Closed ItsMeTheBee closed 3 years ago
@ItsMeTheBee please share the invoking the interpreter script to reproduce the issue.
@hjonnala here you go =)
import numpy as np
import tflite_runtime.interpreter as tflite
import sys
from time import time
import random
EDGETPU_SHARED_LIB = "libedgetpu.so.1"
DEBUG = True
def make_interpreter(model_path, edge_tpu=True):
# Load the TF-Lite model and delegate to Edge TPU
if edge_tpu:
interpreter = tflite.Interpreter(model_path=model_path,
experimental_delegates=[
tflite.load_delegate(EDGETPU_SHARED_LIB)
])
else:
interpreter = tflite.Interpreter(model_path=model_path)
return interpreter
def main(args=None):
make_interpreter("/mnt/Storage/training/unet_multi_class/Multiclass-Segmentation-in-Unet/conversion/model_256x256_edgetpu.tflite", False)
if __name__ == '__main__':
main()
I'm currently working with Python 3.6 and tflite_runtime 2.1.0. I'm guessing I either need to upgrade/ downgrade something or to specify the transpose_conv operator version.
The script is working fine for me with edgetpu output form shared colab notebook. Is the error coming with model_256x256_edgetpu.tflite? If yes, please share model_256x256_edgetpu.tflite file.
Huh interesting, could it be an issue because I am training and converting with TensorFlow 2.3.0 instead of nightly? The model file can be downloaded from my private Google Drive: here.
Hello, I am using python 3.8 and corresponding tflite_runtime whl package from this source: https://github.com/google-coral/pycoral/releases
In my case correct whl would be: https://github.com/google-coral/pycoral/releases/download/v1.0.1/tflite_runtime-2.5.0-cp38-cp38-linux_x86_64.whl
The script is working fine with both True and False flags.
can you please share detail error message.
Ah sweet, it seems like it was an issue with my conda enviroment! I made a new one with Python 3.8 and it worked fine, made another one with Python 3.6 and that one worked as well :D
Thanks a lot!
Do you happen to have some code sample on how to use UNet with tflite_runtime? I'm guessing the image needs to be converted into a numpy array just like during the training (so with normal tensorflow and keras) or are there any differences?
I don't have anything specific to Unet. May be this is https://github.com/google-coral/edgetpu/issues/417#issue-940647083 helpful.
Hey there!
I've trained a UNet model following these instructions but with two classes instead of three. I've also converted the network to tflite and compiled it with the coral edge compiler, but when I try to invoke the interpreter I get this error:
Didn't find op for builtin opcode 'TRANSPOSE_CONV' version '3'
Do you happen to have an example code of running UNet with tflite_runtime, similar to the code in this repo? I know pycoral exists but I need to run everything on an aarch64 system which is why I'm very hesitant to change to another library.
Best regards, Sally