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Merge pull request #25779 from fengyuentau:dnn/fix_onnx_depthtospace
dnn: add DepthToSpace and SpaceToDepth #25779 We are working on updating WeChat QRCode module. One of the new models is a fully convolutional model and hence it should be able to run with different input shapes. However, it has an operator `DepthToSpace`, which is parsed as a subgraph of `Reshape -> Permute -> Reshape` with a fixed shape getting during parsing. The subgraph itself is not a problem, but the true problem is the subgraph with a fixed input and output shape regardless input changes. This does not allow the model to run with different input shapes. Solution is to add a dedicated layer for DepthtoSpace and SpaceToDepth. Backend support: - [x] CPU - [x] CUDA - [x] OpenCL - [x] OpenVINO - [x] CANN - [x] TIMVX - ~Vulkan~ (missing fundamental tools, like permutation and reshape) ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Yuantao Feng
GitHub
parent
acd16e384f
commit
3f13ce797b
@ -1188,6 +1188,16 @@ CV__DNN_INLINE_NS_BEGIN
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static Ptr<GroupNormLayer> create(const LayerParams ¶ms);
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};
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class CV_EXPORTS DepthToSpaceLayer : public Layer {
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public:
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static Ptr<DepthToSpaceLayer> create(const LayerParams ¶ms);
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};
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class CV_EXPORTS SpaceToDepthLayer : public Layer {
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public:
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static Ptr<SpaceToDepthLayer> create(const LayerParams ¶ms);
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};
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//! @}
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//! @}
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CV__DNN_INLINE_NS_END
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@ -265,7 +265,6 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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typename std::enable_if<cxx_utils::is_forward_iterator<ForwardItr>::value, void>
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::type reshape(ForwardItr start, ForwardItr end) {
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CV_Assert(start != end);
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CV_Assert(std::distance(start, end) <= rank());
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using ItrValueType = typename std::iterator_traits<ForwardItr>::value_type;
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@ -284,6 +283,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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auto total = std::accumulate(start, end, 1, std::multiplies<ItrValueType>());
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if (total < 0) {
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/* there is an unknown size */
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CV_CheckEQ(size() % std::abs(total), static_cast<size_type>(0), "cannot be reshaped"); // must be divisible
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if (std::abs(total) <= size()) {
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unknown_size = size() / std::abs(total);
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total = size();
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@ -298,11 +298,9 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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CV_Error(Error::StsBadArg, "new axes do not preserve the tensor element count");
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}
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/* we assume the size of the unspecified axes to be one */
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std::fill(std::begin(shape), std::end(shape), 1);
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std::copy_backward(start, end, std::end(shape));
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/* replace the unknown axis with the correct value */
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/* copy shape from given iterator and reshape -1 with deduced value */
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shape.resize(std::distance(start, end));
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std::copy(start, end, shape.begin());
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std::replace(std::begin(shape), std::end(shape), size_type(-1), unknown_size);
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}
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@ -600,6 +598,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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auto total = std::accumulate(start, end, 1, std::multiplies<ItrValueType>());
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if (total < 0) {
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/* there is an unknown size */
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CV_CheckEQ(size() % std::abs(total), static_cast<size_type>(0), "cannot be reshaped"); // must be divisible
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if (std::abs(total) <= size()) {
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unknown_size = size() / std::abs(total);
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total = size();
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@ -614,11 +613,9 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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CV_Error(Error::StsBadArg, "new axes do not preserve the tensor element count");
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}
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/* we assume the size of the unspecified axes to be one */
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std::fill(std::begin(shape), std::end(shape), 1);
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std::copy_backward(start, end, std::end(shape));
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/* replace the unknown axis with the correct value */
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/* copy shape from given iterator and reshape -1 with deduced value */
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shape.resize(std::distance(start, end));
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std::copy(start, end, shape.begin());
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std::replace(std::begin(shape), std::end(shape), size_type(-1), unknown_size);
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}
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@ -946,7 +943,6 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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typename std::enable_if<!std::is_integral<ForwardItr>::value, void>
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::type reshape(ForwardItr start, ForwardItr end) {
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CV_Assert(start != end);
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CV_Assert(std::distance(start, end) <= rank());
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using ItrValueType = typename std::iterator_traits<ForwardItr>::value_type;
