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 ...
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!