Tencent/ncnn

master

ncnn

Grade	Name	Complexity	Churn	Issues
A	src\layer\x86\batchnorm_x86.h	0	2	-
A	src\layer\x86\binaryop_x86.h	0	2	-
A	src\layer\x86\binaryop_x86_avx512bf16.cpp	20	2	1
A	src\layer\x86\bnll_x86.cpp	12	2	-
A	src\layer\x86\bnll_x86.h	0	2	-
A	src\layer\x86\bnll_x86_avx512bf16.cpp	1	2	-
A	src\layer\x86\clip_x86.cpp	8	2	-
A	src\layer\x86\clip_x86.h	0	2	-
A	src\layer\x86\clip_x86_avx512bf16.cpp	1	2	-
B	src\layer\x86\concat_x86.cpp	234	2	11
A	src\layer\x86\concat_x86.h	0	2	-
B	src\layer\x86\convolution1d_packed.h	256	2	12
B	src\layer\x86\convolution1d_packed_bf16s.h	258	2	11
A	src\layer\x86\convolution1d_x86.cpp	30	2	-
A	src\layer\x86\convolution1d_x86.h	0	2	-
A	src\layer\loongarch\lsx_mathfun.h	19	2	-
A	tools\pnnx\src\utils.h	0	2	-
F	src\layer\x86\convolution_3x3_winograd_bf16s.h	540	2	45
F	src\layer\x86\convolution_3x3_winograd_int8.h	476	2	25
C	src\layer\x86\convolution_im2col_gemm_int8.h	229	2	11