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@ -965,6 +961,7 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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auto total = std::accumulate(start, end, 1, std::multiplies<ItrValueType>());
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if (total < 0) {
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/* there is an unknown size */
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CV_CheckEQ(size() % std::abs(total), static_cast<size_type>(0), "cannot be reshaped"); // must be divisible
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if (std::abs(total) <= size()) {
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unknown_size = size() / std::abs(total);
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total = size();
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@ -979,11 +976,9 @@ namespace cv { namespace dnn { namespace cuda4dnn { namespace csl {
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CV_Error(Error::StsBadArg, "new axes do not preserve the tensor element count");
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}
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/* we assume the size of the unspecified axes to be one */
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std::fill(std::begin(shape), std::end(shape), 1);
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std::copy_backward(start, end, std::end(shape));
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/* replace the unknown axis with the correct value */
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/* copy shape from given iterator and reshape -1 with deduced value */
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shape.resize(std::distance(start, end));
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std::copy(start, end, shape.begin());
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std::replace(std::begin(shape), std::end(shape), size_type(-1), unknown_size);
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}
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76
modules/dnn/src/cuda4dnn/primitives/depth_space_ops.hpp
Normal file
76
modules/dnn/src/cuda4dnn/primitives/depth_space_ops.hpp
Normal file
@ -0,0 +1,76 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_DEPTH_SPACE_OPS_HPP
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#define OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_DEPTH_SPACE_OPS_HPP
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#include "../../op_cuda.hpp"
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#include "../csl/stream.hpp"
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#include "../csl/tensor.hpp"
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#include "../csl/tensor_ops.hpp"
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#include "../csl/memory.hpp"
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#include "../kernels/permute.hpp"
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#include <utility>
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namespace cv { namespace dnn { namespace cuda4dnn {
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template <class T>
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class DepthSpaceOps final : public CUDABackendNode {
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public:
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using wrapper_type = GetCUDABackendWrapperType<T>;
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DepthSpaceOps(csl::Stream stream_, const std::vector<int> &internal_shape_,
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const std::vector<size_t> &permutation_)
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: stream(std::move(stream_)), internal_shape(internal_shape_),
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permutation(permutation_)
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{
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transposed_internal_shape = std::vector<int>(internal_shape.size());
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for (size_t i = 0; i < permutation.size(); i++) {
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transposed_internal_shape[i] = internal_shape[permutation[i]];
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}
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size_t num_elements = std::accumulate(internal_shape.begin(), internal_shape.end(), 1, std::multiplies<size_t>());
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csl::WorkspaceBuilder builder;
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builder.require<T>(num_elements);
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scratch_mem_in_bytes = builder.required_workspace_size();
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}
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void forward(const std::vector<cv::Ptr<BackendWrapper>> &inputs,
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const std::vector<cv::Ptr<BackendWrapper>> &outputs,
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csl::Workspace &workspace) override {
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CV_CheckEQ(inputs.size(), size_t(1), "DepthSpaceOps: only one input is accepted");
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CV_CheckEQ(outputs.size(), size_t(1), "DepthSpaceOps: only one output is accepted");
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auto input_wrapper = inputs.front().dynamicCast<wrapper_type>();
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auto input = input_wrapper->getView();
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CV_CheckEQ(input.rank(), size_t(4), "DepthSpaceOps: input needs to be 4-dimensional [N, C, H, W]");
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auto output_wrapper = outputs.front().dynamicCast<wrapper_type>();
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auto output = output_wrapper->getSpan();
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auto ws_allocator = csl::WorkspaceAllocator(workspace);
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auto transposed_internal = ws_allocator.get_tensor_span<T>(transposed_internal_shape.begin(), transposed_internal_shape.end());
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// Call reshape on input so that it has the correct shape for permutation
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input.reshape(internal_shape.begin(), internal_shape.end());
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kernels::permute(stream, transposed_internal, input, permutation);
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// Only copying is needed as output already has the expected shape
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auto t = csl::TensorView<T>(transposed_internal);
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csl::memcpy(output.get(), t.get(), output.size(), stream);
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}
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std::size_t get_workspace_memory_in_bytes() const noexcept override { return scratch_mem_in_bytes; }
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private:
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csl::Stream stream;
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std::vector<int> internal_shape;
