基于Java的Tensorflow推理速度极慢

Question

我下载了用于python3推理的DeepLabv3示例，该示例使用了预先训练过的模型。在我使用的CPU上，实际推断的运行时大约是19秒。Tensorflow是用pip安装的：

pip install intel-tensorflow

这是colab木星笔记本上的代码：

#!/usr/bin/python

import os
from io import BytesIO
import tarfile
import tempfile
from six.moves import urllib

from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
from timeit import default_timer as timer

#%tensorflow_version 1.x
import tensorflow.compat.v1 as tf
#import tensorflow as tf

class DeepLabModel(object):
  """Class to load deeplab model and run inference."""

  INPUT_TENSOR_NAME = 'ImageTensor:0'
  OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
  INPUT_SIZE = 513
  FROZEN_GRAPH_NAME = 'frozen_inference_graph'

  def __init__(self, tarball_path):
    """Creates and loads pretrained deeplab model."""
    self.graph = tf.Graph()

    graph_def = None
    # Extract frozen graph from tar archive.
    tar_file = tarfile.open(tarball_path)
    for tar_info in tar_file.getmembers():
      if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
        file_handle = tar_file.extractfile(tar_info)
        graph_def = tf.GraphDef.FromString(file_handle.read())
        break

    tar_file.close()

    if graph_def is None:
      raise RuntimeError('Cannot find inference graph in tar archive.')

    with self.graph.as_default():
      tf.import_graph_def(graph_def, name='')

    self.sess = tf.Session(graph=self.graph)

  def run(self, image):
    """Runs inference on a single image.

    Args:
      image: A PIL.Image object, raw input image.

    Returns:
      resized_image: RGB image resized from original input image.
      seg_map: Segmentation map of `resized_image`.
    """
    width, height = image.size
    resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
    target_size = (int(resize_ratio * width), int(resize_ratio * height))
    resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
    start = timer()
    batch_seg_map = self.sess.run(
        self.OUTPUT_TENSOR_NAME,
        feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
    end = timer()
    print("Inference duration: ", end-start)
    seg_map = batch_seg_map[0]
    return resized_image, seg_map


def create_pascal_label_colormap():
  """Creates a label colormap used in PASCAL VOC segmentation benchmark.

  Returns:
    A Colormap for visualizing segmentation results.
  """
  colormap = np.zeros((256, 3), dtype=int)
  ind = np.arange(256, dtype=int)

  for shift in reversed(range(8)):
    for channel in range(3):
      colormap[:, channel] |= ((ind >> channel) & 1) << shift
    ind >>= 3

  return colormap


def label_to_color_image(label):
  """Adds color defined by the dataset colormap to the label.

  Args:
    label: A 2D array with integer type, storing the segmentation label.

  Returns:
    result: A 2D array with floating type. The element of the array
      is the color indexed by the corresponding element in the input label
      to the PASCAL color map.

  Raises:
    ValueError: If label is not of rank 2 or its value is larger than color
      map maximum entry.
  """
  if label.ndim != 2:
    raise ValueError('Expect 2-D input label')

  colormap = create_pascal_label_colormap()

  if np.max(label) >= len(colormap):
    raise ValueError('label value too large.')

  return colormap[label]


def vis_segmentation(image, seg_map):
  """Visualizes input image, segmentation map and overlay view."""
  plt.figure(figsize=(15, 5))
  grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])

  plt.subplot(grid_spec[0])
  plt.imshow(image)
  plt.axis('off')
  plt.title('input image')

  plt.subplot(grid_spec[1])
  seg_image = label_to_color_image(seg_map).astype(np.uint8)
  plt.imshow(seg_image)
  plt.axis('off')
  plt.title('segmentation map')

  plt.subplot(grid_spec[2])
  plt.imshow(image)
  plt.imshow(seg_image, alpha=0.7)
  plt.axis('off')
  plt.title('segmentation overlay')

  unique_labels = np.unique(seg_map)
  ax = plt.subplot(grid_spec[3])
  plt.imshow(
      FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')
  ax.yaxis.tick_right()
  plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
  plt.xticks([], [])
  ax.tick_params(width=0.0)
  plt.grid('off')
  plt.show()


LABEL_NAMES = np.asarray([
    'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
    'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
    'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
])

FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)

MODEL_NAME = 'xception_coco_voctrainval'  # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']

_DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
_MODEL_URLS = {
    'mobilenetv2_coco_voctrainaug':
        'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
    'mobilenetv2_coco_voctrainval':
        'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
    'xception_coco_voctrainaug':
        'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
    'xception_coco_voctrainval':
        'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
}
_TARBALL_NAME = 'deeplab_model.tar.gz'

model_dir = 'model'
tf.io.gfile.makedirs(model_dir)

download_path = os.path.join(model_dir, _TARBALL_NAME)
print('downloading model, this might take a while...')
urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],
                   download_path)
print('download completed! loading DeepLab model...')

MODEL = DeepLabModel(download_path)
print('model loaded successfully!')

