80 lines
3.3 KiB
C++
80 lines
3.3 KiB
C++
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/****************************************************************************
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*
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* Copyright (c) 2023 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops.h"
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#include "tim/vx/types.h"
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#include "tim/vx/platform/native.h"
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#include "tim/vx/platform/lite/lite_native.h"
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int main() {
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//construct tim-vx graph
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType io_shape({2, 2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto input_t0 = graph->CreateTensor(input_spec);
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auto input_t1 = graph->CreateTensor(input_spec);
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auto output_t = graph->CreateTensor(output_spec);
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auto add = graph->CreateOperation<tim::vx::ops::Add>();
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(*add).BindInputs({input_t0, input_t1}).BindOutputs({output_t});
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std::vector<int> data_vec_i0({1, 2, 3, 4});
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std::vector<int> data_vec_i1({4, 3, 2, 1});
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auto devices = tim::vx::platform::NativeDevice::Enumerate();
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auto device = devices[0];
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auto executor = std::make_shared<tim::vx::platform::LiteNativeExecutor>(device);
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auto executable = executor->Compile(graph);
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auto input0_handle = executable->AllocateTensor(input_spec);
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auto input1_handle = executable->AllocateTensor(input_spec);
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auto output_handle = executable->AllocateTensor(output_spec);
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executable->SetInput(input0_handle);
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executable->SetInput(input1_handle);
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executable->SetOutput(output_handle);
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input0_handle->CopyDataToTensor(data_vec_i0.data(),
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data_vec_i0.size() * sizeof(int));
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input1_handle->CopyDataToTensor(data_vec_i1.data(),
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data_vec_i1.size() * sizeof(int));
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executable->Submit(executable);
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executor->Trigger();
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int* data = (int*)malloc(4 * sizeof(int));
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output_handle->CopyDataFromTensor(data);
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//each output value should be "5" in this demo
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for (int i = 0; i < 4; ++i) {
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std::cout << "output value: " << data[i] << std::endl;
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}
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free(data);
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return 0;
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}
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