Question

我在https://github.com/tensorflow/models/tree/master/slim的自述文件之后为花卉图像训练了一个inception_resnet_v2模型

我在训练后得到了我的graph.pbtxt文件，我使用以下代码将其转换为graph.pb文件：

import tensorflow as tf
from google.protobuf import text_format

def convert_pbtxt_to_graphdef(filename):
  """Returns a `tf.GraphDef` proto representing the data in the given pbtxt file.

  Args:
    filename: The name of a file containing a GraphDef pbtxt (text-formatted
      `tf.GraphDef` protocol buffer data).

  Returns:
    A `tf.GraphDef` protocol buffer.
  """
  with tf.gfile.FastGFile(filename, 'r') as f:
    graph_def = tf.GraphDef()

    file_content = f.read()

    # Merges the human-readable string in `file_content` into `graph_def`.
    text_format.Merge(file_content, graph_def)
  return graph_def

with tf.gfile.FastGFile('/foo/bar/workspace/results/graph.pb', 'wb') as f:
    f.write(convert_pbtxt_to_graphdef('/foo/bar/workspace/results/graph.pbtxt'))

获取此文件后，我尝试使用tensorflow的classify_image.py为训练模型提供随机图像：
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/imagenet/classify_image.py

使用我的.pb，.pbtxt和我的标签文件，但是，我收到以下错误：

Traceback (most recent call last):
  File "classify_image.py", line 212, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 30, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "classify_image.py", line 208, in main
    run_inference_on_image(image)
  File "classify_image.py", line 170, in run_inference_on_image
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2615, in get_tensor_by_name
    return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2466, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2508, in _as_graph_element_locked
    "graph." % (repr(name), repr(op_name)))
KeyError: "The name 'softmax:0' refers to a Tensor which does not exist. The operation, 'softmax', does not exist in the graph."

Answer 1

苗条的，实际上是张量流/模型的问题在于，框架等所生成的模型并不适合预测用例：

TF-slim是TensorFlow的一个新的轻量级高级API （tensorflow.contrib.slim）用于定义，培训和评估 复杂的模型。（来源https://github.com/tensorflow/models/tree/master/slim）

预测时的主要问题是，它只适用于Saver类创建的检查点文件。使用检查点文件时，assign_from_checkpoint_fn（）方法可用于使用检查点中包含的训练参数初始化所有变量。另一方面，在只有GraphDef文件* .pb的情况下，你有点丢失。虽然有一个很好的技巧。

关键思想是在将训练过的模型保存为检查点后，将输入图像的tf.placeholder变量注入计算图。以下脚本（ convert_checkpoint_to_pb.py ）读取检查点，插入占位符，将图形变量转换为常量并将其转储到* .pb文件。

import tensorflow as tf
from tensorflow.contrib import slim

from nets import inception
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
from preprocessing import inception_preprocessing

checkpoints_dir = '/path/to/your/checkpoint_dir/'
OUTPUT_PB_FILENAME = 'minimal_graph.proto'
NUM_CLASSES = 2

# We need default size of image for a particular network.
# The network was trained on images of that size -- so we
# resize input image later in the code.
image_size = inception.inception_resnet_v2.default_image_size

with tf.Graph().as_default():
    # Inject placeholder into the graph
    input_image_t = tf.placeholder(tf.string, name='input_image')
    image = tf.image.decode_jpeg(input_image_t, channels=3)

    # Resize the input image, preserving the aspect ratio
    # and make a central crop of the resulted image.
    # The crop will be of the size of the default image size of
    # the network.
    # I use the "preprocess_for_eval()" method instead of "inception_preprocessing()"
    # because the latter crops all images to the center by 85% at
    # prediction time (training=False).
    processed_image = inception_preprocessing.preprocess_for_eval(image,
                                                                  image_size,
                                                                  image_size, central_fraction=None)

    # Networks accept images in batches.
    # The first dimension usually represents the batch size.
    # In our case the batch size is one.
    processed_images = tf.expand_dims(processed_image, 0)

    # Load the inception network structure
    with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
        logits, _ = inception.inception_resnet_v2(processed_images,
                                                  num_classes=NUM_CLASSES,
                                                  is_training=False)
    # Apply softmax function to the logits (output of the last layer of the network)
    probabilities = tf.nn.softmax(logits)

    model_path = tf.train.latest_checkpoint(checkpoints_dir)

    # Get the function that initializes the network structure (its variables) with
    # the trained values contained in the checkpoint
    init_fn = slim.assign_from_checkpoint_fn(
        model_path,
        slim.get_model_variables())

    with tf.Session() as sess:
        # Now call the initialization function within the session
        init_fn(sess)

        # Convert variables to constants and make sure the placeholder input_image is included
        # in the graph as well as the other neccesary tensors.
        constant_graph = convert_variables_to_constants(sess, sess.graph_def, ["input_image", "DecodeJpeg",
                                                                               "InceptionResnetV2/Logits/Predictions"])

        # Define the input and output layer properly
        optimized_constant_graph = optimize_for_inference(constant_graph, ["eval_image"],
                                                          ["InceptionResnetV2/Logits/Predictions"],
                                                          tf.string.as_datatype_enum)
        # Write the production ready graph to file.
        tf.train.write_graph(optimized_constant_graph, '.', OUTPUT_PB_FILENAME, as_text=False)

（模型/超薄代码必须在您的python路径中才能执行此代码）

要使用转换后的模型预测新图像（现在显示为* .pb文件），请使用文件 minimal_predict.py 中的代码：

import tensorflow as tf
import urllib2


def create_graph(model_file):
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(model_file, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')


model_file = "/your/path/to/minimal_graph.proto"

url = ("http://pictureparadise.net/funny-babies/funny-babies02/funny-babies-053.jpg")

# Open specified url and load image as a string
image_string = urllib2.urlopen(url).read()

with tf.Graph().as_default():
    with tf.Session() as new_sess:
        create_graph(model_file)

        softmax = new_sess.graph.get_tensor_by_name("InceptionResnetV2/Logits/Predictions:0")

        # Loading the injected placeholder
        input_placeholder = new_sess.graph.get_tensor_by_name("input_image:0")

        probabilities = new_sess.run(softmax, {input_placeholder: image_string})
        print probabilities

要使用这些脚本，只需运行python

python convert_checkpoint_to_pb.py
python minimal_predicty.py

虽然你的PYTHONPATH中有tensorflow和tensorflow / models / slim。

Answer 2

在@Maximilian的convert_checkpoint_to_pb.pyprovided中，[＆＃34; eval_image＆＃34;]在

中

optimized_constant_graph = optimize_for_inference(constant_graph, ["eval_image"],["InceptionResnetV2/Logits/Predictions"],tf.string.as_datatype_enum)

应替换为[＆＃34; input_image＆＃34;]否则您将收到错误说明＆＃34;未找到以下输入节点：{＆＃39; eval_image＆＃39;} \ n＆＃34 ;

Tensorflow Inception_Resnet_V2对图像进行分类

2 个答案: