Remove denoise module and thirdparty OIDN.

This is replaced by a much lighter weight and faster JNLM denoiser. OIDN is still much more accurate, and may be provided as an optional backend in the future, but the JNLM denoiser seems good enough for most use cases and removing OIDN reduces the build system complexity, binary size, and build times very significantly.

1 files changed, 0 insertions, 381 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp
deleted file mode 100644
index 65b934e123..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp
+++ /dev/null
@@ -1,381 +0,0 @@
-/*******************************************************************************
-* Copyright 2016-2018 Intel Corporation
-*
-* Licensed under the Apache License, Version 2.0 (the "License");
-* you may not use this file except in compliance with the License.
-* You may obtain a copy of the License at
-*
-*     http://www.apache.org/licenses/LICENSE-2.0
-*
-* 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 <assert.h>
-#include <math.h>
-
-#include "c_types_map.hpp"
-#include "math_utils.hpp"
-#include "mkldnn_thread.hpp"
-#include "nstl.hpp"
-#include "type_helpers.hpp"
-
-#include "ref_pooling.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-template <data_type_t data_type, data_type_t acc_type>
-void ref_pooling_fwd_t<data_type, acc_type>::execute_forward(
-        const exec_ctx_t &ctx) const {
-    using namespace alg_kind;
-    using namespace prop_kind;
-
-    auto alg = pd()->desc()->alg_kind;
-
-    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
-    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
-    auto ws = CTX_OUT_MEM(unsigned char *, MKLDNN_ARG_WORKSPACE);
-
-    const memory_desc_wrapper src_d(pd()->src_md());
-    const memory_desc_wrapper dst_d(pd()->dst_md());
-    const memory_desc_wrapper ws_d(pd()->workspace_md());
-    const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;
-
-    const int ID = pd()->ID();
-    const int IH = pd()->IH();
-    const int IW = pd()->IW();
-    const int KD = pd()->KD();
-    const int KH = pd()->KH();
-    const int KW = pd()->KW();
-    const int SD = pd()->KSD();
-    const int SH = pd()->KSH();
-    const int SW = pd()->KSW();
-    const int padF = pd()->padFront();
-    const int padT = pd()->padT();
-    const int padL = pd()->padL();
-
-    const bool is_3d = pd()->desc()->src_desc.ndims == 5;
-
-    auto apply_offset = [=](int index, int offset) {
-        return (index > offset) ? index - offset : 0;
-    };
-
-    auto set_ws = [=](int mb, int oc, int od, int oh, int ow, int value) {
-        if (ws) {
-            assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
-            size_t offset = is_3d
-                ? ws_d.off(mb, oc, od, oh, ow) : ws_d.off(mb, oc, oh, ow);;
-            if (ws_dt == data_type::u8) {
-                assert(0 <= value && value <= 255);
-                ws[offset] = value;
-            } else
-                reinterpret_cast<int *>(ws)[offset] = value;
-        }
-    };
-
-    auto ker_max = [=](data_t *d, int mb, int oc, int oh, int ow) {
-        for (int kh = 0; kh < KH; ++kh) {
-            for (int kw = 0; kw < KW; ++kw) {
-                const int ih = oh * SH - padT + kh;
-                const int iw = ow * SW - padL + kw;
-
-                if (ih < 0 || ih >= IH) continue;
-                if (iw < 0 || iw >= IW) continue;
-
-                auto s = src[src_d.off(mb, oc, ih, iw)];
-                if (s > d[0]) {
-                    d[0] = s;
-                    set_ws(mb, oc, 1, oh, ow, kh*KW + kw);
-                }
-            }
-        }
-    };
-
-    auto ker_avg = [=](data_t *d, int mb, int oc, int oh, int ow) {
-        auto ih_start = apply_offset(oh*SH, padT);
-        auto iw_start = apply_offset(ow*SW, padL);
-        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
-        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
-        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH
-            : (ih_end - ih_start)*(iw_end - iw_start);
-
-        acc_data_t dst = 0;
-        for (int ih = ih_start; ih < ih_end; ++ih) {
-            for (int iw = iw_start; iw < iw_end; ++iw) {
-                dst += src[src_d.off(mb, oc, ih, iw)];
-            }
-        }
-
-        d[0] = math::out_round<data_t>((float)dst / num_summands);
-    };
-
-    auto ker_max_3d = [=](data_t *d, int mb, int oc, int od, int oh, int ow) {
-        for (int kd = 0; kd < KD; ++kd) {
-            for (int kh = 0; kh < KH; ++kh) {
-                for (int kw = 0; kw < KW; ++kw) {
-                    const int id = od * SD - padF + kd;
