141 lines
5.2 KiB
C++
141 lines
5.2 KiB
C++
/****************************************************************************
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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/unstack.h"
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#include "gtest/gtest.h"
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TEST(Unstack, shape_4_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({4,3});
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tim::vx::ShapeType output_shape({3});
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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_tensor = graph->CreateTensor(input_spec);
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auto output1_tensor = graph->CreateTensor(output_spec);
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auto output2_tensor = graph->CreateTensor(output_spec);
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auto output3_tensor = graph->CreateTensor(output_spec);
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auto output4_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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1,2,3,4,
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5,6,7,8,
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9,10,11,12,
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};
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std::vector<float> golden1 = {
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1,5,9
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};
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std::vector<float> golden2 = {
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2,6,10
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};
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std::vector<float> golden3 = {
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3,7,11
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};
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std::vector<float> golden4 = {
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4,8,12
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Unstack>(0, 4);
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(*op).BindInputs({input_tensor}).BindOutputs(
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{output1_tensor, output2_tensor, output3_tensor, output4_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output1(golden1.size());
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std::vector<float> output2(golden2.size());
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std::vector<float> output3(golden3.size());
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std::vector<float> output4(golden4.size());
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EXPECT_TRUE(output1_tensor->CopyDataFromTensor(output1.data()));
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EXPECT_TRUE(output2_tensor->CopyDataFromTensor(output2.data()));
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EXPECT_TRUE(output3_tensor->CopyDataFromTensor(output3.data()));
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EXPECT_TRUE(output4_tensor->CopyDataFromTensor(output4.data()));
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EXPECT_EQ(golden1, output1);
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EXPECT_EQ(golden2, output2);
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EXPECT_EQ(golden3, output3);
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EXPECT_EQ(golden4, output4);
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}
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TEST(Unstack, shape_4_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({4,3});
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tim::vx::ShapeType output_shape({4});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
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input_shape, tim::vx::TensorAttribute::INPUT, quant);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
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output_shape, tim::vx::TensorAttribute::OUTPUT, quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output1_tensor = graph->CreateTensor(output_spec);
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auto output2_tensor = graph->CreateTensor(output_spec);
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auto output3_tensor = graph->CreateTensor(output_spec);
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std::vector<uint8_t> in_data = {
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2,4,6,8,
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10,12,14,16,
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18,20,22,24,
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};
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std::vector<uint8_t> golden1 = {
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2,4,6,8
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};
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std::vector<uint8_t> golden2 = {
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10,12,14,16,
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};
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std::vector<uint8_t> golden3 = {
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18,20,22,24,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::Unstack>(1, 3);
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(*op).BindInputs({input_tensor}).BindOutputs(
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{output1_tensor, output2_tensor, output3_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<uint8_t> output1(golden1.size());
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std::vector<uint8_t> output2(golden2.size());
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std::vector<uint8_t> output3(golden3.size());
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EXPECT_TRUE(output1_tensor->CopyDataFromTensor(output1.data()));
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EXPECT_TRUE(output2_tensor->CopyDataFromTensor(output2.data()));
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EXPECT_TRUE(output3_tensor->CopyDataFromTensor(output3.data()));
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EXPECT_EQ(golden1, output1);
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EXPECT_EQ(golden2, output2);
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EXPECT_EQ(golden3, output3);
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}
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