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std::vector<size_t> permutation;
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std::vector<int> transposed_internal_shape;
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std::size_t scratch_mem_in_bytes;
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};
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}}} // namespace cv::dnn::cuda4dnn
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#endif // OPENCV_DNN_SRC_CUDA4DNN_PRIMITIVES_DEPTH_SPACE_OPS_HPP
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@ -164,6 +164,10 @@ void initializeLayerFactory()
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CV_DNN_REGISTER_LAYER_CLASS(InstanceNormalization, InstanceNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Attention, AttentionLayer);
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CV_DNN_REGISTER_LAYER_CLASS(GroupNormalization, GroupNormLayer);
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CV_DNN_REGISTER_LAYER_CLASS(DepthToSpace, DepthToSpaceLayer)
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CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepth, SpaceToDepthLayer)
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CV_DNN_REGISTER_LAYER_CLASS(DepthToSpaceInt8, DepthToSpaceLayer)
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CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepthInt8, SpaceToDepthLayer)
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CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
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CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer);
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492
modules/dnn/src/layers/depth_space_ops_layer.cpp
Normal file
492
modules/dnn/src/layers/depth_space_ops_layer.cpp
Normal file
@ -0,0 +1,492 @@
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "../precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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// OpenCL backend
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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#endif
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// OpenVINO backend
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#ifdef HAVE_DNN_NGRAPH
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#include "../op_inf_engine.hpp"
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#include "../ie_ngraph.hpp"
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#endif
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// CUDA backend
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#ifdef HAVE_CUDA
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#include "../op_cuda.hpp"
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#include "../cuda4dnn/primitives/depth_space_ops.hpp"
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#endif
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// CANN backend
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#ifdef HAVE_CANN
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#include "../op_cann.hpp"
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#endif
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// TIM-VX backend
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#ifdef HAVE_TIMVX
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#include "../op_timvx.hpp"
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#endif
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namespace cv { namespace dnn {
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struct DepthSpaceOps {
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MatShape internal_shape;
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MatShape transposed_internal_shape;
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std::vector<int> permutation;
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#ifdef HAVE_OPENCL
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UMat umat_permutation;
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UMat umat_internal_strides;
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UMat umat_transposed_internal_strides;
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#endif
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void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) {
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transposed_internal_shape = MatShape(internal_shape.size());
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for (size_t i = 0; i < permutation.size(); i++) {
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transposed_internal_shape[i] = internal_shape[permutation[i]];
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}
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#ifdef HAVE_OPENCL
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umat_permutation.release();
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umat_internal_strides.release();
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umat_transposed_internal_strides.release();
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#endif
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}
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void cpuCompute(const Mat &input, Mat &output) {
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const auto output_shape = shape(output);
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Mat tmp;
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cv::transposeND(input.reshape(1, internal_shape), permutation, tmp);
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tmp.reshape(1, output_shape).copyTo(output);
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}
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#ifdef HAVE_OPENCL
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bool oclCompute(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) {
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std::vector<UMat> inputs, outputs;
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inputs_arr.getUMatVector(inputs);
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outputs_arr.getUMatVector(outputs);
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if (umat_permutation.empty() || umat_internal_strides.empty() || umat_transposed_internal_strides.empty()) {
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Mat mat_permutation(1, permutation.size(), CV_32S, permutation.data());
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mat_permutation.copyTo(umat_permutation);
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std::vector<int> internal_strides(permutation.size(), 1), transposed_internal_stides(permutation.size(), 1);
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for (int i = static_cast<int>(permutation.size()) - 2; i >= 0; i--) {
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internal_strides[i] = internal_strides[i + 1] * internal_shape[i + 1];
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transposed_internal_stides[i] = transposed_internal_stides[i + 1] * transposed_internal_shape[i + 1];
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}
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Mat mat_internal_strides(1, internal_strides.size(), CV_32S, internal_strides.data());
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mat_internal_strides.copyTo(umat_internal_strides);
