182 lines
5.8 KiB
C++
182 lines
5.8 KiB
C++
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/****************************************************************************
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*
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* Copyright (c) 2021 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/stack.h"
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#include "gtest/gtest.h"
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TEST(Stack, shape_2_3_axis_2) {
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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 input_shape({2,3});
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tim::vx::ShapeType output_shape({2,3,2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor1 = graph->CreateTensor(input_spec);
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auto input_tensor2 = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data1 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> in_data2 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> golden = {
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1,4,
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2,5,
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3,6,
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1,4,
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2,5,
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3,6
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};
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EXPECT_TRUE(input_tensor1->CopyDataToTensor(
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in_data1.data(), in_data1.size() * sizeof(float)));
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EXPECT_TRUE(input_tensor2->CopyDataToTensor(
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in_data2.data(), in_data2.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Stack>(2, 2);
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(*op).BindInputs({input_tensor1,input_tensor2}).BindOutputs(
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{output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Stack, shape_2_3_axis_1) {
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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 input_shape({2,3});
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tim::vx::ShapeType output_shape({2,3,2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor1 = graph->CreateTensor(input_spec);
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auto input_tensor2 = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data1 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> in_data2 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> golden = {
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1,4,
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1,4,
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2,5,
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2,5,
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3,6,
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3,6,
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};
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EXPECT_TRUE(input_tensor1->CopyDataToTensor(
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in_data1.data(), in_data1.size() * sizeof(float)));
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EXPECT_TRUE(input_tensor2->CopyDataToTensor(
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in_data2.data(), in_data2.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Stack>(1, 2);
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(*op).BindInputs({input_tensor1,input_tensor2}).BindOutputs(
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{output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Stack, shape_2_3_axis_0) {
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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 input_shape({2,3});
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tim::vx::ShapeType output_shape({2,3,2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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input_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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output_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor1 = graph->CreateTensor(input_spec);
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auto input_tensor2 = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data1 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> in_data2 = {
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1,4,
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2,5,
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3,6
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};
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std::vector<float> golden = {
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1, 1,
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4, 4,
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2, 2,
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5, 5,
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3, 3,
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6, 6
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};
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EXPECT_TRUE(input_tensor1->CopyDataToTensor(
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in_data1.data(), in_data1.size() * sizeof(float)));
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EXPECT_TRUE(input_tensor2->CopyDataToTensor(
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in_data2.data(), in_data2.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Stack>(0, 2);
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(*op).BindInputs({input_tensor1,input_tensor2}).BindOutputs(
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{output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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