def get_train_val_test_sureface(self, data_set_path_train, label_set_path_train, data_set_path_test, label_set_path_test, split_size, device): print("Dataset Size") train_data_set, labels_set = self.__read_dataset( data_set_path_train, label_set_path_train) X_train, X_val, Y_train, Y_val = self.__spilt_data_set( train_data_set, labels_set, split_size=split_size) X_test, Y_test = self.__read_dataset(data_set_path_test, label_set_path_test) print("Train set:") print(Y_train.shape[0]) print("Val set:") print(Y_val.shape[0]) print("Test set:") print(Y_test.shape[0]) train_set = Util.convert_to_tensor(X_train, Y_train, device) val_set = Util.convert_to_tensor(X_val, Y_val, device) test_set = Util.convert_to_tensor(X_test, Y_test, device) return train_set, Y_train.shape[0], val_set, Y_val.shape[ 0], test_set, Y_test.shape[0]
def __split_train_test_validation_set_texture(self, data_set_path, label_set_path, split_size, device): print("Texture Dataset Size") train_data_set, labels_set = self.__read_dataset(data_set_path, label_set_path) self.texture_set_size = labels_set.shape[0] X_train, X_val, Y_train, Y_val = self.__spilt_data_set(train_data_set, labels_set, split_size=split_size) train_set = Util.convert_to_tensor(X_train, Y_train, device) val_set = Util.convert_to_tensor(X_val, Y_val, device) return train_set, val_set, train_data_set, labels_set
def get_tensor_set(self, dataset_path, label_set_path, device): train_data_set, labels_set = self.__read_dataset( dataset_path, label_set_path) texture_data_set_size = labels_set.shape[0] train_set = Util.convert_to_tensor(train_data_set, labels_set, device) return train_set, texture_data_set_size
def pre_process_test_texture(self, data_set_path, label_set_path, device): test_data, labels_set = self.__read_dataset(data_set_path, label_set_path) processed_dataset = Util.convert_to_tensor(test_data, labels_set, device) self.texture_set_size = labels_set.shape[0] return processed_dataset
def split_train_test_validation_set_image_net(self, data_set_path, label_set_path, device): """ This method splits the data set into train, test and validation set. Also this method resize the images based on image dimensions specified by image_dims parameter. :param data_set_path: :param label_set_path: :param split_size: :param device: :param flag: :return train, test and validation set and their corresponding sizes """ print("ImageNet Dataset Size") train_data_set, labels_set = self.__read_dataset( data_set_path, label_set_path) train_set_size = labels_set.shape[0] # X_train, X_val, Y_train, Y_val = self.__spilt_data_set(train_data_set, # labels_set, # split_size=split_size) train_set = Util.convert_to_tensor(train_data_set, labels_set, device) # val_set = Util.convert_to_tensor(X_val, Y_val, device) return train_set, train_set_size