from OpenGL.GL import *
from gui.constants import StatisticLink
from gui.ui_window import OptionGui
from opengl_helper.screenshot import create_screenshot
from processing.network_processing import NetworkProcessor
from utility.file import FileHandler
from utility.log_handling import setup_logger
from utility.performance import track_time
from utility.types import CameraPose
from utility.window import WindowHandler, Window
global options_gui
options_gui = OptionGui()
setup_logger("tool")
def compute_render(some_name: str):
global options_gui
width, height = 1920, 1200
FileHandler().read_statistics()
window_handler: WindowHandler = WindowHandler()
window: Window = window_handler.create_window()
window.set_callbacks()
window.activate()
logging.info(
os.path.abspath(
os.path.join(os.path.dirname(sys.modules[__name__].__file__), "..")))
from typing import List
from data.mnist_data_handler import split_mnist_data
from data.model_data import ModelData, ModelTrainType
from definitions import DATA_PATH
from neural_network_preprocessing.create_mnist_model import create
from neural_network_preprocessing.importance import ImportanceType, get_importance_type_name
from neural_network_preprocessing.neural_network import ProcessedNetwork
from processing.processing_handler import RecordingProcessingHandler
from utility.log_handling import setup_logger
from utility.recording_config import RecordingConfig
setup_logger("sample_processing")
# -------------------------------------------------change these settings-----------------------------------------------#
name: str = "default"
class_selection: List[int] or None = None # [0, 1, 2, 3, 4]
importance_type: ImportanceType = ImportanceType(ImportanceType.GAMMA
| ImportanceType.L1)
basic_model_data: ModelData = create(name=name,
batch_size=128,
epochs=15,
layer_data=[81, 49],
regularized=False,
class_selection=class_selection)
# ---------------------------------------------------------------------------------------------------------------------#
def evaluate_importance(model_data: ModelData, importance_type: ImportanceType,
importance_calculation: ImportanceCalculation):
model_data.reload_model()
importance_handler: ImportanceEvaluator = ImportanceEvaluator(model_data)
importance_handler.setup(importance_type, importance_calculation)
(x_train, y_train), (x_test,
y_test), input_shape, num_classes = get_prepared_data(
model_data.get_class_selection())
importance_handler.set_train_and_test_data(x_train, y_train, x_test,
y_test)
importance_handler.create_evaluation_data(10)
setup_logger("evaluation")
logging.info("Evaluation will take some time...")
setup_plot()
name: str = "default_all"
layer_data: List[int] = [81, 49]
model_data: ModelData = create(name=name,
batch_size=128,
epochs=15,
layer_data=layer_data,
regularized=False)
split_suffix: str = ""
if not os.path.exists("%smnist/mnist_train_split%s" %
(DATA_PATH, split_suffix)) or not os.path.exists(
"%smnist/mnist_test_split" % DATA_PATH):
from data.mnist_data_handler import get_prepared_data
from data.model_data import ModelData
from evaluation.evaluator import ImportanceEvaluator
from utility.log_handling import setup_logger
setup_logger("sample_evaluation")
name: str = "default_all"
model_data: ModelData = ModelData(name)
model_data.reload_model()
importance_handler: ImportanceEvaluator = ImportanceEvaluator(model_data)
importance_handler.setup()
(x_train, y_train), (x_test, y_test), input_shape, num_classes = get_prepared_data(model_data.get_class_selection())
importance_handler.set_train_and_test_data(x_train, y_train, x_test, y_test)
importance_handler.create_evaluation_data(10)