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Merge branch 'master' into python-ncnn-with-vulkan
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@Hideousmon
Hideousmon authored Oct 6, 2023
2 parents 6d64456 + 54a9a56 commit 7e9ed71
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18 changes: 9 additions & 9 deletions .github/workflows/release-python.yml
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Expand Up @@ -73,7 +73,7 @@ jobs:
# build wheels for ubuntu-20.04
- name: Build wheels for ubuntu manylinux
if: matrix.os == 'ubuntu-20.04' && matrix.build != 'cp*-musllinux*'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_LINUX: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -94,7 +94,7 @@ jobs:

- name: Build wheels for ubuntu musllinux
if: matrix.os == 'ubuntu-20.04' && matrix.build == 'cp*-musllinux*'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_LINUX: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -116,7 +116,7 @@ jobs:
# build wheels for windows-2019
- name: Build wheels for windows amd64
if: matrix.os == 'windows-2019' && matrix.arch == 'AMD64'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_WINDOWS: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -139,7 +139,7 @@ jobs:

- name: Build wheels for windows x86
if: matrix.os == 'windows-2019' && matrix.arch == 'x86'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_WINDOWS: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -162,7 +162,7 @@ jobs:

- name: Build wheels for windows ARM64
if: matrix.os == 'windows-2019' && matrix.arch == 'ARM64'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_WINDOWS: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand Down Expand Up @@ -254,7 +254,7 @@ jobs:
- name: Build wheels for macos x86_64
if: matrix.os == 'macos-latest' && matrix.arch == 'x86_64'
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_MACOS: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -271,7 +271,7 @@ jobs:
- name: Build wheels for macos arm64 and macos universal2
if: (matrix.os == 'macos-latest' && matrix.arch == 'arm64') ||
(matrix.os == 'macos-latest' && matrix.arch == 'universal2')
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_MACOS: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand Down Expand Up @@ -342,7 +342,7 @@ jobs:
(matrix.build != 'cp38-musllinux*') && (matrix.build != 'cp39-musllinux*') &&
(matrix.build != 'cp310-musllinux*') && (matrix.build != 'cp311-musllinux*') &&
(matrix.build != 'cp312-musllinux*')
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_LINUX: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
Expand All @@ -365,7 +365,7 @@ jobs:
(matrix.build == 'cp38-musllinux*') || (matrix.build == 'cp39-musllinux*') ||
(matrix.build == 'cp310-musllinux*') || (matrix.build == 'cp311-musllinux*') ||
(matrix.build == 'cp312-musllinux*')
uses: pypa/cibuildwheel@v2.15.0
uses: pypa/cibuildwheel@v2.16.2
env:
CIBW_ARCHS_LINUX: ${{ matrix.arch }}
CIBW_BUILD: ${{ matrix.build }}
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133 changes: 133 additions & 0 deletions benchmark/README.md
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Expand Up @@ -1631,7 +1631,92 @@ cooling_down = 1
vision_transformer min = 6605.19 max = 6606.66 avg = 6605.73
FastestDet min = 52.11 max = 52.97 avg = 52.61
```
### Raspberry Pi 5 Broadcom BCM2712, Cortex-A76 (ARMv8) (2.4GHz x 4)
```
pi@raspberrypi:~/ncnn/benchmark $ ./benchncnn 10 4 0 -1 1
loop_count = 10
num_threads = 4
powersave = 0
gpu_device = -1
cooling_down = 1
squeezenet min = 8.56 max = 8.65 avg = 8.61
