Alexander Alekhin
efc9837df1
Merge pull request #24892 from opencv-pushbot:gitee/alalek/dnn_avoid_16s_usage
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DNN: avoid CV_16S usage for FP16 #24892
**Merge after**: #24918
TODO:
- [x] measure performance changes
- [x] optimize convertTo for OpenCL: #24918
12700K iGPU:
|Name of Test|0|1|1 vs 0 (x-factor)|
|---|:-:|:-:|:-:|
|AlexNet::DNNTestNetwork::OCV/OCL_FP16|7.441|7.480|0.99|
|CRNN::DNNTestNetwork::OCV/OCL_FP16|10.776|10.736|1.00|
|DenseNet_121::DNNTestNetwork::OCV/OCL_FP16|52.762|52.833|1.00|
|EAST_text_detection::DNNTestNetwork::OCV/OCL_FP16|60.694|60.721|1.00|
|EfficientNet::DNNTestNetwork::OCV/OCL_FP16|33.373|33.173|1.01|
|FastNeuralStyle_eccv16::DNNTestNetwork::OCV/OCL_FP16|81.840|81.724|1.00|
|GoogLeNet::DNNTestNetwork::OCV/OCL_FP16|20.965|20.927|1.00|
|Inception_5h::DNNTestNetwork::OCV/OCL_FP16|22.204|22.173|1.00|
|Inception_v2_SSD_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|47.115|47.460|0.99|
|MPHand::DNNTestNetwork::OCV/OCL_FP16|6.760|6.670|1.01|
|MPPalm::DNNTestNetwork::OCV/OCL_FP16|10.188|10.171|1.00|
|MPPose::DNNTestNetwork::OCV/OCL_FP16|12.510|12.561|1.00|
|MobileNet_SSD_Caffe::DNNTestNetwork::OCV/OCL_FP16|17.290|17.072|1.01|
|MobileNet_SSD_v1_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|19.473|19.306|1.01|
|MobileNet_SSD_v2_TensorFlow::DNNTestNetwork::OCV/OCL_FP16|22.874|23.404|0.98|
|OpenFace::DNNTestNetwork::OCV/OCL_FP16|9.568|9.517|1.01|
|OpenPose_pose_mpi_faster_4_stages::DNNTestNetwork::OCV/OCL_FP16|539.899|539.845|1.00|
|PPHumanSeg::DNNTestNetwork::OCV/OCL_FP16|18.015|18.769|0.96|
|PPOCRv3::DNNTestNetwork::OCV/OCL_FP16|63.122|63.540|0.99|
|ResNet_50::DNNTestNetwork::OCV/OCL_FP16|34.947|34.925|1.00|
|SFace::DNNTestNetwork::OCV/OCL_FP16|10.249|10.206|1.00|
|SSD::DNNTestNetwork::OCV/OCL_FP16|213.068|213.108|1.00|
|SqueezeNet_v1_1::DNNTestNetwork::OCV/OCL_FP16|4.867|4.878|1.00|
|VIT_B_32::DNNTestNetwork::OCV/OCL_FP16|200.563|190.788|1.05|
|VitTrack::DNNTestNetwork::OCV/OCL_FP16|7.528|7.173|1.05|
|YOLOX::DNNTestNetwork::OCV/OCL_FP16|132.858|132.701|1.00|
|YOLOv3::DNNTestNetwork::OCV/OCL_FP16|209.559|208.809|1.00|
|YOLOv4::DNNTestNetwork::OCV/OCL_FP16|221.357|220.924|1.00|
|YOLOv4_tiny::DNNTestNetwork::OCV/OCL_FP16|24.446|24.382|1.00|
|YOLOv5::DNNTestNetwork::OCV/OCL_FP16|43.922|44.080|1.00|
|YOLOv8::DNNTestNetwork::OCV/OCL_FP16|64.159|63.842|1.00|
|YuNet::DNNTestNetwork::OCV/OCL_FP16|10.177|10.231|0.99|
|opencv_face_detector::DNNTestNetwork::OCV/OCL_FP16|15.121|15.445|0.98|
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
2024-01-26 16:34:17 +03:00
SamFC10
fa90e14b06
int8 layers and 8-bit quantization support
2021-08-19 09:56:47 +05:30
Yashas Samaga B L
613c12e590
Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low
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CUDA backend for the DNN module
* stub cuda4dnn design
* minor fixes for tests and doxygen
* add csl public api directory to module headers
* add low-level CSL components
* add high-level CSL components
* integrate csl::Tensor into backbone code
* switch to CPU iff unsupported; otherwise, fail on error
* add fully connected layer
* add softmax layer
* add activation layers
* support arbitary rank TensorDescriptor
* pass input wrappers to `initCUDA()`
* add 1d/2d/3d-convolution
* add pooling layer
* reorganize and refactor code
* fixes for gcc, clang and doxygen; remove cxx14/17 code
* add blank_layer
* add LRN layer
* add rounding modes for pooling layer
* split tensor.hpp into tensor.hpp and tensor_ops.hpp
* add concat layer
* add scale layer
* add batch normalization layer
* split math.cu into activations.cu and math.hpp
* add eltwise layer
* add flatten layer
* add tensor transform api
* add asymmetric padding support for convolution layer
* add reshape layer
* fix rebase issues
* add permute layer
* add padding support for concat layer
* refactor and reorganize code
* add normalize layer
* optimize bias addition in scale layer
* add prior box layer
* fix and optimize normalize layer
* add asymmetric padding support for pooling layer
* add event API
* improve pooling performance for some padding scenarios
* avoid over-allocation of compute resources to kernels
* improve prior box performance
* enable layer fusion
* add const layer
* add resize layer
* add slice layer
* add padding layer
* add deconvolution layer
* fix channelwise ReLU initialization
