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opencv/modules/dnn/src/init.cpp
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yyyisyyy11 b887e2fd3e Merge pull request #29018 from yyyisyyy11:project4_Lunhan_Yan 
dnn: add DynamicQuantizeLinear ONNX layer support #29018

### Pull Request Readiness Checklist

- [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 target platform(s)
- [x] There is an accuracy test
- [ ] There is a performance test

### Description

Implements the ONNX `DynamicQuantizeLinear` operator (opset 11) for the OpenCV DNN module.

**What it does:**

- Adds `QuantizeDynamicLayer` and `DequantizeDynamicLayer` layer classes
- Registers layers and adds ONNX importer dispatch for `DynamicQuantizeLinear`
- Computes scale and zero-point at runtime from activation min/max
- Quantizes FP32 input to int8 (stored as uint8 - 128, matching OpenCV convention)

**Framework limitation & workaround:**
Due to the single-dtype-per-layer constraint in `LayerData::dtype` (see #29017), the float32 scale output cannot be passed through the CV_8S blob pipeline directly. As a workaround, the scale is encoded as 4 raw bytes in a CV_8S `{1,4}` blob using `memcpy`, and decoded by the downstream `DequantizeDynamic` layer.

**Testing:**

- Custom accuracy tests reproduce all 3 ONNX conformance test cases: `test_dynamicquantizelinear`, `test_dynamicquantizelinear_max_adjusted`, `test_dynamicquantizelinear_min_adjusted`
- Each test verifies: quantized values, scale, zero point, and round-trip dequantize accuracy
- All existing quantization regression tests pass (42/42)

**Conformance tests:**
The 6 conformance tests for `DynamicQuantizeLinear` remain in the parser denylist (they were already denylisted before this PR) because the framework cannot produce mixed-type outputs. The custom tests provide equivalent coverage.

