TensorFlow:使用ThreadPool的多CPU推断

Question

我有很多我想并行处理的图像。

默认情况下可以使用多个内核，以下是一些信息https://stackoverflow.com/a/41233901/1179925

"目前，这意味着每个线程池将有一个线程 您的机器中的CPU核心。"

通过查看HTOP，我可以看到并非所有内核都在此默认设置中100％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型号，它的性能甚至更糟。

。

在我的笔记本上带有8个核心：

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

使用多处理的代码：

Model init time: 0.78 sec
Processing time: 39.14 sec

这是我使用multiprocessing的代码，我缺少某些内容？：

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：

用multiprocessing.Pool替换multiprocessing.pool.ThreadPool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')

我有一个错误：

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unswer：https：//stackoverflow.com/a/46779776/1179925

它起作用，但并不比Tensorflow本身提供的默认副词快得多。

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)