如何预测.meta和检查点文件的张量流？

Question

我正在学习有关MobileNet的这些日子，我对tensorflow很陌生。经过ssd-mobilenet模型培训后，我得到了checkpoint文件，.meta文件，graph.pbtxt文件等。当我尝试预测这些文件，我无法得到输出，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“get_tensor_by_name”来获得输出，但我没有.pb文件。那么，如何用.meta和ckpt文件预测图像呢？

BTW，这里是妖预测主要代码：

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

你应该使用tf.train.import_meta_graph()加载图形，然后使用get_tensor_by_name()得到张量。你可以试试：

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

好了，我已经成功使用的代码波纹管加载图：

with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess: 
    #with tf.device("/cpu:0"): 
    new_saver = tf.train.import_meta_graph('./model.ckpt-1281.meta') 
    new_saver.restore(sess, './model.ckpt-1281') 
    detection_graph = tf.get_default_graph() 

    for image_path in TEST_IMAGE_PATHS: 
     image = Image.open(image_path) 
     image_np = load_image_into_numpy_array(image) 
     image_np_expanded = np.expand_dims(image_np, axis=0) 
     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') 
     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}) 
     print scores, boxes 

但我得到这个埃罗：

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

然后，我检查了张量名称在graph和graph.pbtxt文件中，没有像“image_tensor”，“detection_boxes”，“detection_scores”或甚至“softmax”这样的名称。那么我怎样才能预测图的输出呢？