/**************************************************************************** * * Copyright (c) 2021 Vivante Corporation * * Permission is hereby granted, free of charge, to any person obtaining a * copy of this software and associated documentation files (the "Software"), * to deal in the Software without restriction, including without limitation * the rights to use, copy, modify, merge, publish, distribute, sublicense, * and/or sell copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER * DEALINGS IN THE SOFTWARE. * *****************************************************************************/ #include "tim/vx/context.h" #include "tim/vx/graph.h" #include "tim/vx/ops/batchnorm.h" #include "tim/transform/layout_inference.h" #include "gtest/gtest.h" #include "test_utils.h" TEST(BatchNorm, shape_3_3_2_1_fp32_cwhn) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType in_shape({2, 3, 3, 1}); tim::vx::ShapeType out_shape({2, 3, 3, 1}); tim::vx::ShapeType mean_shape({2}); tim::vx::ShapeType var_shape({2}); tim::vx::ShapeType gamma_shape({2}); tim::vx::ShapeType beta_shape({2}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT); tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT); tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT); tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT); tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape, tim::vx::TensorAttribute::OUTPUT); std::vector in_data = { 0.59885779, 0.62662862, 0.63011179, 0.82569427, 0.64772359, 0.42895413, 0.30216458, 0.01351635, 0.32545444, 0.0360674, 0.33967769, 0.18092504, 0.09479915, 0.52258112, 0.46735646, 0.95689111, 0.51619059, 0.82685718}; std::vector golden = { 0.92227477, 0.40612271, 1.09906762, 1.00176775, 1.19869136, -0.18535967, -0.7560139, -1.42843423, -0.62427138, -1.3609569, -0.54381545, -0.92751329, -1.92900686, 0.09479138, 0.17841823, 1.39433545, 0.45465564, 1.0052474, }; std::vector mean_data = { 0.43581513, 0.49090168 }; std::vector var_data = { 0.03025229, 0.11069085 }; std::vector gamma_data = { 1,1 }; std::vector beta_data = { 0, 0 }; auto input_tensor = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); auto mean = graph->CreateTensor(mean_spec, mean_data.data()); auto var = graph->CreateTensor(var_spec, var_data.data()); auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data()); auto beta = graph->CreateTensor(beta_spec, beta_data.data()); float epsilon = 0.001; auto op = graph->CreateOperation(epsilon, tim::vx::DataLayout::CWHN); (*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor}); auto final_graph = tim::transform::LayoutInference(graph, ctx); EXPECT_TRUE(final_graph.first->Compile()); final_graph.second[input_tensor]->CopyDataToTensor( in_data.data(), in_data.size() * sizeof(float)); EXPECT_TRUE(final_graph.first->Run()); std::vector output(golden.size()); EXPECT_TRUE(final_graph.second[output_tensor]->CopyDataFromTensor(output.data())); for (uint32_t idx = 0; idx < golden.size(); idx++) { EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01); } } TEST(BatchNorm, shape_3_3_2_1_fp32_whcn) { auto ctx = tim::vx::Context::Create(); auto graph = ctx->CreateGraph(); tim::vx::ShapeType in_shape({3, 3, 2, 1}); tim::vx::ShapeType out_shape({3, 3, 2, 1}); tim::vx::ShapeType mean_shape({2}); tim::vx::ShapeType var_shape({2}); tim::vx::ShapeType gamma_shape({2}); tim::vx::ShapeType beta_shape({2}); tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape, tim::vx::TensorAttribute::INPUT); tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT); tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT); tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT); tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT); tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape, tim::vx::TensorAttribute::OUTPUT); std::vector in_data = { 0.598858, 0.630112, 0.647724, 0.302165, 0.325454, 0.339678, 0.094799, 0.467356, 0.516191, 0.626629, 0.825694, 0.428954, 0.013516, 0.036067, 0.180925, 0.522581, 0.956891, 0.826857}; std::vector golden = { 0.922275, 1.099068, 1.198692, -0.756014, -0.624271, -0.543815, -1.929007, 0.178418, 0.454656, 0.406123, 1.001768, -0.185360, -1.428434, -1.360957, -0.927513, 0.094791, 1.394335, 1.005247}; std::vector mean_data = { 0.43581513, 0.49090168 }; std::vector var_data = { 0.03025229, 0.11069085 }; std::vector gamma_data = { 1,1 }; std::vector beta_data = { 0, 0 }; auto input_tensor = graph->CreateTensor(input_spec); auto output_tensor = graph->CreateTensor(output_spec); auto mean = graph->CreateTensor(mean_spec, mean_data.data()); auto var = graph->CreateTensor(var_spec, var_data.data()); auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data()); auto beta = graph->CreateTensor(beta_spec, beta_data.data()); float epsilon = 0.001; auto op = graph->CreateOperation(epsilon); (*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor}); EXPECT_TRUE(graph->Compile()); input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)); EXPECT_TRUE(graph->Run()); std::vector output(golden.size()); EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); for (uint32_t idx = 0; idx < golden.size(); idx++) { EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01); } }