Latest supported version of Tensorflow?

[Steckman]

We are starting to look into using machine learning for an image classification task. We currently use Labview to collect and process images using conventional machine vision techniques. We have started working on building Tensorflow trained models to work with LV Deep Learning vi's. We are currently working with Tensorflow 2.1 and when trying to import the SavedModel *.pb format I believe we are getting an error due to version incompatibility. We are using LV2018 at the moment.

Can anyone provide information as to what the latest version of Tensorflow that is currently supported in the latest version of Labview (2019/2020?) as this would be a reason for us to upgrade at this time. I am also having trouble finding any information that might show me how to save a Tensorflow model created using the 2.x tools in a lower version if needed (I know probably not likely but figured I would look and see if something like this exist)

Thanks

I apology we are not experts in Tensorflow/python programming compared to our experience with Labview so 

[Tda10]

Hi,

I'm also intersted in having the Labview Tensorflow Wrapper updated to 2.x and support GPU essentially for Real Time inferences. It's currently 1.4.1 and only CPU.

Does someone have any news about that ? It seems that Matlab has a better support for this library. It is a pity.

Thanks for your help

[alexderjuengere]

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@Tda10 wrote:

. It's currently 1.4.1

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It is possible to train a model in 2.x, export the trained model as frozen.pb and then load this frozen.pb in 1.x

frozen means, variables and weights in one file - there is also a saved.pb which does separate those two

but ...

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@Tda10 wrote:

 Labview Tensorflow  is 1.41 CPU-only

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yes, there is no GPU support in Labview Tensorflow IMAQ as it is conveniently done in Python Tensorflow.

for 2.0 there still exists the  tensorflow-gpu==2.0 but in later versions there is only one tensorflow package, with integrated GPU support

[Tda10]

Thanks for your answer.

The issue is that the frozen model seems to be deprecated in TF2.x and I get errors when trying to export the model as frozen. They suggest :

# Save a frozen model
myfunc_model = tf.function(model).get_concrete_function(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))
mygraph_def = frozen_keras_graph(myfunc_model)
tf.io.write_graph(graph_def, '/tmp/tf_model', 'frozen_graph.pb')

-> Error : tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array

That's the reason why I asked for a more recent implementation fo TF2.x in Labview. This way, we could use the TF2.x saved_model.pb format directly.

[alexderjuengere]

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@Tda10 wrote:

The issue is that the frozen model seems to be deprecated in TF2.x

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using sess is deprecated, but is still a valid way to access the actual Tensorflow graph:

https://www.tensorflow.org/api_docs/python/tf/io/write_graph

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@Tda10 wrote:

Thanks for your answer.

The issue is that the frozen model seems to be deprecated in TF2.x and I get errors when trying to export the model as frozen. They suggest :

# Save a frozen model
myfunc_model = tf.function(model).get_concrete_function(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))
mygraph_def = frozen_keras_graph(myfunc_model)
tf.io.write_graph(graph_def, '/tmp/tf_model', 'frozen_graph.pb')

-> Error : tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot convert a Tensor of dtype resource to a NumPy array

That's the reason why I asked for a more recent implementation fo TF2.x in Labview. This way, we could use the TF2.x saved_model.pb format directly.

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you need another process step before you save to file, which is to convert the model's variables to constants

# define a dummy model

from tensorflow.keras.applications.densenet import DenseNet201

model = DenseNet201(weights=None)

# Convert Keras model to ConcreteFunction

full_model = tf.function(lambda x: model(x))

full_model = full_model.get_concrete_function(

    tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))

from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2

# Get frozen ConcreteFunction

frozen_func = convert_variables_to_constants_v2(full_model)

frozen_func.graph.as_graph_def()

# Save frozen graph from frozen ConcreteFunction to hard drive

tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir = pathPB, name = namePB, as_text = False)

I learned this from this Blog post:

https://blog.csdn.net/huangcong159/article/details/106882311

[Tda10]

Thanks again. I'm in progress.

Now I can load a frozen model (generated from TF2.x) in Labview with "IMAQ DL Create Model"

Nevertheless, for  some operations I get errors. It seems that new attributes have been added that are not understood by the binary interpreter used by Labview. I still have the impression that an upgrade to TF2.x is needed on the Labview side.

For instance :

tf.keras.layers.MaxPooling2D(pool_size=(2, 2), padding="valid")  has a new "explicit_paddings" attribute that I can't remove from the generated binary frozen file.

Error -1074395539 occurred at IMAQ DL Model Create

Error reported from TensorFlow
Error Code: 3
Description: NodeDef mentions attr 'explicit_paddings' not in Op<name=MaxPool; signature=input:T -> output:T; attr=T:type,default=DT_FLOAT,allowed=[DT_HALF, DT_BFLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16, DT_QINT8]; attr=ksize:list(int),min=4; attr=strides:list(int),min=4; attr=padding:string,allowed=["SAME", "VALID"]; attr=data_format:string,default="NHWC",allowed=["NHWC", "NCHW", "NCHW_VECT_C"]>; NodeDef: {{node sequential_2/max_pooling2d/MaxPool}}. (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).

Possible reason(s):

Unable to load the Model file.

[alexderjuengere]

Thanks for sharing -

I think the IMAQ deep learning API is meant to be used for object detection, not for classification.

Maybe this is limiting you right now ...

are you trying to run a simple Convolutional Neural Network?

something like this?

however,

someone successfully ran the infamous cats'n'dogs classification   with the IMAQ deep learning tools, see the comments in:

https://forums.ni.com/t5/LabVIEW-Idea-Exchange/Tensorflow-API-for-LabVIEW/idi-p/3248737

especially the last comment:

https://forums.ni.com/t5/LabVIEW-Idea-Exchange/Tensorflow-API-for-LabVIEW/idc-p/3893684/highlight/tr...

[eya-nagazi]

How did you solve the problem, please? I have the same error and I'm using Labview2021(64 bit) python 3.6 tensorflow 2.5.0

[eya-nagazi]

How did you solve the problem please ??

[alexderjuengere]

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@eya-nagazi wrote:

How did you solve the problem please ??i

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 As this is tensorflow-cpu only, it was way too slow, the last time I tried.

We also tried tensorflow-gpu via the python node, but the data transfer was too slow ... this may change with LabView 2021 introducing python 3.9

so, we still use LabView (of course...) but externalised the neural net stuff on a separate hardware, using ethernet to communicate between LabView and Python