### Files changed

- `modules/dnn/include/opencv2/dnn/all_layers.hpp` — layer class declarations
- `modules/dnn/src/init.cpp` — layer registration
- `modules/dnn/src/onnx/onnx_importer.cpp` — ONNX import dispatch
- `modules/dnn/src/int8layers/quantization_utils.cpp` — layer implementations
- `modules/dnn/test/test_onnx_importer.cpp` — custom accuracy tests
2026-07-09 11:07:12 +03:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <opencv2/dnn/layer.details.hpp>
#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
#include <google/protobuf/stubs/common.h>
#endif
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
static Mutex* __initialization_mutex = NULL;
Mutex& getInitializationMutex()
{
if (__initialization_mutex == NULL)
__initialization_mutex = new Mutex();
return *__initialization_mutex;
}
// force initialization (single-threaded environment)
Mutex* __initialization_mutex_initializer = &getInitializationMutex();
#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
namespace {
using namespace google::protobuf;
class ProtobufShutdown {
public:
bool initialized;
ProtobufShutdown() : initialized(true) {}
~ProtobufShutdown()
{
initialized = false;
google::protobuf::ShutdownProtobufLibrary();
}
};
} // namespace
#endif
void initializeLayerFactory()
{
CV_TRACE_FUNCTION();
#if defined(HAVE_PROTOBUF) && !defined(BUILD_PLUGIN)
static ProtobufShutdown protobufShutdown; CV_UNUSED(protobufShutdown);
#endif
CV_DNN_REGISTER_LAYER_CLASS(Slice, SliceLayer);
CV_DNN_REGISTER_LAYER_CLASS(Split, SplitLayer);
CV_DNN_REGISTER_LAYER_CLASS(Concat, ConcatLayer);
CV_DNN_REGISTER_LAYER_CLASS(Reshape, ReshapeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Flatten, FlattenLayer);
CV_DNN_REGISTER_LAYER_CLASS(Resize, ResizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Interp, InterpLayer);
CV_DNN_REGISTER_LAYER_CLASS(CropAndResize, CropAndResizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Convolution, ConvolutionLayer);
CV_DNN_REGISTER_LAYER_CLASS(Deconvolution, DeconvolutionLayer);
CV_DNN_REGISTER_LAYER_CLASS(Pooling, PoolingLayer);
CV_DNN_REGISTER_LAYER_CLASS(ROIPooling, PoolingLayer);
CV_DNN_REGISTER_LAYER_CLASS(PSROIPooling, PoolingLayer);
CV_DNN_REGISTER_LAYER_CLASS(Reduce, ReduceLayer);
CV_DNN_REGISTER_LAYER_CLASS(LRN, LRNLayer);
CV_DNN_REGISTER_LAYER_CLASS(InnerProduct, InnerProductLayer);
CV_DNN_REGISTER_LAYER_CLASS(Gemm, GemmLayer);
CV_DNN_REGISTER_LAYER_CLASS(MatMul, MatMulLayer);
CV_DNN_REGISTER_LAYER_CLASS(Softmax, SoftmaxLayer);
CV_DNN_REGISTER_LAYER_CLASS(SoftMax, SoftmaxLayer); // For compatibility. See https://github.com/opencv/opencv/issues/16877
CV_DNN_REGISTER_LAYER_CLASS(MVN, MVNLayer);
CV_DNN_REGISTER_LAYER_CLASS(ReLU, ReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(ReLU6, ReLU6Layer);
CV_DNN_REGISTER_LAYER_CLASS(ChannelsPReLU, ChannelsPReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(PReLU, ChannelsPReLULayer);
CV_DNN_REGISTER_LAYER_CLASS(Sigmoid, SigmoidLayer);
CV_DNN_REGISTER_LAYER_CLASS(TanH, TanHLayer);
CV_DNN_REGISTER_LAYER_CLASS(Swish, SwishLayer);
CV_DNN_REGISTER_LAYER_CLASS(Mish, MishLayer);
CV_DNN_REGISTER_LAYER_CLASS(ELU, ELULayer);
CV_DNN_REGISTER_LAYER_CLASS(BNLL, BNLLLayer);
CV_DNN_REGISTER_LAYER_CLASS(AbsVal, AbsLayer);
CV_DNN_REGISTER_LAYER_CLASS(Power, PowerLayer);
CV_DNN_REGISTER_LAYER_CLASS(Exp, ExpLayer);
CV_DNN_REGISTER_LAYER_CLASS(Ceil, CeilLayer);
CV_DNN_REGISTER_LAYER_CLASS(Floor, FloorLayer);
