File "/usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/interpreter.py", line 915, in invoke self._interpreter.Invoke() RuntimeError: tensorflow/lite/kernels/concatenation.cc:158 t->dims->data[d] != t0->dims->data[d] (1 != 2)Node number 304 (CONCATENATION) failed to prepare.

[Mayi-Keiji]

Hi ,
I face a problem when complete the training with UNET + efficientnetb0 and convert the trained model to tflite format, I can convert it to tflite format, but after I call the invoke function, it raise an error as the title.

My envs like:

OS Platform and Distribution (e.g., Linux Ubuntu 20.04):
TensorFlow installation (2.9.1+nv22.9):

Error code in tflite as below:
``import numpy as np
import tensorflow as tf

def main():

interpreter = tf.lite.Interpreter(model_path="model.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)

signatures = interpreter.get_signature_list()
print(signatures)

interpreter.invoke()
return

output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
if name == 'main':
main()
My model code is as below:

`
''' BACKBONE = 'efficientnetb0'
BATCH_SIZE = 1
CLASSES = ["background", "target", "others"]
LR = 0.0001
EPOCHS = 10

preprocess_input = sm.get_preprocessing(BACKBONE)

n_classes = 1 if len(CLASSES) == 1 else (len(CLASSES) + 1) # case for binary and multiclass segmentation
activation = 'sigmoid' if n_classes == 1 else 'softmax'

model = sm.Unet(BACKBONE, classes=n_classes, activation=activation)
'''
`