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, 104 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
deleted file mode 100644
index 1a51d8562b..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
+++ /dev/null
@@ -1,104 +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 {
-status_t bnrm_desc_init(batch_normalization_desc_t *bnrm_desc,
-        prop_kind_t prop_kind, const memory_desc_t *data_desc,
-        const memory_desc_t *diff_data_desc, float epsilon, unsigned flags) {
-    bool args_ok = true
-        && !any_null(bnrm_desc, data_desc)
-        && one_of(prop_kind, forward_training, forward_inference,
-                backward_data, backward)
-        && IMPLICATION(prop_kind & backward, diff_data_desc != nullptr);
-    if (!args_ok) return invalid_arguments;
-
-    auto bd = batch_normalization_desc_t();
-    bd.primitive_kind = primitive_kind::batch_normalization;
-    bd.prop_kind = prop_kind;
-
-    bd.data_desc = *data_desc;
-    bd.diff_data_desc = zero_md();
-    if ( one_of(bd.prop_kind,backward_data, backward) )
-        bd.diff_data_desc = *diff_data_desc;
-
-    dims_t scaleshift_dims = { 2, data_desc->dims[1] };
-    mkldnn_memory_desc_init_by_tag(&bd.data_scaleshift_desc, 2,
-            scaleshift_dims, data_type::f32, mkldnn_nc);
-    bd.diff_data_scaleshift_desc = zero_md();
-    if (bd.prop_kind == backward) {
-        bd.diff_data_scaleshift_desc = bd.data_scaleshift_desc;
-    }
-
-    dims_t stats_dims = { data_desc->dims[1] };
-    mkldnn_memory_desc_init_by_tag(&bd.mean_desc, 1, stats_dims,
-            data_type::f32, mkldnn_x);
-    bd.variance_desc = bd.mean_desc;
-    bd.batch_norm_epsilon = epsilon;
-
-    unsigned bnorm_flags =
-        mkldnn_use_global_stats | mkldnn_use_scaleshift | mkldnn_fuse_bn_relu;
-    if ((~bnorm_flags & flags) != 0) return invalid_arguments;
-
-    bd.flags = flags;
-
-    bool consistency = true
-        && utils::one_of(bd.data_desc.ndims, 2, 4, 5);
-    if (bd.prop_kind == backward_data)
-        consistency = consistency
-            && utils::one_of(bd.diff_data_desc.ndims, 2, 4, 5)
-            && array_cmp(bd.diff_data_desc.dims, bd.data_desc.dims,
-                    bd.diff_data_desc.ndims);
-    if (!consistency) return invalid_arguments;
-
-    *bnrm_desc = bd;
-    return success;
-}
-}
-
-status_t mkldnn_batch_normalization_forward_desc_init(
-        batch_normalization_desc_t *bnrm_desc, prop_kind_t prop_kind,
-        const memory_desc_t *data_desc, float epsilon, unsigned flags) {
-    if (!one_of(prop_kind, forward_training, forward_inference))
-        return invalid_arguments;
-    return bnrm_desc_init(bnrm_desc, prop_kind, data_desc, nullptr,
-            epsilon, flags);
-}
-
-status_t mkldnn_batch_normalization_backward_desc_init(
-        batch_normalization_desc_t *bnrm_desc, prop_kind_t prop_kind,
-        const memory_desc_t *diff_data_desc, const memory_desc_t *data_desc,
-        float epsilon, unsigned flags) {
-    if (!one_of(prop_kind, backward, backward_data))
-        return invalid_arguments;
-    return bnrm_desc_init(bnrm_desc, prop_kind, data_desc, diff_data_desc,
-            epsilon, flags);
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s