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

   1 // Copyright (c) 2018 Intel Corporation
   2 //
   3 // Licensed under the Apache License, Version 2.0 (the "License");
   4 // you may not use this file except in compliance with the License.
   5 // You may obtain a copy of the License at
   6 //
   7 //      http://www.apache.org/licenses/LICENSE-2.0
   8 //
   9 // Unless required by applicable law or agreed to in writing, software
  10 // distributed under the License is distributed on an "AS IS" BASIS,
  11 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 // See the License for the specific language governing permissions and
  13 // limitations under the License.
  14
  15
  16 #include "border_inst.h"
  17
  18 #include "error_handler.h"
  19 #include "json_object.h"
  20 #include "primitive_type_base.h"
  21
  22
  23 namespace cldnn
  24 {
  25 primitive_type_id border_type_id()
  26 {
  27     static primitive_type_base<border> instance;
  28     return &instance;
  29 }
  30
  31 layout border_inst::calc_output_layout(border_node const& node)
  32 {
  33     assert((bool)node.get_primitive()->output_data_type == false
  34            && "Output data type forcing is not supported for border_node!");
  35     auto input_layout = node.input().get_output_layout();
  36     auto desc         = node.get_primitive();
  37
  38     auto&& new_size = input_layout.size;
  39     new_size += desc->left_top_sizes.sub({0, 0, 0, 0});
  40     new_size += desc->right_bottom_sizes.sub({0, 0, 0, 0});
  41
  42     return { input_layout.data_type, input_layout.format, {new_size.batch[0], new_size.feature[0], new_size.spatial[0], new_size.spatial[1]} };
  43 }
  44
  45 std::string border_inst::to_string(border_node const& node)
  46 {
  47     auto desc = node.get_primitive();
  48
  49     const auto& left_top_sizes     = desc->left_top_sizes.sub({0, 0, 0, 0});
  50     const auto& right_bottom_sizes = desc->right_bottom_sizes.sub({0, 0, 0, 0});
  51     const auto& border_value       = std::to_string(desc->border_value);
  52
  53     const char* border_type_str = "unknown";
  54     switch (desc->type)
  55     {
  56     case border_type::constant:   border_type_str = "constant";   break;
  57     case border_type::edge:       border_type_str = "edge";       break;
  58     case border_type::mirror:     border_type_str = "mirror";     break;
  59     case border_type::mirror_101: border_type_str = "mirror-101"; break;
  60     }
  61
  62     auto node_info  = node.desc_to_json();
  63
  64     json_composite border_info;
  65     border_info.add("left/top sizes",     left_top_sizes.to_string());
  66     border_info.add("right/bottom sizes", right_bottom_sizes.to_string());
  67     border_info.add("border type",        border_type_str);
  68     border_info.add("border value",       border_value);
  69
  70     node_info->add("border info", border_info);
  71
  72     std::stringstream primitive_description;
  73     node_info->dump(primitive_description);
  74     return primitive_description.str();
  75 }
  76
  77 border_inst::typed_primitive_inst(network_impl& network, border_node const& node)
  78     : parent(network, node)
  79 {
  80     auto input_layout = node.input().get_output_layout();
  81
  82     const auto input_format = input_layout.format;
  83     const auto& input_sizes = input_layout.size;
  84
  85     auto lt_sizes = argument.left_top_sizes.sub({0, 0, 0, 0});
  86     auto rb_sizes = argument.right_bottom_sizes.sub({0, 0, 0, 0});
  87     auto b_type   = argument.type;
  88
  89     CLDNN_ERROR_NOT_PROPER_FORMAT(node.id(), "Input format", input_format.value, "supported border primitive input formats",
  90                                   format::bfyx, format::yxfb, format::byxf);
  91
  92
  93     // Check if sizes of border are in proper range.
  94     CLDNN_ERROR_TENSOR_SIZES_LESS_THAN(node.id(), "Left/Top border sizes", lt_sizes, "0 value", {0, 0, 0, 0},
  95                                        "Invalid border size: negative value");
  96     CLDNN_ERROR_TENSOR_SIZES_LESS_THAN(node.id(), "Right/Bottom border sizes", rb_sizes, "0 value", {0, 0, 0, 0},
  97                                        "Invalid border size: negative value");
  98
  99     if (b_type == border_type::mirror)
 100     {
 101         CLDNN_ERROR_TENSOR_SIZES_GREATER_THAN(node.id(), "Left/Top border sizes", lt_sizes, "input_sizes", input_sizes,
 102                                               "Not enough data in input to create mirror border of specified size");
 103         CLDNN_ERROR_TENSOR_SIZES_GREATER_THAN(node.id(), "Right/Bottom border sizes", rb_sizes, "input_sizes", input_sizes,
 104                                               "Not enough data in input to create mirror border of specified size");
 105     }
 106     else if (b_type == border_type::mirror_101)
 107     {
 108         auto reduced_input_sizes = input_sizes;
 109         reduced_input_sizes -= tensor(1);
 110         reduced_input_sizes = tensor::max(reduced_input_sizes, tensor());
 111
 112         CLDNN_ERROR_TENSOR_SIZES_GREATER_THAN(node.id(), "Left/Top border sizes", lt_sizes, "input_sizes - 1", reduced_input_sizes,
 113                                               "Not enough data in input to create mirror-101 border of specified size");
 114         CLDNN_ERROR_TENSOR_SIZES_GREATER_THAN(node.id(), "Right/Bottom border sizes", rb_sizes, "input_sizes - 1", reduced_input_sizes,
 115                                               "Not enough data in input to create mirror-101 border of specified size");
 116     }
 117 }
 118 }