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[Clean/iniTest] add logic to erase ini after test
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This patch add logic to erase ini after test for better determinancy
and cleaner build directory.

v2: also deprecating config_str in favor of
`ScopedIni`

**Self evaluation:**
1. Build test: [X]Passed [ ]Failed [ ]Skipped
2. Run test: [X]Passed [ ]Failed [ ]Skipped

Signed-off-by: Jihoon Lee <jhoon.it.lee@samsung.com>
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@zhoonit @jijoongmoon
zhoonit authored and jijoongmoon committed Mar 2, 2021
1 parent d12929d commit 68e7dab
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Showing 5 changed files with 263 additions and 389 deletions.
44 changes: 36 additions & 8 deletions test/ccapi/unittest_ccapi.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -156,18 +156,46 @@ TEST(ccapi_dataset, construct_02_p) {
TEST(nntrainer_ccapi, train_with_config_01_p) {
std::unique_ptr<ml::train::Model> model;

std::string config_file = "./test_train_01_p.ini";
RESET_CONFIG(config_file.c_str());

replaceString("Input_Shape = 1:1:62720", "Input_Shape=1:1:62720", config_file,
config_str);
replaceString("batch_size = 32", "batch_size = 16", config_file, config_str);
replaceString("BufferSize=100", "", config_file, config_str);
static IniSection model_base("Model", "Type = NeuralNetwork"
" | Learning_rate = 0.0001"
" | Decay_rate = 0.96"
" | Decay_steps = 1000"
" | Epochs = 1"
" | Optimizer = adam"
" | Loss = cross"
" | Weight_Regularizer = l2norm"
" | weight_regularizer_constant = 0.005"
" | Save_Path = 'model.bin'"
" | batch_size = 32"
" | beta1 = 0.9"
" | beta2 = 0.9999"
" | epsilon = 1e-7");

static IniSection dataset("Dataset", "BufferSize=100"
" | TrainData = trainingSet.dat"
" | ValidData = valSet.dat"
" | LabelData = label.dat");

static IniSection inputlayer("inputlayer", "Type = input"
"| Input_Shape = 1:1:62720"
"| bias_initializer = zeros"
"| Normalization = true"
"| Activation = sigmoid");

static IniSection outputlayer("outputlayer", "Type = fully_connected"
"| input_layers = inputlayer"
"| Unit = 10"
"| bias_initializer = zeros"
"| Activation = softmax");

ScopedIni s("test_train_01_p",
{model_base + "batch_size = 16", dataset + "-BufferSize",
inputlayer, outputlayer});

EXPECT_NO_THROW(model =
ml::train::createModel(ml::train::ModelType::NEURAL_NET));

EXPECT_EQ(model->loadFromConfig(config_file), ML_ERROR_NONE);
EXPECT_EQ(model->loadFromConfig(s.getIniName()), ML_ERROR_NONE);
EXPECT_EQ(model->compile(), ML_ERROR_NONE);
EXPECT_EQ(model->initialize(), ML_ERROR_NONE);
EXPECT_NO_THROW(model->train());
Expand Down
179 changes: 22 additions & 157 deletions test/include/nntrainer_test_util.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,7 @@ class IniTestWrapper {
* @brief erase ini
*
*/
void erase_ini() { remove(getIniName().c_str()); }
void erase_ini() noexcept { remove(getIniName().c_str()); }

bool operator==(const IniTestWrapper &rhs) const {
return name == rhs.name && sections == rhs.sections;
Expand Down Expand Up @@ -290,150 +290,27 @@ class IniTestWrapper {
Sections sections;
};

