args

    img = cv2.imread(img_filepath)
    output = model.predict(img)


def process_dataset(dataset_dir):
    img_filepaths = get_image_filepaths(dataset_dir)

    start = time.time()
    model = ModelWrapper(MODEL_FILEPATH)
    print('Model init time:', round(time.time() - start, 2), 'sec')

    start = time.time()
    n_cpu = multiprocessing.cpu_count()
    for _ in tqdm(ThreadPool(n_cpu).imap_unordered(process_single_file,
                                                   zip(itertools.repeat(model), img_filepaths)),
                                                   total=len(img_filepaths)):
        pass
    print('Processing time:', round(time.time() - start, 2), 'sec')


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(dest='dataset_dir')
    args = parser.parse_args()

    process_dataset(args.dataset_dir)

更新：

将multiprocessing.pool.ThreadPool替换为multiprocessing.Pool后：

def process_dataset(dataset_dir):
    img_filepaths = get_image_filepaths(dataset_dir)

    start = time.time()
    model = ModelWrapper(MODEL_FILEPATH)
    print('Model init time:', round(time.time() - start, 2), 'sec')

    start = time.time()
    n_cpu = multiprocessing.cpu_count()
    pool = multiprocessing.Pool(n_cpu)

    it = pool.imap_unordered(process_single_file, zip(itertools.repeat(model), img_filepaths))
    for _ in tqdm(it, total=len(img_filepaths)):
        pass

    print('Processing time:', round(time.time() - start, 2), 'sec')

我得到一个错误：

Traceback (most recent call last):
  File "tensorflow_example/multi_core_cpu_inference_multiprocessing.py", line 110, in <module>
    process_dataset(args.dataset_dir)
  File "tensorflow_example/multi_core_cpu_inference_multiprocessing.py", line 99, in process_dataset
    for _ in tqdm(it, total=len(img_filepaths)):
  File "/usr/local/lib/python3.6/site-packages/tqdm/_tqdm.py", line 979, in __iter__
    for obj in iterable:
  File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 735, in next
    raise value
  File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py", line 424, in _handle_tasks
    put(task)
  File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/reduction.py", line 51, in dumps
    cls(buf, protocol).dump(obj)
TypeError: can't pickle _thread.RLock objects

Answer 1

基于此保留：https://stackoverflow.com/a/46779776/1179925

它可以工作，但并没有比tensorflow本身提供的默认并行处理快很多。

import os
import glob
import time
import argparse
import multiprocessing

import tensorflow as tf
import numpy as np
from tqdm import tqdm
import cv2

# Running N_PROCESSES processes using multiprocessing pool

N_PROCESSES = 2
N_CPU = multiprocessing.cpu_count()
INTRA_N_THREADS = max(1, N_CPU // N_PROCESSES)
INTER_N_THREADS = max(1, N_CPU // N_PROCESSES)

print('N_PROCESSES', N_PROCESSES)
print('N_CPU', N_CPU)
print('INTRA_N_THREADS', INTRA_N_THREADS)
print('INTER_N_THREADS', INTER_N_THREADS)

MODEL_FILEPATH = './tensorflow_example/inception_v3_2016_08_28_frozen.pb'

def get_image_filepaths(dataset_dir):
    if not os.path.isdir(dataset_dir):
        raise Exception(dataset_dir, 'not dir!')

    img_filepaths = []
    extensions = ['**/*.jpg', '**/*.png', '**/*.JPG', '**/*.PNG']
    for ext in extensions:
        img_filepaths.extend(glob.iglob(os.path.join(dataset_dir, ext), recursive=True))

    return img_filepaths


class ModelWrapper():
    def __init__(self, model_filepath):
        # TODO: estimate this from graph itself
        # Hardcoded for inception_v3_2016_08_28_frozen.pb
        self.input_node_names = ['input']
        self.output_node_names = ['InceptionV3/Predictions/Reshape_1']
        self.input_img_w = 299
        self.input_img_h = 299

        input_tensor_names = [name + ":0" for name in self.input_node_names]
        output_tensor_names = [name + ":0" for name in self.output_node_names]

        self.graph = self.load_graph(model_filepath)

        self.inputs = []
        for input_tensor_name in input_tensor_names:
            self.inputs.append(self.graph.get_tensor_by_name(input_tensor_name))

        self.outputs = []
        for output_tensor_name in output_tensor_names:
            self.outputs.append(self.graph.get_tensor_by_name(output_tensor_name))

        config_proto = tf.ConfigProto(device_count={'GPU': 0},
                                      intra_op_parallelism_threads=INTRA_N_THREADS,
                                      inter_op_parallelism_threads=INTER_N_THREADS)
        self.sess = tf.Session(graph=self.graph, config=config_proto)

    def load_graph(self, model_filepath):
        # Expects frozen graph in .pb format
        with tf.gfile.GFile(model_filepath, "rb") as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
        with tf.Graph().as_default() as graph:
            tf.import_graph_def(graph_def, name="")
        return graph

    def predict(self, img):
        h, w, c = img.shape
        if h != self.input_img_h or w != self.input_img_w:
            img = cv2.resize(img, (self.input_img_w, self.input_img_h))

        batch = img[np.newaxis, ...]
        feed_dict = {self.inputs[0] : batch}
        outputs = self.sess.run(self.outputs, feed_dict=feed_dict) # (1, 1001)

        return outputs


def process_chunk(img_filepaths):
    start = time.time()
    model = ModelWrapper(MODEL_FILEPATH)
    print('Model init time:', round(time.time() - start, 2), 'sec')

    for img_filepath in img_filepaths:
        img = cv2.imread(img_filepath)
        output = model.predict(img)


def process_dataset(dataset_dir):
    img_filepaths = get_image_filepaths(dataset_dir)

    start = time.time()
    pool = multiprocessing.Pool(N_PROCESSES)

    chunks = []
    n = len(img_filepaths) // N_PROCESSES
    for i in range(0, len(img_filepaths), n):
        chunk = img_filepaths[i:i+n]
        chunks.append(chunk)

    it = pool.imap_unordered(process_chunk, chunks)
    for _ in tqdm(it, total=len(img_filepaths)):
        pass

    print('Processing time:', round(time.time() - start, 2), 'sec')


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(dest='dataset_dir')
    args = parser.parse_args()

    process_dataset(args.dataset_dir)

Tensorflow：使用线程池进行多CPU推断

1 个答案: