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| #include "../src/model/layer/cudnn_pooling.h" |
| #ifdef USE_CUDNN |
| |
| #include "gtest/gtest.h" |
| |
| using singa::CudnnPooling; |
| using singa::Shape; |
| TEST(CudnnPooling, Setup) { |
| CudnnPooling pool; |
| // EXPECT_EQ("CudnnPooling", pool.layer_type()); |
| |
| singa::LayerConf conf; |
| singa::PoolingConf *poolconf = conf.mutable_pooling_conf(); |
| poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX); |
| poolconf->set_kernel_h(1); |
| poolconf->set_kernel_w(2); |
| poolconf->set_pad_h(1); |
| poolconf->set_pad_w(0); |
| poolconf->set_stride_h(2); |
| poolconf->set_stride_w(1); |
| pool.Setup(Shape{1, 3, 3}, conf); |
| |
| EXPECT_EQ(singa::PoolingConf_PoolMethod_MAX, pool.pool_method()); |
| EXPECT_EQ(1u, pool.kernel_h()); |
| EXPECT_EQ(2u, pool.kernel_w()); |
| EXPECT_EQ(1u, pool.pad_h()); |
| EXPECT_EQ(0u, pool.pad_w()); |
| EXPECT_EQ(2u, pool.stride_h()); |
| EXPECT_EQ(1u, pool.stride_w()); |
| EXPECT_EQ(1u, pool.channels()); |
| EXPECT_EQ(3u, pool.height()); |
| EXPECT_EQ(3u, pool.width()); |
| } |
| |
| TEST(CudnnPooling, Forward) { |
| const size_t batchsize = 1, c = 1, h = 3, w = 3; |
| const float x[batchsize * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, |
| 6.0f, 7.0f, 8.0f, 9.0f}; |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(singa::Shape{batchsize, c, h, w}, cuda); |
| in.CopyDataFromHostPtr(x, batchsize * c * h * w); |
| |
| CudnnPooling pool; |
| singa::LayerConf conf; |
| singa::PoolingConf *poolconf = conf.mutable_pooling_conf(); |
| poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX); |
| poolconf->set_kernel_h(2); |
| poolconf->set_kernel_w(2); |
| poolconf->set_pad_h(0); |
| poolconf->set_pad_w(0); |
| poolconf->set_stride_h(1); |
| poolconf->set_stride_w(1); |
| pool.Setup(Shape{1, 3, 3}, conf); |
| |
| // Parameter "flag" does not influence pooling |
| singa::Tensor out1 = pool.Forward(singa::kTrain, in); |
| out1.ToHost(); |
| const float *outptr1 = out1.data<float>(); |
| // Input: 3*3; kernel: 2*2; stride: 1*1; no padding. |
| EXPECT_EQ(4u, out1.Size()); |
| EXPECT_EQ(5.0f, outptr1[0]); |
| EXPECT_EQ(6.0f, outptr1[1]); |
| EXPECT_EQ(8.0f, outptr1[2]); |
| EXPECT_EQ(9.0f, outptr1[3]); |
| } |
| |
| TEST(CudnnPooling, Backward) { |
| // src_data |
| const size_t batchsize = 1, c = 1, src_h = 3, src_w = 3; |
| const float x[batchsize * src_h * src_w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, |
| 6.0f, 7.0f, 8.0f, 9.0f}; |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(singa::Shape{batchsize, c, src_h, src_w}, cuda); |
| in.CopyDataFromHostPtr(x, batchsize * c * src_h * src_w); |
| |
| CudnnPooling pool; |
| singa::LayerConf conf; |
| singa::PoolingConf *poolconf = conf.mutable_pooling_conf(); |
| poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX); |
| poolconf->set_kernel_h(2); |
| poolconf->set_kernel_w(2); |
| poolconf->set_pad_h(0); |
| poolconf->set_pad_w(0); |
| poolconf->set_stride_h(1); |
| poolconf->set_stride_w(1); |
| pool.Setup(Shape{1, 3, 3}, conf); |
| |
| singa::Tensor out1 = pool.Forward(singa::kTrain, in); |
| |
| // grad |
| const size_t grad_h = 2, grad_w = 2; |
| const float dy[batchsize * c * grad_h * grad_w] = {0.1f, 0.2f, 0.3f, 0.4f}; |
| singa::Tensor grad(singa::Shape{batchsize, c, grad_h, grad_w}, cuda); |
| grad.CopyDataFromHostPtr(dy, batchsize * c * grad_h * grad_w); |
| |
| const auto ret = pool.Backward(singa::kTrain, grad); |
| singa::Tensor in_grad = ret.first; |
| in_grad.ToHost(); |
| const float *dx = in_grad.data<float>(); |
| EXPECT_EQ(9u, in_grad.Size()); |
| EXPECT_EQ(0.0f, dx[0]); |
| EXPECT_EQ(0.0f, dx[1]); |
| EXPECT_EQ(0.0f, dx[2]); |
| EXPECT_EQ(0.0f, dx[3]); |
| EXPECT_EQ(0.1f, dx[4]); |
| EXPECT_EQ(0.2f, dx[5]); |
| EXPECT_EQ(0.0f, dx[6]); |
| EXPECT_EQ(0.3f, dx[7]); |
| EXPECT_EQ(0.4f, dx[8]); |
| } |
| #endif // USE_CUDNN |