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62 Commits

Author SHA1 Message Date
Alexander Alekhin
afe9993376 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2020-07-28 17:25:20 +00:00
Sinitsina
0ac2f0e04c mish_functor_update 2020-07-23 09:02:00 +03:00
Alexander Alekhin
c81d785ada Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2020-06-23 19:15:47 +00:00
Dmitry Kurtaev
1491934d17 Optimize Mish for CPU backend 2020-06-22 23:27:47 +03:00
Alexander Alekhin
124bf8339f dnn(IE): use HAVE_DNN_IE_NN_BUILDER_2019 for NN Builder API code 
- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019
 2020-03-03 08:07:54 +00:00
Alexander Alekhin
29d214474f dnn(IE): use HAVE_DNN_IE_NN_BUILDER_2019 for NN Builder API code 
- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019
 2020-03-03 07:45:09 +00:00
Alexander Alekhin
f3237fdc6e Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2020-02-14 19:54:59 +03:00
Dmitry Kurtaev
9a4cafa319 Resolve #14566 2020-02-14 00:21:38 +03:00
Alexander Alekhin
92b9888837 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-12-12 13:02:19 +03:00
Liubov Batanina
660a709840 Support Swish and Mish activations 2019-12-06 11:27:59 +03:00
Alexander Alekhin
4b0132ed7a Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-12-02 16:26:52 +03:00
Lubov Batanina
7523c777c5 Merge pull request #15537 from l-bat:ngraph 
* Support nGraph

* Fix resize
 2019-12-02 16:16:06 +03:00
Manjunath Bhat
78c5e41c23 Merge pull request #15808 from thebhatman:Mish_swish 
* Added Swish and Mish activations

* Fixed whitespace errors

* Kernel implementation done

* Added function for launching kernel

* Changed type of 1.0

* Attempt to add test for Swish and Mish

* Resolving type mismatch for log

* exp from device

* Use log1pexp instead of adding 1

* Added openCL kernels
 2019-12-02 00:06:17 +03:00
thebhatman
8a18d132fc Port Swish and Mish layers 2019-12-01 11:55:39 +03:00
Yashas Samaga B L
613c12e590 Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low 
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
Alexander Alekhin
174b4ce29d Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-08-05 18:11:43 +00:00
Dmitry Kurtaev
77d4e3e8d2 Fix 2019R2 tests 2019-07-27 13:30:15 +03:00
Alexander Alekhin
b95e93c20a Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-06-26 20:19:04 +00:00
Alexander Alekhin
24790e4061
Merge pull request #14899 from alalek:dnn_fix_bnll_layer 
* dnn: fix BNLLLayer implementation

details: https://github.com/BVLC/caffe/blame/1.0/src/caffe/layers/bnll_layer.cpp#L17

* dnn: enable OCV/OpenCL BNLL layer
 2019-06-26 23:04:26 +03:00
Alexander Alekhin
66d7956e67 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-06-15 16:25:11 +00:00
Dmitry Kurtaev
eba696a41e Merge pull request #14792 from dkurt:dnn_ie_min_version_r5 
* Remove Inference Engine 2018R3 and 2018R4

* Fix 2018R5
 2019-06-14 18:17:02 +03:00
Alexander Alekhin
4635356435 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-04-13 20:00:54 +00:00
Dmitry Kurtaev
a2bbfa1db5 Enable some tests for Inference Engine 2019R1 2019-04-12 15:21:42 +03:00
Alexander Alekhin
8bde6aea4b Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-02-19 19:49:13 +00:00
Dmitry Kurtaev
ca5976e3d4 Fix IE backend considering future changes. 2019-02-18 19:26:04 +03:00
Alexander Alekhin
631b246881 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2019-01-22 18:00:34 +00:00
Dmitry Kurtaev
f0ddf302b2 Move Inference Engine to new API 2019-01-17 14:28:48 +03:00
Alexander Alekhin
8f4e5c2fb8 Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2018-11-26 15:37:45 +03:00
Dmitry Kurtaev
84ce2cc211 Enable some dnn tests according to the new Intel's Inference Engine release (R4) 2018-11-26 13:02:24 +03:00
Alexander Alekhin
8409aa9eba Merge remote-tracking branch 'upstream/3.4' into merge-3.4 2018-11-14 19:41:09 +00:00
catree
10b482ff1e Fix code and missing intrin header. Remove useless header. 2018-11-14 19:00:59 +01:00
WuZhiwen
6e3ea8b49d Merge pull request #12703 from wzw-intel:vkcom 
* dnn: Add a Vulkan based backend

This commit adds a new backend "DNN_BACKEND_VKCOM" and a
new target "DNN_TARGET_VULKAN". VKCOM means vulkan based
computation library.

This backend uses Vulkan API and SPIR-V shaders to do
the inference computation for layers. The layer types
that implemented in DNN_BACKEND_VKCOM include:
Conv, Concat, ReLU, LRN, PriorBox, Softmax, MaxPooling,
AvePooling, Permute

This is just a beginning work for Vulkan in OpenCV DNN,
more layer types will be supported and performance
tuning is on the way.

Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* dnn/vulkan: Add FindVulkan.cmake to detect Vulkan SDK

In order to build dnn with Vulkan support, need installing
Vulkan SDK and setting environment variable "VULKAN_SDK" and
add "-DWITH_VULKAN=ON" to cmake command.

