Tensorflow:在方法中使用会话/图表

Question

我的情况是这样的:

我有一个训练张量流模型的脚本.在此脚本中,我实例化了一个提供训练数据的类.该类的初始化依次实例化另一个名为"image"的类,以进行数据扩充的各种操作,而不是.

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

我的问题是我试图通过传递会话本身或图形来使用tensorflow在这个图像类中做一些操作.但我收效甚微.

有效的方法(但速度太慢)

我现在所拥有的,但工作缓慢,就像这样(简化):

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,但它为每个图像创建一个新的Session:

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 ...

不起作用的方法(应该更快)

例如,什么不起作用,我无法弄清楚为什么,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.

任何帮助将非常感激!

Answer 1

Image不创建一个类，而是创建一个ImageAugmenter在初始化时进行会话的类，然后使用 Tensorflow 处理图像，怎么样？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)

有了这个，您就不会为每个图像创建一个新会话，它应该为您提供您想要的内容。

注意如何sess.run()调用；我传递给它的 feed 字典的关键是上面定义的占位符张量。根据您的错误跟踪，您可能试图从代码中尚未定义的sess.run()部分进行调用，或者它已被定义为除im_placeholdertf.placeholder.

ImageAugmenter.augment()此外，您可以通过更改方法以接收下参数和上参数作为该方法的输入来进一步改进代码tf.image.random_saturation()，或者您可以使用特定形状来初始化ImageAugmenter，而不是对其进行硬编码。