CV_DNN_REGISTER_LAYER_CLASS(Log, LogLayer);
CV_DNN_REGISTER_LAYER_CLASS(Round, RoundLayer);
CV_DNN_REGISTER_LAYER_CLASS(Sqrt, SqrtLayer);
CV_DNN_REGISTER_LAYER_CLASS(Not, NotLayer);
CV_DNN_REGISTER_LAYER_CLASS(Acos, AcosLayer);
CV_DNN_REGISTER_LAYER_CLASS(Acosh, AcoshLayer);
CV_DNN_REGISTER_LAYER_CLASS(Asin, AsinLayer);
CV_DNN_REGISTER_LAYER_CLASS(Asinh, AsinhLayer);
CV_DNN_REGISTER_LAYER_CLASS(Atan, AtanLayer);
CV_DNN_REGISTER_LAYER_CLASS(Atanh, AtanhLayer);
CV_DNN_REGISTER_LAYER_CLASS(Cos, CosLayer);
CV_DNN_REGISTER_LAYER_CLASS(Cosh, CoshLayer);
CV_DNN_REGISTER_LAYER_CLASS(Erf, ErfLayer);
CV_DNN_REGISTER_LAYER_CLASS(HardSwish, HardSwishLayer);
CV_DNN_REGISTER_LAYER_CLASS(Sin, SinLayer);
CV_DNN_REGISTER_LAYER_CLASS(Sinh, SinhLayer);
CV_DNN_REGISTER_LAYER_CLASS(Sign, SignLayer);
CV_DNN_REGISTER_LAYER_CLASS(Shrink, ShrinkLayer);
CV_DNN_REGISTER_LAYER_CLASS(Softplus, SoftplusLayer);
CV_DNN_REGISTER_LAYER_CLASS(Softsign, SoftsignLayer);
CV_DNN_REGISTER_LAYER_CLASS(Tan, TanLayer);
CV_DNN_REGISTER_LAYER_CLASS(Celu, CeluLayer);
CV_DNN_REGISTER_LAYER_CLASS(HardSigmoid, HardSigmoidLayer);
CV_DNN_REGISTER_LAYER_CLASS(Selu, SeluLayer);
CV_DNN_REGISTER_LAYER_CLASS(ThresholdedRelu,ThresholdedReluLayer);
CV_DNN_REGISTER_LAYER_CLASS(Gelu, GeluLayer);
CV_DNN_REGISTER_LAYER_CLASS(GeluApproximation, GeluApproximationLayer);
CV_DNN_REGISTER_LAYER_CLASS(BatchNorm, BatchNormLayer);
CV_DNN_REGISTER_LAYER_CLASS(MaxUnpool, MaxUnpoolLayer);
CV_DNN_REGISTER_LAYER_CLASS(Dropout, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(Identity, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(Silence, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(Const, ConstLayer);
CV_DNN_REGISTER_LAYER_CLASS(Arg, ArgLayer);
CV_DNN_REGISTER_LAYER_CLASS(Reciprocal, ReciprocalLayer);
CV_DNN_REGISTER_LAYER_CLASS(Gather, GatherLayer);
CV_DNN_REGISTER_LAYER_CLASS(GatherElements, GatherElementsLayer);
CV_DNN_REGISTER_LAYER_CLASS(LayerNormalization, LayerNormLayer);
CV_DNN_REGISTER_LAYER_CLASS(Expand, ExpandLayer);
CV_DNN_REGISTER_LAYER_CLASS(InstanceNormalization, InstanceNormLayer);
CV_DNN_REGISTER_LAYER_CLASS(Attention, AttentionLayer);
CV_DNN_REGISTER_LAYER_CLASS(GroupNormalization, GroupNormLayer);
CV_DNN_REGISTER_LAYER_CLASS(DepthToSpace, DepthToSpaceLayer)
CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepth, SpaceToDepthLayer)
CV_DNN_REGISTER_LAYER_CLASS(DepthToSpaceInt8, DepthToSpaceLayer)
CV_DNN_REGISTER_LAYER_CLASS(SpaceToDepthInt8, SpaceToDepthLayer)
CV_DNN_REGISTER_LAYER_CLASS(Crop, CropLayer);
CV_DNN_REGISTER_LAYER_CLASS(Eltwise, EltwiseLayer);
CV_DNN_REGISTER_LAYER_CLASS(NaryEltwise, NaryEltwiseLayer);
CV_DNN_REGISTER_LAYER_CLASS(Permute, PermuteLayer);
CV_DNN_REGISTER_LAYER_CLASS(ShuffleChannel, ShuffleChannelLayer);
CV_DNN_REGISTER_LAYER_CLASS(PriorBox, PriorBoxLayer);
CV_DNN_REGISTER_LAYER_CLASS(PriorBoxClustered, PriorBoxLayer);
CV_DNN_REGISTER_LAYER_CLASS(Reorg, ReorgLayer);
CV_DNN_REGISTER_LAYER_CLASS(Region, RegionLayer);
CV_DNN_REGISTER_LAYER_CLASS(DetectionOutput, DetectionOutputLayer);
CV_DNN_REGISTER_LAYER_CLASS(NormalizeBBox, NormalizeBBoxLayer);
CV_DNN_REGISTER_LAYER_CLASS(Normalize, NormalizeBBoxLayer);
CV_DNN_REGISTER_LAYER_CLASS(Shift, ShiftLayer);
CV_DNN_REGISTER_LAYER_CLASS(Padding, PaddingLayer);
CV_DNN_REGISTER_LAYER_CLASS(Proposal, ProposalLayer);
CV_DNN_REGISTER_LAYER_CLASS(Scale, ScaleLayer);
