Tensorflow：在方法中使用会话/图形

Question

我的情况是这样的：

我有一个训练 tensorflow 模型的脚本。在这个脚本中，我实例化了一个提供训练数据的类。该类的初始化反过来又实例化了另一个名为“image”的类来执行各种数据增强操作。

main script -> instantiates data_feed class -> instantiates image class

我的问题是我正在尝试使用 tensorflow 通过传递会话本身或图形来在此图像类中执行一些操作。但我收效甚微。

可行的方法（但太慢）

我现在所拥有的，但工作速度非常缓慢，是这样的（简化的）：

class image(object):
    def __init__(self, im):
        self.im = im

    def augment(self):
        aux_im = tf.image.random_saturation(self.im, 0.6)

        sess = tf.Session(graph=aux_im.graph)
        self.im = sess.run(aux_im)

class data_feed(object):
    def __init__(self, data_dir):
        self.images = load_data(data_dir)

    def process_data(self):
        for im in self.images:
            image = image(im)
            image.augment()

if __name__ == "__main__":
    # initialize everything tensorflow related here, including model
    sess = tf.Session()
    # next load the data
    data_feed = data_feed(TRAIN_DATA_DIR)
    train_data = data_feed.process_data()

这种方法有效，但它为每张图片创建一个新会话：

I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
etc ...

行不通的方法（应该更快）

例如，什么不起作用，我不知道为什么，是从我的主脚本传递图形或会话，如下所示：

class image(object):
    def __init__(self, im):
        self.im = im

    def augment(self, tf_sess):
        with tf_sess.as_default():
            aux_im = tf.image.random_saturation(self.im, 0.6)

            self.im = tf_sess.run(aux_im)

class data_feed(object):
    def __init__(self, data_dir, tf_sess):
        self.images = load_data(data_dir)
        self.tf_sess = tf_sess

    def process_data(self):
        for im in self.images:
            image = image(im)
            image.augment(self.tf_sess)

if __name__ == "__main__":
    # initialize everything tensorflow related here, including model
    sess = tf.Session()
    # next load the data
    data_feed = data_feed(TRAIN_DATA_DIR, sess)
    train_data = data_feed.process_data()

这是我得到的错误：

Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 754, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 409, in data_generator_task
    generator_output = next(generator)
  File "/home/mathetes/Dropbox/ML/load_gluc_data.py", line 198, in generate
    yield self.next_batch()
  File "/home/mathetes/Dropbox/ML/load_gluc_data.py", line 192, in next_batch
    X, y, l = self.process_image(json_im, X, y, l)
  File "/home/mathetes/Dropbox/ML/load_gluc_data.py", line 131, in process_image
    im.augment_with_tf(self.tf_sess)
  File "/home/mathetes/Dropbox/ML/load_gluc_data.py", line 85, in augment_with_tf
    self.im = sess.run(saturation, {im_placeholder: np.asarray(self.im)})
  File "/home/mathetes/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 766, in run
    run_metadata_ptr)
  File "/home/mathetes/.local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 921, in _run
    + e.args[0])
TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder:0", shape=(96, 96, 3), dtype=float32) is not an element of this graph.

任何帮助将不胜感激！

Answer 1

不如创建一个 ImageAugmenter 类而不是 Image 类，该类在初始化时接受一个会话，然后使用 Tensorflow 处理您的图像？你可以这样做：

import tensorflow as tf
import numpy as np

class ImageAugmenter(object):
    def __init__(self, sess):
        self.sess = sess
        self.im_placeholder = tf.placeholder(tf.float32, shape=[1,784,3])

    def augment(self, image):
        augment_op = tf.image.random_saturation(self.im_placeholder, 0.6, 0.8)
        return self.sess.run(augment_op, {self.im_placeholder: image})

class DataFeed(object):
    def __init__(self, data_dir, sess):
        self.images = load_data(data_dir)
        self.augmenter = ImageAugmenter(sess)

    def process_data(self):
        processed_images = []
        for im in self.images:
            processed_images.append(self.augmenter.augment(im))
        return processed_images

def load_data(data_dir):
    # True method would read images from disk
    # This is just a mockup
    images = []
    images.append(np.random.random([1,784,3]))
    images.append(np.random.random([1,784,3]))
    return images

if __name__ == "__main__":
    TRAIN_DATA_DIR = '/some/dir/'
    sess = tf.Session()
    data_feed = DataFeed(TRAIN_DATA_DIR, sess)
    train_data = data_feed.process_data()
    print(train_data)

这样你就不会为每张图片创建一个新的会话，它应该给你你想要的。

注意sess.run() 的调用方式；我传递给它的 feed dict 的键是 上面定义的占位符张量。根据您的错误跟踪，您可能正试图从代码中未定义 im_placeholder 的部分调用 sess.run()，或者它已被定义为 tf.placeholder 以外的部分。

此外，您可以通过更改ImageAugmenter.augment() 方法来进一步改进代码，以接收lower 和upper 参数作为tf.image.random_saturation() 方法的输入，或者您可以使用特定形状初始化ImageAugmenter 而不是使用例如，它是硬编码的。