squeezenet_int8 min = 11.65 max = 12.64 avg = 11.94
mobilenet min = 11.32 max = 13.46 avg = 11.75
mobilenet_int8 min = 11.30 max = 11.60 avg = 11.45
mobilenet_v2 min = 13.57 max = 13.77 avg = 13.63
mobilenet_v3 min = 9.18 max = 10.52 avg = 9.48
shufflenet min = 4.56 max = 6.19 avg = 5.98
shufflenet_v2 min = 5.04 max = 5.13 avg = 5.09
mnasnet min = 8.27 max = 9.86 avg = 8.65
proxylessnasnet min = 9.36 max = 11.18 avg = 9.62
efficientnet_b0 min = 14.77 max = 14.96 avg = 14.87
efficientnetv2_b0 min = 19.91 max = 20.11 avg = 19.99
regnety_400m min = 11.91 max = 12.10 avg = 11.96
blazeface min = 2.26 max = 2.29 avg = 2.28
googlenet min = 32.80 max = 33.17 avg = 32.97
googlenet_int8 min = 32.63 max = 32.99 avg = 32.78
resnet18 min = 23.95 max = 24.21 avg = 24.12
resnet18_int8 min = 32.50 max = 32.79 avg = 32.68
alexnet min = 25.31 max = 25.75 avg = 25.51
vgg16 min = 162.19 max = 165.08 avg = 163.75
vgg16_int8 min = 187.46 max = 191.21 avg = 189.09
resnet50 min = 55.95 max = 56.61 avg = 56.29
resnet50_int8 min = 73.34 max = 73.97 avg = 73.59
squeezenet_ssd min = 40.48 max = 41.39 avg = 40.92
squeezenet_ssd_int8 min = 45.67 max = 46.35 avg = 46.06
mobilenet_ssd min = 31.15 max = 31.73 avg = 31.48
mobilenet_ssd_int8 min = 31.09 max = 31.44 avg = 31.27
mobilenet_yolo min = 71.51 max = 72.38 avg = 71.95
mobilenetv2_yolov3 min = 47.86 max = 48.41 avg = 48.04
yolov4-tiny min = 55.95 max = 56.51 avg = 56.19
nanodet_m min = 14.26 max = 14.68 avg = 14.48
yolo-fastest-1.1 min = 6.48 max = 8.10 avg = 7.30
yolo-fastestv2 min = 6.03 max = 7.33 avg = 7.04
vision_transformer min = 613.62 max = 637.97 avg = 629.51
FastestDet min = 6.53 max = 6.66 avg = 6.59
pi@raspberrypi:~/ncnn/benchmark $ ./benchncnn 10 1 0 -1 1
loop_count = 10
num_threads = 1
powersave = 0
gpu_device = -1
cooling_down = 1
squeezenet min = 13.18 max = 13.27 avg = 13.22
squeezenet_int8 min = 15.69 max = 15.93 avg = 15.78
mobilenet min = 21.42 max = 21.55 avg = 21.46
mobilenet_int8 min = 14.92 max = 20.91 avg = 17.34
mobilenet_v2 min = 18.56 max = 23.06 avg = 19.24
mobilenet_v3 min = 13.16 max = 13.33 avg = 13.25
shufflenet min = 7.25 max = 11.14 avg = 8.43
shufflenet_v2 min = 7.17 max = 11.15 avg = 7.70
mnasnet min = 13.89 max = 13.94 avg = 13.91
proxylessnasnet min = 17.01 max = 17.26 avg = 17.07
efficientnet_b0 min = 26.19 max = 26.30 avg = 26.24
efficientnetv2_b0 min = 39.69 max = 40.12 avg = 39.97
regnety_400m min = 17.30 max = 17.44 avg = 17.36
blazeface min = 4.74 max = 4.78 avg = 4.76
googlenet min = 57.64 max = 57.84 avg = 57.72
googlenet_int8 min = 55.80 max = 56.01 avg = 55.93
resnet18 min = 31.90 max = 32.09 avg = 32.00
resnet18_int8 min = 56.92 max = 57.16 avg = 57.01
alexnet min = 39.84 max = 40.12 avg = 39.92
vgg16 min = 208.33 max = 211.06 avg = 209.64
vgg16_int8 min = 437.53 max = 440.55 avg = 439.35
resnet50 min = 95.75 max = 96.68 avg = 96.28
resnet50_int8 min = 116.80 max = 118.01 avg = 117.57
squeezenet_ssd min = 47.75 max = 47.97 avg = 47.86
squeezenet_ssd_int8 min = 61.98 max = 62.90 avg = 62.47
mobilenet_ssd min = 52.83 max = 53.39 avg = 53.07
mobilenet_ssd_int8 min = 46.15 max = 46.60 avg = 46.35
mobilenet_yolo min = 117.68 max = 117.97 avg = 117.81
mobilenetv2_yolov3 min = 67.37 max = 67.67 avg = 67.48
yolov4-tiny min = 73.85 max = 74.35 avg = 74.10
nanodet_m min = 22.78 max = 23.33 avg = 22.96
yolo-fastest-1.1 min = 8.82 max = 8.91 avg = 8.87
yolo-fastestv2 min = 8.18 max = 11.42 avg = 8.59
vision_transformer min = 1267.90 max = 1269.45 avg = 1268.82
FastestDet min = 7.79 max = 11.14 avg = 9.03
```
### Raspberry Pi Zero 2 W Broadcom BCM2710A1, Cortex-A53 (ARMv8) (1.0GHz x 4)

