Tensorflow session and graph context - tensorflow

Question

My question is about context and the TensorFlow default sessions and graph.
The problem:
Tensorflow is unable to feed a placeholder in the following scenario:
Function Test defines a graph.
Function Test_Once defines a session.
When Function Test calls Test_Once -> Feeding fails.
When I change the code so function Test declares the graph + the session -> all is working.
Here is the code:
def test_once(g, saver, summary_writer, logits, images, summary_op):
"""Run a session once for a givven test image.
Args:
saver: Saver.
summary_writer: Summary writer.
logits:
summary_op: Summary op.
"""
with tf.Session(graph=g) as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found')
return
images.astype(np.float32)
predictions = sess.run(logits, feed_dict={'InputPlaceHolder/TestInput:0':images})
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary_writer.add_summary(summary, global_step)
return (predictions)
def test():
"""Test LCPR with a test image"""
with tf.Graph().as_default() as g:
# Get image for testing
images, labels = lcpr.test_input()
# Build a Graph that computes the logits predictions from the
# inference model.
with tf.name_scope('InputPlaceHolder'):
test_image_placeholder = tf.placeholder(tf.float32, (None,None,None,3), 'TestInput')
# Display the training images in the visualizer.
# The 'max_outputs' default is 3. Not stated. (Max number of batch elements to generate images for.)
#tf.summary.image('input_images', test_image_placeholder)
with tf.name_scope('Inference'):
logits = lcpr.inference(test_image_placeholder)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
lcpr.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
writer = tf.summary.FileWriter("/tmp/lcpr/test")
writer.add_graph(g)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.test_dir, g)
#Sadly, this will not work:
predictions = test_once(g, saver, summary_writer, logits, images, summary_op)
'''Alternative working option :
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found')
return
x = sess.run(logits, feed_dict={'InputPlaceHolder/TestInput:0':images})
print(x)
'''
The above code yeilds an error that the placeholder is not fed:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'InputPlaceHolder/TestInput' with dtype float
And it's not that TensorFlow does not recognize the placeholder. If I change the name from 'InputPlaceHolder/TestInput:0' to 'InputPlaceHolder/TestInput:1' I receive a message calming that 'InputPlaceHolder/TestInput' exists but has only 1 output. This makes sense, and I guess the session runs on my default graph.
Things only work for me if I stay within the same def:
If I change the code by running the commented part (starting ' with tf.Session() as sess:) directly from within the first function all works.
I wonder what am I missing?
My guess that is context related, maybe not assigning the session to the graph?

Answer 1

Solved. Stupid mistake
test_once calls sess.run twice. On the second time, indeed no placeholder is fed.... : summary.ParseFromString(sess.run(summary_op))