inference-engine/thirdparty/mkl-dnn/src/cpu/nchw_pooling.hpp

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   2 * Copyright 2017-2018 Intel Corporation
   3 *
   4 * Licensed under the Apache License, Version 2.0 (the "License");
   5 * you may not use this file except in compliance with the License.
   6 * You may obtain a copy of the License at
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   8 *     http://www.apache.org/licenses/LICENSE-2.0
   9 *
  10 * Unless required by applicable law or agreed to in writing, software
  11 * distributed under the License is distributed on an "AS IS" BASIS,
  12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13 * See the License for the specific language governing permissions and
  14 * limitations under the License.
  15 *******************************************************************************/
  16
  17 #ifndef CPU_NCHW_POOLING_HPP
  18 #define CPU_NCHW_POOLING_HPP
  19
  20 #include <assert.h>
  21
  22 #include "c_types_map.hpp"
  23 #include "cpu_pooling_pd.hpp"
  24 #include "cpu_engine.hpp"
  25 #include "type_helpers.hpp"
  26 #include "utils.hpp"
  27
  28 namespace mkldnn {
  29 namespace impl {
  30 namespace cpu {
  31
  32 using namespace mkldnn::impl::memory_format;
  33
  34 template <impl::data_type_t data_type>
  35 struct nchw_pooling_fwd_t: public cpu_primitive_t {
  36     struct pd_t: public cpu_pooling_fwd_pd_t {
  37         pd_t(engine_t *engine, const pooling_desc_t *adesc,
  38                 const primitive_attr_t *attr,
  39                 const pooling_fwd_pd_t *hint_fwd_pd)
  40             : cpu_pooling_fwd_pd_t(engine, adesc, attr, hint_fwd_pd) {}
  41
  42         DECLARE_COMMON_PD_T("nchw_pooling:any", nchw_pooling_fwd_t);
  43
  44         virtual status_t init() override {
  45             using namespace prop_kind;
  46             using namespace alg_kind;
  47             assert(engine()->kind() == engine_kind::cpu);
  48             auto src_format = src_pd()->desc()->format;
  49             bool ok = true
  50                 && set_default_params() == status::success
  51                 && utils::one_of(desc()->prop_kind, forward_training,
  52                         forward_inference)
  53                 && utils::one_of(desc()->alg_kind, pooling_max,
  54                         pooling_avg_include_padding,
  55                         pooling_avg_exclude_padding)
  56                 && !has_zero_dim_memory()
  57                 && utils::everyone_is(data_type, src_pd()->desc()->data_type,
  58                         dst_pd()->desc()->data_type)
  59                 && utils::one_of(src_format, nchw, ncdhw)
  60                 && (src_format == dst_pd()->desc()->format)
  61                 && attr()->has_default_values();
  62             if (!ok) return status::unimplemented;
  63
  64             bool is_training = desc_.prop_kind == forward_training;
  65             if (desc()->alg_kind == pooling_max && is_training) {
  66                 auto indices_desc = *dst_pd()->desc();
  67                 indices_desc.data_type = pooling_index_data_type(desc());
  68                 ws_pd_ = cpu_memory_t::pd_t(engine_, &indices_desc);
  69             }
  70
  71             return status::success;
  72         }
  73     };
  74
  75     nchw_pooling_fwd_t(const pd_t *apd, const input_vector &inputs,
  76             const output_vector &outputs)
  77         : cpu_primitive_t(apd, inputs, outputs) {}
  78     typedef typename prec_traits<data_type>::type data_t;
  79
  80     virtual void execute(event_t *e) const {
  81         execute_forward();
  82         e->set_state(event_t::ready);
  83     }
  84
  85 private:
  86     void execute_forward() const;
  87     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
  88 };
  89
  90 template <impl::data_type_t data_type>
  91 struct nchw_pooling_bwd_t: public cpu_primitive_t {
  92     struct pd_t: public cpu_pooling_bwd_pd_t {
  93         pd_t(engine_t *engine, const pooling_desc_t *adesc,
  94                 const primitive_attr_t *attr,
  95                 const pooling_fwd_pd_t *hint_fwd_pd)
  96             : cpu_pooling_bwd_pd_t(engine, adesc, attr, hint_fwd_pd) {}
  97
  98         DECLARE_COMMON_PD_T("nchw:any", nchw_pooling_bwd_t);
  99
 100         virtual status_t init() override {
 101             using namespace prop_kind;
 102             using namespace alg_kind;
 103             assert(engine()->kind() == engine_kind::cpu);
 104             auto diff_dst_format = diff_dst_pd()->desc()->format;
 105             bool ok = true
 106                 && set_default_params() == status::success
 107                 && utils::one_of(desc()->prop_kind, backward_data)
 108                 && utils::one_of(desc()->alg_kind, pooling_max,
 109                         pooling_avg_include_padding,
 110                         pooling_avg_exclude_padding)
 111                 && !has_zero_dim_memory()
 112                 && utils::everyone_is(data_type,
 113                         diff_dst_pd()->desc()->data_type,
 114                         diff_src_pd()->desc()->data_type)
 115                 && utils::one_of(diff_dst_format, nchw, ncdhw)
 116                 && (diff_dst_format == diff_src_pd()->desc()->format)
 117                 && attr()->has_default_values();
 118             if (!ok) return status::unimplemented;
 119
 120             if (desc()->alg_kind == pooling_max) {
 121                 bool ws_ok = true
 122                     && hint_fwd_pd_
 123                     && hint_fwd_pd_->workspace_pd()
 124                     && utils::one_of(
 125                             hint_fwd_pd_->workspace_pd()->desc()->format,
 126                             nchw, nChw8c, nChw16c, ncdhw, nCdhw8c, nCdhw16c);
 127                 if (!ws_ok) return status::unimplemented;
 128
 129                 ws_pd_ = *(cpu_memory_t::pd_t*)hint_fwd_pd_->workspace_pd();
 130             }
 131
 132             return status::success;
 133         }
 134     };
 135
 136     nchw_pooling_bwd_t(const pd_t *apd, const input_vector &inputs,
 137             const output_vector &outputs)
 138         : cpu_primitive_t(apd, inputs, outputs) {}
 139     typedef typename prec_traits<data_type>::type data_t;
 140
 141     virtual void execute(event_t *e) const {
 142         execute_backward();
 143         e->set_state(event_t::ready);
 144     }
 145
 146 private:
 147     void execute_backward() const;
 148     const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
 149 };
 150
 151 }
 152 }
 153 }
 154
 155 #endif
 156
 157 // vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s