yolov3-tiny model float16 quantization (InvalidArgument: [ONNXRuntimeError] )

Hi everyone,

I quantized yolov3-tiny model with float16 and run the model in onnxruntime

ort_session = ort.SessionOptions()
ort_session.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
sess = ort.InferenceSession(model_path, ort_session, providers=['CPUExecutionProvider'])
image = np.ones(shape=(1, 3, 416, 416), dtype=np.float16)
image_size = np.random.rand(1, 2).astype('float16')
input_name1 = sess.get_inputs()[0].name
input_name2 = sess.get_inputs()[1].name
output = sess.run(None, {input_name1: image, input_name2: image_size})

But I have this issue :
InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : Load model from ./tiny_yolov3_fp16.onnx failed:This is an invalid model. Type Error: Type 'tensor(float16)' of input parameter (yolo_evaluation_layer_1/concat_6:0_btc) of operator (NonMaxSuppression) in node (yolonms_layer_1/non_max_suppression/NonMaxSuppressionV3) is invalid.

Is there way I can fix this non_max_suppresion problem?
I would really appreciate it if you give me any idea. Thank you for your time.

model file :
tiny_yolov3_fp16.zip

Start with the attached tiny_yolov3_fp16.zip model and the Python reproduction using CPUExecutionProvider. Inspect the NonMaxSuppressionV3 node and its tensor(float16) input, then verify the model against ONNX Runtime's supported input types. Done means the model loads and the provided sess.run call completes without the invalid-graph error.