tests/python/contrib/test_edgetpu_runtime.py

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#
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import os
import tvm
from tvm import te
import numpy as np
from tvm import rpc
from tvm.contrib import util, tflite_runtime

# import tflite_runtime.interpreter as tflite


def skipped_test_tflite_runtime():
    def get_tflite_model_path(target_edgetpu):
        # Return a path to the model
        edgetpu_path = os.getenv("EDGETPU_PATH", "/home/mendel/edgetpu")
        # Obtain mobilenet model from the edgetpu repo path
        if target_edgetpu:
            model_path = os.path.join(
                edgetpu_path, "test_data/mobilenet_v1_1.0_224_quant_edgetpu.tflite"
            )
        else:
            model_path = os.path.join(edgetpu_path, "test_data/mobilenet_v1_1.0_224_quant.tflite")
        return model_path

    def init_interpreter(model_path, target_edgetpu):
        # Initialize interpreter
        if target_edgetpu:
            edgetpu_path = os.getenv("EDGETPU_PATH", "/home/mendel/edgetpu")
            libedgetpu = os.path.join(edgetpu_path, "libedgetpu/direct/aarch64/libedgetpu.so.1")
            interpreter = tflite.Interpreter(
                model_path=model_path, experimental_delegates=[tflite.load_delegate(libedgetpu)]
            )
        else:
            interpreter = tflite.Interpreter(model_path=model_path)
        return interpreter

    def check_remote(target_edgetpu=False):
        tflite_model_path = get_tflite_model_path(target_edgetpu)

        # inference via tflite interpreter python apis
        interpreter = init_interpreter(tflite_model_path, target_edgetpu)
        interpreter.allocate_tensors()
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()

        input_shape = input_details[0]["shape"]
        tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.uint8)
        interpreter.set_tensor(input_details[0]["index"], tflite_input)
        interpreter.invoke()
        tflite_output = interpreter.get_tensor(output_details[0]["index"])

        # inference via remote tvm tflite runtime
        server = rpc.Server("localhost")
        remote = rpc.connect(server.host, server.port)
        ctx = remote.cpu(0)

        with open(tflite_model_path, "rb") as model_fin:
            runtime = tflite_runtime.create(model_fin.read(), ctx)
            runtime.set_input(0, tvm.nd.array(tflite_input, ctx))
            runtime.invoke()
            out = runtime.get_output(0)
            np.testing.assert_equal(out.asnumpy(), tflite_output)

    # Target CPU on coral board
    check_remote()
    # Target EdgeTPU on coral board
    check_remote(target_edgetpu=True)


if __name__ == "__main__":
    # skipped_test_tflite_runtime()
    pass