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, 395 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp
deleted file mode 100644
index c0a979c4cf..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp
+++ /dev/null
@@ -1,395 +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 "c_types_map.hpp"
-#include "math_utils.hpp"
-#include "mkldnn_thread.hpp"
-#include "mkldnn_traits.hpp"
-#include "type_helpers.hpp"
-
-#include "ref_convolution.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-using math::saturate;
-using math::get_bias;
-
-template <data_type_t src_type, data_type_t wei_type,
-         data_type_t dst_type, data_type_t acc_type>
-void ref_convolution_fwd_t<src_type, wei_type, dst_type, acc_type>::
-execute_forward(const exec_ctx_t &ctx) const {
-    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
-    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
-    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
-    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
-
-    const memory_desc_wrapper src_d(pd()->src_md());
-    const memory_desc_wrapper dst_d(pd()->dst_md());
-    const memory_desc_wrapper weights_d(pd()->weights_md(0));
-    const memory_desc_wrapper bias_d(pd()->weights_md(1));
-
-    const bool with_groups = pd()->with_groups();
-
-    const int G = pd()->G();
-    const int MB = pd()->MB();
-    const int OD = pd()->OD();
-    const int OH = pd()->OH();
-    const int OW = pd()->OW();
-    const int ID = pd()->ID();
-    const int IH = pd()->IH();
-    const int IW = pd()->IW();
-
-    const int OC = pd()->OC() / G;
-    const int IC = pd()->IC() / G;
-    const int KD = pd()->KD();
-    const int KH = pd()->KH();
-    const int KW = pd()->KW();
-
-    const int KSD = pd()->KSD();
-    const int KSH = pd()->KSH();
-    const int KSW = pd()->KSW();
-
-    const int KDD = pd()->KDD();
-    const int KDH = pd()->KDH();
-    const int KDW = pd()->KDW();
-
-    const int padFront = pd()->padFront();
-    const int padT = pd()->padT();
-    const int padL = pd()->padL();
-
-    const bool with_relu = 0; // TODO: change if support post_ops
-    const float nslope = 0.f;
-
-    const int ndims = pd()->desc()->src_desc.ndims;
-
-    auto ker = [=](int g, int mb, int oc, int od, int oh,
-            int ow) {
-        acc_data_t d = 0;
-        for (int ic = 0; ic < IC; ++ic)
-        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 * KSD - padFront + kd * (1 + KDD);
-            const int ih = oh * KSH - padT + kh * (1 + KDH);
-            const int iw = ow * KSW - padL + kw * (1 + KDW);
-
-            if (id < 0 || id >= ID) continue;
-            if (ih < 0 || ih >= IH) continue;
-            if (iw < 0 || iw >= IW) continue;
-
-            if (ndims == 5)
-                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, id, ih, iw)]
-                    * (with_groups
-                    ? weights[weights_d.off(g, oc, ic, kd, kh, kw)]
-                    : weights[weights_d.off(oc, ic, kd, kh, kw)]);
-            else if (ndims == 4)
-                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, ih, iw)]
-                    * (with_groups
-                    ? weights[weights_d.off(g, oc, ic, kh, kw)]
-                    : weights[weights_d.off(oc, ic, kh, kw)]);
-            else if (ndims == 3)
-                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, iw)]
-                    * (with_groups
-                    ? weights[weights_d.off(g, oc, ic, kw)]
-                    : weights[weights_d.off(oc, ic, kw)]);
-           else
-               assert(false);
-
-        }
-        return d;
-    };
-
-    parallel_nd(G, MB, OC, OD, OH, OW,
-        [&](int g, int mb, int oc, int od, int oh, int ow) {
-        float a = bias
-            ? get_bias(bias, bias_d.off(g * OC + oc),
-                    pd()->desc()->bias_desc.data_type)
-            : 0;
-        a += ker(g, mb, oc, od, oh, ow);
-        if (with_relu && a < 0)
-            a = a * nslope;
-        if (ndims == 5)
-            dst[dst_d.off(mb, g*OC + oc, od, oh, ow)] = saturate<dst_data_t>(a);
-        else if (ndims == 4)
-            dst[dst_d.off(mb, g*OC + oc, oh, ow)] = saturate<dst_data_t>(a);
-        else if (ndims == 3)
-            dst[dst_d.off(mb, g*OC + oc, ow)] = saturate<dst_data_t>(a);
-        else
-            assert(false);
-   });
-}
-
-template <data_type_t diff_src_type, data_type_t wei_type,
-         data_type_t diff_dst_type, data_type_t acc_type>
-void ref_convolution_bwd_data_t<diff_src_type, wei_type, diff_dst_type,
-     acc_type>::execute_backward_data(const exec_ctx_t &ctx) const {
-    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
