| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| import pytest |
| |
| import tvm |
| from tvm import te |
| import numpy as np |
| from tvm import rpc |
| from tvm.contrib import util, tflite_runtime |
| |
| |
| def _create_tflite_model(): |
| if not tvm.runtime.enabled("tflite"): |
| print("skip because tflite runtime is not enabled...") |
| return |
| if not tvm.get_global_func("tvm.tflite_runtime.create", True): |
| print("skip because tflite runtime is not enabled...") |
| return |
| |
| try: |
| import tensorflow as tf |
| except ImportError: |
| print("skip because tensorflow not installed...") |
| return |
| |
| root = tf.Module() |
| root.const = tf.constant([1.0, 2.0], tf.float32) |
| root.f = tf.function(lambda x: root.const * x) |
| |
| input_signature = tf.TensorSpec( |
| shape=[ |
| 2, |
| ], |
| dtype=tf.float32, |
| ) |
| concrete_func = root.f.get_concrete_function(input_signature) |
| converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func]) |
| tflite_model = converter.convert() |
| return tflite_model |
| |
| |
| @pytest.mark.skip("skip because accessing output tensor is flakey") |
| def test_local(): |
| if not tvm.runtime.enabled("tflite"): |
| print("skip because tflite runtime is not enabled...") |
| return |
| if not tvm.get_global_func("tvm.tflite_runtime.create", True): |
| print("skip because tflite runtime is not enabled...") |
| return |
| |
| try: |
| import tensorflow as tf |
| except ImportError: |
| print("skip because tensorflow not installed...") |
| return |
| |
| tflite_fname = "model.tflite" |
| tflite_model = _create_tflite_model() |
| temp = util.tempdir() |
| tflite_model_path = temp.relpath(tflite_fname) |
| open(tflite_model_path, "wb").write(tflite_model) |
| |
| # inference via tflite interpreter python apis |
| interpreter = tf.lite.Interpreter(model_path=tflite_model_path) |
| 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.float32) |
| interpreter.set_tensor(input_details[0]["index"], tflite_input) |
| interpreter.invoke() |
| tflite_output = interpreter.get_tensor(output_details[0]["index"]) |
| |
| # inference via tvm tflite runtime |
| with open(tflite_model_path, "rb") as model_fin: |
| runtime = tflite_runtime.create(model_fin.read(), tvm.cpu(0)) |
| runtime.set_input(0, tvm.nd.array(tflite_input)) |
| runtime.invoke() |
| out = runtime.get_output(0) |
| np.testing.assert_equal(out.asnumpy(), tflite_output) |
| |
| |
| def test_remote(): |
| if not tvm.runtime.enabled("tflite"): |
| print("skip because tflite runtime is not enabled...") |
| return |
| if not tvm.get_global_func("tvm.tflite_runtime.create", True): |
| print("skip because tflite runtime is not enabled...") |
| return |
| |
| try: |
| import tensorflow as tf |
| except ImportError: |
| print("skip because tensorflow not installed...") |
| return |
| |
| tflite_fname = "model.tflite" |
| tflite_model = _create_tflite_model() |
| temp = util.tempdir() |
| tflite_model_path = temp.relpath(tflite_fname) |
| open(tflite_model_path, "wb").write(tflite_model) |
| |
| # inference via tflite interpreter python apis |
| interpreter = tf.lite.Interpreter(model_path=tflite_model_path) |
| 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.float32) |
| 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) |
| a = remote.upload(tflite_model_path) |
| |
| with open(tflite_model_path, "rb") as model_fin: |
| runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0)) |
| runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0))) |
| runtime.invoke() |
| out = runtime.get_output(0) |
| np.testing.assert_equal(out.asnumpy(), tflite_output) |
| |
| server.terminate() |
| |
| |
| if __name__ == "__main__": |
| test_local() |
| test_remote() |