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Mat mat_transposed_internal_strides(1, transposed_internal_stides.size(), CV_32S, transposed_internal_stides.data());
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mat_transposed_internal_strides.copyTo(umat_transposed_internal_strides);
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}
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const auto output_shape = shape(outputs.front());
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UMat tmp = inputs.front().reshape(1, static_cast<int>(internal_shape.size()), internal_shape.data());
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bool use_half = (inputs_arr.depth() == CV_16F);
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std::string permute_options = cv::format("-DDtype=%s", use_half ? "half" : "float");
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ocl::Kernel permute_kernel("permute", ocl::dnn::permute_oclsrc, permute_options);
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if (permute_kernel.empty()) {
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return false;
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}
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UMat transposed_tmp(static_cast<int>(transposed_internal_shape.size()), transposed_internal_shape.data(), inputs_arr.depth());
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size_t num_element = static_cast<size_t>(std::accumulate(internal_shape.begin(), internal_shape.end(), 1, std::multiplies<int>()));
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permute_kernel.set(0, static_cast<int>(num_element));
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permute_kernel.set(1, ocl::KernelArg::PtrReadOnly(tmp));
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permute_kernel.set(2, ocl::KernelArg::PtrReadOnly(umat_permutation));
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permute_kernel.set(3, ocl::KernelArg::PtrReadOnly(umat_internal_strides));
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permute_kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_transposed_internal_strides));
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permute_kernel.set(5, static_cast<int>(permutation.size()));
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permute_kernel.set(6, ocl::KernelArg::PtrWriteOnly(transposed_tmp));
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if (!permute_kernel.run(1, &num_element, NULL, false)) {
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return false;
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}
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transposed_tmp.reshape(1, static_cast<int>(output_shape.size()), output_shape.data()).copyTo(outputs.front());
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return true;
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}
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#endif // HAVE_OPENCL
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};
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class DepthToSpaceLayerImpl CV_FINAL : public DepthToSpaceLayer, public DepthSpaceOps {
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public:
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DepthToSpaceLayerImpl(const LayerParams ¶ms) {
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setParamsFrom(params);
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CV_CheckTrue(params.has("blocksize"), "DepthSpaceLayer: blocksize is required");
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blocksize = params.get<int>("blocksize");
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auto mode = params.get<std::string>("mode", "DCR");
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if (mode == "CRD") {
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is_crd = true;
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permutation = {0, 1, 4, 2, 5, 3};
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} else if (mode == "DCR") {
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is_crd = false;
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permutation = {0, 3, 4, 1, 5, 2};
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} else {
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CV_Error(Error::StsBadArg, cv::format("DepthToSpace: unsupported mode %s\n", mode.c_str()));
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}
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE {
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
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backendId == DNN_BACKEND_CUDA ||
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backendId == DNN_BACKEND_CANN ||
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(backendId == DNN_BACKEND_TIMVX && is_crd);
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}
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virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const CV_OVERRIDE {
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CV_CheckEQ(inputs.size(), static_cast<size_t>(1), "DepthSpaceLayer: accepts only one input");
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const auto &input = inputs.front();
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CV_CheckEQ(input.size(), static_cast<size_t>(4), "DepthSpaceLayer: input needs to be 4-dimensional [N, C, H, W]");
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int batch = input[0], input_depth = input[1], input_height = input[2], input_width = input[3];
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int output_depth = -1, output_height = -1, output_width = -1;
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CV_CheckEQ(input_depth % (blocksize * blocksize), 0,
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"DepthSpaceLayer: requires input depth to be a multiple of (blocksize * blocksize)");
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output_depth = input_depth / blocksize / blocksize;
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output_height = input_height * blocksize;
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output_width = input_width * blocksize;
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outputs.assign(1, MatShape{batch, output_depth, output_height, output_width});
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return false;
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}
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virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
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std::vector<Mat> inputs;
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inputs_arr.getMatVector(inputs);
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auto input_shape = shape(inputs.front());
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int batch = input_shape[0], input_depth = input_shape[1], input_height = input_shape[2], input_width = input_shape[3];
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if (is_crd) {
|
||||
internal_shape = MatShape{batch, input_depth / (blocksize * blocksize), blocksize, blocksize, input_height, input_width};
|
||||
} else {
|
||||
internal_shape = MatShape{batch, blocksize, blocksize, input_depth / (blocksize * blocksize), input_height, input_width};
|
||||
}
|
||||
|
||||
DepthSpaceOps::finalize(inputs_arr, outputs_arr);
|
||||
}
|
||||
|
||||
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE {
