103 lines
4.7 KiB
C++
103 lines
4.7 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020-2023 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/localresponsenormalization.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(localresponsenormalization, axis_0_shape_6_1_1_1_float) {
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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({6, 1, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1};
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std::vector<float> golden = {-0.264926, 0.125109, 0.140112,
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0.267261, -0.161788, 0.0244266};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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int radius = 2;
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int size = radius * 2;
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float alpha = 4.0, beta = 0.5, bias = 9.0;
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auto op = graph->CreateOperation<tim::vx::ops::LocalResponseNormalization>(
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size, alpha, beta, bias, 0);
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(*op).BindInputs({input_tensor}).BindOutputs({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_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(localresponsenormalization, axis_1_shape_2_6_float) {
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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, 6});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, io_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {-1.100000023841858f, -1.100000023841858f, 0.6000000238418579f,
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0.6000000238418579f, 0.699999988079071f, 0.699999988079071f,
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1.2000000476837158f, 1.2000000476837158f, -0.699999988079071f,
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-0.699999988079071f, 0.10000000149011612f, 0.10000000149011612f};
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std::vector<float> golden = {-0.26492568850517273f, -0.26492568850517273f, 0.12510864436626434f,
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0.12510864436626434f, 0.14011213183403015f, 0.14011213183403015f,
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0.267261266708374f, 0.267261266708374f, -0.16178755462169647f,
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-0.16178755462169647f, 0.024426599964499474f, 0.024426599964499474f};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float)));
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int radius = 2;
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int size = radius * 2;
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float alpha = 4.0, beta = 0.5, bias = 9.0;
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auto op = graph->CreateOperation<tim::vx::ops::LocalResponseNormalization>(
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size, alpha, beta, bias, 1);
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(*op).BindInputs({input_tensor}).BindOutputs({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_TRUE(ArraysMatch(golden, output, 1e-5f));
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} |