Hi Dave
I also tried Armbian Stretch (Debian 9), but nothing changed. I got an Illegal Instruction error as well.
Then I managed to get an RPi 3. I set it up by following this tutorial. On this platform, my simple test program runs correctly:
pi@raspberrypi:~/devel/glover-image-classifier-0.1.0 $ python3 image-classifier.py
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.AttrValue is deprecated. Please use tf.compat.v1.AttrValue instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.COMPILER_VERSION is deprecated. Please use tf.version.COMPILER_VERSION instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.CXX11_ABI_FLAG is deprecated. Please use tf.sysconfig.CXX11_ABI_FLAG instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/__init__.py:98: The name tf.ConditionalAccumulator is deprecated. Please use tf.compat.v1.ConditionalAccumulator instead.
2019-10-22 15:42:53,478 - DEBUG - Starting ...
2019-10-22 15:42:53,479 - DEBUG - Importing the TF graph ...
Classified as: Red Apple
2019-10-22 15:42:58,061 - DEBUG - Prediction time = 1.8572380542755127 s
Avocado 2.246000076411292e-05
Banana 3.769999921132694e-06
Green Apple 0.029635459184646606
Hand 4.4839998736279085e-05
Orange 0.0009084499906748533
Red Apple 0.9693851470947266
2019-10-22 15:42:58,067 - DEBUG - Exiting ...
I used mounted the same raspbian root file system used with RPi from my embedded platform and I got an Illegal Instruction error again.
So it seems there is a structural incompatibility between one of the software layers (maybe TensorFlow) and my platform, which is based on NXP i.MX6Q.
Hey, I had a brief look at armbian and I spotted that it was on a fairly old kernel release - 3.x from memory. I think Stretch on RPi was on 4.3 or something similar. I did wonder if that was where the issue is. There is nothing to stop you from retargeting the Custom Vision model Docker image to different base a image... I think you said you got the CV/Tensorflow running directly on Armbian so that might be a good starting point...
Actually, I used only the armbian root file system.
Regarding the Linux kernel, I used the one that belongs to the latest official BSP of our platform. It is based on release 4.9.11.
Anyway, I agree with you, in the sense that I can't exclude that the root cause is somehow related to the kernel.
Hi Dave,
finally, I managed to solve the problem.
The root cause is related to how the Tensor Flow packages I used were built. Because of the compiler's flags, these packages make use of instructions that are not supported by the i.MX6Q SoC.
So I rebuilt TF with the proper flags ... et voilà:
$ python3 image-classifier.py
2019-10-25 11:17:15,288 - DEBUG - Starting ...
2019-10-25 11:17:15,289 - DEBUG - Importing the TF graph ...
Classified as: Red Apple
2019-10-25 11:17:21,591 - DEBUG - Prediction time = 2.567471504211426 s
Avocado 2.246000076411292e-05
Banana 3.769999921132694e-06
Green Apple 0.029635440558195114
Hand 4.4839998736279085e-05
Orange 0.0009084499906748533
Red Apple 0.9693851470947266
2019-10-25 11:17:21,594 - DEBUG - Exiting ...
I had a stupid bug in my code.
I fixed it and now everything works fine. I'm gonna run it on my embedded device.
Yah awesome!
Hi Dave,
installing tensorflow and all its dependencies wasn't easy on armbian at all!
I tried several TF/Python combinations, but none of them worked :(
This table lists the combinations I tried and the reason why they fail.
I think that the Illegal instruction problem might explain why your container doesn't work either on this device.
By the way, does your container make use of Python 2.x o 3.x?
In the meantime, I think I'm gonna try a different distro.
Hi Dave
I also tried Armbian Stretch (Debian 9), but nothing changed. I got an Illegal Instruction error as well.
Then I managed to get an RPi 3. I set it up by following this tutorial. On this platform, my simple test program runs correctly:
I used mounted the same raspbian root file system used with RPi from my embedded platform and I got an Illegal Instruction error again.
So it seems there is a structural incompatibility between one of the software layers (maybe TensorFlow) and my platform, which is based on NXP i.MX6Q.
Hey, I had a brief look at armbian and I spotted that it was on a fairly old kernel release - 3.x from memory. I think Stretch on RPi was on 4.3 or something similar. I did wonder if that was where the issue is. There is nothing to stop you from retargeting the Custom Vision model Docker image to different base a image... I think you said you got the CV/Tensorflow running directly on Armbian so that might be a good starting point...
Actually, I used only the armbian root file system.
Regarding the Linux kernel, I used the one that belongs to the latest official BSP of our platform. It is based on release 4.9.11.
Anyway, I agree with you, in the sense that I can't exclude that the root cause is somehow related to the kernel.
Hi Dave,
finally, I managed to solve the problem.
The root cause is related to how the Tensor Flow packages I used were built. Because of the compiler's flags, these packages make use of instructions that are not supported by the i.MX6Q SoC.
So I rebuilt TF with the proper flags ... et voilà:
Woohoo, well done!