assignment not allowed to field tensor in protocol message object

Send Downlink command as JSON via gRPC and Python with use of payload formatter

Hey, I’m trying to send a downlink command via gRPC and Python to an existing device. It works to a point, but I tried to send a JSON, which then should get processed via the payload formatter. The problem is that if I send the JSON as an utf-8 encoded string, it does not get passed through the payload format but gets enqueued directly to the device. I tried to assign the req.queue_item.object, but it gives me an error (“AttributeError: Assignment not allowed to message, map, or repeated field “object” in the protocol message object.”). Is there a way to send a downlink to chirpstack with a JSON so that the payload formatter can encode it for the device?

Are you testing this against ChirpStack v3 or v4?

Hey, We use ChirpStack v4 and the corresponding PyPI library.

@brocaar , is this a bug, or is this functionality not yet implemented?

Where in v3 you had to pass the JSON object as string, you must pass it as JSON object in v4 ( https://github.com/chirpstack/chirpstack/blob/master/api/proto/api/device.proto#L483 ).

@brocaar I know, but if I set it to either a python JSON object or a google.protobuf.Struct it returns:

And what do I do with the data field, I can’t let it be empty.

I’m not familiar with the Python API for Protobuf struct types, but maybe this could help you? dictionary - How do you create a Protobuf Struct from a Python Dict? - Stack Overflow

@brocaar I tried that before but same message:

I think there is something else wrong. Maybe the word object is conflicting with something in python? Or something went wrong with the code generation for python. But I honestly don’t know how to debug this.

Found the solution. If you use:

Instead of:

Then everything works.

Can I add a new node in the middle of a Tensorflow model graph using the graphsurgeon tool?

Hello, I’m using TensorRT version 4.0.1.6.

I successfully replaced an unsupported Tensorflow operation with a TensorRT PlugIn layer using the graphsurgeon APIs.

Now, I want to add a totally new node in the middle of the model.

I saw that the graphsurgeon support these APIs: create_node, create_plugin_node, extend, append

But I couldn’t understand if I can use them for my purpose.

I know how to find the specific model location where I want to add my new node but I cannot find the right way to use the graphsurgeon APIs to do that.

Please advise,

You can convert graph_def to GraphSurgeons DynamicGraph, modify it and finally write it as uff:

Thanks juko.lam

My purpose is to add a debug node multiple times in the middle of the grpah in order to implement a simple functionallity: Save to disk the tensor input data.

By this way I will have the ability to investigate the graph nodes tensor outputs data correctness.

So, as I wrote previously, I have the following abilities:

• uff file generation using uff.from_tensorflow_frozen_model API
• Replace an existing nodes in the graph using graphsurgeon API
• Remove an existing nodes in the graph using graphsurgeon API

What I don’t have and till now I couldn’t succeeded to do is to add totally new node somewhere in the graph

Can you provide me please an example of how to add new node in the middle of the graph using graphsurgeon APIs?

Based on my constraints (I have only the frozen graph pb file), Do you have any other idea (not related to the graphsurgeon) to to achieve my purpose?

You can load the frozen pb file which gives you the graph, then convert the graph to DynamicGraph and modify it. So something like this (probably not runnable directly):

I suggest that you inspect graphs created by the TRT examples just before they are written to UFF file.

I tried this to replace the ResizeBilinear with Conv2DTranspose but I got error :

“convert_lanenet_uff.py”, line 32, in add_plugin node.input = next_.input AttributeError: Assignment not allowed to repeated field “input” in protocol message object.

