Source code for denspp.offline.dnn.training.classifier_dataset

import numpy as np
from torch import is_tensor
from torch.utils.data import Dataset
from denspp.offline.dnn import DatasetFromFile




class DatasetClassifier(Dataset):
    def __init__(self, dataset: DatasetFromFile):
        """Dataset Loader for Classification Tasks
        :param dataset: Dataclass DatasetFromFile with ['data', 'label', 'names', 'mean']
        :return:        Dataclass Dataset used in PyTorch Training Routine
        """
        self.__data = np.array(dataset.data, dtype=np.float32)
        self.__label = np.array(dataset.label, dtype=np.uint8)
        self.__name = dataset.dict if isinstance(dataset.dict, list) else []



    def __len__(self):
        return self.__data.shape[0]




    def __getitem__(self, idx):
        if is_tensor(idx):
            idx = idx.tolist()

        return {
            'in': self.__data[idx,:],
            'out': self.__label[idx]
        }


    @property
    def get_dictionary(self) -> list:
        """Getting the dictionary of labeled dataset"""
        return self.__name

    @property
    def get_topology_type(self) -> str:
        """Getting the information of used deep learning topology"""
        return 'Classifier'

    @property
    def get_cluster_num(self) -> int:
        """Getting the number of clusters"""
        return int(np.unique(self.__label).size)