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, 68 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp b/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp
deleted file mode 100644
index 82848e3d1f..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp
+++ /dev/null
@@ -1,68 +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 "memory_desc_wrapper.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 softmax_desc_init(softmax_desc_t *softmax_desc, prop_kind_t prop_kind,
-        const memory_desc_t *data_desc, const memory_desc_t *diff_desc, int softmax_axis) {
-    bool args_ok = true
-        && !any_null(softmax_desc, data_desc)
-        && 0 <= softmax_axis
-        && softmax_axis < data_desc->ndims;
-    if (!args_ok) return invalid_arguments;
-
-    auto sd = softmax_desc_t();
-    sd.primitive_kind = primitive_kind::softmax;
-    sd.prop_kind = prop_kind;
-
-    bool is_bwd = (sd.prop_kind == backward_data);
-    sd.data_desc = *data_desc;
-    sd.diff_desc = is_bwd ? *diff_desc : zero_md();
-    sd.softmax_axis = softmax_axis;
-
-    *softmax_desc = sd;
-    return success;
-}
-}
-
-status_t mkldnn_softmax_forward_desc_init(softmax_desc_t *softmax_desc,
-        prop_kind_t prop_kind, const memory_desc_t *data_desc,
-        int softmax_axis) {
-    if (!one_of(prop_kind, forward_inference, forward_training))
-        return invalid_arguments;
-    return softmax_desc_init(softmax_desc, prop_kind, data_desc, nullptr, softmax_axis);
-}
-
-status_t mkldnn_softmax_backward_desc_init(softmax_desc_t *softmax_desc,
-        const memory_desc_t *diff_desc, const mkldnn_memory_desc_t *data_desc,
-        int softmax_axis) {
-    return softmax_desc_init(softmax_desc, prop_kind::backward_data,
-            data_desc, diff_desc, softmax_axis);
-}
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s