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Merge pull request #16087 from YashasSamaga:cuda4dnn-eltwise-div

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pull/16091/head
Alexander Alekhin 5 years ago
parent b2b6f52d14 a91eca6ec2
commit 202ba124a5
5 changed files with 58 additions and 2 deletions
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  1. 48
      modules/dnn/src/cuda/eltwise_ops.cu
  2. 3
      modules/dnn/src/cuda4dnn/kernels/eltwise_ops.hpp
  3. 5
      modules/dnn/src/cuda4dnn/primitives/eltwise.hpp
  4. 3
      modules/dnn/src/layers/eltwise_layer.cpp
  5. 1
      modules/dnn/test/test_onnx_importer.cpp

48
modules/dnn/src/cuda/eltwise_ops.cu
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@ -102,6 +102,26 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
v_store(output_vPtr[i], vec_x);
}
}
template <class T, std::size_t N>
__global__ void eltwise_div_2_vec(Span<T> output, View<T> x, View<T> y) {
using vector_type = get_vector_type_t<T, N>;
auto output_vPtr = vector_type::get_pointer(output.data());
auto x_vPtr = vector_type::get_pointer(x.data());
auto y_vPtr = vector_type::get_pointer(y.data());
for (auto i : grid_stride_range(output.size() / vector_type::size())) {
vector_type vec_x, vec_y;
v_load(vec_x, x_vPtr[i]);
v_load(vec_y, y_vPtr[i]);
for (int j = 0; j < vector_type::size(); j++)
vec_x.data[j] = vec_x.data[j] / vec_y.data[j];
v_store(output_vPtr[i], vec_x);
}
}
}
template <class T, std::size_t N>
@ -221,4 +241,32 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
template void eltwise_prod_2(const Stream& stream, Span<__half> output, View<__half> x, View<__half> y);
template void eltwise_prod_2(const Stream& stream, Span<float> output, View<float> x, View<float> y);
template <class T, std::size_t N>
void launch_vectorized_eltwise_div_2(const Stream& stream, Span<T> output, View<T> x, View<T> y) {
CV_Assert(is_fully_aligned<T>(output, N));
CV_Assert(is_fully_aligned<T>(x, N));
CV_Assert(is_fully_aligned<T>(y, N));
auto kernel = raw::eltwise_div_2_vec<T, N>;
auto policy = make_policy(kernel, output.size() / N, 0, stream);
launch_kernel(kernel, policy, output, x, y);
}
template <class T>
void eltwise_div_2(const Stream& stream, Span<T> output, View<T> x, View<T> y) {
CV_Assert(x.size() == y.size());
CV_Assert(x.size() == output.size());
if (is_fully_aligned<T>(output, 4) && is_fully_aligned<T>(x, 4) && is_fully_aligned<T>(y, 4)) {
launch_vectorized_eltwise_div_2<T, 4>(stream, output, x, y);
} else if (is_fully_aligned<T>(output, 2) && is_fully_aligned<T>(x, 2) && is_fully_aligned<T>(y, 2)) {
launch_vectorized_eltwise_div_2<T, 2>(stream, output, x, y);
} else {
launch_vectorized_eltwise_div_2<T, 1>(stream, output, x, y);
}
}
template void eltwise_div_2(const Stream& stream, Span<__half> output, View<__half> x, View<__half> y);
template void eltwise_div_2(const Stream& stream, Span<float> output, View<float> x, View<float> y);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */

3
modules/dnn/src/cuda4dnn/kernels/eltwise_ops.hpp
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@ -24,6 +24,9 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace kernels {
template <class T>
void eltwise_prod_2(const csl::Stream& stream, csl::Span<T> output, csl::View<T> x, csl::View<T> y);
template <class T>
void eltwise_div_2(const csl::Stream& stream, csl::Span<T> output, csl::View<T> x, csl::View<T> y);
}}}} /* namespace cv::dnn::cuda4dnn::kernels */
#endif /* OPENCV_DNN_SRC_CUDA4DNN_KERNELS_ELTWISE_OPS_HPP */

5
modules/dnn/src/cuda4dnn/primitives/eltwise.hpp
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@ -24,7 +24,8 @@ namespace cv { namespace dnn { namespace cuda4dnn {
enum class EltwiseOpType {
MAX,
SUM,
PRODUCT
PRODUCT,
DIV
};
template <class T>
@ -64,6 +65,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
{
case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, input_x, input_y); break;
case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, input_x, input_y); break;
case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, input_x, input_y); break;
case EltwiseOpType::SUM:
if (coeffs.empty() || (coeffs[0] == 1 && coeffs[1] == 1))
kernels::eltwise_sum_2<T>(stream, output, input_x, input_y);
@ -89,6 +91,7 @@ namespace cv { namespace dnn { namespace cuda4dnn {
{
case EltwiseOpType::MAX: kernels::eltwise_max_2<T>(stream, output, output, input); break;
case EltwiseOpType::PRODUCT: kernels::eltwise_prod_2<T>(stream, output, output, input); break;
case EltwiseOpType::DIV: kernels::eltwise_div_2<T>(stream, output, output, input); break;
case EltwiseOpType::SUM:
if (coeffs.empty() || coeffs[i] == 1)
kernels::eltwise_sum_2<T>(stream, output, output, input);

3
modules/dnn/src/layers/eltwise_layer.cpp
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@ -108,7 +108,7 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_CUDA && op != DIV) || // TODO: not implemented, see PR #15811
backendId == DNN_BACKEND_CUDA ||
(backendId == DNN_BACKEND_HALIDE && op != DIV) || // TODO: not implemented, see PR #15811
((((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (preferableTarget != DNN_TARGET_OPENCL || coeffs.empty()))
|| backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && !variableChannels));
@ -471,6 +471,7 @@ public:
case MAX: return cuda4dnn::EltwiseOpType::MAX;
case SUM: return cuda4dnn::EltwiseOpType::SUM;
case PROD: return cuda4dnn::EltwiseOpType::PRODUCT;
case DIV: return cuda4dnn::EltwiseOpType::DIV;
}
return cuda4dnn::EltwiseOpType::SUM;
}();

1
modules/dnn/test/test_onnx_importer.cpp
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@ -380,6 +380,7 @@ TEST_P(Test_ONNX_layers, Div)
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(Test_ONNX_layers, DynamicReshape)

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