SAMPLE_IMAGE = 'image1'  # @param ['image1', 'image2', 'image3']
IMAGE_URL = 'file:///home/rhobincu/man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg'  #@param {type:"string"}

_SAMPLE_URL = ('https://github.com/tensorflow/models/blob/master/research/'
               'deeplab/g3doc/img/%s.jpg?raw=true')


def run_visualization(url):
  """Inferences DeepLab model and visualizes result."""
  try:
    f = urllib.request.urlopen(url)
    jpeg_str = f.read()
    original_im = Image.open(BytesIO(jpeg_str))
  except IOError:
    print('Cannot retrieve image. Please check url: ' + url)
    return

  print('running deeplab on image %s...' % url)
  resized_im, seg_map = MODEL.run(original_im)

  vis_segmentation(resized_im, seg_map)


image_url = IMAGE_URL or _SAMPLE_URL % SAMPLE_IMAGE
run_visualization(image_url)

产出：

rhobincu@ml:~/gitroot/test$ ./test.py 
downloading model, this might take a while...
download completed! loading DeepLab model...
2020-04-08 14:51:24.066757: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199980000 Hz
2020-04-08 14:51:24.080415: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5561af0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-08 14:51:24.080567: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-08 14:51:24.081792: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
model loaded successfully!
running deeplab on image file:///home/rhobincu/man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg...
Inferrence duration:  18.454864561999784

我已经尝试用Java重写它。我通过克隆https://github.com/tensorflow/tensorflow标记v2.1.0并运行

bazel build -c opt --copt=-mavx --copt=-msse2 --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mfpmath=both //tensorflow:install_headers //tensorflow:libtensorflow_cc.so //tensorflow:libtensorflow_framework.so //tensorflow/java:tensorflow  //tensorflow/java:libtensorflow_jni

以下是对应的Java代码：

package tensorflowapp;

import java.io.IOException;
import java.io.PrintStream;
import java.nio.ByteBuffer;
import java.nio.charset.Charset;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;
import org.tensorflow.DataType;
import org.tensorflow.Graph;
import org.tensorflow.Output;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.TensorFlow;
import org.tensorflow.types.UInt8;

/**
 * Sample use of the TensorFlow Java API to label images using a pre-trained
 * model.
 */
public class LabelImage {

    static {
        System.load("/usr/local/share/java/opencv4/libopencv_java420.so");
    System.load("/opt/tensorflow/java/native/libtensorflow_jni.so");
    }

    static Session loadDeeplabModel() throws IOException {
        Graph graph = new Graph();
        graph.importGraphDef(Files.readAllBytes(Paths.get("model/deeplabv3_pascal_trainval/frozen_inference_graph.pb")));
        Session session = new Session(graph);
        return session;
    }

    static Tensor<UInt8> matToTensor(Mat image) {
        byte[] byteData = new byte[(int) image.total() * image.channels()];
        image.get(0, 0, byteData);
        return Tensor.create(UInt8.class, new long[]{1, 1, image.width() * image.height(), 3}, ByteBuffer.wrap(byteData));
    }

    public static void main(String[] args) throws IOException {
        Session session = loadDeeplabModel();
        Mat image = Imgcodecs.imread(args[0], Imgcodecs.IMREAD_COLOR);
        Mat resized = new Mat();
        double scale = 513.0 / Math.max(image.width(), image.height());
        Size destinationSize = new Size(image.width() * scale, image.height() * scale);
        System.out.println("Destination size: " + destinationSize);
        Imgproc.resize(image, resized, destinationSize);
        Tensor<UInt8> imageTensor = matToTensor(resized);

        List<Tensor<?>> result = session.runner().feed("ImageTensor:0", imageTensor).fetch("SemanticPredictions:0").run();//.get(0).expect(Float.class);
        System.out.println("Done");
    }

}

运行以下命令：

time java -cp /opt/tensorflow/java/*:dist/TensorFlowApp.jar:/usr/local/share/java/opencv4/opencv-420.jar tensorflowapp.LabelImage ../../man-in-white-dress-shirt-sitting-on-black-rolling-chair-840996.jpg

产生以下输出：

2020-04-08 13:26:14.611201: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199980000 Hz
2020-04-08 13:26:14.626568: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f7038dea6d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-08 13:26:14.626612: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Destination size: 513x342
2020-04-08 13:46:59.913359: W tensorflow/core/framework/op_kernel.cc:1655] OP_REQUIRES failed at spacetobatch_op.cc:219 : Invalid argument: padded_shape[1]=21942 is not divisible by block_shape[1]=4
Exception in thread "main" java.lang.IllegalArgumentException: padded_shape[1]=21942 is not divisible by block_shape[1]=4
     [[{{node xception_65/exit_flow/block2/unit_1/xception_module/separable_conv1_depthwise/depthwise/SpaceToBatchND}}]]
    at org.tensorflow.Session.run(Native Method)
    at org.tensorflow.Session.access$100(Session.java:48)
    at org.tensorflow.Session$Runner.runHelper(Session.java:326)
    at org.tensorflow.Session$Runner.run(Session.java:276)
    at tensorflowapp.LabelImage.main(LabelImage.java:58)
Command exited with non-zero status 1
21166.66user 3912.49system 20:48.87elapsed 2008%CPU (0avgtext+0avgdata 27929748maxresident)k
0inputs+408outputs (0major+269297302minor)pagefaults 0swaps

除了错误本身，运行时间是3912秒.

Answer 1

对于推理时间，您是否尝试使用同一会话第二次运行它？TensorFlow可以在第一次运行时惰性地初始化一些资源，因此您可能希望将相同的会话也用于所有其他推理运行，而不是为每个会话创建一个新的会话。

一种常见的做法是，在进行真正的推断之前，使用虚拟运行一次把它热起来 (这个链接只显示TFX是如何做到的，但对于Java来说这是相同的原则)。