图像失真返回错误: Reshape_5:0返回的张量无效
图像失真返回错误: Reshape_5:0返回的张量无效
提问于 2017-01-08 16:23:19
我试图将图像失真添加到我的ConvNet模型中，并得到一个非常奇怪的错误。我的数据是TFRecords格式的，我正在使用来自CFIAR10代码的color_distorter()函数。下面是一些我拼凑在一起的虚拟代码，以确保所有的事情都在做我希望它做的事情。当我看到图像被扭曲后，就没有问题了。这个问题似乎是在张量变平后或在运行之后出现的。由于某种原因，它将执行一次，但是第二次它会抛出一个错误。下面是我的代码和它返回的错误。我目前的怀疑是它可能是tf.map_fn()，但我不知道。
def color_distorer(image, thread_id=0, scope=None):
with tf.op_scope([image], scope, 'distort_color'):
color_ordering = thread_id % 2
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
# The random_* ops do not necessarily clamp.
image = tf.clip_by_value(image, 0.0, 1.0)
return image
X_test_batch, y_test_batch = inputs(FLAGS.train_dir,
FLAGS.test_file,
FLAGS.batch_size,
FLAGS.n_epochs,
FLAGS.n_classes,
one_hot_labels=True,
imshape=160*160*3)
with tf.Session() as sess:
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# X = sess.run([X_test_batch])
for i in range(5): #epochs
image = tf.reshape(X_test_batch, [-1, 160, 160, 3])
result = tf.map_fn(lambda img: color_distorer(img), image)
X, y = sess.run([result, y_test_batch]) #to see what the distortion did
for i in range(50): #all the images look distorted..
if i%25 ==0:
plt.title(y[i])
plt.imshow(X[i])
plt.show()
print('******************') #This runs once... then breaks. Why?
result = tf.reshape(result, [-1, 76800])
dX, dy = sess.run([result, y_test_batch])
print(dX)错误：
******************
[[ 1. 1. 1. ..., 1. 1. 1.]
[ 1. 1. 1. ..., 1. 1. 1.]
[ 1. 1. 1. ..., 1. 1. 1.]
...,
[ 1. 1. 1. ..., 1. 1. 1.]
[ 1. 1. 1. ..., 1. 1. 1.]
[ 1. 1. 1. ..., 1. 1. 1.]]
WARNING:tensorflow:tf.op_scope(values, name, default_name) is deprecated, use tf.name_scope(name, default_name, values)
Traceback (most recent call last):
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call
return fn(*args)
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1003, in _run_fn
status, run_metadata)
File "/home/mcamp/anaconda3/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: The tensor returned for map_1/TensorArrayPack_1/TensorArrayGatherV2:0 was not valid.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/media/mcamp/Local SSHD/Python Projects/Garage Door Project/FreshStart/ReadTFREcords.py", line 80, in <module>
dX, dy = sess.run([result, y_test_batch])
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 766, in run
run_metadata_ptr)
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 964, in _run
feed_dict_string, options, run_metadata)
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run
target_list, options, run_metadata)
File "/home/mcamp/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1034, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: The tensor returned for map_1/TensorArrayPack_1/TensorArrayGatherV2:0 was not valid.回答 1
Stack Overflow用户
回答已采纳
发布于 2017-01-08 19:34:18
好的，我认为正在发生的是我调整了X_test_batch的大小，使之回到图像的形状。然后我在它上运行我的失真，并将其命名为result。这就是我跑过去看的。在查看之后，我调整了模型的大小，使之成为平面图像。在此期间，我更改了一些名为image和result的东西的名称。如果代码从上到下都是红色的，我认为它会很好，但是Tensorflow有它工作的图形，所以当它到达第二次迭代时，它期望结果是一个三维张量，但是它实际上是一个平面张量，试图通过tf.map_fn。tf.map_fn是驻留在图形上的东西，直到sess.run()才会被执行，因此这将是我看到的错误的原因。我希望这是有意义的，并帮助其他人的道路上。
with tf.name_scope('TestingData'):
X_test_batch, y_test_batch = inputs(FLAGS.train_dir,
FLAGS.test_file,
FLAGS.batch_size,
FLAGS.n_epochs,
FLAGS.n_classes,
one_hot_labels=True,
imshape=160*160*3)
image = tf.reshape(X_test_batch, [-1, 160, 160, 3])
image = tf.map_fn(lambda img: color_distorer(img), image)
with tf.Session() as sess:
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
# X = sess.run([X_test_batch])
for i in range(5):
# dX, dy = sess.run([result, y_test_batch])
print('******************')
image = tf.reshape(image, [-1, 76800])
dX, dy = sess.run([image, y_test_batch])
distorted = np.reshape(dX, (-1, 160, 160, 3))
print(dX.shape)
print(distorted.shape)
for i in range(50):
if i%25 ==0:
print(distorted[i], dy[i])
plt.title(dy[i])
plt.imshow(distorted[i])
plt.show()
print(dX.shape)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/41534866
复制相关文章
相似问题
腾讯云开发者