|
||||
CV_TRACE_FUNCTION();
|
||||
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
||||
|
||||
// TODO: support 8-bit int in permute kernel
|
||||
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) && inputs_arr.depth() != CV_8S,
|
||||
DepthSpaceOps::oclCompute(inputs_arr, outputs_arr, internals_arr))
|
||||
|
||||
if (inputs_arr.depth() == CV_16F) {
|
||||
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
||||
return;
|
||||
}
|
||||
|
||||
std::vector<Mat> inputs, outputs;
|
||||
inputs_arr.getMatVector(inputs);
|
||||
outputs_arr.getMatVector(outputs);
|
||||
|
||||
DepthSpaceOps::cpuCompute(inputs.front(), outputs.front());
|
||||
}
|
||||
|
||||
#ifdef HAVE_DNN_NGRAPH
|
||||
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendNode>> &nodes) CV_OVERRIDE {
|
||||
using namespace ov::op;
|
||||
auto input_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
|
||||
std::shared_ptr<ov::Node> output_node;
|
||||
if (is_crd) {
|
||||
output_node = std::make_shared<v0::DepthToSpace>(input_node, v0::DepthToSpace::DepthToSpaceMode::DEPTH_FIRST, static_cast<size_t>(blocksize));
|
||||
} else {
|
||||
output_node = std::make_shared<v0::DepthToSpace>(input_node, v0::DepthToSpace::DepthToSpaceMode::BLOCKS_FIRST, static_cast<size_t>(blocksize));
|
||||
}
|
||||
return Ptr<BackendNode>(new InfEngineNgraphNode(output_node));
|
||||
}
|
||||
#endif // HAVE_DNN_NGRAPH
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
Ptr<BackendNode> initCUDA(void *context_,
|
||||
const std::vector<Ptr<BackendWrapper>>& inputs,
|
||||
const std::vector<Ptr<BackendWrapper>>& outputs) override {
|
||||
using namespace cv::dnn::cuda4dnn;
|
||||
auto context = reinterpret_cast<csl::CSLContext*>(context_);
|
||||
std::vector<size_t> perm(permutation.begin(), permutation.end());
|
||||
return make_cuda_node<cuda4dnn::DepthSpaceOps>(preferableTarget, std::move(context->stream), internal_shape, perm);
|
||||
}
|
||||
#endif // HAVE_CUDA
|
||||
|
||||
#ifdef HAVE_CANN
|
||||
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendWrapper>> &outputs,
|
||||
const std::vector<Ptr<BackendNode>> &nodes) CV_OVERRIDE {
|
||||
CV_CheckEQ(inputs.size(), static_cast<size_t>(1), "DepthToSpace/CANN: only accepts one input wrapper");
|
||||
CV_CheckEQ(nodes.size(), static_cast<size_t>(1), "DepthToSpace/CANN: only accepts one input node");
|
||||
|
||||
auto input_tensor_wrapper = inputs.front().dynamicCast<CannBackendWrapper>();
|
||||
auto input_tensor_desc = input_tensor_wrapper->getTensorDesc();
|
||||
auto input_node = nodes.front().dynamicCast<CannBackendNode>()->getOp();
|
||||
|
||||
auto node = std::make_shared<ge::op::DepthToSpace>(name);
|
||||
|
||||
node->set_attr_block_size(blocksize);
|
||||
if (is_crd) {
|
||||
node->set_attr_mode("CRD");
|
||||
} else {
|
||||
node->set_attr_mode("DCR");
|
||||
}
|
||||
node->set_attr_data_format("NCHW");
|
||||
|
||||
node->set_input_x_by_name(*input_node, input_tensor_wrapper->name.c_str());
|
||||
node->update_input_desc_x(*input_tensor_desc);
|
||||
|
||||
auto output_tensor_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
|
||||
node->update_output_desc_y(*output_tensor_desc);
|
||||
|
||||
return Ptr<BackendNode>(new CannBackendNode(node));
|
||||
}
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_TIMVX
|
||||
virtual Ptr<BackendNode> initTimVX(void* timvx_info_,
|
||||
const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendWrapper>> &outputs,
|
||||
bool isLast) CV_OVERRIDE {
|
||||
auto info = reinterpret_cast<TimVXInfo*>(timvx_info_);
|
||||
CV_Assert(info);
|
||||
auto timvx_graph = info->getGraph();
|
||||
CV_Assert(timvx_graph);
|
||||
auto graph = timvx_graph->graph;
|
||||
|
||||
auto input_wrapper = inputs.front().dynamicCast<TimVXBackendWrapper>();
|
||||
int input_wrapper_index = -1;
|
||||
if (input_wrapper->isTensor()) {
|
||||
input_wrapper_index = timvx_graph->getTensorIndex(input_wrapper->getTensor());
|
||||
if (input_wrapper_index == -1) {
|
||||
auto tmp = input_wrapper->getMat();
|
||||
input_wrapper = std::make_shared<TimVXBackendWrapper>(tmp);
|
||||
}
|
||||
}
|
||||
if (!input_wrapper->isTensor() || input_wrapper_index == 1) {
|
||||
auto input_node_quant = Ptr<tim::vx::Quantization>(new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0));
|
||||
input_wrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, input_node_quant);
|
||||
input_wrapper_index = timvx_graph->addWrapper(input_wrapper);
|
||||
}
|
||||
|
||||
auto output_wrapper = outputs.front().dynamicCast<TimVXBackendWrapper>();
|
||||
auto output_node_quant = input_wrapper->getTensorQuantization();
|
||||
if (isLast) {
|
||||
auto shape_type = getShapeTypeFromMat(output_wrapper->getMat());
|
||||
output_wrapper->setTensorShape(shape_type);
|
||||
output_wrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, output_node_quant);
|
||||
} else {
|
||||
output_wrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, output_node_quant);
|
||||
}
|
||||
int output_wrapper_index = timvx_graph->addWrapper(output_wrapper);
|
||||
|
||||
std::shared_ptr<tim::vx::Operation> timvx_node;
|
||||
timvx_node = graph->CreateOperation<tim::vx::ops::DepthToSpace>(blocksize);
|
||||
std::vector<int> input_wrapper_indices{input_wrapper_index}, output_wrapper_indices{output_wrapper_index};
|
||||
return Ptr<BackendNode>(new TimVXBackendNode(timvx_graph, timvx_node, input_wrapper_indices, output_wrapper_indices));
|
||||
}
|
||||
#endif
|
||||
|
||||
virtual bool tryQuantize(const std::vector<std::vector<float>> &scales,
|
||||
const std::vector<std::vector<int>> &zeropoints, LayerParams ¶ms) CV_OVERRIDE {
|
||||
return true;
|
||||
}
|
||||
|
||||
private:
|
||||
int blocksize;
|
||||
|
||||
bool is_crd;
|
||||
};
|
||||
|
||||
Ptr<DepthToSpaceLayer> DepthToSpaceLayer::create(const LayerParams ¶ms) {
|
||||
return makePtr<DepthToSpaceLayerImpl>(params);
|
||||
}
|
||||
|
||||
class SpaceToDepthLayerImpl CV_FINAL : public SpaceToDepthLayer, public DepthSpaceOps {
|
||||
public:
|
||||
SpaceToDepthLayerImpl(const LayerParams ¶ms) {
|
||||
setParamsFrom(params);
|
||||
|
||||
CV_CheckTrue(params.has("blocksize"), "SpaceToDepthLayer: blocksize is required");
|
||||
blocksize = params.get<int>("blocksize");
|
||||
|
||||
permutation = {0, 3, 5, 1, 2, 4};
|
||||
}
|
||||
|
||||
virtual bool supportBackend(int backendId) CV_OVERRIDE {
|
||||
return backendId == DNN_BACKEND_OPENCV ||
|
||||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
|
||||
backendId == DNN_BACKEND_CUDA ||
|
||||
backendId == DNN_BACKEND_CANN ||
|
||||
(backendId == DNN_BACKEND_TIMVX);
|
||||
}
|
||||
|
||||
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
|
||||
const int requiredOutputs,
|
||||
std::vector<MatShape> &outputs,
|
||||
std::vector<MatShape> &internals) const CV_OVERRIDE {
|
||||
CV_CheckEQ(inputs.size(), static_cast<size_t>(1), "SpaceToDepthLayer: accepts only one input");
|
||||
const auto &input = inputs.front();
|
||||
CV_CheckEQ(input.size(), static_cast<size_t>(4), "SpaceToDepthLayer: input needs to be 4-dimensional [N, C, H, W]");
|
||||
int batch = input[0], input_depth = input[1], input_height = input[2], input_width = input[3];
|
||||
int output_depth = -1, output_height = -1, output_width = -1;
|
||||
|
||||
CV_CheckEQ(input_height % blocksize, 0, "SpaceToDepthLayer: requires input height to be a multiple of blocksize");
|
||||
CV_CheckEQ(input_width % blocksize, 0, "SpaceToDepthLayer: requires input width to be a multiple of blocksize");
