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data.py
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data.py
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import os
import torch
import torchvision.datasets as datasets
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.sampler import RandomSampler
from torch.utils.data import Subset
from torch._utils import _accumulate
from utils.regime import Regime
from utils.dataset import IndexedFileDataset
from preprocess import get_transform
from itertools import chain
from copy import deepcopy
import warnings
import numpy as np
from PIL import Image
warnings.filterwarnings("ignore", "(Possibly )?corrupt EXIF data", UserWarning)
def get_dataset(name, split='train', transform=None,
target_transform=None, download=True, datasets_path='~/Datasets'):
train = (split == 'train')
root = os.path.join(os.path.expanduser(datasets_path), name)
if name == 'cifar10':
return datasets.CIFAR10(root=root,
train=train,
transform=transform,
target_transform=target_transform,
download=download)
elif name == 'cifar100':
return datasets.CIFAR100(root=root,
train=train,
transform=transform,
target_transform=target_transform,
download=download)
elif name == 'mnist':
return datasets.MNIST(root=root,
train=train,
transform=transform,
target_transform=target_transform,
download=download)
elif name == 'stl10':
return datasets.STL10(root=root,
split=split,
transform=transform,
target_transform=target_transform,
download=download)
elif name == 'imagenet':
if train:
root = os.path.join(root, 'train')
else:
root = os.path.join(root, 'val')
return datasets.ImageFolder(root=root,
transform=transform,
target_transform=target_transform)
elif name == 'imagenet_calib':
if train:
root = os.path.join(root.replace('imagenet_calib','imagenet'), 'calib')
else:
root = os.path.join(root, 'val')
return datasets.ImageFolder(root=root,
transform=transform,
target_transform=target_transform)
elif name == 'imagenet_calib_10K':
if train:
root = os.path.join(root.replace('imagenet_calib_10K','imagenet'), 'calib_10K')
else:
root = os.path.join(root, 'val')
return datasets.ImageFolder(root=root,
transform=transform,
target_transform=target_transform)
elif name == 'imagenet_tar':
if train:
root = os.path.join(root, 'imagenet_train.tar')
else:
root = os.path.join(root, 'imagenet_validation.tar')
return IndexedFileDataset(root, extract_target_fn=(
lambda fname: fname.split('/')[0]),
transform=transform,
target_transform=target_transform)
_DATA_ARGS = {'name', 'split', 'transform',
'target_transform', 'download', 'datasets_path'}
_DATALOADER_ARGS = {'batch_size', 'shuffle', 'sampler', 'batch_sampler',
'num_workers', 'collate_fn', 'pin_memory', 'drop_last',
'timeout', 'worker_init_fn'}
_TRANSFORM_ARGS = {'transform_name', 'input_size', 'scale_size', 'normalize', 'augment',
'cutout', 'duplicates', 'num_crops', 'autoaugment'}
_OTHER_ARGS = {'distributed'}
#class ImageNetCalib(datasets.ImageFolder):
