* skip compile caffe tools

* add convolution_int8 requant test

* fix

* test int8requant

* revert code

* clean code

* fix CI error

* resolve review advices

* Update convolution_arm.cpp

Co-authored-by: nihui <shuizhuyuanluo@126.com>

* Fix warning C4244 in src/layer/normalize.cpp

C4244: '=': conversion from 'double' to 'float', possible loss of data

* Fix warning C4244 in src/layer/requantize.cpp

C4244: 'initializing': conversion from 'double' to 'int', possible loss of data

* Fix warning C4244 in src/mat_pixel_resize.cpp

C4244: '=': conversion from 'double' to 'int', possible loss of data

* Fix warning C4244 in src/mat_pixel.cpp

C4244: '=': conversion from 'int' to 'float', possible loss of data

* Fix warning C4267 in src/modelbin.cpp

C4267: 'initializing': conversion from 'size_t' to 'int', possible loss of data

* Fix warning C4244 in src/layer/batchnorm.cpp

C4244: 'initializing': conversion from 'double' to 'float',
    possible loss of data

* Fix warning C4244 in src/layer/padding.cpp

C4244: 'argument': conversion from 'const float' to 'T', possible loss of data

* Fix warning C4244 in src/layer/priorbox.cpp

C4244: '=': conversion from 'double' to 'float',
    possible loss of data
C4244: 'initializing': conversion from 'double' to 'float',
    possible loss of data

* constraint input value to [-127, +127]

* keep new line at the end

* 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