[WIP] new int8 implement,better accuracy #749
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This was referenced Feb 21, 2019
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can anyone please tell me how to get the accuracy for a particular model (say mobilenet-yolov3) while running the benchmark? |
nihui
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Jul 3, 2019
* add the armv7a conv3x3s1 implement without overflow,remove old codes * fix the bug of conv3x3s2 packed int8 * new int8 implement,weight quant by perchanel,better accuracy~ * fix the bug of conv3x3s1 packed int8 neon * add the naive c fp32 and int8 winograd F(2,3) * add the neon intrinsic int8 winograd F(2,3) * optimize the armv7a int8 winograd F(2,3) with neon assembly * optimize the armv7a int8 winograd F(2,3) input transform with assembly. * add the requantize layer and int8 relu implement. * add graph optimize conv1x1s2 -> conv1x1s1,begin optimize int8 aarch64. * fix int8 bugs * add the c naive im2col with sgemm * add aarch64 int8 winograd f23, conv3x3s2 naive implement * add the int8 sgemm conv7x7s2 on x86/armv7a platform * optimize the int8 sgemm by neon intrinsic and packed kernel * optimize the int8 sgemm with packed data * optimize the int8 sgemm with armv7a neon assembly * add the int8 sgemm on arm64-v8a platform * perpare to merge latest codes from master * add the int8 param files * In the Class Net,add the fuse_network method
nihui
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This reverts commit 0009ad6.
This was referenced Oct 18, 2020
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It's a WIP,
I find that quantize the weight data split by outch num can get a better accuracy.So it needs some change.
Better Accuracy
I have implemented the int8 winograd F(2,3),It has the same accuracy as original int8 conv3x3s1 : )
Faster Inference
Platform : Hisi3519(Cortex-A17@880MHz)
Unit : ms
Runtime Memory : mbytes
Storage Memory : mbytes
new convert tool
x86-simulator
arm
New Feature
x86 simulator
armv7a(fix overflow)
arm64-v8a(fix overflow)
Another Int8 layers
x86 simulator
armv7a
arm64-v8a