我一直在使用Tensorflow对象检测API - 在我的情况下，我试图使用来自模型动物园的kitti训练模型（faster_rcnn_resnet101_kitti_2018_01_28）检测静止图像中的车辆，并且我正在使用从object_detection_tutorial jupyter笔记本包含在github存储库中。
我在下面包含了我修改后的代码，但是我从github的原始笔记本中找到了相同的结果。
当使用深度学习AMI在Amazon AWS g3x4large (GPU)实例上的jupyter笔记本服务器上运行时，处理单个图像只需要4秒钟。推理函数的时间为1.3-1.5秒（参见下面的代码） - 对于模型的报告推理时间（20ms），它似乎非常高。虽然我不希望达到报告的标记，但我的时间似乎不符合我的需求并且不切实际。我正在考虑一次处理100万个图像，并且无法承受46天的处理时间。鉴于该模型用于视频帧捕获....我认为应该可以将每个图像的时间减少到1秒以下，至少。
我的问题是：
1）减少推理时间有哪些解释/解决方案？
2）将图像转换为numpy（处理前）是否需要1.5秒？
3）如果这是我能想到的最佳性能，我希望从重新设计模型到批处理图像可以获得多少时间增加？
感谢您的帮助！
来自python笔记本的代码：
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import json
import collections
import os.path
import datetime
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
# This is needed to display the images.
get_ipython().magic('matplotlib inline')
#Setup variables
PATH_TO_TEST_IMAGES_DIR = 'test_images'
MODEL_NAME = 'faster_rcnn_resnet101_kitti_2018_01_28'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'kitti_label_map.pbtxt')
NUM_CLASSES = 2
from utils import label_map_util
from utils import visualization_utils as vis_util
def get_scores(
boxes,
classes,
scores,
category_index,
min_score_thresh=.5
):
import collections
# Create a display string (and color) for every box location, group any boxes
# that correspond to the same location.
box_to_display_str_map = collections.defaultdict(list)
for i in range(boxes.shape[0]):
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
if scores is None:
box_to_color_map[box] = groundtruth_box_visualization_color
else:
display_str = ''
if classes[i] in category_index.keys():
class_name = category_index[classes[i]]['name']
else:
class_name = 'N/A'
display_str = str(class_name)
if not display_str:
display_str = '{}%'.format(int(100*scores[i]))
else:
display_str = '{}: {}%'.format(display_str, int(100*scores[i]))
box_to_display_str_map[i].append(display_str)
return box_to_display_str_map
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
#get list of paths
exten='.jpg'
TEST_IMAGE_PATHS=[]
for dirpath, dirnames, files in os.walk(PATH_TO_TEST_IMAGES_DIR):
for name in files:
if name.lower().endswith(exten):
#print(os.path.join(dirpath,name))
TEST_IMAGE_PATHS.append(os.path.join(dirpath,name))
print((len(TEST_IMAGE_PATHS), 'Images To Process'))
#load model graph for inference
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
#setup class labeling parameters
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
#placeholder for timings
myTimings=[]
myX = 1
myResults = collections.defaultdict(list)
for image_path in TEST_IMAGE_PATHS:
if os.path.exists(image_path):
print(myX,"--------------------------------------",datetime.datetime.time(datetime.datetime.now()))
print(myX,"Image:", image_path)
myTimings.append((myX,"Image", image_path))
print(myX,"Open:",datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Open",datetime.datetime.time(datetime.datetime.now()).__str__()))
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
print(myX,"Numpy:",datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Numpy",datetime.datetime.time(datetime.datetime.now()).__str__()))
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
print(myX,"Expand:",datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Expand",datetime.datetime.time(datetime.datetime.now()).__str__()))
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
print(myX,"Detect:",datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Detect",datetime.datetime.time(datetime.datetime.now()).__str__()))
output_dict = run_inference_for_single_image(image_np, detection_graph)
# Visualization of the results of a detection.
print(myX,"Export:",datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Export",datetime.datetime.time(datetime.datetime.now()).__str__()))
op=get_scores(
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
min_score_thresh=.2)
myResults[image_path].append(op)
print(myX,"Done:", datetime.datetime.time(datetime.datetime.now()))
myTimings.append((myX,"Done", datetime.datetime.time(datetime.datetime.now()).__str__()))
myX= myX + 1
#save results
with open((OUTPUTS_BASENAME+'_Results.json'), 'w') as fout:
json.dump(myResults, fout)
with open((OUTPUTS_BASENAME+'_Timings.json'), 'w') as fout:
json.dump(myTimings, fout)
计时的例子：
[1, "Image", "test_images/DE4T_11Jan2018/MFDC4612.JPG"]
[1, "Open", "19:20:08.029423"]
[1, "Numpy", "19:20:08.052679"]
[1, "Expand", "19:20:09.977166"]
[1, "Detect", "19:20:09.977250"]
[1, "Export", "19:23:13.902443"]
[1, "Done", "19:23:13.903012"]
[2, "Image", "test_images/DE4T_11Jan2018/MFDC4616.JPG"]
[2, "Open", "19:23:13.903885"]
[2, "Numpy", "19:23:13.906320"]
[2, "Expand", "19:23:15.756308"]
[2, "Detect", "19:23:15.756597"]
[2, "Export", "19:23:17.153233"]
[2, "Done", "19:23:17.153699"]
[3, "Image", "test_images/DE4T_11Jan2018/MFDC4681.JPG"]
[3, "Open", "19:23:17.154510"]
[3, "Numpy", "19:23:17.156576"]
[3, "Expand", "19:23:19.012935"]
[3, "Detect", "19:23:19.013013"]
[3, "Export", "19:23:20.323839"]
[3, "Done", "19:23:20.324307"]
[4, "Image", "test_images/DE4T_11Jan2018/MFDC4697.JPG"]
[4, "Open", "19:23:20.324791"]
[4, "Numpy", "19:23:20.327136"]
[4, "Expand", "19:23:22.175578"]
[4, "Detect", "19:23:22.175658"]
[4, "Export", "19:23:23.472040"]
[4, "Done", "19:23:23.472297"]
答案 0 :(得分：1)
1）您可以做的是直接加载视频而不是图像，然后更改“run_inference_for_single_image（）”以创建会话一次并在其中加载图像/视频（重新创建图形非常慢）。此外，您可以编辑管道配置文件以减少提案数量，这将直接加速推断。请注意，您必须在之后重新导出图表（https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/exporting_models.md）。批处理也有帮助（虽然我很抱歉，我忘记了多少），最后，您可以使用多处理来卸载CPU特定的操作（绘制边界框，加载数据）以更好地利用GPU。
2）将图像转换为numpy（处理前）为1.5秒，不合格，这是非常慢的，并且还有很大的改进空间。
3）虽然我不知道AWS上的确切gpu（k80？），你应该可以在一个包含所有修复的geforce 1080TI上获得超过10fps，这与他们报告的79ms时间一致（其中你得到20ms更快 - rcnn_resnet_101 ??）