The problem is in your init method. You load everything here. Don’t do that.
Move the image loading logic to getitem because that is the method for loading. Init is only for creating and storing the file paths and labels.
And also can’t you use the CocoDataset directly? Since you can just instantiate the dataset and pass it to a dataloader.
class CocoDetection(data.Dataset):
    """`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.

    Args:
        root (string): Root directory where images are downloaded to.
        annFile (string): Path to json annotation file.
        transform (callable, optional): A function/transform that  takes in an PIL image
            and returns a transformed version. E.g, ``transforms.ToTensor``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
    """

    def __init__(self, root, annFile, transform=None, target_transform=None):
        from pycocotools.coco import COCO
        self.root = root
        self.coco = COCO(annFile)
        self.ids = list(self.coco.imgs.keys())
        self.transform = transform
        self.target_transform = target_transform

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
        """
        coco = self.coco
        img_id = self.ids[index]
        ann_ids = coco.getAnnIds(imgIds=img_id)
        target = coco.loadAnns(ann_ids)

        path = coco.loadImgs(img_id)[0]['file_name']

        img = Image.open(os.path.join(self.root, path)).convert('RGB')
        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target


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

    def __repr__(self):
        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
        fmt_str += '    Root Location: {}\n'.format(self.root)
        tmp = '    Transforms (if any): '
        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
        tmp = '    Target Transforms (if any): '
        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
        return fmt_str



  
      pytorch.org
  
  
    

torchvision.datasets.coco — PyTorch master documentation 114