|
||||
output_depth = input_depth * blocksize * blocksize;
|
||||
output_height = input_height / blocksize;
|
||||
output_width = input_width / blocksize;
|
||||
|
||||
outputs.assign(1, MatShape{batch, output_depth, output_height, output_width});
|
||||
return false;
|
||||
}
|
||||
|
||||
virtual void finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr) CV_OVERRIDE {
|
||||
std::vector<Mat> inputs;
|
||||
inputs_arr.getMatVector(inputs);
|
||||
|
||||
auto input_shape = shape(inputs.front());
|
||||
int batch = input_shape[0], input_depth = input_shape[1], input_height = input_shape[2], input_width = input_shape[3];
|
||||
internal_shape = MatShape{batch, input_depth, input_height / blocksize, blocksize, input_width / blocksize, blocksize};
|
||||
|
||||
DepthSpaceOps::finalize(inputs_arr, outputs_arr);
|
||||
}
|
||||
|
||||
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE {
|
||||
CV_TRACE_FUNCTION();
|
||||
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
|
||||
|
||||
// TODO: support 8-bit int in permute kernel
|
||||
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) && inputs_arr.depth() != CV_8S,
|
||||
DepthSpaceOps::oclCompute(inputs_arr, outputs_arr, internals_arr))
|
||||
|
||||
if (inputs_arr.depth() == CV_16F) {
|
||||
forward_fallback(inputs_arr, outputs_arr, internals_arr);
|
||||
return;
|
||||
}
|
||||
|
||||
std::vector<Mat> inputs, outputs;
|
||||
inputs_arr.getMatVector(inputs);
|
||||
outputs_arr.getMatVector(outputs);
|
||||
|
||||
DepthSpaceOps::cpuCompute(inputs.front(), outputs.front());
|
||||
}
|
||||
|
||||
#ifdef HAVE_DNN_NGRAPH
|
||||
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendNode>> &nodes) CV_OVERRIDE {
|
||||
using namespace ov::op;
|
||||
auto input_node = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
|
||||
std::shared_ptr<ov::Node> output_node;
|
||||
output_node = std::make_shared<v0::SpaceToDepth>(input_node, v0::SpaceToDepth::SpaceToDepthMode::BLOCKS_FIRST, static_cast<size_t>(blocksize));
|
||||
return Ptr<BackendNode>(new InfEngineNgraphNode(output_node));
|
||||
}
|
||||
#endif // HAVE_DNN_NGRAPH
|
||||
|
||||
#ifdef HAVE_CUDA
|
||||
Ptr<BackendNode> initCUDA(void *context_,
|
||||
const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendWrapper>> &outputs) override {
|
||||
using namespace cv::dnn::cuda4dnn;
|
||||
auto context = reinterpret_cast<csl::CSLContext*>(context_);
|
||||
std::vector<size_t> perm(permutation.begin(), permutation.end());
|
||||
return make_cuda_node<cuda4dnn::DepthSpaceOps>(preferableTarget, std::move(context->stream), internal_shape, perm);
|
||||
}
|
||||
#endif // HAVE_CUDA
|
||||
|
||||
#ifdef HAVE_CANN
|
||||
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendWrapper>> &outputs,
|
||||
const std::vector<Ptr<BackendNode>> &nodes) CV_OVERRIDE {
|
||||
CV_CheckEQ(inputs.size(), static_cast<size_t>(1), "DepthToSpace/CANN: only accepts one input wrapper");
|
||||
CV_CheckEQ(nodes.size(), static_cast<size_t>(1), "DepthToSpace/CANN: only accepts one input node");
|
||||
|
||||
auto input_tensor_wrapper = inputs.front().dynamicCast<CannBackendWrapper>();
|
||||
auto input_tensor_desc = input_tensor_wrapper->getTensorDesc();
|
||||
auto input_node = nodes.front().dynamicCast<CannBackendNode>()->getOp();
|
||||
|
||||
auto node = std::make_shared<ge::op::SpaceToDepth>(name);
|
||||
|
||||
node->set_attr_block_size(blocksize);
|
||||
node->set_attr_data_format("NCHW");
|
||||
|
||||
node->set_input_x_by_name(*input_node, input_tensor_wrapper->name.c_str());
|
||||
node->update_input_desc_x(*input_tensor_desc);
|
||||
|
||||
auto output_tensor_desc = std::make_shared<ge::TensorDesc>(ge::Shape(), ge::FORMAT_NCHW, ge::DT_FLOAT);
|
||||
node->update_output_desc_y(*output_tensor_desc);
|
||||
|
||||
return Ptr<BackendNode>(new CannBackendNode(node));
|
||||
}
|
||||
#endif
|
||||
|
||||
#ifdef HAVE_TIMVX
|
||||
virtual Ptr<BackendNode> initTimVX(void* timvx_info_,
|
||||
const std::vector<Ptr<BackendWrapper>> &inputs,
|
||||
const std::vector<Ptr<BackendWrapper>> &outputs,
|
||||
bool isLast) CV_OVERRIDE {
|
||||
auto info = reinterpret_cast<TimVXInfo*>(timvx_info_);
|
||||
CV_Assert(info);
|
||||
auto timvx_graph = info->getGraph();
|
||||
CV_Assert(timvx_graph);
|
||||
auto graph = timvx_graph->graph;
|
||||
|
||||
auto input_wrapper = inputs.front().dynamicCast<TimVXBackendWrapper>();
|
||||
int input_wrapper_index = -1;
|
||||
if (input_wrapper->isTensor()) {
|
||||
input_wrapper_index = timvx_graph->getTensorIndex(input_wrapper->getTensor());
|
||||
if (input_wrapper_index == -1) {
|
||||
auto tmp = input_wrapper->getMat();
|
||||
input_wrapper = std::make_shared<TimVXBackendWrapper>(tmp);
|
||||
}
|
||||
}
|
||||
if (!input_wrapper->isTensor() || input_wrapper_index == 1) {
|
||||
auto input_node_quant = Ptr<tim::vx::Quantization>(new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0));
|
||||
input_wrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, input_node_quant);
|
||||
input_wrapper_index = timvx_graph->addWrapper(input_wrapper);
|
||||
}
|
||||
|
||||
auto output_wrapper = outputs.front().dynamicCast<TimVXBackendWrapper>();
|
||||
auto output_node_quant = input_wrapper->getTensorQuantization();
|
||||
if (isLast) {
|
||||
auto shape_type = getShapeTypeFromMat(output_wrapper->getMat());
|
||||
output_wrapper->setTensorShape(shape_type);
|
||||
output_wrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, output_node_quant);
|
||||
} else {
|
||||
output_wrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, output_node_quant);
|
||||
}
|
||||
int output_wrapper_index = timvx_graph->addWrapper(output_wrapper);
|
||||
|
||||
std::shared_ptr<tim::vx::Operation> timvx_node;
|
||||
timvx_node = graph->CreateOperation<tim::vx::ops::SpaceToDepth>(std::vector<int>{blocksize, blocksize});
|
||||
std::vector<int> input_wrapper_indices{input_wrapper_index}, output_wrapper_indices{output_wrapper_index};
|
||||
return Ptr<BackendNode>(new TimVXBackendNode(timvx_graph, timvx_node, input_wrapper_indices, output_wrapper_indices));
|
||||
}
|
||||
#endif
|
||||
|
||||
virtual bool tryQuantize(const std::vector<std::vector<float>> &scales,
|
||||
const std::vector<std::vector<int>> &zeropoints, LayerParams ¶ms) CV_OVERRIDE {
|
||||
return true;
|
||||
}
|
||||
|
||||
private:
|
||||
int blocksize;
|
||||
};
|
||||
|
||||
Ptr<SpaceToDepthLayer> SpaceToDepthLayer::create(const LayerParams ¶ms) {
|
||||
return makePtr<SpaceToDepthLayerImpl>(params);
|
||||
}
|
||||
|
||||
}} // namespace cv::dnn
|
||||
@ -189,7 +189,7 @@ private:
|
||||
void parseDetectionOutput (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseCumSum (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseElementWise (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseDepthToSpace (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseDepthSpaceOps (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseRange (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseScatter (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
void parseTile (LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto);
|
||||
@ -2916,82 +2916,8 @@ void ONNXImporter::parseElementWise(LayerParams& layerParams, const opencv_onnx:
|
||||
addLayer(layerParams, node_proto);
|
||||
}
|
||||
|
||||
void ONNXImporter::parseDepthToSpace(LayerParams& layerParams, const opencv_onnx::NodeProto& node_proto_)
|
||||
{
|
||||
// We parse "DepthToSpace" and "SpaceToDepth" in this function.
