Tencent/ncnn

master

ncnn

Grade	Name	Complexity	Churn	Issues
F	src\layer\arm\convolution_5x5_pack4_bf16s.h	26	1	118
F	src\layer\arm\gemm_int8.h	857	1	95
F	src\layer\arm\convolution_im2col_gemm_bf16s.h	249	2	75
F	src\layer\arm\convolution_3x3_winograd.h	484	1	69
F	src\layer\arm\convolution_im2col_gemm_int8.h	323	1	69
F	src\layer\arm\gemm_int8_fp16s.h	740	1	68
F	src\layer\arm\gemm_int8_bf16s.h	659	1	62
F	src\layer\arm\gemm_bf16s.h	829	3	62
F	src\layer\x86\convolution_3x3_winograd.h	714	3	59
F	src\layer\x86\gemm_int8.h	1091	3	57
F	src\layer\arm\convolution_im2col_gemm.h	201	1	57
F	src\layer\x86\gemm_bf16s.h	1007	6	51
F	src\layer\x86\convolution_3x3_winograd_bf16s.h	540	2	45
F	src\layer\x86\gemm_x86.cpp	1141	5	43
F	src\layer\arm\gemm_arm.cpp	952	3	42
F	src\layer\arm\convolution_3x3_winograd_bf16s.h	348	1	41
F	src\layer\arm\gemm_fp16s.h	572	2	35
F	src\layer\arm\convolution_7x7_pack1to4_bf16s.h	15	1	35
F	src\layer\x86\convolution_im2col_gemm_bf16s.h	405	4	33
F	src\layer\arm\convolution_5x5_pack4.h	16	1	32