1 files changed, 104 insertions, 0 deletions
diff --git a/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
new file mode 100644
index 0000000000..1a51d8562b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
@@ -0,0 +1,104 @@
+/*******************************************************************************
+* 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