addons: Entity could not be transformed

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 18.04
TensorFlow installed from (source or binary): binary
TensorFlow version (use command below): 2.0.0-dev20190317 (gpu nightly)
TensorFlow Addons installed from (source, PyPi): source
TensorFlow Addons version: master
Python version and type (eg. Anaconda Python, Stock Python as in Mac, or homebrew installed Python etc): anaconda (conda-forge) python 3.7.1
Bazel version (if compiling from source): 0.20.0
GCC/Compiler version (if compiling from source): 7.3.0
Is GPU used? (yes/no): yes
GPU model (if used): RTX 2070

I’m seeing the following warning appear when trying to use the transform op. The simplest way to reproduce is to simply run the tests via python:

python ~/src/addons/tensorflow_addons/image/transform_test.py
[...]
W0317 18:03:00.753756 139906711668544 tf_logging.py:161] Entity <function image_projective_transform_v2 at 0x7f3e73a05a60> could not be transformed and will be staged without change. Error details can be found in the logs when running with the env variable AUTOGRAPH_VERBOSITY >= 1. Please report this to the AutoGraph team. Cause: Unexpected error transforming <function image_projective_transform_v2 at 0x7f3e73a05a60>. If you believe this is due to a bug, please set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output when filing the bug report. Caused by: Unable to locate the source code of <function image_projective_transform_v2 at 0x7f3e73a05a60>. Note that functions defined in certain environments, like the interactive Python shell do not expose their source code. If that is the case, you should to define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code

export AUTOGRAPH_VERBOSITY=10
python ~/src/addons/tensorflow_addons/image/transform_test.py
[...]
I0317 18:11:38.742995 140338201401152 ag_logging.py:132] Error transforming <function image_projective_transform_v2 at 0x7fa2ea6b2a60>
Traceback (most recent call last):
  File "~/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/autograph/pyct/parser.py", line 51, in parse_entity
    source = tf_inspect.getsource_no_unwrap(entity)
  File "~/miniconda3/envs/tf2/lib/python3.7/site-packages/tensorflow/python/util/tf_inspect.py", line 408, in getsource_no_unwrap
    lines, lnum = _inspect.findsource(obj)
  File "~/miniconda3/envs/tf2/lib/python3.7/inspect.py", line 786, in findsource
    raise OSError('could not get source code')
OSError: could not get source code

Is this expected? I expect this is likely an artifact of a broken source build on my end, but I imagine others may experience similar issues until the CI/CD system is building nightly versions for all platforms and python versions.

About this issue

Original URL
State: closed
Created 5 years ago
Comments: 27 (19 by maintainers)

Commits related to this issue

Mark TF plugins so that autograph can recognize and whitelist them. Add special handling for exec-generated code, which does have a __code__ object but no source. Fixes https://github.com/tensorflow/a... — committed to testkevinbonz/tensorflow by deleted user 5 years ago
Mark TF plugins so that autograph can recognize and whitelist them. Add special handling for exec-generated code, which does have a __code__ object but no source. Fixes https://github.com/tensorflow/a... — committed to ashwin/tensorflow by deleted user 5 years ago

Most upvoted comments

I just checked the test with the nightly and the warnings should go away now - can you confirm?

mdanatg on May 3, 2019

Thanks, the docker container worked, and I found the culprit as well. It seems that our own op loader generates some dynamic code using exec, which discards source code information. I’ll have a fix shortly to handle those properly.

The warning is otherwise absolutely benign, but sorry for the annoyance it created!

mdanatg on May 2, 2019

feature_extractor_url ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2" #@param {type:"string"}

feature_extrator_layer = hub.KerasLayer(feature_extractor_url, input_shape=IMAGE_SHAPE+(3,))

model = tf.keras.Sequential([
  feature_extractor_layer,
  layers.Dense(image_data_train.num_classes, activation='softmax')
])

model.summary()

WARNING:tensorflow:Entity <bound method KerasLayer.call of <tensorflow_hub.keras_layer.KerasLayer object at 0x13180d390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module ‘gast’ has no attribute ‘Num’ WARNING:tensorflow:Entity <bound method KerasLayer.call of <tensorflow_hub.keras_layer.KerasLayer object at 0x13180d390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module ‘gast’ has no attribute ‘Num’ WARNING: Entity <bound method KerasLayer.call of <tensorflow_hub.keras_layer.KerasLayer object at 0x13180d390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output. Cause: module ‘gast’ has no attribute ‘Num’

ruifengma on Sep 28, 2019

Will do! Feel free to add me to the Assignees list but we linked an internal bug to the issue.

mdanatg on May 2, 2019

Last note: I’ve seen the same warning in very basic non-test calculations, but I figured executing the test script manually is a pretty good way to reproduce the issue initially.

edit Here’s a minimal example that triggers the same warning:

import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow_addons.image.transform_ops import angles_to_projective_transforms

minval = 10 * np.pi / 180.
maxval = -1.0 * minval

n_images = 10
x = tf.eye(28, 28, [n_images])
random_angles = tf.random.uniform(shape=[1], minval=minval, maxval=maxval)
transforms = angles_to_projective_transforms(random_angles, 28, 28)
transforms = transforms[0:1]
rotated_images = tfa.image.transform(x, transforms)

kyleabeauchamp on Mar 18, 2019