```
Expand Down Expand Up @@ -1788,6 +1873,54 @@ cooling_down = 1
yolo-fastestv2 min = 316.93 max = 319.86 avg = 318.33
```

### Avaota Aim T527, Allwinner T527 (Cortex-A55 2.2GHz x 4 + Cortex-A55 1.8GHz x 4)

```
./benchncnn 4 4 2 -1 1
loop_count = 4
num_threads = 4
powersave = 2
gpu_device = -1
cooling_down = 1
squeezenet min = 14.15 max = 14.21 avg = 14.17
squeezenet_int8 min = 21.05 max = 21.12 avg = 21.09
mobilenet min = 19.22 max = 19.30 avg = 19.25
mobilenet_int8 min = 18.65 max = 19.52 avg = 19.07
mobilenet_v2 min = 20.23 max = 21.01 avg = 20.63
mobilenet_v3 min = 15.34 max = 15.48 avg = 15.41
shufflenet min = 10.30 max = 10.37 avg = 10.33
shufflenet_v2 min = 9.18 max = 9.34 avg = 9.23
mnasnet min = 15.58 max = 15.62 avg = 15.60
proxylessnasnet min = 19.64 max = 19.73 avg = 19.67
efficientnet_b0 min = 25.62 max = 25.81 avg = 25.69
efficientnetv2_b0 min = 36.95 max = 37.46 avg = 37.17
regnety_400m min = 23.75 max = 24.13 avg = 23.90
blazeface min = 3.37 max = 3.42 avg = 3.40
googlenet min = 57.36 max = 58.32 avg = 57.88
googlenet_int8 min = 60.80 max = 62.30 avg = 61.50
resnet18 min = 39.99 max = 40.34 avg = 40.17
resnet18_int8 min = 54.18 max = 56.08 avg = 55.16
alexnet min = 41.87 max = 42.21 avg = 42.08
vgg16 min = 260.14 max = 260.94 avg = 260.51
vgg16_int8 min = 347.42 max = 348.90 avg = 348.30
resnet50 min = 90.91 max = 91.26 avg = 91.07
resnet50_int8 min = 121.94 max = 122.56 avg = 122.28
squeezenet_ssd min = 57.11 max = 57.57 avg = 57.37
squeezenet_ssd_int8 min = 74.70 max = 75.18 avg = 74.91
mobilenet_ssd min = 49.60 max = 49.96 avg = 49.71
mobilenet_ssd_int8 min = 49.45 max = 49.93 avg = 49.63
mobilenet_yolo min = 114.98 max = 115.37 avg = 115.18
mobilenetv2_yolov3 min = 75.74 max = 75.97 avg = 75.87
yolov4-tiny min = 99.09 max = 99.43 avg = 99.25
nanodet_m min = 29.40 max = 29.77 avg = 29.60
yolo-fastest-1.1 min = 13.78 max = 13.85 avg = 13.82
yolo-fastestv2 min = 12.91 max = 13.10 avg = 12.98
vision_transformer min = 1641.78 max = 1648.71 avg = 1646.65
FastestDet min = 12.24 max = 12.61 avg = 12.42
```


### Khadas VIM3, Amlogic A311D (Cortex-A73 2.2GHz x 4 + Cortex-A53 1.8GHz x 2)

```
Expand Down
16 changes: 16 additions & 0 deletions docs/developer-guide/operators.md
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Expand Up @@ -72,6 +72,7 @@
* [RNN](#rnn)
* [Scale](#scale)
* [SELU](#selu)
* [Shrink](#shrink)
* [ShuffleChannel](#shufflechannel)
* [Sigmoid](#sigmoid)
* [Slice](#slice)
Expand Down Expand Up @@ -1675,6 +1676,21 @@ else y = x * lambda
| 0 | alpha | float | 1.67326324f| |
| 1 | lambda | float | 1.050700987f| |

