Line data    Source code

       1              : // SPDX-License-Identifier: Apache-2.0
       2              : /**
       3              :  * Copyright (C) 2021 Jihoon Lee <jhoon.it.lee@samsung.com>
       4              :  *
       5              :  * @file previous_input_realizer.cpp
       6              :  * @date 18 November 2021
       7              :  * @brief NNTrainer graph realizer which connects input to previous one if empty
       8              :  * @see https://github.com/nnstreamer/nntrainer
       9              :  * @author Jihoon Lee <jhoon.it.lee@samsung.com>
      10              :  * @bug No known bugs except for NYI items
      11              :  */
      12              : #include <algorithm>
      13              : #include <compiler_fwd.h>
      14              : #include <memory>
      15              : #include <stdexcept>
      16              : #include <vector>
      17              : 
      18              : #include <connection.h>
      19              : #include <layer_node.h>
      20              : #include <nntrainer_log.h>
      21              : #include <previous_input_realizer.h>
      22              : 
      23              : namespace nntrainer {
      24              : 
      25          753 : PreviousInputRealizer::PreviousInputRealizer(
      26          753 :   const std::vector<Connection> &identified_inputs_) :
      27          753 :   identified_inputs(identified_inputs_) {}
      28              : 
      29         1502 : PreviousInputRealizer::~PreviousInputRealizer() {}
      30              : 
      31              : GraphRepresentation
      32          753 : PreviousInputRealizer::realize(const GraphRepresentation &reference) {
      33          753 :   GraphRepresentation processed(reference.begin(), reference.end());
      34              : 
      35              :   /**
      36              :    * @brief for node has input connection, below function determines if the node
      37              :    * should be input node or add input_layers from previous layer
      38              :    *
      39              :    */
      40         1586 :   auto is_actually_an_input_node = [this](const LayerNode &node) {
      41         2000 :     return node.hasInputShapeProperty() or
      42              :            std::any_of(
      43          414 :              identified_inputs.begin(), identified_inputs.end(),
      44          546 :              [&node](auto &conn) { return node.getName() == conn.getName(); });
      45          753 :   };
      46              : 
      47         4952 :   for (auto iter = processed.begin(); iter != processed.end(); ++iter) {
      48              :     auto &node = *iter;
      49         4202 :     if (node->getNumInputConnections() != 0) {
      50         2616 :       continue;
      51              :     }
      52              : 
      53         1586 :     if (is_actually_an_input_node(*node)) {
      54         1175 :       continue;
      55              :     }
      56              : 
      57              :     /**
      58              :      * @brief Weight layer can't have a previous input
      59              :      *
      60              :      */
      61          411 :     if (node->getType() == "weight")
      62            0 :       continue;
      63              : 
      64          411 :     NNTR_THROW_IF(iter == processed.begin(), std::invalid_argument)
      65              :       << "First node must be identified as an input if it is qualified to be "
      66              :          "input, name: "
      67            6 :       << node->getName();
      68              : 
      69              :     auto &prev_node = *(iter - 1);
      70          816 :     ml_logi(
      71              :       "%s is identified as a non-input node and default input layer(%s) is "
      72              :       "being set ",
      73              :       node->getName().c_str(), prev_node->getName().c_str());
      74              : 
      75         1632 :     node->setProperty({"input_layers=" + prev_node->getName()});
      76              :   }
      77              : 
      78          750 :   return processed;
      79          411 : }
      80              : 
      81              : } // namespace nntrainer