Merge pull request #38386 from reduz/new-lightmapper

New GPU lightmapper

1 files changed, 114 insertions, 0 deletions

diff --git a/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp b/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp
new file mode 100644
index 0000000000..be96e654ff
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp
@@ -0,0 +1,114 @@
+/*******************************************************************************
+* 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 pooling_desc_init(pooling_desc_t *pool_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t kernel, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    bool args_ok = true
+        && !any_null(pool_desc, src_desc, dst_desc, strides, kernel, padding_l)
+        && one_of(alg_kind, pooling_max,
+                pooling_avg_include_padding,
+                pooling_avg_exclude_padding)
+        && one_of(padding_kind, padding_kind::padding_zero);
+    if (!args_ok) return invalid_arguments;
+
+    if (padding_r == nullptr) padding_r = padding_l;
+
+    auto pd = pooling_desc_t();
+    pd.primitive_kind = primitive_kind::pooling;
+    pd.prop_kind = prop_kind;
+    pd.alg_kind = alg_kind;
+    pd.src_desc.ndims = src_desc->ndims;
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+
+    pd.diff_src_desc = pd.src_desc = zero_md();
+    pd.diff_dst_desc = pd.dst_desc = zero_md();
+
+    (is_fwd ? pd.src_desc : pd.diff_src_desc) = *src_desc;
+    (is_fwd ? pd.dst_desc : pd.diff_dst_desc) = *dst_desc;
+
+    int sp_dims = src_desc->ndims - 2;
+    utils::array_copy(pd.strides, strides, sp_dims);
+    utils::array_copy(pd.kernel, kernel, sp_dims);
+    utils::array_copy(pd.padding[0], padding_l, sp_dims);
+    utils::array_copy(pd.padding[1], padding_r, sp_dims);
+
+    pd.padding_kind = padding_kind;
+    if (one_of(alg_kind, pooling_max, pooling_avg_include_padding,
+                pooling_avg_exclude_padding)) {
+        pd.accum_data_type = types::default_accum_data_type(
+                src_desc->data_type, dst_desc->data_type);
+    } else {
+        pd.accum_data_type = dst_desc->data_type;
+    }
+
+    bool consistency = true
+        && utils::one_of(src_desc->ndims, 4, 5)
+        && utils::one_of(dst_desc->ndims, 4, 5)
+        && src_desc->dims[0] == dst_desc->dims[0]
+        && src_desc->dims[1] == dst_desc->dims[1];
+    for (int i = 2; i < src_desc->ndims; ++i)
+        consistency = consistency && (
+                (src_desc->dims[i] - kernel[i - 2] + padding_l[i - 2]
+                 + padding_r[i - 2]) / strides[i - 2] + 1
+                == dst_desc->dims[i]);
+    if (!consistency) return invalid_arguments;
+
+    *pool_desc = pd;
+    return success;
+}
+}
+
+status_t mkldnn_pooling_forward_desc_init(pooling_desc_t *pool_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t kernel, 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 pooling_desc_init(pool_desc, prop_kind, alg_kind, src_desc,
+            dst_desc, strides, kernel, padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_pooling_backward_desc_init(pooling_desc_t *pool_desc,
+        alg_kind_t alg_kind, const memory_desc_t *diff_src_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t kernel, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return pooling_desc_init(pool_desc, prop_kind::backward_data, alg_kind,
+            diff_src_desc, diff_dst_desc, strides, kernel, padding_l,
+            padding_r, padding_kind);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s