TFLite Model Creation

[vdaita]

I used ONNX to convert the .onnx file to a .pb file. However, I can't convert it to a .tflite model because I don't know what the input and output arrays are. I tried to take a look at the pytorch model using Netron, but still don't have any luck with that. Any recommendations?

[glenn-jocher]

This is something on our TODO list, so I can't offer you any advice at the moment. Can you upload your current conversions script here?

[vdaita]

https://colab.research.google.com/drive/1BMYdWS51RXEesAHKW59GWNLks63xkfGV?usp=sharing
https://colab.research.google.com/drive/1e6sYYAKGY4Gm4D1NtavAmWdWwiBLKYd_?usp=sharing
@glenn-jocher

[Misterrendal]

Export to onnx

model.model[-1].export = True 
output = model(img) 
torch.onnx.export(model, 
                  img, 
                  f, 
                  verbose=False, 
                  opset_version=10, 
                  export_params=True,
                  input_names=['images'],
                  keep_initializers_as_inputs=True,
                  output_names=['output_0', 'output_1', 'output_2'])

Export model as .pb file

from onnx_tf.backend import prepare
import onnx

model_onnx = onnx.load('tflite/saved_model.onnx')
tf_rep = prepare(model_onnx, device='cpu')
tf_rep.export_graph('tflite/saved_model.pb')

Read frozen weights

def wrap_frozen_graph(graph_def, inputs, outputs):
    def _imports_graph_def():
        tf.compat.v1.import_graph_def(graph_def, name="")

    wrapped_import = tf.compat.v1.wrap_function(_imports_graph_def, [])
    import_graph = wrapped_import.graph

    return wrapped_import.prune(
        tf.nest.map_structure(import_graph.as_graph_element, inputs),
        tf.nest.map_structure(import_graph.as_graph_element, outputs))

with tf.io.gfile.GFile("tflite/saved_model.pb", "rb") as f:
    graph_def = tf.compat.v1.GraphDef()
    loaded = graph_def.ParseFromString(f.read())

Wrap frozen graph to ConcreteFunctions

frozen_func = wrap_frozen_graph(graph_def=graph_def,
                                inputs=["images:0"],
                                outputs=['output_0:0','output_1:0', 'output_2:0'])

And convert to tflite model

converter = tf.lite.TFLiteConverter.from_concrete_functions([frozen_func])
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tf_lite_model = converter.convert()
open('tflite/yolov5s.tflite', 'wb').write(tf_lite_model)

[Misterrendal]

I could not convert the model to tflite using ops version 11

[batrlatom]

@Misterrendal have you succeed?

[daggarwal01]

@Misterrendal did that solution worked out for you?

[keesschollaart81]

I was able to use @Misterrendal code to create a tflite version!

I had to replace this line with Misterrendal's onnx export statement torch.onnx.export(model, ...) (with the correct input/output names).

Then run the ONNX export as described in #251. This results in a .pb file. Then I pasted the other 4 code blocks in a .py file, I installed some dependencies like pip3 install tensorflow-gpu==2.2.0 and pip install git+https://github.com/onnx/onnx-tensorflow.git.

[batrlatom]

@keesschollaart81 Were you able to successfully run converted tflite model? I have tried @Misterrendal code to convert onnx model to tflite with success, but when trying to infer the model, I am getting segfault.

The code for prediction I have used:

import numpy as np
import tensorflow as tf

interpreter = tf.lite.Interpreter(model_path="yolov5s.tflite")
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()

output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)

[zldrobit]

@batrlatom, maybe you could try #959.
This PR exports yolov5 to both TensorFlow and TFLite models with TF 2.3.

[batrlatom]

@zldrobit looks like it works. Thanks!

[Kar1s]

@zldrobit tested it as well, it works, thanks

[JimBratsos]

I am having a problem with the models module. When I tried pip install I ran to a number of problems. After some searching I found out the package was renamed to doqu, but when I tried using it, it pops up an error about missing module named document_base. Does anyone know a workaround with @zldrobit's PR? Thanks for the great work. Also, is the result of the conversion int8 quantized?

[batrlatom]

@zldrobit Do you think that you could make a complete inference code with nms in tf? So people without deeper knowledge of tf can just use your code in tflite or tfjs? btw, I was able to export your saved_model into tfjs, which is great!

[zldrobit]

@JimBratsos the result is not int 8 quantized.
@batrlatom Thanks :D
I will add tf nms in models/tf.py with an argument, but I am busy with TFLite's GPU delegate problem.
I hope the problem can be solved soon and I can have time to add nms feature.

[ntlex]

@zldrobit Any news on the progress of nms for tensorflow?

[Jacobsolawetz]

@zldrobit Do you think that you could make a complete inference code with nms in tf? So people without deeper knowledge of tf can just use your code in tflite or tfjs? btw, I was able to export your saved_model into tfjs, which is great!

@batrlatom did you find success running in tfjs? I have made the conversion but seeing many of the confidences are off (around 0 or 1). I'm wondering I made a quantization error along the way. Did you run into anything like this?

[Jacobsolawetz]

If anyone encounters what I had above, the problem was not preprocessing the tfjs image correctly, I needed to scale pixel values between [0,1]

[batrlatom]

@Jacobsolawetz Hi, unfortunately I was not able to make nms work so I waiting if @zldrobit would make it avaliable

[zldrobit]

@batrlatom @ntlex I have pushed new commit to support adding nms in SavedModel and GraphDef https://github.com/zldrobit/yolov5/tree/tf-android.
Try using

PYTHONPATH=. python3  models/tf.py --img 640 --weight weights/yolov5s.pt --cfg models/yolov5s.yaml --tf-nms

to export SavedModel and GraphDef, and detect objects with one of

python3 detect.py --img 640 --weight weights/yolov5s.pb --no-tf-nms
python3 detect.py --img 640 --weight weights/yolov5s_saved_model --no-tf-nms

If you have further questions, plz reply in #1127, so I won't miss it :D

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[dewball345]

If anyone encounters what I had above, the problem was not preprocessing the tfjs image correctly, I needed to scale pixel values between [0,1]

How were you even able to do inference in tensorflow.js. I'm getting Cannot read property 'name' of undefined. What was the code you used to do inference? How did you export the model to tf.js? I converted from pytorch to onnx to pb to tensorflow.js, and I got the inference to work on the pb model but not in javascript.