我的情况是这样的:

我有一个训练 tensorflow 模型的脚本。在此脚本中,我实例化了一个提供培训数据的类。该类的初始化又实例化了另一个称为“图像”的类,以进行各种数据增强操作以及不进行数据增强的操作。

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 session 本身或图形来在此图像类中执行一些操作。但是我收效甚微。

可行的方法(但太慢了)

我现在所拥有的,但是工作很慢,却是这样的(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()

此方法有效,但会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 ...

无效的方法(应该已经快了很多)

例如,什么是行不通的,而我不知道为什么,是通过主脚本传递图或 session ,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!

最佳答案

如何创建Image类,而不是使用ImageAugmenter类,该类在初始化时会进行 session ,然后使用Tensorflow处理图像?您可以执行以下操作:

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,您将不会为每个图像创建一个新的 session ,它将为您提供所需的内容。

注意sess.run()的调用方式。我传递给它的提要dict的关键是上面定义的占位符张量。根据您的错误跟踪,您可能正在尝试从未定义sess.run()或已将其定义为im_placeholder之外的代码的一部分调用tf.placeholder

另外,您可以通过更改ImageAugmenter.augment()方法以接收上下参数作为tf.image.random_saturation()方法的输入来进一步改进代码,或者可以使用特定形状初始化ImageAugmenter而不是对其进行硬编码。

关于python - Tensorflow:在方法中使用 session /图形,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/42438170/

10-10 14:11
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