inference-engine/thirdparty/clDNN/src/upsampling.cpp

   1 /*
   2 // Copyright (c) 2016 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 ///////////////////////////////////////////////////////////////////////////////////////////////////
  18 #include "upsampling_inst.h"
  19 #include "primitive_type_base.h"
  20 #include "error_handler.h"
  21
  22 namespace cldnn
  23 {
  24 primitive_type_id upsampling_type_id()
  25 {
  26     static primitive_type_base<upsampling> instance;
  27     return &instance;
  28 }
  29
  30 layout upsampling_inst::calc_output_layout(upsampling_node const& node)
  31 {
  32     assert((bool)node.get_primitive()->output_data_type == false
  33            && "Output data type forcing is not supported for upsampling_node!");
  34     auto desc = node.get_primitive();
  35     auto input_layout = node.input().get_output_layout();
  36     auto scale = desc->scale;
  37
  38     auto result_sizes = tensor(input_layout.size.batch[0], input_layout.size.feature[0], static_cast<size_t>(input_layout.size.spatial[0] * scale),
  39         static_cast<size_t>(input_layout.size.spatial[1] * scale));
  40     auto result = layout({ input_layout.data_type, input_layout.format, result_sizes });
  41
  42     return result;
  43 }
  44
  45 std::string upsampling_inst::to_string(upsampling_node const& node)
  46 {
  47     std::stringstream           primitive_description;
  48     auto desc                   = node.get_primitive();
  49     auto& input_1               = node.input();
  50     auto activation             = desc->with_activation ? " true" : "false";
  51     std::string                 str_type;
  52     switch(desc->sample_type)
  53     {
  54     case upsampling_sample_type::nearest:
  55             str_type = "nearest";
  56             break;
  57     case upsampling_sample_type::bilinear:
  58             str_type = "bilinear";
  59             break;
  60     default:
  61             str_type = "not supported sample type";
  62             break;
  63     }
  64
  65     primitive_description << "id: " << desc->id << ", type: upsampling" <<
  66         "\n\tinput_1: " << input_1.id() << ", count: " << input_1.get_output_layout().count() << ",  size: " << input_1.get_output_layout().size <<
  67         "\n\tscale: " << desc->scale <<
  68         "\n\tnum_filter: " << desc->num_filter <<
  69         "\n\tsample_type: " << str_type <<
  70         "\n\twith activation: " << activation << ", slope: " << desc->activation_negative_slope <<
  71         "\n\toutput padding lower size: " << desc->output_padding.lower_size() <<
  72         "\n\toutput padding upper size: " << desc->output_padding.upper_size() <<
  73         "\n\toutput: count: " << node.get_output_layout().count() << ",  size: " << node.get_output_layout().size << '\n';
  74
  75     return primitive_description.str();
  76 }
  77
  78 upsampling_inst::typed_primitive_inst(network_impl& network, upsampling_node const& node)
  79     :parent(network, node)
  80 {
  81     if(argument.sample_type == upsampling_sample_type::bilinear)
  82         CLDNN_ERROR_MESSAGE(node.id(), "Upsampling primitive instance with bilinear filtering should be replaced by deconvolution!");
  83 }
  84 }