* add vector traits
* add vectorized versions of relu, clipped_relu, power
* add vectorized concat kernels
* improve concat_with_offsets performance
* vectorize scale and bias kernels
* add support for multi-billion element tensors
* vectorize prior box kernels
* fix address alignment check
* improve bias addition performance of conv/deconv/fc layers
* restructure code for supporting multiple targets
* add DNN_TARGET_CUDA_FP64
* add DNN_TARGET_FP16
* improve vectorization
* add region layer
* improve tensor API, add dynamic ranks
1. use ManagedPtr instead of a Tensor in backend wrapper
2. add new methods to tensor classes
- size_range: computes the combined size of for a given axis range
- tensor span/view can be constructed from a raw pointer and shape
3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time)
4. remove device code from tensor classes (as they are unused)
5. enforce strict conditions on tensor class APIs to improve debugging ability
* fix parametric relu activation
* add squeeze/unsqueeze tensor API
* add reorg layer
* optimize permute and enable 2d permute
* enable 1d and 2d slice
* add split layer
* add shuffle channel layer
* allow tensors of different ranks in reshape primitive
* patch SliceOp to allow Crop Layer
* allow extra shape inputs in reshape layer
* use `std::move_backward` instead of `std::move` for insert in resizable_static_array
* improve workspace management
* add spatial LRN
* add nms (cpu) to region layer
* add max pooling with argmax ( and a fix to limits.hpp)
* add max unpooling layer
* rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA
* update supportBackend to be more rigorous
* remove stray include from preventing non-cuda build
* include op_cuda.hpp outside condition #if
* refactoring, fixes and many optimizations
* drop DNN_TARGET_CUDA_FP64
* fix gcc errors
* increase max. tensor rank limit to six
* add Interp layer
* drop custom layers; use BackendNode
* vectorize activation kernels
* fixes for gcc
* remove wrong assertion
* fix broken assertion in unpooling primitive
* fix build errors in non-CUDA build
* completely remove workspace from public API
* fix permute layer
* enable accuracy and perf. tests for DNN_TARGET_CUDA
* add asynchronous forward
* vectorize eltwise ops
* vectorize fill kernel
* fixes for gcc
* remove CSL headers from public API
* remove csl header source group from cmake
* update min. cudnn version in cmake
* add numerically stable FP32 log1pexp
* refactor code
* add FP16 specialization to cudnn based tensor addition
* vectorize scale1 and bias1 + minor refactoring
* fix doxygen build
* fix invalid alignment assertion
* clear backend wrappers before allocateLayers
* ignore memory lock failures
* do not allocate internal blobs
* integrate NVTX
* add numerically stable half precision log1pexp
* fix indentation, following coding style, improve docs
* remove accidental modification of IE code
* Revert "add asynchronous forward"
This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70.
* [cmake] throw error for unsupported CC versions
* fix rebase issues
* add more docs, refactor code, fix bugs
* minor refactoring and fixes
* resolve warnings/errors from clang
* remove haveCUDA() checks from supportBackend()
* remove NVTX integration
* changes based on review comments
* avoid exception when no CUDA device is present
* add color code for CUDA in Net::dump
2019-10-21 14:28:00 +03:00
Dmitry Kurtaev
24ab751547
Merge pull request #12565 from dkurt:dnn_non_intel_gpu
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* Remove isIntel check from deep learning layers
* Remove fp16->fp32 fallbacks where it's not necessary
* Fix Kernel::run to prevent localsize > globalsize
2018-09-26 16:27:00 +03:00
Dmitry Kurtaev
09fa758725
Replace Darknet's Reorg to permute layer
2018-09-12 18:13:39 +03:00
Dmitry Kurtaev
d486204a0d
Merge pull request #12264 from dkurt:dnn_remove_forward_method
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* Remove a forward method in dnn::Layer
* Add a test
* Fix tests
* Mark multiple dnn::Layer::finalize methods as deprecated
* Replace back dnn's inputBlobs to vector of pointers
* Remove Layer::forward_fallback from CV_OCL_RUN scopes
2018-09-06 13:26:47 +03:00
berak
a7b502f04a
dnn: preserve name, type strings for ShuffleLayer
2018-07-11 08:19:23 +02:00
Dmitry Kurtaev
4626246087
Add ShuffleChannel layer
2018-06-21 19:10:42 +03:00