/// @todo: migrate this to datafile unittest
const std::string config_str = "[Model]"
"\n"
"Type = NeuralNetwork"
"\n"
"Learning_rate = 0.0001"
"\n"
"Decay_rate = 0.96"
"\n"
"Decay_steps = 1000"
"\n"
"Epochs = 1"
"\n"
"Optimizer = adam"
"\n"
"Loss = cross"
"\n"
"Weight_Regularizer = l2norm"
"\n"
"weight_regularizer_constant = 0.005"
"\n"
"Save_Path = 'model.bin'"
"\n"
"batch_size = 32"
"\n"
"beta1 = 0.9"
"\n"
"beta2 = 0.9999"
"\n"
"epsilon = 1e-7"
"\n"
"[DataSet]"
"\n"
"BufferSize=100"
"\n"
"TrainData = trainingSet.dat"
"\n"
"ValidData = valSet.dat"
"\n"
"LabelData = label.dat"
"\n"
"[inputlayer]"
"\n"
"Type = input"
"\n"
"Input_Shape = 1:1:62720"
"\n"
"bias_initializer = zeros"
"\n"
"Normalization = true"
"\n"
"Activation = sigmoid"
"\n"
"[outputlayer]"
"\n"
"Type = fully_connected"
"\n"
"input_layers = inputlayer"
"\n"
"Unit = 10"
"\n"
"bias_initializer = zeros"
"\n"
"Activation = softmax"
"\n";

const std::string config_str2 = "[Model]"
"\n"
"Type = NeuralNetwork"
"\n"
"Learning_rate = 0.0001"
"\n"
"Decay_rate = 0.96"
"\n"
"Decay_steps = 1000"
"\n"
"Epochs = 1"
"\n"
"Optimizer = adam"
"\n"
"Loss = cross"
"\n"
"Weight_Regularizer = l2norm"
"\n"
"weight_regularizer_constant = 0.005"
"\n"
"Model = 'model.bin'"
"\n"
"batch_size = 32"
"\n"
"beta1 = 0.9"
"\n"
"beta2 = 0.9999"
"\n"
"epsilon = 1e-7"
"\n"
"[DataSet]"
"\n"
"BufferSize=100"
"\n"
"TrainData = trainingSet.dat"
"\n"
"ValidData = valSet.dat"
"\n"
"LabelData = label.dat"
"\n"
"[conv2dlayer]"
"\n"
"Type = conv2d"
"\n"
"Input_Shape = 3:28:28"
"\n"
"bias_initializer = zeros"
"\n"
"Activation = sigmoid"
"\n"
"weight_regularizer=l2norm"
"\n"
"weight_regularizer_constant=0.005"
"\n"
"filters=6"
"\n"
"kernel_size=5,5"
"\n"
"stride=1,1"
"\n"
"padding=0,0"
"\n"
"weight_initializer=xavier_uniform"
"\n"
"flatten = false"
"\n"
"[outputlayer]"
"\n"
"Type = fully_connected"
"\n"
"input_layers = conv2dlayer"
"\n"
"Unit = 10"
"\n"
"bias_initializer = zeros"
"\n"
"Activation = softmax"
"\n";
/**
* @brief This class wraps IniTestWrapper, this class must live longer than the
* IniTestWrapper contained inside
*
*/
class ScopedIni {
public:
ScopedIni(const IniTestWrapper &ini_) : ini(ini_) { ini.save_ini(); }
ScopedIni(const std::string &name_,
const IniTestWrapper::Sections &sections_) :
ini(name_, sections_) {
ini.save_ini();
}

std::string getIniName() { return ini.getIniName(); }

~ScopedIni() { ini.erase_ini(); }

private:
IniTestWrapper ini;
};

#define GEN_TEST_INPUT(input, eqation_i_j_k_l) \
do { \
Expand All @@ -449,18 +326,6 @@ const std::string config_str2 = "[Model]"
} \
} while (0)

#define RESET_CONFIG(conf_name) \
do { \
std::ifstream file_stream(conf_name, std::ifstream::in); \
if (file_stream.good()) { \
file_stream.close(); \
if (std::remove(conf_name) != 0) \
ml_loge("Error: Cannot delete file: %s", conf_name); \
else \
ml_logi("Info: deleteing file: %s", conf_name); \
} \
} while (0)

/**
* @brief return a tensor filled with contant value with dimension
*/
Expand Down
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