# Shrink
```
if x < -lambd y = x + bias
if x > lambd y = x - bias
else y = x
```

* one_blob_only
* support_inplace

| param id | name | type | default | description |
| --------- | ------------- | ----- | --------- | ----------------- |
| 0 | bias | float | 0.0f | |
| 1 | lambd | float | 0.5f | |

# ShuffleChannel
```
if reverse == 0 y = shufflechannel(x) by group
Expand Down
34 changes: 3 additions & 31 deletions docs/how-to-build/how-to-build.md
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Expand Up @@ -601,39 +601,11 @@ Pick `build-XYZ/install` folder for further usage.

### Build for AllWinner D1

Download c906 toolchain package from https://occ.t-head.cn/community/download?id=4046947553902661632
Download c906 toolchain package from https://xuantie.t-head.cn/community/download?id=4224193099938729984

```shell
tar -xf Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
export RISCV_ROOT_PATH=/home/nihui/osd/Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6
```

You need to fix riscv_vector.h header for workaround vfrec7/vfrsqrt7 bug.

Open ```$RISCV_ROOT_PATH/lib/gcc/riscv64-unknown-linux-gnu/10.2.0/include/riscv_vector.h```, goto the file end, you will find three ```#endif```, and apply changes as the following
```c
#endif

#define vfrec7_v_f32m1(x, vl) vfrdiv_vf_f32m1(x, 1.f, vl)
#define vfrec7_v_f32m2(x, vl) vfrdiv_vf_f32m2(x, 1.f, vl)
#define vfrec7_v_f32m4(x, vl) vfrdiv_vf_f32m4(x, 1.f, vl)
#define vfrec7_v_f32m8(x, vl) vfrdiv_vf_f32m8(x, 1.f, vl)
#define vfrec7_v_f16m1(x, vl) vfrdiv_vf_f16m1(x, 1.f, vl)
#define vfrec7_v_f16m2(x, vl) vfrdiv_vf_f16m2(x, 1.f, vl)
#define vfrec7_v_f16m4(x, vl) vfrdiv_vf_f16m4(x, 1.f, vl)
#define vfrec7_v_f16m8(x, vl) vfrdiv_vf_f16m8(x, 1.f, vl)

#define vfrsqrt7_v_f32m1(x, vl) vfrdiv_vf_f32m1(vfsqrt_v_f32m1(x, vl), 1.f, vl)
#define vfrsqrt7_v_f32m2(x, vl) vfrdiv_vf_f32m2(vfsqrt_v_f32m2(x, vl), 1.f, vl)
#define vfrsqrt7_v_f32m4(x, vl) vfrdiv_vf_f32m4(vfsqrt_v_f32m4(x, vl), 1.f, vl)
#define vfrsqrt7_v_f32m8(x, vl) vfrdiv_vf_f32m8(vfsqrt_v_f32m8(x, vl), 1.f, vl)
#define vfrsqrt7_v_f16m1(x, vl) vfrdiv_vf_f16m1(vfsqrt_v_f16m1(x, vl), 1.f, vl)
#define vfrsqrt7_v_f16m2(x, vl) vfrdiv_vf_f16m2(vfsqrt_v_f16m2(x, vl), 1.f, vl)
#define vfrsqrt7_v_f16m4(x, vl) vfrdiv_vf_f16m4(vfsqrt_v_f16m4(x, vl), 1.f, vl)
#define vfrsqrt7_v_f16m8(x, vl) vfrdiv_vf_f16m8(vfsqrt_v_f16m8(x, vl), 1.f, vl)

#endif
#endif
tar -xf Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.6.1-20220906.tar.gz
export RISCV_ROOT_PATH=/home/nihui/osd/Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.6.1
```

Build ncnn with riscv-v vector and simpleocv enabled:
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1 change: 1 addition & 0 deletions src/CMakeLists.txt
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Expand Up @@ -165,6 +165,7 @@ ncnn_add_layer(CopyTo)
ncnn_add_layer(Erf)
ncnn_add_layer(Diag)
ncnn_add_layer(CELU)
ncnn_add_layer(Shrink)

if(NCNN_VULKAN)
ncnn_add_shader(${CMAKE_CURRENT_SOURCE_DIR}/convert_ycbcr.comp)
Expand Down
83 changes: 0 additions & 83 deletions src/layer/arm/convolution_1x1_int8.h
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