|
||||
opencv_onnx::NodeProto node_proto = node_proto_;
|
||||
const std::string& layer_type = node_proto.op_type();
|
||||
CV_Assert(layer_type == "DepthToSpace" || layer_type == "SpaceToDepth");
|
||||
|
||||
// Get blocksize
|
||||
CV_Assert(layerParams.has("blocksize"));
|
||||
int blocksize = layerParams.get<int>("blocksize");
|
||||
CV_Assert(blocksize > 0);
|
||||
|
||||
// Get mode, only for "DepthToSpace"
|
||||
std::string modeType = layerParams.get<std::string>("mode", "DCR");
|
||||
|
||||
MatShape inpShape = outShapes[node_proto.input(0)];
|
||||
CV_Assert(inpShape.size() == 4);
|
||||
int N = inpShape[0], C = inpShape[1], H = inpShape[2], W = inpShape[3];
|
||||
|
||||
// Implement DepthToSpace and SpaceToDepth by the Reshape and Permute layer.
|
||||
std::array<int, 6> shape0, perm;
|
||||
std::array<int, 4> shape1;
|
||||
|
||||
if (layer_type == "DepthToSpace")
|
||||
{
|
||||
if (modeType == "DCR")
|
||||
{
|
||||
shape0 = {N, blocksize, blocksize, C/(blocksize * blocksize), H, W};
|
||||
perm = {0, 3, 4, 1, 5, 2};
|
||||
shape1 = {N, C/(blocksize * blocksize), H * blocksize, W * blocksize};
|
||||
}
|
||||
else if (modeType == "CRD")
|
||||
{
|
||||
shape0 = {N, C/(blocksize * blocksize), blocksize, blocksize, H, W};
|
||||
perm = {0, 1, 4, 2, 5, 3};
|
||||
shape1 = {N, C/(blocksize * blocksize), H * blocksize, W * blocksize};
|
||||
}
|
||||
else
|
||||
CV_Error(Error::StsNotImplemented, "The mode of " + modeType + " in " + layer_type + " Layer is not supported");
|
||||
}
|
||||
else // SpaceToDepth
|
||||
{
|
||||
shape0 = {N, C, H/blocksize, blocksize, W/blocksize, blocksize};
|
||||
perm = {0, 3, 5, 1, 2, 4};
|
||||
shape1 = {N, C * blocksize * blocksize, H/blocksize, W/blocksize};
|
||||
}
|
||||
|
||||
// Step1: Reshape
|
||||
LayerParams reshapeLp;
|
||||
reshapeLp.name = layerParams.name + "/reshape";
|
||||
reshapeLp.type = "Reshape";
|
||||
CV_Assert(layer_id.find(reshapeLp.name) == layer_id.end());
|
||||
reshapeLp.set("dim", DictValue::arrayInt(shape0.data(), shape0.size()));
|
||||
|
||||
opencv_onnx::NodeProto protoReshape;
|
||||
protoReshape.add_input(node_proto.input(0));
|
||||
protoReshape.add_output(reshapeLp.name);
|
||||
addLayer(reshapeLp, protoReshape);
|
||||
|
||||
// Step2: Transpose
|
||||
LayerParams permuteLp;
|
||||
permuteLp.name = layerParams.name + "/permute";
|
||||
permuteLp.type = "Permute";
|
||||
CV_Assert(layer_id.find(permuteLp.name) == layer_id.end());
|
||||
permuteLp.set("order", DictValue::arrayInt(perm.data(), perm.size()));
|
||||
|
||||
opencv_onnx::NodeProto protoPermute;
|
||||
protoPermute.add_input(reshapeLp.name);
|
||||
protoPermute.add_output(permuteLp.name);
|
||||
addLayer(permuteLp, protoPermute);
|
||||
|
||||
// Step3: Reshape
|
||||
layerParams.type = "Reshape";
|
||||
layerParams.set("dim", DictValue::arrayInt(shape1.data(), shape1.size()));
|
||||
|
||||
node_proto.set_input(0, permuteLp.name);
|
||||
void ONNXImporter::parseDepthSpaceOps(LayerParams &layerParams, const opencv_onnx::NodeProto& node_proto) {
|
||||
CV_CheckTrue(layerParams.has("blocksize"), "blocksize is required but not found");
|
||||
addLayer(layerParams, node_proto);
|
||||
}
|
||||
|
||||
@ -4002,7 +3928,7 @@ void ONNXImporter::buildDispatchMap_ONNX_AI(int opset_version)
|
||||
dispatch["SoftMax"] = dispatch["Softmax"] = dispatch["LogSoftmax"] = &ONNXImporter::parseSoftMax;
|
||||
dispatch["DetectionOutput"] = &ONNXImporter::parseDetectionOutput;
|
||||
dispatch["CumSum"] = &ONNXImporter::parseCumSum;
|
||||
dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter::parseDepthToSpace;
|
||||
dispatch["SpaceToDepth"] = dispatch["DepthToSpace"] = &ONNXImporter::parseDepthSpaceOps;
|
||||
dispatch["ScatterElements"] = dispatch["Scatter"] = dispatch["ScatterND"] = &ONNXImporter::parseScatter;
|
||||
dispatch["Tile"] = &ONNXImporter::parseTile;
|
||||
