inference-engine/thirdparty/clDNN/src/gpu/roi_pooling_gpu.cpp

   1 /*
   2 // Copyright (c) 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
   7 //
   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 #include "roi_pooling_inst.h"
  18 #include "primitive_gpu_base.h"
  19 #include "implementation_map.h"
  20 #include "error_handler.h"
  21 #include "kernel_selector_helper.h"
  22 #include "roi_pooling/roi_pooling_kernel_selector.h"
  23 #include "roi_pooling/roi_pooling_kernel_ref.h"
  24
  25 namespace cldnn { namespace gpu {
  26
  27 namespace {
  28     static inline bool hasSingleBatchOutput(const program_node & node)
  29     {
  30         const auto & batch = node.get_output_layout().size.batch;
  31
  32         return batch.empty() || (batch.size() == 1 && batch[0] == 1);
  33     }
  34
  35     kernel_selector::pool_type cldnn_2_pool_type(pooling_mode mode)
  36     {
  37         switch (mode)
  38         {
  39             case pooling_mode::max:
  40                 return kernel_selector::pool_type::MAX;
  41             case pooling_mode::average:
  42                 return kernel_selector::pool_type::AVG;
  43             case pooling_mode::average_no_padding:
  44                 return kernel_selector::pool_type::AVG;
  45             case pooling_mode::bilinear:
  46                 return kernel_selector::pool_type::BILINEAR;
  47             default:
  48                 assert(0);
  49                 return kernel_selector::pool_type::MAX;
  50         }
  51     }
  52 }
  53
  54
  55 struct roi_pooling_gpu : typed_primitive_gpu_impl<roi_pooling>
  56 {
  57     using parent = typed_primitive_gpu_impl<roi_pooling>;
  58     using parent::parent;
  59
  60 protected:
  61
  62     virtual kernel::kernel_arguments_data get_arguments(typed_primitive_inst<roi_pooling>& instance, int32_t) const override
  63     {
  64         kernel::kernel_arguments_data args;
  65
  66         args.inputs = { &instance.input_memory(), &instance.rois_memory() };
  67         args.output = &instance.output_memory();
  68
  69         return args;
  70     }
  71
  72 public:
  73
  74     static primitive_impl* create(const roi_pooling_node& arg)
  75     {
  76         const auto& input_layout    = arg.input().get_output_layout();
  77         const auto& output_layout   = arg.get_output_layout();
  78         const auto& rois_layout     = arg.rois().get_output_layout();
  79         const auto& primitive       = arg.get_primitive();
  80
  81         const auto padding_filling_value = output_layout.data_padding.filling_value();
  82
  83         CLDNN_ERROR_NOT_EQUAL(arg.id(), "roi_pooling padding filling value", padding_filling_value, "padding mode", 0.0f, "Unknown padding mode in roi_pooling.");
  84         CLDNN_ERROR_NOT_PROPER_FORMAT(arg.id(), "Input_layout.format", input_layout.format.value, "output_layout.format", output_layout.format);
  85
  86         auto roi_params = get_default_params<kernel_selector::roi_pooling_params>(arg);
  87         auto roi_optional_params = get_default_optional_params<kernel_selector::roi_pooling_optional_params>(arg.get_program());
  88
  89         const auto& out = roi_params.output;
  90
  91         const auto roi_bfyx = convert_data_tensor(rois_layout);
  92         const auto roi_bf = roi_bfyx.FlattenFeatureAndSpatials();
  93         roi_params.inputs.push_back(roi_bf);
  94         roi_params.output = { out.GetDims(), out.GetDType(), kernel_selector::data_layout::brfyx, out.GetViewOffset(), out.PhysicalSize(), out.GetPaddedVal() }; // TOOD: it's an hack - cldnn doesn't support roi pooling with batching
  95         roi_params.mode               = cldnn_2_pool_type(primitive->mode);
  96         roi_params.position_sensitive = primitive->position_sensitive;
  97         roi_params.pooledWidth        = primitive->pooled_width;
  98         roi_params.pooledHeight       = primitive->pooled_height;
  99         roi_params.spatialScale       = primitive->spatial_scale;
 100         roi_params.spatial_bins_x     = primitive->spatial_bins_x;
 101         roi_params.spatial_bins_y     = primitive->spatial_bins_y;
 102
 103         auto& kernel_selector = kernel_selector::roi_pooling_kernel_selector::Instance();
 104         auto best_kernels = kernel_selector.GetBestKernels(roi_params, roi_optional_params);
 105
 106         CLDNN_ERROR_BOOL(arg.id(), "Best_kernel.empty()", best_kernels.empty(), "Cannot find a proper kernel with this arguments");
 107
 108         auto roi_pool = new roi_pooling_gpu(arg, best_kernels[0]);
 109
 110         return roi_pool;
 111     }
 112 };
 113
 114 namespace {
 115     struct attach
 116     {
 117         attach()
 118         {
 119             implementation_map<roi_pooling>::add(std::make_tuple(engine_types::ocl, data_types::f16, format::bfyx), roi_pooling_gpu::create);
 120             implementation_map<roi_pooling>::add(std::make_tuple(engine_types::ocl, data_types::f32, format::bfyx), roi_pooling_gpu::create);
 121         }
 122
 123         ~attach() {}
 124     };
 125
 126     attach attach_impl;
 127 }
 128 } }