Comments (1)
According to the page http://pytorch.org/docs/master/_modules/torch/utils/data/dataset.html#Dataset ,
you need to implement the following methods: __getitem__ and __len__
class Dataset(object):
"""An abstract class representing a Dataset.
All other datasets should subclass it. All subclasses should override
``__len__``, that provides the size of the dataset, and ``__getitem__``,
supporting integer indexing in range from 0 to len(self) exclusive.
"""
def __getitem__(self, index):
raise NotImplementedError
def __len__(self):
raise NotImplementedError
def __add__(self, other):
return ConcatDataset([self, other])
This is a sample showing what I have done before.
class Dataset(torch.utils.data.Dataset):
def __init__(self, img_dir, label_dir):
# Load images & labels
self.image_files = [f for f in glob.glob(img_dir + '/*.bmp')]
self.label_files = [label_dir + '/' + f.split('/')[-1].split('.')[0] + '.bmp'
for f in self.image_files]
# Do something
def __len__(self):
return len(self.image_files)
def __getitem__(self, idx):
# Do something
image = self.images[idx]
label = self.labels[idx]
return image, label
from yolo2-pytorch.
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from yolo2-pytorch.