dispatch["LayerNormalization"] = &ONNXImporter::parseLayerNorm;
|
||||
|
||||
@ -509,6 +509,61 @@ TEST_P(Test_Int8_layers, Eltwise)
|
||||
testLayer("split_max", "ONNX", 0.004, 0.012);
|
||||
}
|
||||
|
||||
TEST_P(Test_Int8_layers, DepthSpaceOps) {
|
||||
auto test_layer_with_onnx_conformance_models = [&](const std::string &model_name, double l1, double lInf) {
|
||||
std::string model_path = _tf("onnx/conformance/node/test_" + model_name + "/model.onnx");
|
||||
auto net = readNet(model_path);
|
||||
|
||||
// load reference inputs and outputs
|
||||
std::string data_base_path = _tf("onnx/conformance/node/test_" + model_name + "/test_data_set_0");
|
||||
Mat input = readTensorFromONNX(data_base_path + "/input_0.pb");
|
||||
Mat ref_output = readTensorFromONNX(data_base_path + "/output_0.pb");
|
||||
|
||||
std::vector<float> input_scales, output_scales;
|
||||
std::vector<int> input_zeropoints, output_zeropoints;
|
||||
auto qnet = net.quantize(std::vector<Mat>{input}, CV_8S, CV_8S, false);
|
||||
qnet.getInputDetails(input_scales, input_zeropoints);
|
||||
qnet.getOutputDetails(output_scales, output_zeropoints);
|
||||
qnet.setPreferableBackend(backend);
|
||||
qnet.setPreferableTarget(target);
|
||||
|
||||
Mat quantized_input, quantized_output;
|
||||
input.convertTo(quantized_input, CV_8S, 1.f / input_scales.front(), input_zeropoints.front());
|
||||
qnet.setInput(quantized_input);
|
||||
quantized_output = qnet.forward();
|
||||
|
||||
Mat output;
|
||||
quantized_output.convertTo(output, CV_32F, output_scales.front(), -(output_scales.front() * output_zeropoints.front()));
|
||||
normAssert(ref_output, output, model_name.c_str(), l1, lInf);
|
||||
};
|
||||
|
||||
double l1 = default_l1, lInf = default_lInf;
|
||||
{
|
||||
l1 = 0.001; lInf = 0.002;
|
||||
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.001; lInf = 0.002; }
|
||||
test_layer_with_onnx_conformance_models("spacetodepth", l1, lInf);
|
||||
}
|
||||
{
|
||||
l1 = 0.022; lInf = 0.044;
|
||||
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.022; lInf = 0.044; }
|
||||
test_layer_with_onnx_conformance_models("spacetodepth_example", l1, lInf);
|
||||
}
|
||||
{
|
||||
l1 = 0.001; lInf = 0.002;
|
||||
if (backend == DNN_BACKEND_TIMVX) { l1 = 0.24; lInf = 0.99; }
|
||||
test_layer_with_onnx_conformance_models("depthtospace_crd_mode", l1, lInf);
|
||||
}
|
||||
test_layer_with_onnx_conformance_models("depthtospace_dcr_mode", 0.001, 0.002);
|
||||
test_layer_with_onnx_conformance_models("depthtospace_example", 0.07, 0.14);
|
||||
|
||||
{
|
||||
l1 = 0.07; lInf = 0.14;
|
||||
if (backend == DNN_BACKEND_TIMVX) // diff too huge, l1 = 13.6; lInf = 27.2
|
||||
applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
|
||||
test_layer_with_onnx_conformance_models("depthtospace_crd_mode_example", l1, lInf);
|
||||
}
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, dnnBackendsAndTargetsInt8());
|
||||
|
||||
class Test_Int8_nets : public DNNTestLayer
|
||||
|
||||
@ -544,10 +544,20 @@ CASE(test_cumsum_2d_negative_axis)
|
||||
// no filter
|
||||
CASE(test_depthtospace_crd_mode)
|
||||
// no filter
|
||||
if (target == DNN_TARGET_OPENCL)
|
||||
{
|
||||
default_l1 = 1e-4; // Expected: (normL1) <= (l1), actual: 9.33057e-05 vs 1e-05
|
||||
default_lInf = 2.5e-4; // Expected: (normInf) <= (lInf), actual: 0.000243843 vs 0.0001
|
||||
}
|
||||
CASE(test_depthtospace_crd_mode_example)
|
||||
// no filter
|
||||
CASE(test_depthtospace_dcr_mode)
|
||||
// no filter
|
||||
if (target == DNN_TARGET_OPENCL)
|
||||
{
|
||||
default_l1 = 1e-4; // Expected: (normL1) <= (l1), actual: 9.33057e-05 vs 1e-05
|
||||
default_lInf = 2.5e-4; // Expected: (normInf) <= (lInf), actual: 0.000243843 vs 0.0001
|
||||
}
|
||||
CASE(test_depthtospace_example)
|
||||
// no filter
|
||||
CASE(test_dequantizelinear)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user