* get_physical_cpu_count api family

* set default to physical big cpu

* always treat smt core as big core

* is_smt_cpu

* get max freq mhz on windows

* windows thread affinity

* Simple miss count for better space efficiency

* Simple double ended greedy;

* Add size drop threshold setter;

* set workspace allocator cr to zero as we had some sort of recylcing capability :P

Co-authored-by: LinHeLurking <LinHeLurking@users.noreply.github.com>
Co-authored-by: nihuini <nihuini@tencent.com>

* clear bom marker, avoid vector data function

* feat(src/layer): add vision_transformer benchmark and relative layer

* refactor(testutil.h): add para for RandomMat

* add the param file of nanodet_m in benchmark

* add Noop layer into nanodet_m param for benchmark

* update nanodet_m.param

* openmp on webasm works

* fix compile flags

* dynamic kmp runtime initialization

* clang simpleomp ci

* fix dispatch on unique cpu

* 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

* Add yolov4 example option.
Add yolov4-tiny for benchmark.

* Restyled by astyle

* Restyled by clang-format

* Restyled by astyle

Co-authored-by: Restyled.io <commits@restyled.io>

* online pipeline cache wip

* device-wide pipeline cache

* enable model-wide pipeline cache

* drop pre-created shader modules

* always use pipeline cache

* use implicit model-wide pipeline cache, code format

* code clean

* Add channel padding and blazeface model support.

* remove python binding

* remove std::min usage

* fix reference blob usage

* Increased padding test  coverage

* implement requested changes

* wip

* wip

* fix

* image and imageview can not be destroyed until command execution ends

* fast copy path for tightly packed data

* wip

* texture load works

* 1d 3d image

* record clone image, multiple commands share one image reference

* upload download image

* layer forward accept vkimagemat

* vkimagemat graph works

* staging vkimagemat for passing dynamic parameters, macro for fp32+image shader, padding image shader

* vkimagemat elemsize

* convolution test pass

* conv1x1s1 image shader

* fast staging image allocator from host memory, pooling image shader

* convolutiondepthwise image shader

* innerproduct image shader

* packing image shader

* crop deconvolution image shader

* resolve spirv binding types

* image fp16 and fp16a, cast image shader

* eltwise image shader

* wip

* absval image shader

* deconvolutiondepthwise image shader

* concat image shader, squeezenet works

* noop split image shader

* uniform precision hint

* layer support_image_storage

* wip

* vulkan device utility operator

* command is storage and packing option aware

* fallback to cpu on image allocation failed, mobilenetssd works

* flatten image shader, enable more test

* ci test

* check imgfp32 imgfp16 imgfp16a features

* fix ci test

* fix ci test

* upgrade swiftshader

* wip

* opt aggressive

* imgfp16p

* opt none

* convolution winograd image shader

* fix flush range, fast copy path for continous buffer

* minor fix

* fix innerproduct

* wip ...

* wip

* cast fix

* packing test

* wip

* image fp16p is fp16p

* wip

* silence

* more line info

* code clean

* softmax image shader

* add option for cooling down

* add option for cooling down
add the option usage
add benchmark of RTX2080

* add  benchmark of rtx2060

* Change DataReader::read()'s signature to fix warning C4267

This CL fixes lots of warning "C4267: 'initializing': conversion from
'size_t' to 'int'" in our codebase by matching DataReader::read()'s
signature to fread().

* Fix warnings C4244 and C4267 in tools/ncnnoptimize.cpp

C4244: 'initializing': conversion from 'double' to 'float',
    possible loss of data
C4267: 'initializing': conversion from 'size_t' to 'int',
    possible loss of data

* Fix warning C4244 in src\layer\selu.cpp

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

* Fix warning C4244 in src\layer\cast.cpp

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

* Fix warning C4244 in src\layer\psroipooling.cpp

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

* Add mobilenetv2_yolov3 ,and remove old model

* Recovery yolov2

* Update link

* CMake improvement

* Fix bugs

* Fix typo

* Propagate vulkan dependency

* import vulkan

* add config files, now exported target cmake should be able to find packages

* Propagate no-rtti and no-exception

* Provide a option to control rtti and exception in mobile platform

* Make cmake clean

* Resolve conflicts

* Update CMake
PIE is propagated by INTERFACE_POSITION_INDEPENDENT_CODE

* Remove bad things

* initial the ncnn post training quantization tools

* clear some comments of tools

* fix the Travis ci compiler error

* 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