"""
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Copyright 2020 Huawei Technologies Co., Ltd
Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://spdx.org/licenses/BSD-3-Clause.html
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
""" 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,
repeats=0,
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, repeats=repeats)
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]
x_list = [self._normalize(x)]
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)