custom_data_io.py

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


def default_loader(img):
    return Image.open(img)


class custom_dset(Dataset):
    def __init__(self,
                 img_path,
                 txt_path,
                 img_transform=None,
                 loader=default_loader):
        with open(txt_path, 'r') as f:
            lines = f.readlines()
            self.img_list = [
                os.path.join(img_path, i.split()[0]) for i in lines
            ]
            self.label_list = [i.split()[1] for i in lines]
        self.img_transform = img_transform
        self.loader = loader

    def __getitem__(self, index):
        img_path = self.img_list[index]
        label = self.label_list[index]
        # img = self.loader(img_path)
        img = img_path
        if self.img_transform is not None:
            img = self.img_transform(img)
        return img, label

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


def collate_fn(batch):
    batch.sort(key=lambda x: len(x[1]), reverse=True)
    img, label = zip(*batch)
    pad_label = []
    lens = []
    max_len = len(label[0])
    for i in range(len(label)):
        temp_label = [0] * max_len
        temp_label[:len(label[i])] = label[i]
        pad_label.append(temp_label)
        lens.append(len(label[i]))
    return img, pad_label, lens