mirror of
https://github.com/lordmathis/CUDANet.git
synced 2025-11-07 02:04:26 +00:00
Split tests to cpu and cuda
This commit is contained in:
281
test/cuda/layers/test_avg_pooling.cu
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281
test/cuda/layers/test_avg_pooling.cu
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#include <cuda_runtime.h>
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#include <gtest/gtest.h>
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#include <vector>
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#include "avg_pooling.cuh"
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class AvgPoolingLayerTest : public ::testing::Test {
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protected:
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shape2d inputSize;
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int nChannels;
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shape2d poolingSize;
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shape2d stride;
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shape2d padding;
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std::vector<float> input;
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std::vector<float> expected;
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float *d_input;
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float *d_output;
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CUDANet::Layers::AvgPooling2d *avgPoolingLayer;
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virtual void SetUp() override {
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d_input = nullptr;
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d_output = nullptr;
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avgPoolingLayer = nullptr;
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}
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virtual void TearDown() override {
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if (d_input) {
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cudaFree(d_input);
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}
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}
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void runTest() {
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cudaError_t cudaStatus;
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avgPoolingLayer = new CUDANet::Layers::AvgPooling2d(
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inputSize, nChannels, poolingSize, stride, padding,
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CUDANet::Layers::ActivationType::NONE
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);
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cudaStatus = cudaMalloc(
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(void **)&d_input,
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sizeof(float) * inputSize.first * inputSize.second * nChannels
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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cudaStatus = cudaMemcpy(
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d_input, input.data(),
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sizeof(float) * inputSize.first * inputSize.second * nChannels,
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cudaMemcpyHostToDevice
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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d_output = avgPoolingLayer->forward(d_input);
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int outputSize = avgPoolingLayer->getOutputSize();
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std::vector<float> output(outputSize);
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cudaStatus = cudaMemcpy(
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output.data(), d_output, sizeof(float) * outputSize,
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cudaMemcpyDeviceToHost
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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for (int i = 0; i < output.size(); ++i) {
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EXPECT_NEAR(expected[i], output[i], 1e-4);
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}
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delete avgPoolingLayer;
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}
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};
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TEST_F(AvgPoolingLayerTest, AvgPoolForwardTest) {
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inputSize = {4, 4};
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nChannels = 2;
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poolingSize = {2, 2};
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stride = {2, 2};
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padding = {0, 0};
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input = {
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// clang-format off
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// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f,
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0.243f, 0.316f, 0.573f, 0.619f,
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0.712f, 0.055f, 0.243f, 0.316f,
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0.573f, 0.619f, 0.742f, 0.055f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f,
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0.073f, 0.362f, 0.973f, 0.059f,
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0.473f, 0.455f, 0.283f, 0.416f,
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0.532f, 0.819f, 0.732f, 0.850f
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// clang-format on
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};
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expected = {0.43775f, 0.49475f, 0.48975f, 0.339f,
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0.45675f, 0.303f, 0.56975f, 0.57025f};
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runTest();
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}
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TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquareInputTest) {
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inputSize = {4, 6}; // Non-square input
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nChannels = 2;
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poolingSize = {2, 2};
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stride = {2, 2};
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padding = {0, 0};
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input = {// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f, 0.123f, 0.234f, 0.243f, 0.316f,
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0.573f, 0.619f, 0.456f, 0.789f, 0.712f, 0.055f, 0.243f, 0.316f,
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0.654f, 0.987f, 0.573f, 0.619f, 0.742f, 0.055f, 0.321f, 0.654f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f, 0.321f, 0.654f, 0.073f, 0.362f,
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0.973f, 0.059f, 0.654f, 0.987f, 0.473f, 0.455f, 0.283f, 0.416f,
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0.789f, 0.123f, 0.532f, 0.819f, 0.732f, 0.850f, 0.987f, 0.321f
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};
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expected = {0.43775f, 0.49475f, 0.4005f, 0.48975f, 0.339f, 0.654f,
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0.45675f, 0.303f, 0.654f, 0.56975f, 0.57025f, 0.555f};
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runTest();
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}
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TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquarePoolingTest) {
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inputSize = {4, 4};
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nChannels = 2;
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poolingSize = {2, 3}; // Non-square pooling
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stride = {2, 2};
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padding = {0, 0};
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input = {// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
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0.712f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f, 0.742f, 0.055f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f, 0.073f, 0.362f, 0.973f, 0.059f,
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0.473f, 0.455f, 0.283f, 0.416f, 0.532f, 0.819f, 0.732f, 0.850f
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};
