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, 265 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp
deleted file mode 100644
index d79b1a034b..0000000000
--- a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp
+++ /dev/null
@@ -1,265 +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 "type_helpers.hpp"
-#include "mkldnn_thread.hpp"
-#include "simple_q10n.hpp"
-
-#include "ref_batch_normalization.hpp"
-
-namespace mkldnn {
-namespace impl {
-namespace cpu {
-
-template <impl::data_type_t data_type>
-void ref_batch_normalization_fwd_t<data_type>::execute_forward(
-        const exec_ctx_t &ctx) const {
-    /* fast return */
-    if (this->pd()->has_zero_dim_memory()) return;
-
-    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
-    auto scaleshift = CTX_IN_MEM(const float *, MKLDNN_ARG_SCALE_SHIFT);
-
-    auto mean = pd()->stats_is_src()
-        ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_MEAN))
-        : CTX_OUT_MEM(float *, MKLDNN_ARG_MEAN);
-    auto variance = pd()->stats_is_src()
-        ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_VARIANCE))
-        : CTX_OUT_MEM(float *, MKLDNN_ARG_VARIANCE);
-
-    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
-    auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE);
-
-    const memory_desc_wrapper data_d(pd()->src_md());
-    const memory_desc_wrapper scaleshift_d(pd()->weights_md());
-
-    const dim_t N = pd()->MB();
-    const dim_t C = pd()->C();
-    dim_t H = 1, W = 1, D = 1;
-    const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5);
-    if (has_spatial) {
-        D = pd()->D();
-        H = pd()->H();
-        W = pd()->W();
-    }
-
-    const float eps = pd()->desc()->batch_norm_epsilon;
-    const bool use_scaleshift = pd()->use_scaleshift();;
-    const bool save_stats = pd()->is_training();
-    const bool is_training = pd()->is_training();
-    const bool fuse_bn_relu = pd()->fuse_bn_relu();
-    const bool calculate_stats = !pd()->stats_is_src();
-
-    const bool with_relu = pd()->with_relu_post_op();
-    auto maybe_post_op = [&](float res) {
-        return (with_relu && res < 0.0f) ? 0.0f : res;
-    };
-    const bool is_3d = data_d.ndims() == 5;
-
-    auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c,
-            dim_t d, dim_t h, dim_t w) {
-        if (has_spatial) {
-            if (is_3d)
-                return data_d.off(n, c, d, h, w);
-            else
-                return data_d.off(n, c, h, w);
-        } else
-            return data_d.off(n, c);
-    };
-
-    parallel_nd(C, [&](dim_t c) {
-        float v_mean = calculate_stats ? 0 : mean[c];
-        float v_variance = calculate_stats ? 0 : variance[c];
-
-        if (calculate_stats) {
-            for (dim_t n = 0; n < N; ++n)
-            for (dim_t d = 0; d < D; ++d)
-            for (dim_t h = 0; h < H; ++h)
-            for (dim_t w = 0; w < W; ++w)
-                v_mean += src[data_offset(data_d, n, c, d, h, w)];
-            v_mean /= W*N*H*D;
-
-            for (dim_t n = 0; n < N; ++n)
-            for (dim_t d = 0; d < D; ++d)
-            for (dim_t h = 0; h < H; ++h)
-            for (dim_t w = 0; w < W; ++w) {
-                float m = src[data_offset(data_d, n, c, d, h, w)] - v_mean;
-                v_variance += m*m;
-            }
-            v_variance /= W*H*N*D;
-        }
-
-        float sqrt_variance = sqrtf(v_variance + eps);
-        float sm = (use_scaleshift
-            ? scaleshift[scaleshift_d.off(0, c)]
-            : 1.0f) / sqrt_variance;
-        float sv = use_scaleshift ? scaleshift[scaleshift_d.off(1, c)] : 0;
-
-        for (dim_t n = 0; n < N; ++n)
-        for (dim_t d = 0; d < D; ++d)
-        for (dim_t h = 0; h < H; ++h)
-        for (dim_t w = 0; w < W; ++w) {
-            auto d_off = data_offset(data_d,n,c,d,h,w);
-            float bn_res = sm * ((float)src[d_off] - v_mean) + sv;
-            if (fuse_bn_relu) {
-                if (bn_res <= 0) {
-                    bn_res = 0;
-                    if (is_training)
-                        ws[d_off] = 0;
-                } else {
-                    if (is_training)
-                        ws[d_off] = 1;
-                }
-            }
-            if (data_type == data_type::s8) {
-                dst[d_off] = qz_a1b0<float, data_t>()(maybe_post_op(bn_res));
-            } else {
-                dst[d_off] = static_cast<data_t>(maybe_post_op(bn_res));
-            }
-        }
-