CV_DNN_REGISTER_LAYER_CLASS(Compare, CompareLayer);
CV_DNN_REGISTER_LAYER_CLASS(DataAugmentation, DataAugmentationLayer);
CV_DNN_REGISTER_LAYER_CLASS(Correlation, CorrelationLayer);
CV_DNN_REGISTER_LAYER_CLASS(Accum, AccumLayer);
CV_DNN_REGISTER_LAYER_CLASS(FlowWarp, FlowWarpLayer);
CV_DNN_REGISTER_LAYER_CLASS(LSTM, LSTMLayer);
CV_DNN_REGISTER_LAYER_CLASS(GRU, GRULayer);
CV_DNN_REGISTER_LAYER_CLASS(CumSum, CumSumLayer);
CV_DNN_REGISTER_LAYER_CLASS(Einsum, EinsumLayer);
CV_DNN_REGISTER_LAYER_CLASS(Scatter, ScatterLayer);
CV_DNN_REGISTER_LAYER_CLASS(ScatterND, ScatterNDLayer);
CV_DNN_REGISTER_LAYER_CLASS(Tile, TileLayer);
CV_DNN_REGISTER_LAYER_CLASS(TopK, TopKLayer);
CV_DNN_REGISTER_LAYER_CLASS(RandomNormalLike, RandomNormalLikeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Quantize, QuantizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Dequantize, DequantizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(Requantize, RequantizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(QuantizeDynamic, QuantizeDynamicLayer);
CV_DNN_REGISTER_LAYER_CLASS(DequantizeDynamic, DequantizeDynamicLayer);
CV_DNN_REGISTER_LAYER_CLASS(ConvolutionInt8, ConvolutionLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(InnerProductInt8, InnerProductLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(PoolingInt8, PoolingLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(EltwiseInt8, EltwiseLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(BatchNormInt8, BatchNormLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ScaleInt8, ScaleLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ShiftInt8, ShiftLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ReLUInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ReLU6Int8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(SigmoidInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(TanHInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(SwishInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(HardSwishInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(MishInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ELUInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(BNLLInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(AbsValInt8, ActivationLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(SoftmaxInt8, SoftmaxLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(SoftMaxInt8, SoftmaxLayerInt8);
CV_DNN_REGISTER_LAYER_CLASS(ConcatInt8, ConcatLayer);
CV_DNN_REGISTER_LAYER_CLASS(FlattenInt8, FlattenLayer);
CV_DNN_REGISTER_LAYER_CLASS(PaddingInt8, PaddingLayer);
CV_DNN_REGISTER_LAYER_CLASS(BlankInt8, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(DropoutInt8, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(IdentityInt8, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(SilenceInt8, BlankLayer);
CV_DNN_REGISTER_LAYER_CLASS(ConstInt8, ConstLayer);
CV_DNN_REGISTER_LAYER_CLASS(ReshapeInt8, ReshapeLayer);
CV_DNN_REGISTER_LAYER_CLASS(ResizeInt8, ResizeLayer);
CV_DNN_REGISTER_LAYER_CLASS(SplitInt8, SplitLayer);
CV_DNN_REGISTER_LAYER_CLASS(SliceInt8, SliceLayer);
CV_DNN_REGISTER_LAYER_CLASS(CropInt8, CropLayer);
CV_DNN_REGISTER_LAYER_CLASS(PermuteInt8, PermuteLayer);
CV_DNN_REGISTER_LAYER_CLASS(ReorgInt8, ReorgLayer);
CV_DNN_REGISTER_LAYER_CLASS(ShuffleChannelInt8, ShuffleChannelLayer);
}
CV__DNN_INLINE_NS_END
}} // namespace
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