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pytorch-image-models/timm/data/dataset.py

146 lines
4.4 KiB

""" Quick n Simple Image Folder, Tarfile based DataSet
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.utils.data as data
import os
import torch
import logging
from PIL import Image
from .parsers import create_parser
_logger = logging.getLogger(__name__)
_ERROR_RETRY = 50
class ImageDataset(data.Dataset):
def __init__(
self,
root,
parser=None,
class_map='',
load_bytes=False,
transform=None,
):
if parser is None or isinstance(parser, str):
parser = create_parser(parser or '', root=root, class_map=class_map)
self.parser = parser
self.load_bytes = load_bytes
self.transform = transform
self._consecutive_errors = 0
def __getitem__(self, index):
img, target = self.parser[index]
try:
img = img.read() if self.load_bytes else Image.open(img).convert('RGB')
except Exception as e:
_logger.warning(f'Skipped sample (index {index}, file {self.parser.filename(index)}). {str(e)}')
self._consecutive_errors += 1
if self._consecutive_errors < _ERROR_RETRY:
return self.__getitem__((index + 1) % len(self.parser))
else:
raise e
self._consecutive_errors = 0
if self.transform is not None:
img = self.transform(img)
if target is None:
target = torch.tensor(-1, dtype=torch.long)
return img, target
def __len__(self):
return len(self.parser)
def filename(self, index, basename=False, absolute=False):
return self.parser.filename(index, basename, absolute)
def filenames(self, basename=False, absolute=False):
return self.parser.filenames(basename, absolute)
class IterableImageDataset(data.IterableDataset):
def __init__(
self,
root,
parser=None,
split='train',
is_training=False,
batch_size=None,
class_map='',
load_bytes=False,
transform=None,
):
assert parser is not None
if isinstance(parser, str):
self.parser = create_parser(
parser, root=root, split=split, is_training=is_training, batch_size=batch_size)
else:
self.parser = parser
self.transform = transform
self._consecutive_errors = 0
def __iter__(self):
for img, target in self.parser:
if self.transform is not None:
img = self.transform(img)
if target is None:
target = torch.tensor(-1, dtype=torch.long)
yield img, target
def __len__(self):
if hasattr(self.parser, '__len__'):
return len(self.parser)
else:
return 0
def filename(self, index, basename=False, absolute=False):
assert False, 'Filename lookup by index not supported, use filenames().'
def filenames(self, basename=False, absolute=False):
return self.parser.filenames(basename, absolute)
class AugMixDataset(torch.utils.data.Dataset):
"""Dataset wrapper to perform AugMix or other clean/augmentation mixes"""
def __init__(self, dataset, num_splits=2):
self.augmentation = None
self.normalize = None
self.dataset = dataset
if self.dataset.transform is not None:
self._set_transforms(self.dataset.transform)
self.num_splits = num_splits
def _set_transforms(self, x):
assert isinstance(x, (list, tuple)) and len(x) == 3, 'Expecting a tuple/list of 3 transforms'
self.dataset.transform = x[0]
self.augmentation = x[1]
self.normalize = x[2]
@property
def transform(self):
return self.dataset.transform
@transform.setter
def transform(self, x):
self._set_transforms(x)
def _normalize(self, x):
return x if self.normalize is None else self.normalize(x)
def __getitem__(self, i):
x, y = self.dataset[i] # all splits share the same dataset base transform
x_list = [self._normalize(x)] # first split only normalizes (this is the 'clean' split)
# run the full augmentation on the remaining splits
for _ in range(self.num_splits - 1):
x_list.append(self._normalize(self.augmentation(x)))
return tuple(x_list), y
def __len__(self):
return len(self.dataset)