Question

我正在学习有关MobileNet的知识，而且我对tensorflow很新。在使用ssd-mobilenet模型进行培训后，我获得了checkpoint文件，.meta文件，graph.pbtxt文件等。当我尝试使用这些文件进行预测时，我无法获得输出，例如box_pred，classs_scores ......

然后我发现预测演示代码使用.pb文件来加载图形，并使用＆＃34; get_tensor_by_name＆＃34;得到输出，但我没有.pb文件。那么，如何使用.meta和ckpt文件预测图像？

BTW，这里是预测恶魔的主要代码：

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import time

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image

#%matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

from utils import label_map_util
from utils import visualization_utils as vis_util

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# 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', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

#Load a (frozen) Tensorflow model into memory.
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='')

#load label map
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)

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)


#detection
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    for image_path in TEST_IMAGE_PATHS:
      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.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      # Each score represent how level of confidence for each of the objects.
      # Score is shown on the result image, together with the class label.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      # Actual detection.
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})

Answer 1

您应该使用tf.train.import_meta_graph()加载图表，然后使用get_tensor_by_name()获取张量。你可以尝试：

model_path = "model.ckpt"
detection_graph = tf.Graph()
with tf.Session(graph=detection_graph) as sess:
    # Load the graph with the trained states
    loader = tf.train.import_meta_graph(model_path+'.meta')
    loader.restore(sess, model_path)

    # Get the tensors by their variable name
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    scores = detection_graph.get_tensor_by_name('detection_scores:0')
    ...
    # Make predictions
    _boxes, _scores = sess.run([boxes, scores], feed_dict={image_tensor: image_np_expanded})

Answer 2

仅针对像wu ruize和CoupDeMistral这样的问题：

但是我遇到了这个错误：i

在使用detection_graph.get_tensor_by_name之前，您需要先命名张量。

例如，如下所示：

"The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."

请注意，上面的张量已被命名为“准确性”。

之后，您可以通过以下方式享受还原操作：

accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32),name='accuracy')

现在玩得开心：P！

如何使用tensorflow中的.meta和checkpoint文件进行预测？

2 个答案: