如何使用.meta和tensorflow中的检查点文件进行预测？ [英] how to predict with .meta and checkpoint files in tensorflow?

问题描述

这些天，我正在学习有关MobileNet的知识，而且对tensorflow还是陌生的。经过ssd-mobilenet模型的训练，我得到了检查点文件，.meta文件，graph.pbtxt文件等。当我尝试使用这些文件进行预测时，我无法获得诸如box_pred，classs_scores ...

I'm learning about MobileNet thesedays and i'm new to tensorflow. After training with ssd-mobilenet model,i got checkpoint file , .meta file , graph.pbtxt file and so on. When I try to predict with these files, i can't get the output such as box_pred, classs_scores...

的输出，然后我发现使用.pb文件来预测演示代码加载图，并使用 get_tensor_by_name获取输出，但我没有.pb文件。那么，如何使用.meta和ckpt文件预测图像？

Then I found predict demo code used .pb file to load graph ,and used "get_tensor_by_name" to get output, but Ｉ don't have .pb file. So, how can I predict an image with .meta and ckpt files ?

BTW，这是预测恶魔的主要代码：

BTW, here is predict demon main code:

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})

推荐答案

您应使用 tf.train.import_meta_graph加载图形（），然后使用 get_tensor_by_name（）获得张量。您可以尝试：

You should load the graph using tf.train.import_meta_graph() and then get the tensors using get_tensor_by_name(). You can try:

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}) 

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