| # 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 tvm |
| from tvm import te |
| import numpy as np |
| from tvm import rpc |
| from tvm.contrib import util, xcode, coreml_runtime |
| |
| import pytest |
| import os |
| |
| proxy_host = os.environ.get("TVM_IOS_RPC_PROXY_HOST", "localhost") |
| proxy_port = os.environ.get("TVM_IOS_RPC_PROXY_PORT", 9090) |
| destination = os.environ.get("TVM_IOS_RPC_DESTINATION", "") |
| key = "iphone" |
| |
| |
| @pytest.mark.skip("skip because coremltools is not available in CI") |
| def test_coreml_runtime(): |
| |
| import coremltools |
| from coremltools.models.neural_network import NeuralNetworkBuilder |
| |
| def create_coreml_model(): |
| shape = (2,) |
| alpha = 2 |
| |
| inputs = [ |
| ("input0", coremltools.models.datatypes.Array(*shape)), |
| ("input1", coremltools.models.datatypes.Array(*shape)), |
| ] |
| outputs = [ |
| ("output0", coremltools.models.datatypes.Array(*shape)), |
| ("output1", coremltools.models.datatypes.Array(*shape)), |
| ] |
| builder = NeuralNetworkBuilder(inputs, outputs) |
| builder.add_elementwise( |
| name="Add", input_names=["input0", "input1"], output_name="output0", mode="ADD" |
| ) |
| builder.add_elementwise( |
| name="Mul", alpha=alpha, input_names=["input0"], output_name="output1", mode="MULTIPLY" |
| ) |
| return coremltools.models.MLModel(builder.spec) |
| |
| def verify(coreml_model, model_path, ctx): |
| coreml_model = create_coreml_model() |
| |
| out_spec = coreml_model.output_description._fd_spec |
| out_names = [spec.name for spec in out_spec] |
| |
| # inference via coremltools |
| inputs = {} |
| for in_spec in coreml_model.input_description._fd_spec: |
| name = in_spec.name |
| shape = in_spec.type.multiArrayType.shape |
| inputs[name] = np.random.random_sample(shape) |
| |
| coreml_outputs = [coreml_model.predict(inputs)[name] for name in out_names] |
| |
| # inference via tvm coreml runtime |
| runtime = coreml_runtime.create("main", model_path, ctx) |
| for name in inputs: |
| runtime.set_input(name, tvm.nd.array(inputs[name], ctx)) |
| runtime.invoke() |
| tvm_outputs = [runtime.get_output(i).asnumpy() for i in range(runtime.get_num_outputs())] |
| |
| for c_out, t_out in zip(coreml_outputs, tvm_outputs): |
| np.testing.assert_almost_equal(c_out, t_out, 3) |
| |
| def check_remote(coreml_model): |
| temp = util.tempdir() |
| compiled_model = xcode.compile_coreml(coreml_model, out_dir=temp.temp_dir) |
| xcode.popen_test_rpc( |
| proxy_host, proxy_port, key, destination=destination, libs=[compiled_model] |
| ) |
| compiled_model = os.path.basename(compiled_model) |
| remote = rpc.connect(proxy_host, proxy_port, key=key) |
| ctx = remote.cpu(0) |
| verify(coreml_model, compiled_model, ctx) |
| |
| def check_local(coreml_model): |
| temp = util.tempdir() |
| compiled_model = xcode.compile_coreml(coreml_model, out_dir=temp.temp_dir) |
| ctx = tvm.cpu(0) |
| verify(coreml_model, compiled_model, ctx) |
| |
| coreml_model = create_coreml_model() |
| check_remote(coreml_model) |
| check_local(coreml_model) |
| |
| |
| if __name__ == "__main__": |
| test_coreml_runtime() |