Code snippet : import tensorflow as tf from tensorflow.core.framework import types_pb2 import graphsurgeon as gs

graph = gs.DynamicGraph(‘frozen_graph.pb’)

Add new node

node = tf.NodeDef() node.op = ‘Conv2DTranspose’ node.name = ‘Conv2DTranspose’ graph.append(node)

want to replace ResizeBilinear with Conv2DTranspose layer

next = graph.find_nodes_by_op(“ResizeBilinear”)[0] # assume only one node is found node.input = next.input next.input = [node.name]

python assignment not allowed to field in protocol message object

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Assignment not allowed to repeated field "conversions" in protocol message object #559

panukuulu commented Jan 11, 2022

sakkammadam commented Jan 11, 2022 • edited Loading

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panukuulu commented Jan 12, 2022

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Assignment not allowed to repeated field "conversions" in protocol message object

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TypeError: 'Tensor' object does not support item assignment in TensorFlow

I try to run this code:

But I get error on last line: TypeError: 'Tensor' object does not support item assignment It seems I can not assign to tensor, how can I fix it?

5 Answers 5

In general, a TensorFlow tensor object is not assignable, so you cannot use it on the left-hand side of an assignment.

The easiest way to do what you're trying to do is to build a Python list of tensors, and tf.stack() them together at the end of the loop:

 * With the exception of tf.Variable objects, using the Variable.assign() etc. methods. However, rnn.rnn() likely returns a tf.Tensor object that does not support this method.

• 1 Note that tf.pack() has been replaced by tf.stack() since TensorFlow 1.0. –  CNugteren Commented Jun 15, 2017 at 14:20
• I have the same problem with the issue in stackoverflow.com/questions/55871023/… , but I do not know how can I do this with tf.stack. could please guide about this issue? –  david Commented Apr 26, 2019 at 16:03
• How would one do this?: for x in range(int(r.shape[2]/2)): for d in range(1,int(r.shape[0]/2)): r[d, :, x, :] = r[0, :, x+d, :] –  zendevil Commented Aug 20, 2020 at 0:07

Another way you can do it is like this.

then the output is:

ref: https://www.tensorflow.org/api_docs/python/tf/Variable#assign

• I seem to be getting an error when trying to implement it this way: x = tf.Variable(tf.ones([1,2,2,3], tf.float32)) x = x[:,:,:,1].assign(2.0) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) x_data = sess.run(x) –  Moondra Commented Sep 26, 2017 at 20:17
• note: tf.Variable maintains its value across session runs, this is not always the right solution. –  Viktor Tóth Commented Aug 15, 2018 at 14:09
• 2 This answer has the worst performance in all speed tests I've done to assign 1D vectors to 2D array. Don't use this method! The fastest so far is mrry's answer with appending to a list and stacking. –  Krzysztof Maliszewski Commented Jan 12, 2021 at 23:34
• 3 doesn't work in TF2: EagerTensor object has no attribute 'assign' –  Dan D. Commented Oct 6, 2021 at 9:04

When you have a tensor already, convert the tensor to a list using tf.unstack (TF2.0) and then use tf.stack like @mrry has mentioned. (when using a multi-dimensional tensor, be aware of the axis argument in unstack)

• this worked for me. thanks. –  rsonx Commented Sep 20, 2023 at 21:39

Neither tf.Tensor nor tf.Variable is element-wise-assignable. There is a trick however which is not the most efficient way of course, especially when you do it iteratively.

You can create a mask and a new_layer tensor with new values and then

do a Hadamard product (element-wise product).

The original * mask part sets the specified values of original to 0 and the second part, new_layer*(1-mask) assigns new_layer tensor whatever you want without modifying the elements assigned to 0 by the mask tensor in the previous step.

Another way is to use numpy instead:

Use Pytorch:

As this comment says, a workaround would be to create a NEW tensor with the previous one and a new one on the zones needed.

• Create a mask of shape outputs with 0's on the indices you want to replace and 1's elsewhere (Can work also with True and False )
• Create new matrix of shape outputs with the new desired value: new_values
• Replace only the needed indexes with: outputs_new = outputs* mask + new_values * (1 - mask)

If you would provide me with an MWE I could do the code for you.

A good reference is this note: How to Replace Values by Index in a Tensor with TensorFlow-2.0

• How would one do this? for x in range(int(r.shape[2]/2)): for d in range(1,int(r.shape[0]/2)): r[d, :, x, :] = r[0, :, x+d, :] –  zendevil Commented Aug 20, 2020 at 0:07

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