-        if (calculate_stats) {
-            if (save_stats) {
-                mean[c] = v_mean;
-                variance[c] = v_variance;
-            }
-        }
-    });
-}
-
-template struct ref_batch_normalization_fwd_t<data_type::f32>;
-template struct ref_batch_normalization_fwd_t<data_type::s8>;
-
-template <impl::data_type_t data_type>
-void ref_batch_normalization_bwd_t<data_type>::execute_backward(
-        const exec_ctx_t &ctx) const {
-    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
-    auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN);
-    auto variance = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE);
-    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
-    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
-    auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE);
-
-    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
-    auto diff_scaleshift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT);
-
-    const memory_desc_wrapper data_d(pd()->src_md());
-    const memory_desc_wrapper diff_data_d(pd()->diff_src_md());
-    const memory_desc_wrapper scaleshift_d(pd()->weights_md());
-    const memory_desc_wrapper diff_scaleshift_d(pd()->diff_weights_md());
-
-    const dim_t C = pd()->C();
-
-    /* fast return */
-    if (this->pd()->has_zero_dim_memory()) {
-        if (diff_scaleshift) {
-            for (dim_t c = 0; c < C; ++c) {
-                diff_scaleshift[diff_scaleshift_d.off(0, c)] = 0;
-                diff_scaleshift[diff_scaleshift_d.off(1, c)] = 0;
-            }
-        }
-        return;
-    }
-
-    const dim_t N = pd()->MB();
-    dim_t H = 1, W = 1, D = 1;
-    const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5);
-    if (has_spatial) {
-        D = pd()->D();
-        H = pd()->H();
-        W = pd()->W();
-    }
-
-    const float eps = pd()->desc()->batch_norm_epsilon;
-    const bool use_scaleshift = pd()->use_scaleshift();
-    const bool calculate_diff_stats = !pd()->use_global_stats();
-    const bool fuse_bn_relu = pd()->fuse_bn_relu();
-
-    const bool is_3d = data_d.ndims() == 5;
-
-    auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c,
-            dim_t d, dim_t h, dim_t w) {
-        if (has_spatial) {
-            if (is_3d)
-                return data_d.off(n, c, d, h, w);
-            else
-                return data_d.off(n, c, h, w);
-        } else
-            return data_d.off(n, c);
-    };
-
-    parallel_nd(C, [&](dim_t c) {
-        data_t v_mean = mean[c];
-        data_t v_variance = variance[c];
-        data_t sqrt_variance = static_cast<data_t>(1.0f / sqrtf(v_variance + eps));
-        data_t gamma = use_scaleshift ? scaleshift[scaleshift_d.off(0, c)] : 1;
-        data_t diff_gamma = data_t(0);
-        data_t diff_beta = data_t(0);
-        diff_gamma = 0.0;
-        diff_beta = 0.0;
-
-        for (dim_t n = 0; n < N; ++n)
-        for (dim_t d = 0; d < D; ++d)
-        for (dim_t h = 0; h < H; ++h)
-        for (dim_t w = 0; w < W; ++w) {
-            const size_t s_off = data_offset(data_d, n, c, d, h, w);
-            data_t dd = diff_dst[data_offset(diff_data_d, n, c, d, h, w)];
-            if (fuse_bn_relu && !ws[s_off])
-                dd = 0;
-
-            diff_gamma += (src[s_off] - v_mean) * dd;
-            diff_beta += dd;
-        }
-        diff_gamma *= sqrt_variance;
-
-        if (diff_scaleshift) {
-            diff_scaleshift[diff_scaleshift_d.off(0, c)] = diff_gamma;
-            diff_scaleshift[diff_scaleshift_d.off(1, c)] = diff_beta;
-        }
-
-        for (dim_t n = 0; n < N; ++n)
-        for (dim_t d = 0; d < D; ++d)
-        for (dim_t h = 0; h < H; ++h)
-        for (dim_t w = 0; w < W; ++w) {
-            const size_t s_off = data_offset(data_d, n, c, d, h, w);
-            const size_t dd_off = data_offset(diff_data_d, n, c, d, h, w);
-            data_t dd = diff_dst[dd_off];
-            if (fuse_bn_relu && !ws[s_off])
-                dd = 0;
-
-            data_t v_diff_src = dd;
-            if (calculate_diff_stats) {
-                v_diff_src -= diff_beta/(D*W*H*N) +
-                    (src[s_off] - v_mean) *
-                    diff_gamma*sqrt_variance/(D*W*H*N);
-            }
-            v_diff_src *= gamma*sqrt_variance;
-            diff_src[dd_off] = v_diff_src;
-        }
-    });
-}
-
-template struct ref_batch_normalization_bwd_t<data_type::f32>;
-
-}
-}
-}
-
-// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s