数据集 ¶

from torch.utils.data import Dataset
from PIL import Image
import os

# class Dataset(typing.Generic)
#  |  An abstract class representing a :class:`Dataset`.
#  |  
#  |  All datasets that represent a map from keys to data samples should subclass
#  |  it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a
#  |  data sample for a given key. Subclasses could also optionally overwrite
#  |  :meth:`__len__`, which is expected to return the size of the dataset by many
#  |  :class:`~torch.utils.data.Sampler` implementations and the default options
#  |  of :class:`~torch.utils.data.DataLoader`. Subclasses could also
#  |  optionally implement :meth:`__getitems__`, for speedup batched samples
#  |  loading. This method accepts list of indices of samples of batch and returns
#  |  list of samples.

class MyDataset(Dataset):
    """
    Oriented towards hymenoptera data
    """
    def __init__(self, root_dir, label_dir):
        self.root_dir = root_dir
        self.label_dir = label_dir

        self.path = os.path.join(self.root_dir, self.label_dir)
        self.img_path = os.listdir(self.path)
        pass

    def __getitem__(self, idx):
        img_name = self.img_path[idx]
        img_item_path = os.path.join(self.root_dir, self.label_dir, img_name)
        img = Image.open(img_item_path)
        label = self.label_dir
        return img, label

    def __len__(self):
        return len(self.img_path)

root_dir = 'data/hymenoptera_data/train'
label_dir = 'ants'
ants_dataset = MyDataset(root_dir, label_dir)
# print(ants_dataset[0])
label_dir = 'bees'
bees_dataset = MyDataset(root_dir, label_dir)

train_dataset = ants_dataset + bees_dataset