| /************************************************************ |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| * |
| *************************************************************/ |
| |
| #include "gtest/gtest.h" |
| #include "singa/core/device.h" |
| #include "singa/core/tensor.h" |
| #include "singa/model/loss.h" |
| |
| using singa::Tensor; |
| class TestMSE : public ::testing::Test { |
| protected: |
| virtual void SetUp() { |
| p.Resize(singa::Shape{2, 3}); |
| t.Resize(singa::Shape{2, 3}); |
| p.CopyDataFromHostPtr(pdat, sizeof(pdat) / sizeof(float)); |
| t.CopyDataFromHostPtr(tdat, sizeof(pdat) / sizeof(float)); |
| } |
| const float pdat[6] = {0.1f, 1.1f, 2.1f, 0.3f, 2.2f, 1.8f}; |
| const float tdat[6] = {0.1f, 1.1f, 2.0f, 0.3f, 2.2f, 1.8f}; |
| |
| singa::Tensor p, t; |
| }; |
| |
| #ifdef USE_CBLAS |
| TEST_F(TestMSE, CppForward) { |
| singa::MSE mse; |
| const Tensor& loss = mse.Forward(singa::kEval, p, t); |
| auto ldat = loss.data<float>(); |
| |
| for (size_t i = 0, k = 0; i < loss.Size(); i++) { |
| float l = 0.f; |
| for (size_t j = 0; j < p.Size() / loss.Size(); j++) { |
| l += (pdat[k] - tdat[k]) * (pdat[k] - tdat[k]); |
| k++; |
| } |
| EXPECT_FLOAT_EQ(ldat[i], 0.5f * l); |
| } |
| } |
| |
| TEST_F(TestMSE, CppBackward) { |
| singa::MSE mse; |
| mse.Forward(singa::kTrain, p, t); |
| const Tensor& grad = mse.Backward(); |
| |
| auto gdat = grad.data<float>(); |
| |
| for (size_t i = 0; i < grad.Size(); i++) |
| EXPECT_FLOAT_EQ(gdat[i], (1.0f / p.shape().at(0)) * (pdat[i] - tdat[i])); |
| } |
| #endif |
| #ifdef USE_CUDA |
| TEST_F(TestMSE, CudaForward) { |
| singa::MSE* mse = new singa::MSE(); |
| auto dev = std::make_shared<singa::CudaGPU>(); |
| p.ToDevice(dev); |
| t.ToDevice(dev); |
| Tensor loss = mse->Forward(singa::kEval, p, t); |
| |
| loss.ToHost(); |
| auto ldat = loss.data<float>(); |
| |
| for (size_t i = 0, k = 0; i < loss.Size(); i++) { |
| float l = 0.f; |
| for (size_t j = 0; j < p.Size() / loss.Size(); j++) { |
| l += (pdat[k] - tdat[k]) * (pdat[k] - tdat[k]); |
| k++; |
| } |
| EXPECT_FLOAT_EQ(ldat[i], 0.5 * l); |
| } |
| p.ToHost(); |
| t.ToHost(); |
| delete mse; |
| } |
| |
| TEST_F(TestMSE, CudaBackward) { |
| singa::MSE mse; |
| auto dev = std::make_shared<singa::CudaGPU>(); |
| p.ToDevice(dev); |
| t.ToDevice(dev); |
| mse.Forward(singa::kTrain, p, t); |
| Tensor grad = mse.Backward(); |
| grad.ToHost(); |
| auto gdat = grad.data<float>(); |
| |
| for (size_t i = 0; i < grad.Size(); i++) |
| EXPECT_FLOAT_EQ(gdat[i], (1.0f / p.shape().at(0)) * (pdat[i] - tdat[i])); |
| p.ToHost(); |
| t.ToHost(); |
| } |
| #endif |