From 32308f62c583a256107a75038355b34969708e4e Mon Sep 17 00:00:00 2001 From: chxin66 <57057788+chxin66@users.noreply.github.com> Date: Wed, 19 Jan 2022 14:54:39 +0800 Subject: [PATCH] Add softmax unit test (#274) https://github.com/VeriSilicon/TIM-VX/issues/266 Signed-off-by: Chen Xin Co-authored-by: Chen Xin --- src/tim/vx/ops/softmax_test.cc | 279 +++++++++++++++++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 src/tim/vx/ops/softmax_test.cc diff --git a/src/tim/vx/ops/softmax_test.cc b/src/tim/vx/ops/softmax_test.cc new file mode 100644 index 0000000..b33f062 --- /dev/null +++ b/src/tim/vx/ops/softmax_test.cc @@ -0,0 +1,279 @@ +/**************************************************************************** +* +* Copyright (c) 2022 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/softmax.h" +#include "test_utils.h" +#include "gtest/gtest.h" + +TEST(Softmax, shape_3_1_float_axis_0) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 1}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + -1, 0, 1, + }; + std::vector golden = { + 0.09003057, 0.24472848, 0.66524094 + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,0); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(Softmax, shape_3_4_float_axis_0) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 4}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + }; + std::vector golden = { + 0.09003057, 0.24472848, 0.66524094, + 0.09003057, 0.24472848, 0.66524094, + 0.09003057, 0.24472848, 0.66524094, + 0.09003057, 0.24472848, 0.66524094, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,0); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); + // EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(Softmax, shape_3_4_float_axis_1) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 4}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + }; + std::vector golden = { + 0.25, 0.25, 0.25, + 0.25, 0.25, 0.25, + 0.25, 0.25, 0.25, + 0.25, 0.25, 0.25, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,1); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); + // EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(Softmax, shape_3_3_2_float_axis_0) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 3, 2}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + + 2, 3, 5, + 2, 3, 5, + 2, 3, 5, + }; + std::vector golden = { + 0.09003057, 0.24472848, 0.66524094, + 0.09003057, 0.24472848, 0.66524094, + 0.09003057, 0.24472848, 0.66524094, + + 0.04201007, 0.11419519, 0.8437947, + 0.04201007, 0.11419519, 0.8437947, + 0.04201007, 0.11419519, 0.8437947, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,0); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); + // EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(Softmax, shape_3_3_2_float_axis_1) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 3, 2}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + + 2, 3, 5, + 2, 3, 5, + 2, 3, 5, + }; + std::vector golden = { + 0.33333334, 0.33333334, 0.33333334, + 0.33333334, 0.33333334, 0.33333334, + 0.33333334, 0.33333334, 0.33333334, + + 0.33333334, 0.33333334, 0.33333334, + 0.33333334, 0.33333334, 0.33333334, + 0.33333334, 0.33333334, 0.33333334, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,1); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); + // EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} + +TEST(Softmax, shape_3_3_2_float_axis_2) { + auto ctx = tim::vx::Context::Create(); + auto graph = ctx->CreateGraph(); + + tim::vx::ShapeType io_shape({3, 3, 2}); + tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::INPUT); + tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, + io_shape, tim::vx::TensorAttribute::OUTPUT); + + auto input_tensor = graph->CreateTensor(input_spec); + auto output_tensor = graph->CreateTensor(output_spec); + + std::vector in_data = { + 1, 2, 3, + 1, 2, 3, + 1, 2, 3, + + 2, 3, 5, + 2, 3, 5, + 2, 3, 5, + }; + std::vector golden = { + 0.26894143, 0.26894143, 0.11920291, + 0.26894143, 0.26894143, 0.11920291, + 0.26894143, 0.26894143, 0.11920291, + + 0.7310586 , 0.7310586 , 0.880797, + 0.7310586 , 0.7310586 , 0.880797, + 0.7310586 , 0.7310586 , 0.880797, + }; + + EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float))); + + auto op = graph->CreateOperation(1,2); + (*op).BindInputs({input_tensor}).BindOutputs({output_tensor}); + + EXPECT_TRUE(graph->Compile()); + EXPECT_TRUE(graph->Run()); + + std::vector output(golden.size()); + EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data())); + EXPECT_EQ(golden, output); + // EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f)); +} \ No newline at end of file