-    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
-    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
-    auto diff_src = CTX_OUT_MEM(diff_src_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 weights_d(pd()->weights_md(0));
-    const memory_desc_wrapper bias_d(pd()->weights_md(1));
-
-    const bool with_groups = pd()->with_groups();
-
-    const int G = pd()->G();
-    const int MB = pd()->MB();
-    const int OD = pd()->OD();
-    const int OH = pd()->OH();
-    const int OW = pd()->OW();
-    const int ID = pd()->ID();
-    const int IH = pd()->IH();
-    const int IW = pd()->IW();
-
-    const int OC = pd()->OC() / G;
-    const int IC = pd()->IC() / G;
-    const int KD = pd()->KD();
-    const int KH = pd()->KH();
-    const int KW = pd()->KW();
-
-    const int KSD = pd()->KSD();
-    const int KSH = pd()->KSH();
-    const int KSW = pd()->KSW();
-
-    const int KDD = pd()->KDD();
-    const int KDH = pd()->KDH();
-    const int KDW = pd()->KDW();
-
-    const int padFront = pd()->padFront();
-    const int padT = pd()->padT();
-    const int padL = pd()->padL();
-
-    const int ndims = pd()->desc()->diff_src_desc.ndims;
-
-    auto ker = [=](int g, int mb, int ic, int id, int ih,
-            int iw) {
-        acc_data_t d = 0;
-        for (int oc = 0; oc < OC; ++oc)
-        for (int kd = 0; kd < KD; ++kd)
-        for (int kh = 0; kh < KH; ++kh)
-        for (int kw = 0; kw < KW; ++kw) {
-            if (iw + padL < kw * (1 + KDW)
-                || ih + padT < kh * (1 + KDH)
-                || id + padFront < kd * (1 + KDD))
-                continue;
-            int ow = iw - kw * (1 + KDW) + padL;
-            int oh = ih - kh * (1 + KDH) + padT;
-            int od = id - kd * (1 + KDD) + padFront;
-            if (ow % KSW != 0 || oh % KSH != 0 || od % KSD != 0)
-                continue;
-
-            ow /= KSW;
-            oh /= KSH;
-            od /= KSD;
-
-            if (od < OD && oh < OH && ow < OW) {
-                if (ndims == 5)
-                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
-                        + oc, od, oh, ow)] * (with_groups
-                        ? weights[weights_d.off(g, oc, ic, kd, kh, kw)]
-                        : weights[weights_d.off(oc, ic, kd, kh, kw)]);
-                else if (ndims == 4)
-                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
-                        + oc, oh, ow)] * (with_groups
-                        ? weights[weights_d.off(g, oc, ic, kh, kw)]
-                        : weights[weights_d.off(oc, ic, kh, kw)]);
-                else if (ndims == 3)
-                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
-                        + oc, ow)] * (with_groups
-                        ? weights[weights_d.off(g, oc, ic, kw)]
-                        : weights[weights_d.off(oc, ic, kw)]);
-                else
-                    assert(false);
-            }
-        }
-        return d;
-    };
-
-    parallel_nd(G, MB, IC, ID, IH, IW,
-        [&](int g, int mb, int ic, int id, int ih, int iw) {
-        auto ds_idx = (ndims == 5)
-            ? diff_src_d.off(mb, g*IC + ic, id, ih, iw)
-            : (ndims == 4)
-            ? diff_src_d.off(mb, g*IC + ic, ih, iw)
-            : diff_src_d.off(mb, g*IC + ic, iw);
-        float a = bias
-            ? get_bias(bias, bias_d.off(g * IC + ic),
-                    pd()->desc()->bias_desc.data_type)
-            : 0;
-        a += ker(g, mb, ic, id, ih, iw);
-        diff_src[ds_idx] = saturate<diff_src_data_t>(a);
-    });
-}
-
-template <data_type_t src_type, data_type_t diff_wei_type,
-         data_type_t diff_dst_type, data_type_t acc_type>
-void ref_convolution_bwd_weights_t<src_type, diff_wei_type, diff_dst_type,
-     acc_type>::execute_backward_weights(const exec_ctx_t &ctx) const {
-    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
-    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
-    auto diff_weights = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
-    auto diff_bias = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_BIAS);
-
-    const memory_desc_wrapper src_d(pd()->src_md());
-    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
-    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
-    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));
-
-    const bool with_groups = pd()->with_groups();
-
-    const int G = pd()->G();
-    const int MB = pd()->MB();
-    const int OD = pd()->OD();
-    const int OH = pd()->OH();
-    const int OW = pd()->OW();
-    const int ID = pd()->ID();
-    const int IH = pd()->IH();
-    const int IW = pd()->IW();
-