You can download Vulkan SDK from:
https://vulkan.lunarg.com/sdk/home#linux

For how to install, see
https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html
https://vulkan.lunarg.com/doc/sdk/latest/windows/getting_started.html
https://vulkan.lunarg.com/doc/sdk/latest/mac/getting_started.html
respectively for linux, windows and mac.

To run the vulkan backend, also need installing mesa driver.
On Ubuntu, use this command 'sudo apt-get install mesa-vulkan-drivers'

To test, use command '$BUILD_DIR/bin/opencv_test_dnn --gtest_filter=*VkCom*'

Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* dnn/Vulkan: dynamically load Vulkan runtime

No compile-time dependency on Vulkan library.
If Vulkan runtime is unavailable, fallback to CPU path.

Use environment "OPENCL_VULKAN_RUNTIME" to specify path to your
own vulkan runtime library.

Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>

* dnn/Vulkan: Add a python script to compile GLSL shaders to SPIR-V shaders

The SPIR-V shaders are in format of text-based 32-bit hexadecimal
numbers, and inserted into .cpp files as unsigned int32 array.

* dnn/Vulkan: Put Vulkan headers into 3rdparty directory and some other fixes

Vulkan header files are copied from
https://github.com/KhronosGroup/Vulkan-Docs/tree/master/include/vulkan
to 3rdparty/include

Fix the Copyright declaration issue.

Refine OpenCVDetectVulkan.cmake

* dnn/Vulkan: Add vulkan backend tests into existing ones.

Also fixed some test failures.

- Don't use bool variable as uniform for shader
- Fix dispathed group number beyond max issue
- Bypass "group > 1" convolution. This should be support in future.

* dnn/Vulkan: Fix multiple initialization in one thread.
 2018-10-29 17:51:26 +03:00
Alexander Alekhin
9d02d42afe dnn(ocl4dnn): don't use getUMat() 
especially in CPU only processing
 2018-10-05 15:24:51 +03:00
Dmitry Kurtaev
d486204a0d Merge pull request #12264 from dkurt:dnn_remove_forward_method 
* 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
Dmitry Kurtaev
50bceea038 Include preprocessing nodes to object detection TensorFlow networks (#12211) 
* Include preprocessing nodes to object detection TensorFlow networks

* Enable more fusion

* faster_rcnn_resnet50_coco_2018_01_28 test
 2018-08-31 15:41:56 +03:00
Dmitry Kurtaev
3e027df583 Enable more deep learning tests using Intel's Inference Engine backend 2018-08-27 18:37:35 +03:00
Dmitry Kurtaev
2c291bc2fb Enable FastNeuralStyle and OpenFace networks with IE backend 2018-06-09 15:57:12 +03:00
Dmitry Kurtaev
b781ac7346 Make Intel's Inference Engine backend is default if no preferable backend is specified. 2018-06-04 18:31:46 +03:00
Dmitry Kurtaev
f96f934426 Update Intel's Inference Engine deep learning backend (#11587) 
* Update Intel's Inference Engine deep learning backend

* Remove cpu_extension dependency

* Update Darknet accuracy tests
 2018-05-31 14:05:21 +03:00
Tomoaki Teshima
2e9e71ab9e make ocl4dnn available to run on other platform than Intel GPU 2018-05-29 19:18:10 +09:00
Li Peng
3dd916882a fp16 ocl support for googlenet 
Signed-off-by: Li Peng <peng.li@intel.com>
 2018-05-16 22:45:02 +08:00
zuoshaobo
4ff6a1bc7b Merge pull request #11425 from zuoshaobo:relu_negative_slope 
* FIX INF_ENGINE RELU ERROR

* set slope to variable

* tab in indentwq
 2018-05-03 13:36:49 +03:00
Alexander Alekhin
1060c0f439 dnn: apply CV_OVERRIDE/CV_FINAL 2018-03-28 18:43:27 +03:00
Alexander Alekhin
6c051a55e5 cmake: don't add include <module>/src directory to avoid conflicts 
during opencv_world builds
 2018-03-19 11:14:15 +03:00
Alexander Alekhin
1b83bc48a1 dnn: make OpenCL DNN code optional 2018-03-01 12:12:40 +03:00
Li Peng
2863f950d6 ReLU6 layer ocl support 
include relu6 ocl kernel and layer fusion support

Signed-off-by: Li Peng <peng.li@intel.com>
 2018-02-20 15:11:09 +08:00
Dmitry Kurtaev
7fe97376c2 MobileNet-SSD from TensorFlow 1.3 and Inception-V2-SSD using Inference Engine backend 2018-02-09 13:45:45 +03:00
Dmitry Kurtaev
10e1de74d2 Intel Inference Engine deep learning backend (#10608) 
* Intel Inference Engine deep learning backend.

* OpenFace network using Inference Engine backend
 2018-02-06 11:57:35 +03:00
Li Peng
7bc017601f Power, Tanh and Channels ReLU layer ocl support 
Signed-off-by: Li Peng <peng.li@intel.com>
 2018-01-17 17:11:27 +08:00
Li Peng
8f99083726 Add new layer forward interface 
Add layer forward interface with InputArrayOfArrays and
OutputArrayOfArrays parameters, it allows UMat buffer to be
processed and transferred in the layers.

Signed-off-by: Li Peng <peng.li@intel.com>
 2017-11-09 15:59:39 +08:00