我已经包含了我的修改代码,但是在github上的原始笔记本中发现了相同的结果。
当在Amazon AWS g3x4large(GPU)实例上的jupyter笔记本服务器上运行时,在深度学习AMI上处理单个图像需要不到4秒钟。 推理函数的时间为1.3-1.5秒(请参见下面的代码),这似乎对于模型报告的推理时间(20ms)来说过高。 尽管我不希望达到报告的标准,但我的时间似乎不合适,对于我的需求来说也不切实际。 我正在考虑一次处理100万张以上的图像,而且不能承受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缩短至少1秒。
我的问题是:
1)有哪些解释/解决方案可以减少推理时间?
2)将图像转换为numpy(在处理之前)需要1.5秒,是否过高?
3)如果这是我可以期望的最佳性能,那么从重新设计批处理图像的模型中可以获得多少时间增加?
感谢您的帮助!
Python笔记本代码:
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)
时间示例:
[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"]