diff options
Diffstat (limited to 'inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp')
| -rw-r--r-- | inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp | 70 |
1 files changed, 70 insertions, 0 deletions
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp index 3c86b4c7a..a3cbc0a75 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp +++ b/inference-engine/thirdparty/clDNN/tests/test_cases/convolution_grad_input_gpu_test.cpp @@ -25,6 +25,7 @@ #include <api/CPP/network.hpp> #include <api/CPP/engine.hpp> #include "test_utils/test_utils.h" +#include "api/CPP/eltwise.hpp" using namespace cldnn; using namespace tests; @@ -136,4 +137,73 @@ TEST(convolution_grad_input_f32_fw_gpu, basic_wsiz2x2_in2x2x1x2_bfyx_stride2_pad { EXPECT_FLOAT_EQ(expected_output_vec[i], output_ptr[i]); } +} + +TEST(convolution_grad_input_f32_fw_gpu, basic_wsiz2x2_in2x2x1x2_bfyx_stride2_fusion) { + // Filter : 2x2 + // Input : 2x2x1x2 + // Output : 2x2x1x2 + // Stride : 2x2 + // + // Input: + // 8 0.5 1 3 + // 6 9 2 4 + // + // Filter + // -2 2 + // 7 -0.5 + // + // Output: + // -4 3.5 -0.5 21 + // 12 -18 4 -9 + + engine engine; + + auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 2, 2 } }); + auto weights = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 2 } }); + auto scale_in = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 1, 1 } }); + auto elt_data = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 2, 1, 2 } }); + + set_values(input, { 8.f, 0.5f, 6.f, 9.f, 1.f, 3.f, 2.f, 4.f }); + set_values(weights, { -2.f, 2.f, 7.f, -0.5f }); + set_values(scale_in, { 1.0f }); + set_values(elt_data, { 1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f }); + + topology topology( + input_layout("input", input.get_layout()), + data("weights", weights), + data("scale_in", scale_in), + data("elt_data", elt_data), + convolution_grad_input("conv", "input", { "weights" }, { 1, 1, 2, 2 }, { 0, 0, -1, -1 }), + eltwise("elt", "conv", "elt_data", eltwise_mode::sum), + scale("scale", "elt", "scale_in") + ); + + build_options options; + options.set_option(build_option::optimize_data(true)); + + network network(engine, topology, options); + network.set_input_data("input", input); + + auto outputs = network.execute(); + auto primitives = network.get_all_primitive_ids(); + auto exec_prim = network.get_executed_primitive_ids(); + EXPECT_EQ(outputs.size(), size_t(1)); + EXPECT_EQ(outputs.begin()->first, "scale"); + EXPECT_TRUE(std::find(primitives.begin(), primitives.end(), "elt") == primitives.end()); + EXPECT_TRUE(std::find(exec_prim.begin(), exec_prim.end(), "elt") == exec_prim.end()); + + auto output_prim = outputs.begin()->second.get_memory(); + + auto output_ptr = output_prim.pointer<float>(); + + std::vector<float> expected_output_vec = { + -3.f, 5.5f, 15.f, -14.f, + 4.5f, 27.f, 11.f, 0.f + }; + + for (unsigned int i = 0; i < expected_output_vec.size(); i++) + { + EXPECT_FLOAT_EQ(expected_output_vec[i], output_ptr[i]); + } }
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