Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

add DIV support to EltwiseOp

Browse Source
pull/16087/head
YashasSamaga 5 years ago
parent 4b0132ed7a
commit a91eca6ec2
5 changed files with 58 additions and 2 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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
Unescape View File

@ -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)

Write Preview
Loading…
Cancel
Save