-                    const int ih = oh * SH - padT + kh;
-                    const int iw = ow * SW - padL + kw;
-
-                    if (id < 0 || id >= ID) continue;
-                    if (ih < 0 || ih >= IH) continue;
-                    if (iw < 0 || iw >= IW) continue;
-
-                    auto s = src[src_d.off(mb, oc, id, ih, iw)];
-                    if (s > d[0]) {
-                        d[0] = s;
-                        set_ws(mb, oc, od, oh, ow, kd * KH * KW + kh*KW + kw);
-                    }
-                }
-            }
-        }
-    };
-
-    auto ker_avg_3d = [=](data_t *d, int mb, int oc, int od, int oh, int ow) {
-        auto id_start = apply_offset(od*SD, padF);
-        auto ih_start = apply_offset(oh*SH, padT);
-        auto iw_start = apply_offset(ow*SW, padL);
-        auto id_end = nstl::min(od*SD - padF + KD, ID);
-        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
-        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
-        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH*KD
-            : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);
-
-        acc_data_t dst = 0;
-        for (int id = id_start; id < id_end; ++id) {
-            for (int ih = ih_start; ih < ih_end; ++ih) {
-                for (int iw = iw_start; iw < iw_end; ++iw) {
-                    dst += src[src_d.off(mb, oc, id, ih, iw)];
-                }
-            }
-        }
-
-        d[0] = math::out_round<data_t>((float)dst / num_summands);
-    };
-
-    const int MB = pd()->MB();
-    const int OC = pd()->C();
-    const int OD = pd()->OD();
-    const int OH = pd()->OH();
-    const int OW = pd()->OW();
-
-    if (alg == pooling_max) {
-        parallel_nd(MB, OC, OD, OH, OW,
-            [&](int mb, int oc, int od, int oh, int ow) {
-            data_t *d = is_3d
-                ? &dst[dst_d.off(mb, oc, od, oh, ow)]
-                : &dst[dst_d.off(mb, oc, oh, ow)];
-                d[0] = nstl::numeric_limits<data_t>::lowest();
-                set_ws(mb, oc, od, oh, ow, 0);
-                if (is_3d) ker_max_3d(d, mb, oc, od, oh, ow);
-                else ker_max(d, mb, oc, oh, ow);
-        });
-    } else {
-        parallel_nd(MB, OC, OD, OH, OW,
-            [&](int mb, int oc, int od, int oh, int ow) {
-            data_t *d = is_3d
-                ? &dst[dst_d.off(mb, oc, od, oh, ow)]
-                : &dst[dst_d.off(mb, oc, oh, ow)];
-            d[0] = 0;
-            if (is_3d) ker_avg_3d(d, mb, oc, od, oh, ow);
-            else ker_avg(d, mb, oc, oh, ow);
-        });
-    }
-}
-
-template <data_type_t data_type, data_type_t acc_type>
-void ref_pooling_bwd_t<data_type, acc_type>::execute_backward(
-        const exec_ctx_t &ctx) const {
-    using namespace alg_kind;
-
-    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
-    auto ws = CTX_IN_MEM(const unsigned char *, MKLDNN_ARG_WORKSPACE);
-    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-
-    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
-    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
-    const memory_desc_wrapper ws_d(pd()->workspace_md());
-
-    const int ID = pd()->ID();
-    const int IH = pd()->IH();
-    const int IW = pd()->IW();
-    const int KD = pd()->KD();
-    const int KH = pd()->KH();
-    const int KW = pd()->KW();
-    const int SD = pd()->KSD();
-    const int SH = pd()->KSH();
-    const int SW = pd()->KSW();
-    const int padF = pd()->padFront();
-    const int padT = pd()->padT();
-    const int padL = pd()->padL();
-
-    const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
-
-    auto alg = pd()->desc()->alg_kind;
-
-    auto apply_offset = [=](int index, int offset) {
-        return (index > offset) ? index - offset : 0;
-    };
-
-    auto ker_zero = [=](int _mb, int _oc) {
-        for (int ih = 0; ih < IH; ++ih) {
-            for (int iw = 0; iw < IW; ++iw) {
-                diff_src[diff_src_d.off(_mb, _oc, ih, iw)] = data_type_t(0);
-            }
-        }
-    };
-
-    auto ker_max = [=](const data_t *d, int mb, int oc, int oh, int ow) {
-        const size_t ws_off = ws_d.off(mb, oc, oh, ow);
-        const int index = ws_d.data_type() == data_type::u8
-            ? (int)ws[ws_off] : ((int *)ws)[ws_off];
-        const int kw = index % KW;
-        const int kh = index / KW;
-        const int ih = oh * SH - padT + kh;
-        const int iw = ow * SW - padL + kw;
-
-        // If padding area could fit the kernel,
-        // then input displacement would be out of bounds.
-        // No need to back propagate there as padding is
-        // virtual in pooling_max case.
