126 lines
5.5 KiB
C++
126 lines
5.5 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2022 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 "test_utils.h"
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#include "gtest/gtest.h"
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TEST(Reduce_sum, NotKeepDims) {
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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, 1});
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tim::vx::ShapeType output_shape({2, 1});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.00784313772,
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127);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec dc_spec1(tim::vx::DataType::UINT8, {0, 0, 0},
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tim::vx::TensorAttribute::TRANSIENT, quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto dc_tensor1 = graph->CreateTensor(dc_spec1);
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auto dc1_op = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*dc1_op).BindInputs({input_tensor}).BindOutputs({dc_tensor1});
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tim::vx::TensorSpec reduce_sum_spec(tim::vx::DataType::UINT8, {0, 0, 0},
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tim::vx::TensorAttribute::TRANSIENT,
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quant);
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auto reduce_sum_out = graph->CreateTensor(reduce_sum_spec);
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std::vector<int32_t> axis = {1};
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auto reduce_sum =
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graph->CreateOperation<tim::vx::ops::ReduceSum>(axis, false);
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(*reduce_sum).BindInputs({dc_tensor1}).BindOutputs({reduce_sum_out});
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto dc2_op = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*dc2_op).BindInputs({reduce_sum_out}).BindOutputs({output_tensor});
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std::vector<float> in_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
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std::vector<float> golden = {
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1.003922,
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1.003922,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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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(Reduce_sum, KeepDims) {
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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({1, 3});
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tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.00784313772,
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127);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec dc_spec1(tim::vx::DataType::UINT8, {0, 0, 0},
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tim::vx::TensorAttribute::TRANSIENT, quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto dc_tensor1 = graph->CreateTensor(dc_spec1);
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auto dc1_op = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*dc1_op).BindInputs({input_tensor}).BindOutputs({dc_tensor1});
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tim::vx::TensorSpec reduce_sum_spec(tim::vx::DataType::UINT8, {0, 0, 0},
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tim::vx::TensorAttribute::TRANSIENT,
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quant);
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auto reduce_sum_out = graph->CreateTensor(reduce_sum_spec);
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std::vector<int32_t> axis = {0};
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auto reduce_sum = graph->CreateOperation<tim::vx::ops::ReduceSum>(axis, true);
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(*reduce_sum).BindInputs({dc_tensor1}).BindOutputs({reduce_sum_out});
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto output_tensor = graph->CreateTensor(output_spec);
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auto dc2_op = graph->CreateOperation<tim::vx::ops::DataConvert>();
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(*dc2_op).BindInputs({reduce_sum_out}).BindOutputs({output_tensor});
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std::vector<float> in_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6};
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std::vector<float> golden = {
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0.596078,
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0.698039,
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1.003922,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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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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} |