-    const int OC = pd()->OC() / G;
-    const int IC = pd()->IC() / G;
-    const int KD = pd()->KD();
-    const int KH = pd()->KH();
-    const int KW = pd()->KW();
-
-    const int KSD = pd()->KSD();
-    const int KSH = pd()->KSH();
-    const int KSW = pd()->KSW();
-
-    const int KDD = pd()->KDD();
-    const int KDH = pd()->KDH();
-    const int KDW = pd()->KDW();
-
-    const int padFront = pd()->padFront();
-    const int padT = pd()->padT();
-    const int padL = pd()->padL();
-
-    const int ndims = pd()->desc()->src_desc.ndims;
-
-auto ker = [=](acc_data_t &d, int g, int oc, int ic, int kd, int kh, int kw) {
-        for (int mb = 0; mb < MB; ++mb)
-        for (int od = 0; od < OD; ++od)
-        for (int oh = 0; oh < OH; ++oh)
-        for (int ow = 0; ow < OW; ++ow) {
-            if (ow*KSW + kw * (1 + KDW) < padL
-                || oh*KSH + kh * (1 + KDH) < padT
-                || od*KSD + kd * (1 + KDD) < padFront
-                || ow*KSW + kw * (1 + KDW) >= IW + padL
-                || oh*KSH + kh * (1 + KDH) >= IH + padT
-                || od*KSD + kd * (1 + KDD) >= ID + padFront)
-                continue;
-
-            int id = od*KSD - padFront + kd * (1 + KDD);
-            int ih = oh*KSH - padT + kh * (1 + KDH);
-            int iw = ow*KSW - padL + kw * (1 + KDW);
-            if (ndims == 5)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od,
-                    oh, ow)] * src[src_d.off(mb, g*IC + ic, id, ih, iw)];
-            else if (ndims == 4)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh, ow)]
-                    * src[src_d.off(mb, g*IC + ic, ih, iw)];
-            else if (ndims == 3)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)]
-                    * src[src_d.off(mb, g*IC + ic, iw)];
-            else
-                assert(false);
-        }
-    };
-
-    auto ker_bias = [=](acc_data_t &d, int g, int oc) {
-        for (int mb = 0; mb < MB; ++mb)
-        for (int od = 0; od < OD; ++od)
-        for (int oh = 0; oh < OH; ++oh)
-        for (int ow = 0; ow < OW; ++ow) {
-            if (ndims == 5)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od, oh,
-                     ow)];
-            else if (ndims == 4)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh,
-                     ow)];
-            else if (ndims == 3)
-                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)];
-            else
-                assert(false);
-        }
-    };
-
-    parallel_nd(G, OC, [&](int g, int oc) {
-        if (diff_bias) {
-            // XXX: loss of precision when bias is a float...
-            acc_data_t db = 0;
-            ker_bias(db, g, oc);
-            diff_bias[diff_bias_d.off(g*OC+oc)]
-                = saturate<diff_wei_data_t>(db);
-        }
-
-        for (int ic = 0; ic < IC; ++ic)
-        for (int kd = 0; kd < KD; ++kd)
-        for (int kh = 0; kh < KH; ++kh)
-        for (int kw = 0; kw < KW; ++kw) {
-            acc_data_t dw = 0;
-            ker(dw, g, oc, ic, kd, kh, kw);
-
-            if (ndims == 5) {
-                auto idx = with_groups
-                    ? diff_weights_d.off(g, oc, ic, kd, kh, kw)
-                    : diff_weights_d.off(oc, ic, kd, kh, kw);
-                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
-            } else if (ndims == 4) {
-                auto idx = with_groups
-                    ? diff_weights_d.off(g, oc, ic, kh, kw)
-                    : diff_weights_d.off(oc, ic, kh, kw);
-                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
-            } else if (ndims == 3) {
-                auto idx = with_groups
-                    ? diff_weights_d.off(g, oc, ic, kw)
-                    : diff_weights_d.off(oc, ic, kw);
-                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
-            } else {
-                 assert(false);
-            }
-        }
-    });
-}
-
-using namespace data_type;
-
-template struct ref_convolution_fwd_t<f32>;
-
-template struct ref_convolution_fwd_t<u8, s8, f32, s32>;
-template struct ref_convolution_fwd_t<u8, s8, s32, s32>;
-template struct ref_convolution_fwd_t<u8, s8, s8, s32>;
-template struct ref_convolution_fwd_t<u8, s8, u8, s32>;
-
-template struct ref_convolution_bwd_data_t<f32, f32, f32, f32>;
-
-template struct ref_convolution_bwd_data_t<f32, s8, u8, s32>;
-template struct ref_convolution_bwd_data_t<s32, s8, u8, s32>;
-template struct ref_convolution_bwd_data_t<s8, s8, u8, s32>;
-template struct ref_convolution_bwd_data_t<u8, s8, u8, s32>;
-
-template struct ref_convolution_bwd_weights_t<f32, f32, f32, f32>;
-
-}
-}
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s