Commit Graph

18 Commits

Author SHA1 Message Date
Dmitry Kurtaev
08112f3821 Faster-RCNN models support 2017-12-15 12:16:21 +03:00
Li Peng
59cbaca4d3 detection_output layer ocl implementation
Signed-off-by: Li Peng <peng.li@intel.com>
2017-12-06 22:35:59 +08:00
Alexander Alekhin
0f34628af7 dnn: drop OpenCL code path for DetectionOutputLayer
getUMat()/getMat() calls are scope based. Results of these calls can't be
stored somewhere for future usage.
2017-11-21 17:28:42 +03:00
Dmitry Kurtaev
6c5dd5cf6d Replace caffe::NormalizedBBox to local structure 2017-11-20 18:03:31 +03:00
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
Dmitry Kurtaev
03cefa7bfe Set zero confidences in case of no detections 2017-10-30 10:17:57 +03:00
Vladislav Sovrasov
7e3e9144de dnn: add an accuracy test for NMS 2017-10-25 13:40:56 +03:00
Vladislav Sovrasov
c704942b8a dnn: add a documentation for NMS, fix missing experimantal namespace 2017-10-25 13:35:49 +03:00
Vladislav Sovrasov
acedb4a579 dnn: make NMS function public 2017-10-25 13:35:49 +03:00
Dmitry Kurtaev
e4aa39f9e5 Text TensorFlow graphs parsing. MobileNet-SSD for 90 classes. 2017-10-08 22:25:29 +03:00
Aleksandr Rybnikov
ce1cc352d9 MobileNet SSD sample 2017-08-01 12:30:27 +03:00
Alexander Alekhin
c3e6de293f dnn: code cleanup, refactor detection output layer 2017-07-13 19:00:34 +03:00
Maksim Shabunin
ace0701a46 Merge pull request #9019 from alalek:dnn_trace 2017-06-29 07:33:46 +00:00
Alexander Alekhin
ed10383359 dnn: added trace macros 2017-06-28 14:57:26 +03:00
Vadim Pisarevsky
c5faa9aefa Merge pull request #9013 from arrybn:ssd_last_layers_optim 2017-06-28 10:38:55 +00:00
Aleksandr Rybnikov
ec321e651f Removed usage of std::map in DetectionOutput layer 2017-06-28 11:31:38 +03:00
Vadim Pisarevsky
8b3d6603d5 another round of dnn optimization (#9011)
* another round of dnn optimization:
* increased malloc alignment across OpenCV from 16 to 64 bytes to make it AVX2 and even AVX-512 friendly
* improved SIMD optimization of pooling layer, optimized average pooling
* cleaned up convolution layer implementation
* made activation layer "attacheable" to all other layers, including fully connected and addition layer.
* fixed bug in the fusion algorithm: "LayerData::consumers" should not be cleared, because it desctibes the topology.
* greatly optimized permutation layer, which improved SSD performance
* parallelized element-wise binary/ternary/... ops (sum, prod, max)

* also, added missing copyrights to many of the layer implementation files

* temporarily disabled (again) the check for intermediate blobs consistency; fixed warnings from various builders
2017-06-28 11:15:22 +03:00
Alexander Alekhin
93729784bb dnn: move module from opencv_contrib
e6f63c7a38/modules/dnn
2017-06-26 13:41:51 +03:00