168 lines
7.1 KiB
C++
168 lines
7.1 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/batchnorm.h"
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#include "tim/transform/layout_inference.h"
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#include "gtest/gtest.h"
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#include "test_utils.h"
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TEST(BatchNorm, shape_3_3_2_1_fp32_cwhn) {
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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 in_shape({2, 3, 3, 1});
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tim::vx::ShapeType out_shape({2, 3, 3, 1});
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tim::vx::ShapeType mean_shape({2});
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tim::vx::ShapeType var_shape({2});
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tim::vx::ShapeType gamma_shape({2});
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tim::vx::ShapeType beta_shape({2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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std::vector<float> in_data = {
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0.59885779, 0.62662862, 0.63011179, 0.82569427, 0.64772359, 0.42895413,
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0.30216458, 0.01351635, 0.32545444, 0.0360674, 0.33967769, 0.18092504,
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0.09479915, 0.52258112, 0.46735646, 0.95689111, 0.51619059, 0.82685718};
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std::vector<float> golden = {
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0.92227477, 0.40612271, 1.09906762,
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1.00176775, 1.19869136, -0.18535967,
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-0.7560139, -1.42843423, -0.62427138,
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-1.3609569, -0.54381545, -0.92751329,
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-1.92900686, 0.09479138, 0.17841823,
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1.39433545, 0.45465564, 1.0052474,
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};
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std::vector<float> mean_data = {
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0.43581513, 0.49090168
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};
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std::vector<float> var_data = {
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0.03025229, 0.11069085
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};
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std::vector<float> gamma_data = {
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1,1
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};
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std::vector<float> beta_data = {
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0, 0
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};
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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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auto mean = graph->CreateTensor(mean_spec, mean_data.data());
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auto var = graph->CreateTensor(var_spec, var_data.data());
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auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data());
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auto beta = graph->CreateTensor(beta_spec, beta_data.data());
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float epsilon = 0.001;
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auto op = graph->CreateOperation<tim::vx::ops::BatchNorm>(epsilon, tim::vx::DataLayout::CWHN);
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(*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor});
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auto final_graph = tim::transform::LayoutInference(graph, ctx);
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EXPECT_TRUE(final_graph.first->Compile());
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final_graph.second[input_tensor]->CopyDataToTensor(
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in_data.data(), in_data.size() * sizeof(float));
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EXPECT_TRUE(final_graph.first->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(final_graph.second[output_tensor]->CopyDataFromTensor(output.data()));
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for (uint32_t idx = 0; idx < golden.size(); idx++) {
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EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01);
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}
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}
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TEST(BatchNorm, shape_3_3_2_1_fp32_whcn) {
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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 in_shape({3, 3, 2, 1});
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tim::vx::ShapeType out_shape({3, 3, 2, 1});
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tim::vx::ShapeType mean_shape({2});
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tim::vx::ShapeType var_shape({2});
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tim::vx::ShapeType gamma_shape({2});
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tim::vx::ShapeType beta_shape({2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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std::vector<float> in_data = {
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0.598858, 0.630112, 0.647724, 0.302165, 0.325454, 0.339678,
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0.094799, 0.467356, 0.516191, 0.626629, 0.825694, 0.428954,
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0.013516, 0.036067, 0.180925, 0.522581, 0.956891, 0.826857};
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std::vector<float> golden = {
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0.922275, 1.099068, 1.198692, -0.756014, -0.624271, -0.543815,
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-1.929007, 0.178418, 0.454656, 0.406123, 1.001768, -0.185360,
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-1.428434, -1.360957, -0.927513, 0.094791, 1.394335, 1.005247};
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std::vector<float> mean_data = {
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0.43581513, 0.49090168
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};
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std::vector<float> var_data = {
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0.03025229, 0.11069085
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};
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std::vector<float> gamma_data = {
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1,1
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};
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std::vector<float> beta_data = {
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0, 0
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};
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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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auto mean = graph->CreateTensor(mean_spec, mean_data.data());
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auto var = graph->CreateTensor(var_spec, var_data.data());
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auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data());
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auto beta = graph->CreateTensor(beta_spec, beta_data.data());
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float epsilon = 0.001;
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auto op = graph->CreateOperation<tim::vx::ops::BatchNorm>(epsilon);
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(*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(in_data.data(),
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in_data.size() * sizeof(float));
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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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for (uint32_t idx = 0; idx < golden.size(); idx++) {
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EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01);
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}
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} |