test/unittest/models/unittest_models.cpp

   1 // SPDX-License-Identifier: Apache-2.0
   2 /**
   3  * Copyright (C) 2021 Parichay Kapoor <pk.kapoor@samsung.com>
   4  *
   5  * @file unittest_models_v2.cpp
   6  * @date 25 Nov 2021
   7  * @brief unittest models for v2 version
   8  * @see https://github.com/nnstreamer/nntrainer
   9  * @author Parichay Kapoor <pk.kapoor@samsung.com>
  10  * @bug No known bugs except for NYI items
  11  */
  12
  13 #include <gtest/gtest.h>
  14
  15 #include <memory>
  16
  17 #include <ini_wrapper.h>
  18 #include <neuralnet.h>
  19 #include <nntrainer_test_util.h>
  20
  21 #include <models_golden_test.h>
  22
  23 using namespace nntrainer;
  24
  25 static inline constexpr const int NOT_USED_ = 1;
  26
  27 static IniSection nn_base("model", "type = NeuralNetwork");
  28 static std::string fc_base = "type = Fully_connected";
  29 static std::string red_mean_base = "type = reduce_mean";
  30 static IniSection sgd_base("optimizer", "Type = sgd");
  31 static IniSection constant_loss("loss", "type = constant_derivative");
  32
  33 IniWrapper reduce_mean_last("reduce_mean_last",
  34                             {
  35                               nn_base + "batch_size=3",
  36                               sgd_base + "learning_rate=0.1",
  37                               IniSection("fc_1") + fc_base +
  38                                 "unit=7 | input_shape=1:1:2",
  39                               IniSection("red_mean") + red_mean_base + "axis=3",
  40                               constant_loss,
  41                             });
  42
  43 static std::unique_ptr<NeuralNetwork> makeMolAttention() {
  44   std::unique_ptr<NeuralNetwork> nn(new NeuralNetwork());
  45   nn->setProperty({"batch_size=3"});
  46
  47   auto outer_graph = makeGraph({
  48     {"input", {"name=in3", "input_shape=1:1:5"}},
  49     {"input", {"name=in2", "input_shape=1:4:6"}},
  50     {"input", {"name=in1", "input_shape=1:1:6"}},
  51     {"mol_attention",
  52      {"name=mol", "input_layers=in1,in2,in3", "unit=8", "mol_k=5"}},
  53     {"constant_derivative", {"name=loss", "input_layers=mol"}},
  54   });
  55
  56   for (auto &node : outer_graph) {
  57     nn->addLayer(node);
  58   }
  59
  60   nn->setOptimizer(ml::train::createOptimizer("sgd", {"learning_rate = 0.1"}));
  61   return nn;
  62 }
  63
  64 INSTANTIATE_TEST_CASE_P(
  65   model, nntrainerModelTest,
  66   ::testing::ValuesIn({
  67     mkModelIniTc(reduce_mean_last, DIM_UNUSED, NOT_USED_,
  68                  ModelTestOption::COMPARE_V2),
  69     mkModelTc_V2(makeMolAttention, "mol_attention",
  70                  ModelTestOption::COMPARE_V2),
  71   }),
  72   [](const testing::TestParamInfo<nntrainerModelTest::ParamType> &info) {
  73     return std::get<1>(info.param);
  74   });