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, 200 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp b/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp
deleted file mode 100644
index 0c5c02bcd1..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp
+++ /dev/null
@@ -1,200 +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 "mkldnn.h"
-
-#include "c_types_map.hpp"
-#include "type_helpers.hpp"
-#include "utils.hpp"
-
-using namespace mkldnn::impl;
-using namespace mkldnn::impl::utils;
-using namespace mkldnn::impl::status;
-using namespace mkldnn::impl::prop_kind;
-using namespace mkldnn::impl::alg_kind;
-using namespace mkldnn::impl::types;
-
-namespace mkldnn {
-namespace impl {
-status_t conv_desc_init(convolution_desc_t *conv_desc,
-        prop_kind_t prop_kind, alg_kind_t alg_kind,
-        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
-        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
-        const dims_t strides, const dims_t dilates,
-        const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    bool args_ok = true
-        && !any_null(conv_desc, src_desc, weights_desc, dst_desc, strides,
-                padding_l)
-        && one_of(alg_kind, convolution_auto, convolution_direct, convolution_winograd)
-        && one_of(padding_kind, padding_kind::padding_zero);
-    if (!args_ok) return invalid_arguments;
-
-    if (padding_r == nullptr) padding_r = padding_l;
-
-    auto cd = convolution_desc_t();
-    cd.primitive_kind = primitive_kind::convolution;
-    cd.prop_kind = prop_kind;
-    cd.alg_kind = alg_kind;
-
-    cd.diff_src_desc = cd.src_desc = zero_md();
-    cd.diff_dst_desc = cd.dst_desc = zero_md();
-    cd.diff_weights_desc = cd.weights_desc = zero_md();
-    cd.diff_bias_desc = cd.bias_desc = zero_md();
-
-    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
-    const bool with_bias =
-        bias_desc && bias_desc->format_kind != format_kind::undef;
-    const bool with_groups = weights_desc->ndims == src_desc->ndims + 1;
-
-    (prop_kind == backward_data ? cd.diff_src_desc : cd.src_desc) = *src_desc;
-    (is_fwd ? cd.dst_desc : cd.diff_dst_desc)  = *dst_desc;
-    (prop_kind == backward_weights ? cd.diff_weights_desc : cd.weights_desc) =
-        *weights_desc;
-    if (with_bias)
-        (prop_kind == backward_weights ? cd.diff_bias_desc : cd.bias_desc) =
-            *bias_desc;
-
-    int sp_dims = src_desc->ndims - 2;
-    utils::array_copy(cd.strides, strides, sp_dims);
-    utils::array_copy(cd.padding[0], padding_l, sp_dims);
-    utils::array_copy(cd.padding[1], padding_r, sp_dims);
-    if (dilates)
-        utils::array_copy(cd.dilates, dilates, sp_dims);
-    else
-        utils::array_set(cd.dilates, 0, sp_dims);
-
-    cd.padding_kind = padding_kind;
-    cd.accum_data_type = types::default_accum_data_type(src_desc->data_type,
-            weights_desc->data_type, dst_desc->data_type, prop_kind);
-
-    const int g = with_groups ? weights_desc->dims[0] : 1;
-    const int bias_dim = prop_kind == backward_data
-        ? src_desc->dims[1]
-        : dst_desc->dims[1];
-
-    bool consistency = true
-        && memory_desc_wrapper(weights_desc).nelems()
-        && src_desc->ndims == dst_desc->ndims
-        && utils::one_of(src_desc->ndims, 3, 4, 5)
-        && utils::one_of(weights_desc->ndims, src_desc->ndims,
-                src_desc->ndims + 1)
-        && (with_bias ? bias_desc->ndims == 1 : true)
-        && (with_bias ? bias_desc->dims[0] == bias_dim : true)
-        && src_desc->dims[0] == dst_desc->dims[0]
-        && src_desc->dims[1] == g * weights_desc->dims[with_groups + 1]
-        && dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0];
-    for (int i = 2; i < src_desc->ndims; ++i)
-    {
-        int src = src_desc->dims[i];
-        int ker = weights_desc->dims[with_groups + i];
-        int dil = cd.dilates[i - 2];
-        int pad_l = padding_l[i - 2];
-        int pad_r = padding_r[i - 2];
-        int str = strides[i - 2];
-        int dst = dst_desc->dims[i];
-        int ker_range = 1 + (ker - 1) * (dil + 1);
-
-        if (str < 1) return invalid_arguments;
-        consistency = consistency
-            && dil >= 0
-            && pad_l >= 0
-            && pad_r + str > 0
-            && (src - ker_range + pad_l + pad_r) / str + 1 == dst;
-    }
-    if (!consistency) return invalid_arguments;
-
-    *conv_desc = cd;
-    return success;
-}
-}
-}
-
-status_t mkldnn_convolution_forward_desc_init(convolution_desc_t *conv_desc,
-        prop_kind_t prop_kind, alg_kind_t alg_kind,
-        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
-        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
-        const dims_t strides, const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    if (!one_of(prop_kind, forward_training, forward_inference))
-        return invalid_arguments;
-    return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
-            weights_desc, bias_desc, dst_desc, strides, nullptr,
-            padding_l, padding_r, padding_kind);
-}
-
-status_t mkldnn_dilated_convolution_forward_desc_init(
-        convolution_desc_t *conv_desc, prop_kind_t prop_kind,
-        alg_kind_t alg_kind, const memory_desc_t *src_desc,
-        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
-        const memory_desc_t *dst_desc, const dims_t strides,
-        const dims_t dilates, const dims_t padding_l,
-        const dims_t padding_r, padding_kind_t padding_kind) {
-    if (!one_of(prop_kind, forward_training, forward_inference))
-        return invalid_arguments;
-    return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
-            weights_desc, bias_desc, dst_desc, strides, dilates,
-            padding_l, padding_r, padding_kind);
-}
-
-status_t mkldnn_convolution_backward_data_desc_init(
-        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
-        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
-        const memory_desc_t *diff_dst_desc, const dims_t strides,
-        const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
-            weights_desc, nullptr, diff_dst_desc, strides, nullptr,
-            padding_l, padding_r, padding_kind);
-}
-
-status_t mkldnn_dilated_convolution_backward_data_desc_init(
-        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
-        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
-        const memory_desc_t *diff_dst_desc, const dims_t strides,
-        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
-            weights_desc, nullptr, diff_dst_desc, strides, dilates,
-            padding_l, padding_r, padding_kind);
-}
-
-status_t mkldnn_convolution_backward_weights_desc_init(
-        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
-        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
-        const memory_desc_t *diff_bias_desc,
-        const memory_desc_t *diff_dst_desc, const dims_t strides,
-        const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
-            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
-            nullptr, padding_l, padding_r, padding_kind);
-}
-
-status_t mkldnn_dilated_convolution_backward_weights_desc_init(
-        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
-        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
-        const memory_desc_t *diff_bias_desc,
-        const memory_desc_t *diff_dst_desc, const dims_t strides,
-        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
-        padding_kind_t padding_kind) {
-    return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
-            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
-            dilates, padding_l, padding_r, padding_kind);
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s