* runtime cpu dispatch

* force thread one

* disable openmp for coverage

* simplify test layer

* print NCNN_TARGET_ARCH

* less ci build variants

* weight fp16 storage option

* test convdw int8

* apple a12 a13

* ncnn_add_layer ncnn_add_shader cmake macro

* cpu test

* wip

* ci run test

* travis ci for arm64

* arm64 ctest

* copy vulkan loader

* wip

* run

* Update ccpp.yml

* gpu test

* swiftshader

* cache macos swiftshader

* try MoltenVK

* try vulkaninfo

* give swiftshader another try

* disable failed macos gpu test

* more conv test, fix conv3x3s1 gpu test fail

* fix deconvolution test

* dilation test

* cmake option to build tests

* ncnn_add_layer_test macro

* host barrier before upload and after download, handle packing layout option

* test packing layout

* wip

* wip

* merge deconvolution packing and non-packing code

* merge convolution packing and non-packing code

* pass top_blob_count param

* fix build

* take care of non-coherent mappable memory

* initial the ncnn post training quantization tools

* clear some comments of tools

* fix the Travis ci compiler error

* optimize the conv sgemm int8 on arm64-v8a platform

* optimize int8 arm64-v8a with sadalp ins

* merge requantize op  into latest conv layer

* merge requantize op into conv-int8 op

* update the mobilenet.param in the benchmark

* Update README.md

update Kirin970 and RK3399

* try to fix the travis build error

* requantize layer arm64-v8a neon implement

* convdw3x3s1 arm64-v8a neon implement

* convdw3x3s2 arm64-v8a neon implement

* conv1x1s1 arm64-v8a is optimized by neon assembly

* conv sgemm int8 optimized with neon assembly,kernel transform is offline

* conv conv winograd int8 optimized with neon assembly,fix ci build failed

* conv3x3s2 int8 arm64-v8a optimized with neon assembly,remove old codes.

* 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