I’ve custom trained ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8_3 with my data and exported the model and I’m able to do inferencing . im using tensorflow 2.6 and now I want to convert the model to tflite to run it on mobile .I’ve used the code below to convert the model to tflite and the code run and a tflite model is saved successfully .
converter=tf.lite.TFLiteConverter.from_saved_model(PATH_TO_SAVED_MODE1L)
    converter.optimizations=[tf.lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops=[tf.lite.OpsSet.TFLITE_BUILTINS,tf.lite.OpsSet.SELECT_TF_OPS]
    tflite_model=converter.convert()
    with open('C:/Users/CX/Desktop/model.tflite', 'wb') as f:
      f.write(tflite_model)

Now im trying to test with some with images using the code below :
interpreter = tf.lite.Interpreter(model_path="C:/Users/CX/OneDrive/Desktop/model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
path=r"C:\t3"
for file in pathlib.Path(path).iterdir():

    img = cv2.imread(r"{}".format(file.resolve()))
     
    img1 = img.reshape(1,img.shape[0],img.shape[1],3) 

    interpreter.set_tensor(input_details[0]['shape_signature'],img1)

    interpreter.invoke()


Im getting the error :
TypeError: SetTensor(): incompatible function arguments. The following argument types are supported:
    1. (self: tensorflow.lite.python.interpreter_wrapper._pywrap_tensorflow_interpreter_wrapper.InterpreterWrapper, arg0: int, arg1: handle) -> object

Invoked with: <tensorflow.lite.python.interpreter_wrapper._pywrap_tensorflow_interpreter_wrapper.InterpreterWrapper object at 0x000001E4D88E38F0>, array([ 1, -1, -1,  3])

here is the details from the interpreter.get_input_details() fucntion :

[{'name': 'serving_default_input_tensor:0', 'index': 0, 'shape': array([1, 1, 1, 3]), 'shape_signature': array([ 1, -1, -1,  3]), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]


and interpreter.get_output_details()
[{'name': 'StatefulPartitionedCall:6', 'index': 462, 'shape': array([    1, 12804,     4]), 'shape_signature': array([    1, 12804,     4]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:0', 'index': 1594, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:5', 'index': 1559, 'shape': array([1]), 'shape_signature': array([1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:7', 'index': 482, 'shape': array([    1, 12804,    56]), 'shape_signature': array([    1, 12804,    56]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:1', 'index': 1647, 'shape': array([1, 1, 1]), 'shape_signature': array([ 1, -1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:2', 'index': 1629, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:4', 'index': 1576, 'shape': array([1, 1]), 'shape_signature': array([ 1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}, {'name': 'StatefulPartitionedCall:3', 'index': 1612, 'shape': array([1, 1, 1]), 'shape_signature': array([ 1, -1, -1]), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

Any help really appreciated