- 12 12月, 2019 1 次提交
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由 Alexander Alekhin 提交于
dnn(eltwise): fix handling of different number of channels * dnn(test): reproducer for Eltwise layer issue from PR16063 * dnn(eltwise): rework support for inputs with different channels * dnn(eltwise): get rid of finalize(), variableChannels * dnn(eltwise): update input sorting by number of channels - do not swap inputs if number of channels are same after truncation * dnn(test): skip "shortcut" with batch size 2 on MYRIAD targets
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- 11 12月, 2019 10 次提交
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 shimat 提交于
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由 Alexander Alekhin 提交于
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- 10 12月, 2019 12 次提交
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由 Alexander Alekhin 提交于
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由 Pádraig Brady 提交于
-c is required to avoid linking (and the associated missing "main" message) when linker flags like "-Wl,-z,relro" are passed to GCC
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由 Maksim Shabunin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Dmitry Kurtaev 提交于
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由 Alexander Alekhin 提交于
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由 RAJKIRAN NATARAJAN 提交于
* Add eps error checking for approxPolyDP to allow sensible values only for epsilon value of Douglas-Peucker algorithm. * Review changes for PR
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Dmitry Kurtaev 提交于
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- 09 12月, 2019 5 次提交
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由 Alexander Smorkalov 提交于
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由 Paul Murphy 提交于
* resize: HResizeLinear reduce duplicate work There appears to be a 2x unroll of the HResizeLinear against k, however the k value is only incremented by 1 during the unroll. This results in k - 1 duplicate passes when k > 1. Likewise, the final pass may not respect the work done by the vector loop. Start it with the offset returned by the vector op if implemented. Note, no vector ops are implemented today. The performance is most noticable on a linear downscale. A set of performance tests are added to characterize this. The performance improvement is 10-50% depending on the scaling. * imgproc: vectorize HResizeLinear Performance is mostly gated by the gather operations for x inputs. Likewise, provide a 2x unroll against k, this reduces the number of alpha gathers by 1/2 for larger k. While not a 4x improvement, it still performs substantially better under P9 for a 1.4x improvement. P8 baseline is 1.05-1.10x due to reduced VSX instruction set. For float types, this results in a more modest 1.2x improvement. * Update U8 processing for non-bitexact linear resize * core: hal: vsx: improve v_load_expand_q With a little help, we can do this quickly without gprs on all VSX enabled targets. * resize: Fix cn == 3 step per feedback Per feedback, ensure we don't overrun. This was caught via the failure observed in Test_TensorFlow.inception_accuracy.
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由 Alexander Alekhin 提交于
* imgproc(IPP): wrong result from threshold(THRESH_TOZERO) * imgproc(IPP): disable IPP code to pass THRESH_TOZERO test
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由 Dmitry Kurtaev 提交于
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由 Alexander Alekhin 提交于
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- 08 12月, 2019 1 次提交
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由 Alexander Alekhin 提交于
- don't override current application thread names - set name for own threads only
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- 07 12月, 2019 2 次提交
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由 Lubov Batanina 提交于
Test create custom layer in python * check is contiguos * Add custom layer test * Fix test * Remove assert * Move assert to pyopencv dnn * remove assert * Add unregister * Fix python2 * proto to bytearray * Fix data type
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由 Dmitry Kurtaev 提交于
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- 06 12月, 2019 9 次提交
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Alexander Alekhin 提交于
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由 Liubov Batanina 提交于
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由 Liubov Batanina 提交于
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由 Alexander Alekhin 提交于
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由 Dmitry Kurtaev 提交于
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