-        if (ih < 0 || ih >= IH)
-            return;
-        if (iw < 0 || iw >= IW)
-            return;
-
-        diff_src[diff_src_d.off(mb, oc, ih, iw)] += d[0];
-    };
-
-    auto ker_avg = [=](const data_t *d, int mb, int oc, int oh, int ow) {
-        auto ih_start = apply_offset(oh*SH, padT);
-        auto iw_start = apply_offset(ow*SW, padL);
-        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
-        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
-        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH
-            : (ih_end - ih_start)*(iw_end - iw_start);
-
-        for (int ih = ih_start; ih < ih_end; ++ih) {
-            for (int iw = iw_start; iw < iw_end; ++iw) {
-                diff_src[diff_src_d.off(mb, oc, ih, iw)] += d[0] / num_summands;
-            }
-        }
-    };
-
-    auto ker_zero_3d = [=](int _mb, int _oc) {
-        for (int id = 0; id < ID; ++id) {
-            for (int ih = 0; ih < IH; ++ih) {
-                for (int iw = 0; iw < IW; ++iw) {
-                    diff_src[diff_src_d.off(_mb, _oc, id, ih, iw)] =
-                        data_type_t(0);
-                }
-            }
-        }
-    };
-
-    auto ker_max_3d = [=](const data_t *d, int mb, int oc, int od, int oh,
-            int ow) {
-        const size_t ws_off = ws_d.off(mb, oc, od, oh, ow);
-        const int index = ws_d.data_type() == data_type::u8
-            ? (int)ws[ws_off] : ((int *)ws)[ws_off];
-        const int kw = index % KW;
-        const int kh = (index / KW) % KH;
-        const int kd = (index / KW) / KH;
-        const int id = od * SD - padF + kd;
-        const int ih = oh * SH - padT + kh;
-        const int iw = ow * SW - padL + kw;
-
-        // If padding area could fit the kernel,
-        // then input displacement would be out of bounds.
-        // No need to back propagate there as padding is
-        // virtual in pooling_max case.
-        if (id < 0 || id >= ID)
-            return;
-        if (ih < 0 || ih >= IH)
-            return;
-        if (iw < 0 || iw >= IW)
-            return;
-
-        diff_src[diff_src_d.off(mb, oc, id, ih, iw)] += d[0];
-    };
-
-    auto ker_avg_3d = [=](const data_t *d, int mb, int oc, int od, int oh,
-            int ow) {
-        auto id_start = apply_offset(od*SD, padF);
-        auto ih_start = apply_offset(oh*SH, padT);
-        auto iw_start = apply_offset(ow*SW, padL);
-        auto id_end = nstl::min(od*SD - padF + KD, ID);
-        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
-        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
-
-        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH*KD
-            : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);
-
-        for (int id = id_start; id < id_end; ++id)
-        for (int ih = ih_start; ih < ih_end; ++ih)
-        for (int iw = iw_start; iw < iw_end; ++iw) {
-            diff_src[diff_src_d.off(mb, oc, id, ih, iw)] += d[0] / num_summands;
-        }
-    };
-
-    const int MB = pd()->MB();
-    const int OC = pd()->C();
-    const int OD = pd()->OD();
-    const int OH = pd()->OH();
-    const int OW = pd()->OW();
-
-    if (pd()->desc()->alg_kind == alg_kind::pooling_max) {
-        parallel_nd(MB, OC, [&](int mb, int oc) {
-            if (is_3d) ker_zero_3d(mb, oc);
-            else ker_zero(mb, oc);
-            for (int od = 0; od < OD; ++od) {
-                for (int oh = 0; oh < OH; ++oh) {
-                    for (int ow = 0; ow < OW; ++ow) {
-                        const data_t *d = is_3d
-                            ? &diff_dst[diff_dst_d.off(mb, oc, od, oh, ow)]
-                            : &diff_dst[diff_dst_d.off(mb, oc, oh, ow)];
-                        if (is_3d) ker_max_3d(d, mb, oc, od, oh, ow);
-                        else ker_max(d, mb, oc, oh, ow);
-                    }
-                }
-            }
-        });
-    } else {
-        parallel_nd(MB, OC, [&](int mb, int oc) {
-            if (is_3d) ker_zero_3d(mb, oc);
-            else ker_zero(mb, oc);
-            for (int od = 0; od < OD; ++od) {
-                for (int oh = 0; oh < OH; ++oh) {
-                    for (int ow = 0; ow < OW; ++ow) {
-                        const data_t *d = is_3d
-                            ? &diff_dst[diff_dst_d.off(mb, oc, od, oh, ow)]
-                            : &diff_dst[diff_dst_d.off(mb, oc, oh, ow)];
-                        if (is_3d) ker_avg_3d(d, mb, oc, od, oh, ow);
-                        else ker_avg(d, mb, oc, oh, ow);
-                    }
-                }
-            }
-        });
-    }
-}
-
-template struct ref_pooling_fwd_t<data_type::f32>;
-template struct ref_pooling_fwd_t<data_type::s32>;
-template struct ref_pooling_fwd_t<data_type::s8, data_type::s32>;
-template struct ref_pooling_fwd_t<data_type::u8, data_type::s32>;
-
-template struct ref_pooling_bwd_t<data_type::f32>;
-template struct ref_pooling_bwd_t<data_type::s32>;
-
-}
-}
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s