File size: 3,109 Bytes
be903e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | // Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2022 THL A29 Limited, a Tencent company. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.
#include "cpu.h"
#include "mat.h"
#include "x86_usability.h"
namespace ncnn {
#include "convolution_packed_int8.h"
#include "convolution_im2col_gemm_int8.h"
#include "convolution_3x3_pack8to1_int8.h"
#include "convolution_3x3_pack8to4_int8.h"
// packed
void convolution_transform_kernel_packed_int8_avx2(const Mat& kernel, Mat& kernel_tm, int inch, int outch, int kernel_w, int kernel_h)
{
convolution_transform_kernel_packed_int8(kernel, kernel_tm, inch, outch, kernel_w, kernel_h);
}
void convolution_packed_int8_avx2(const Mat& bottom_blob, Mat& top_blob, const Mat& weight_data_tm, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, const Option& opt)
{
convolution_packed_int8(bottom_blob, top_blob, weight_data_tm, kernel_w, kernel_h, dilation_w, dilation_h, stride_w, stride_h, opt);
}
// gemm
void convolution_im2col_gemm_transform_kernel_int8_avx2(const Mat& kernel, Mat& AT, int inch, int outch, int kernel_w, int kernel_h, const Option& opt)
{
convolution_im2col_gemm_transform_kernel_int8(kernel, AT, inch, outch, kernel_w, kernel_h, opt);
}
void convolution_im2col_gemm_int8_avx2(const Mat& bottom_blob, Mat& top_blob, const Mat& AT, int kernel_w, int kernel_h, int dilation_w, int dilation_h, int stride_w, int stride_h, int nT, const Option& opt)
{
convolution_im2col_gemm_int8(bottom_blob, top_blob, AT, kernel_w, kernel_h, dilation_w, dilation_h, stride_w, stride_h, nT, opt);
}
// winograd
void conv3x3s1_winograd43_transform_kernel_pack8to1_int8_sse_avx2(const Mat& kernel, Mat& kernel_tm, int inch, int outch, const Option& opt)
{
conv3x3s1_winograd43_transform_kernel_pack8to1_int8_sse(kernel, kernel_tm, inch, outch, opt);
}
void conv3x3s1_winograd43_pack8to1_int8_sse_avx2(const Mat& bottom_blob, Mat& top_blob, const Mat& kernel, const Option& opt)
{
conv3x3s1_winograd43_pack8to1_int8_sse(bottom_blob, top_blob, kernel, opt);
}
void conv3x3s1_winograd43_transform_kernel_pack8to4_int8_sse_avx2(const Mat& kernel, Mat& kernel_tm, int inch, int outch, const Option& opt)
{
conv3x3s1_winograd43_transform_kernel_pack8to4_int8_sse(kernel, kernel_tm, inch, outch, opt);
}
void conv3x3s1_winograd43_pack8to4_int8_sse_avx2(const Mat& bottom_blob, Mat& top_blob, const Mat& kernel, const Option& opt)
{
conv3x3s1_winograd43_pack8to4_int8_sse(bottom_blob, top_blob, kernel, opt);
}
} // namespace ncnn
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