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expected = {0.50933f, 0.49067f, 0.4845f, 0.549f};
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runTest();
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}
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TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquareStrideTest) {
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inputSize = {4, 4};
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nChannels = 2;
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poolingSize = {2, 2};
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stride = {1, 2}; // Non-square stride
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padding = {0, 0};
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input = {// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
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0.712f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f, 0.742f, 0.055f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f, 0.073f, 0.362f, 0.973f, 0.059f,
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0.473f, 0.455f, 0.283f, 0.416f, 0.532f, 0.819f, 0.732f, 0.850f
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};
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expected = {0.43775f, 0.49475f, 0.3315f, 0.43775f, 0.48975f, 0.339f,
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0.45675f, 0.303f, 0.34075f, 0.43275f, 0.56975f, 0.57025f};
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runTest();
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}
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TEST_F(AvgPoolingLayerTest, AvgPoolForwardNonSquarePaddingTest) {
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inputSize = {4, 4};
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nChannels = 2;
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poolingSize = {2, 2};
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stride = {2, 2};
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padding = {1, 0}; // Non-square padding
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input = {// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f,
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0.712f, 0.055f, 0.243f, 0.316f, 0.573f, 0.619f, 0.742f, 0.055f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f, 0.073f, 0.362f, 0.973f, 0.059f,
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0.473f, 0.455f, 0.283f, 0.416f, 0.532f, 0.819f, 0.732f, 0.850f
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};
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expected = {0.298f, 0.19675f, 0.3315f, 0.43775f, 0.298f, 0.19925f,
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0.348f, 0.045f, 0.34075f, 0.43275f, 0.33775f, 0.3955f};
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runTest();
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}
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class AdaptiveAvgPoolingLayerTest : public ::testing::Test {
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protected:
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shape2d inputSize;
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shape2d outputSize;
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int nChannels;
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std::vector<float> input;
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std::vector<float> expected;
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float* d_input;
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float* d_output;
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CUDANet::Layers::AdaptiveAvgPooling2d* adaptiveAvgPoolingLayer;
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virtual void SetUp() override {
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d_input = nullptr;
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d_output = nullptr;
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adaptiveAvgPoolingLayer = nullptr;
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}
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virtual void TearDown() override {
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cudaFree(d_input);
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}
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void runTest() {
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cudaError_t cudaStatus;
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adaptiveAvgPoolingLayer = new CUDANet::Layers::AdaptiveAvgPooling2d(
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inputSize, nChannels, outputSize, CUDANet::Layers::ActivationType::NONE
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);
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cudaStatus = cudaMalloc(
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(void**)&d_input,
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sizeof(float) * inputSize.first * inputSize.second * nChannels
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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cudaStatus = cudaMemcpy(
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d_input, input.data(),
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sizeof(float) * inputSize.first * inputSize.second * nChannels,
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cudaMemcpyHostToDevice
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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d_output = adaptiveAvgPoolingLayer->forward(d_input);
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int outputSize = adaptiveAvgPoolingLayer->getOutputSize();
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std::vector<float> output(outputSize);
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cudaStatus = cudaMemcpy(
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output.data(), d_output, sizeof(float) * outputSize,
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cudaMemcpyDeviceToHost
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);
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EXPECT_EQ(cudaStatus, cudaSuccess);
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for (int i = 0; i < output.size(); ++i) {
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EXPECT_NEAR(expected[i], output[i], 1e-5);
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}
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delete adaptiveAvgPoolingLayer;
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}
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};
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TEST_F(AdaptiveAvgPoolingLayerTest, AdaptiveAvgPoolForwardTest) {
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inputSize = {4, 4};
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outputSize = {2, 2};
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nChannels = 2;
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input = {
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// clang-format off
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// Channel 0
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0.573f, 0.619f, 0.732f, 0.055f,
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0.243f, 0.316f, 0.573f, 0.619f,
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0.712f, 0.055f, 0.243f, 0.316f,
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0.573f, 0.619f, 0.742f, 0.055f,
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// Channel 1
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0.473f, 0.919f, 0.107f, 0.073f,
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0.073f, 0.362f, 0.973f, 0.059f,
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0.473f, 0.455f, 0.283f, 0.416f,
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0.532f, 0.819f, 0.732f, 0.850f
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// clang-format on
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};
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expected = {
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// clang-format off
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// Channel 0
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0.43775f, 0.49475f,
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0.48975f, 0.339f,
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// Channel 1
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0.45675f, 0.303f,
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0.56975f, 0.57025f
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// clang-format on
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};
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runTest();
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}
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