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| * Licensed to the Apache Software Foundation (ASF) under one |
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| * distributed with this work for additional information |
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| * http://www.apache.org/licenses/LICENSE-2.0 |
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| *************************************************************/ |
| |
| #include "../src/model/layer/flatten.h" |
| #include "gtest/gtest.h" |
| |
| using singa::Flatten; |
| using singa::Shape; |
| TEST(Flatten, Setup) { |
| Flatten flt; |
| // EXPECT_EQ("Flatten", flt.layer_type()); |
| |
| singa::LayerConf conf; |
| singa::FlattenConf *flattenconf = conf.mutable_flatten_conf(); |
| flattenconf->set_axis(1); |
| |
| flt.Setup(Shape{2}, conf); |
| EXPECT_EQ(1, flt.Axis()); |
| } |
| |
| TEST(Flatten, ForwardCPU) { |
| const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -4.f, |
| 1.5f, -1.5f, 0.f, -0.5f, -2.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| singa::Shape s = {2, 1, 3, 2}; |
| singa::Tensor in(s); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| int axis = 3; |
| Flatten flt; |
| singa::LayerConf conf; |
| singa::FlattenConf *flattenconf = conf.mutable_flatten_conf(); |
| flattenconf->set_axis(axis); |
| flt.Setup(Shape{1, 3, 2}, conf); |
| |
| singa::Tensor out = flt.Forward(singa::kTrain, in); |
| EXPECT_EQ(n, out.Size()); |
| EXPECT_EQ(6u, out.shape(0)); |
| EXPECT_EQ(2u, out.shape(1)); |
| const float *yptr = out.data<float>(); |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(x[i], yptr[i]); |
| } |
| |
| TEST(Flatten, BackwardCPU) { |
| // directly use input as the output_grad for backward |
| // note that only the shape of input really matters |
| const float dy[] = {1.f, 2.f, 3.f, -2.f, -3.f, -4.f, |
| 1.5f, -1.5f, 0.f, -0.5f, -2.f, -1.f}; |
| size_t n = sizeof(dy) / sizeof(float); |
| singa::Tensor in(singa::Shape{2, 1, 3, 2}); |
| in.CopyDataFromHostPtr<float>(dy, n); |
| |
| int axis = 2; |
| Flatten flt; |
| singa::LayerConf conf; |
| singa::FlattenConf *flattenconf = conf.mutable_flatten_conf(); |
| flattenconf->set_axis(axis); |
| flt.Setup(Shape{1, 3, 2}, conf); |
| |
| singa::Tensor temp = flt.Forward(singa::kTrain, in); |
| const auto out = flt.Backward(singa::kTrain, temp); |
| const float *xptr = out.first.data<float>(); |
| EXPECT_EQ(n, out.first.Size()); |
| EXPECT_EQ(2u, out.first.shape(0)); |
| EXPECT_EQ(1u, out.first.shape(1)); |
| EXPECT_EQ(3u, out.first.shape(2)); |
| EXPECT_EQ(2u, out.first.shape(3)); |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dy[i], xptr[i]); |
| } |
| |
| #ifdef USE_CUDA |
| TEST(Flatten, ForwardGPU) { |
| const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -4.f, |
| 1.5f, -1.5f, 0.f, -0.5f, -2.f, -1.f}; |
| size_t n = sizeof(x) / sizeof(float); |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(singa::Shape{2, 1, 3, 2}, cuda); |
| in.CopyDataFromHostPtr<float>(x, n); |
| |
| int axis = 3; |
| Flatten flt; |
| singa::LayerConf conf; |
| singa::FlattenConf *flattenconf = conf.mutable_flatten_conf(); |
| flattenconf->set_axis(axis); |
| flt.Setup(Shape{1, 3, 2}, conf); |
| |
| singa::Tensor out = flt.Forward(singa::kTrain, in); |
| out.ToHost(); |
| EXPECT_EQ(n, out.Size()); |
| EXPECT_EQ(6u, out.shape(0)); |
| EXPECT_EQ(2u, out.shape(1)); |
| const float *yptr = out.data<float>(); |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(x[i], yptr[i]); |
| } |
| |
| TEST(Flatten, BackwardGPU) { |
| // directly use input as the output_grad for backward |
| // note that only the shape of input really matters |
| const float dy[] = {1.f, 2.f, 3.f, -2.f, -3.f, -4.f, |
| 1.5f, -1.5f, 0.f, -0.5f, -2.f, -1.f}; |
| size_t n = sizeof(dy) / sizeof(float); |
| auto cuda = std::make_shared<singa::CudaGPU>(); |
| singa::Tensor in(singa::Shape{2, 1, 3, 2}, cuda); |
| in.CopyDataFromHostPtr<float>(dy, n); |
| |
| int axis = 2; |
| Flatten flt; |
| singa::LayerConf conf; |
| singa::FlattenConf *flattenconf = conf.mutable_flatten_conf(); |
| flattenconf->set_axis(axis); |
| flt.Setup(Shape{1, 3, 2}, conf); |
| |
| singa::Tensor out = flt.Forward(singa::kTrain, in); |
| const auto ret = flt.Backward(singa::kTrain, out); |
| singa::Tensor in_diff = ret.first; |
| in_diff.ToHost(); |
| const float *xptr = in_diff.data<float>(); |
| EXPECT_EQ(n, in_diff.Size()); |
| EXPECT_EQ(2u, in_diff.shape(0)); |
| EXPECT_EQ(1u, in_diff.shape(1)); |
| EXPECT_EQ(3u, in_diff.shape(2)); |
| EXPECT_EQ(2u, in_diff.shape(3)); |
| for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dy[i], xptr[i]); |
| } |
| #endif // USE_CUDA |