# """Small calibration dataset taken from training."""
#
# def __init__(self, root,transform=None, target_transform=None):
# """
# Args:
# csv_file (string): Path to the csv file with annotations.
# root_dir (string): Directory with all the images.
# transform (callable, optional): Optional transform to be applied
# on a sample.
# """
# self.samples,self.target = torch.load(root)
# self.samples = Image.fromarray(np.uint8(self.samples.permute(0,2,3,1).contiguous().numpy()))
# self.root = root
# self.transform = transform
# self.target_transform = target_transform
#
#
# def __len__(self):
# return len(self.target)
#
# def __getitem__(self, idx):
# samples = self.samples[idx]
# target = self.target[idx]
# if self.transform is not None:
# #import pdb; pdb.set_trace()
# print(samples.shape)
# samples = self.transform(samples)
# if self.target_transform is not None:
# target = self.target_transform(target)
# return samples,target #,idx
class DataRegime(object):
def __init__(self, regime, defaults={}):
self.regime = Regime(regime, deepcopy(defaults))
self.epoch = 0
self.steps = None
self.get_loader(True)
def get_setting(self):
setting = self.regime.setting
loader_setting = {k: v for k,
v in setting.items() if k in _DATALOADER_ARGS}
data_setting = {k: v for k, v in setting.items() if k in _DATA_ARGS}
transform_setting = {
k: v for k, v in setting.items() if k in _TRANSFORM_ARGS}
other_setting = {k: v for k, v in setting.items() if k in _OTHER_ARGS}
transform_setting.setdefault('transform_name', data_setting['name'])
return {'data': data_setting, 'loader': loader_setting,
'transform': transform_setting, 'other': other_setting}
def get(self, key, default=None):
return self.regime.setting.get(key, default)
def get_loader(self, force_update=False, override_settings=None, subset_indices=None):
if force_update or self.regime.update(self.epoch, self.steps):
setting = self.get_setting()
if override_settings is not None:
setting.update(override_settings)
self._transform = get_transform(**setting['transform'])
setting['data'].setdefault('transform', self._transform)
self._data = get_dataset(**setting['data'])
if subset_indices is not None:
self._data = Subset(self._data, subset_indices)
if setting['other'].get('distributed', False):
setting['loader']['sampler'] = DistributedSampler(self._data)
setting['loader']['shuffle'] = None
# pin-memory currently broken for distributed
setting['loader']['pin_memory'] = False
self._sampler = setting['loader'].get('sampler', None)
self._loader = torch.utils.data.DataLoader(
self._data, **setting['loader'])
return self._loader
def set_epoch(self, epoch):
self.epoch = epoch
if self._sampler is not None and hasattr(self._sampler, 'set_epoch'):
self._sampler.set_epoch(epoch)
def __len__(self):
return len(self._data)
class SampledDataLoader(object):
def __init__(self, dl_list):
self.dl_list = dl_list
self.epoch = 0
def generate_order(self):
order = [[idx]*len(dl) for idx, dl in enumerate(self.dl_list)]
order = list(chain(*order))
g = torch.Generator()
g.manual_seed(self.epoch)
return torch.tensor(order)[torch.randperm(len(order), generator=g)].tolist()
def __len__(self):
return sum([len(dl) for dl in self.dl_list])
def __iter__(self):
order = self.generate_order()
iterators = [iter(dl) for dl in self.dl_list]
for idx in order:
yield next(iterators[idx])
return
class SampledDataRegime(DataRegime):
def __init__(self, data_regime_list, probs, split_data=True):
self.probs = probs
self.data_regime_list = data_regime_list
self.split_data = split_data
def get_setting(self):
return [data_regime.get_setting() for data_regime in self.data_regime_list]
def get(self, key, default=None):
return [data_regime.get(key, default) for data_regime in self.data_regime_list]
def get_loader(self, force_update=False):
settings = self.get_setting()
if self.split_data:
dset_sizes = [len(get_dataset(**s['data'])) for s in settings]
assert len(set(dset_sizes)) == 1, \
"all datasets should be same size"
dset_size = dset_sizes[0]
lengths = [int(prob * dset_size) for prob in self.probs]
lengths[-1] = dset_size - sum(lengths[:-1])
indices = torch.randperm(dset_size).tolist()
indices_split = [indices[offset - length:offset]
for offset, length in zip(_accumulate(lengths), lengths)]
loaders = [data_regime.get_loader(force_update=True, subset_indices=indices_split[i])
for i, data_regime in enumerate(self.data_regime_list)]
else:
loaders = [data_regime.get_loader(
force_update=force_update) for data_regime in self.data_regime_list]
self._loader = SampledDataLoader(loaders)
self._loader.epoch = self.epoch
return self._loader
def set_epoch(self, epoch):
self.epoch = epoch
if hasattr(self, '_loader'):
self._loader.epoch = epoch
for data_regime in self.data_regime_list:
if data_regime._sampler is not None and hasattr(data_regime._sampler, 'set_epoch'):
data_regime._sampler.set_epoch(epoch)
def __len__(self):
return sum([len(data_regime._data)
for data_regime in self.data_regime_list])
if __name__ == '__main__':
reg1 = DataRegime(None, {'name': 'imagenet', 'batch_size': 16})
reg2 = DataRegime(None, {'name': 'imagenet', 'batch_size': 32})
reg1.set_epoch(0)
reg2.set_epoch(0)
mreg = SampledDataRegime([reg1, reg2])
for x, _ in mreg.get_loader():
print(x.shape)