Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,088 Bytes
5f093a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# A dataset base class that you can easily resize and combine.
# --------------------------------------------------------
import numpy as np
from dust3r.datasets.base.batched_sampler import BatchedRandomSampler
class EasyDataset:
""" a dataset that you can easily resize and combine.
Examples:
---------
2 * dataset ==> duplicate each element 2x
10 @ dataset ==> set the size to 10 (random sampling, duplicates if necessary)
dataset1 + dataset2 ==> concatenate datasets
"""
def __add__(self, other):
return CatDataset([self, other])
def __rmul__(self, factor):
return MulDataset(factor, self)
def __rmatmul__(self, factor):
return ResizedDataset(factor, self)
def set_epoch(self, epoch):
pass # nothing to do by default
def make_sampler(self, batch_size, shuffle=True, world_size=1, rank=0, drop_last=True):
if not (shuffle):
raise NotImplementedError() # cannot deal yet
num_of_aspect_ratios = len(self._resolutions)
return BatchedRandomSampler(self, batch_size, num_of_aspect_ratios, world_size=world_size, rank=rank, drop_last=drop_last)
class MulDataset (EasyDataset):
""" Artifically augmenting the size of a dataset.
"""
multiplicator: int
def __init__(self, multiplicator, dataset):
assert isinstance(multiplicator, int) and multiplicator > 0
self.multiplicator = multiplicator
self.dataset = dataset
def __len__(self):
return self.multiplicator * len(self.dataset)
def __repr__(self):
return f'{self.multiplicator}*{repr(self.dataset)}'
def __getitem__(self, idx):
if isinstance(idx, tuple):
idx, other = idx
return self.dataset[idx // self.multiplicator, other]
else:
return self.dataset[idx // self.multiplicator]
@property
def _resolutions(self):
return self.dataset._resolutions
class ResizedDataset (EasyDataset):
""" Artifically changing the size of a dataset.
"""
new_size: int
def __init__(self, new_size, dataset):
assert isinstance(new_size, int) and new_size > 0
self.new_size = new_size
self.dataset = dataset
def __len__(self):
return self.new_size
def __repr__(self):
size_str = str(self.new_size)
for i in range((len(size_str)-1) // 3):
sep = -4*i-3
size_str = size_str[:sep] + '_' + size_str[sep:]
return f'{size_str} @ {repr(self.dataset)}'
def set_epoch(self, epoch):
# this random shuffle only depends on the epoch
rng = np.random.default_rng(seed=epoch+777)
# shuffle all indices
perm = rng.permutation(len(self.dataset))
# rotary extension until target size is met
shuffled_idxs = np.concatenate([perm] * (1 + (len(self)-1) // len(self.dataset)))
self._idxs_mapping = shuffled_idxs[:self.new_size]
assert len(self._idxs_mapping) == self.new_size
def __getitem__(self, idx):
assert hasattr(self, '_idxs_mapping'), 'You need to call dataset.set_epoch() to use ResizedDataset.__getitem__()'
if isinstance(idx, tuple):
idx, other = idx
return self.dataset[self._idxs_mapping[idx], other]
else:
return self.dataset[self._idxs_mapping[idx]]
@property
def _resolutions(self):
return self.dataset._resolutions
class CatDataset (EasyDataset):
""" Concatenation of several datasets
"""
def __init__(self, datasets):
for dataset in datasets:
assert isinstance(dataset, EasyDataset)
self.datasets = datasets
self._cum_sizes = np.cumsum([len(dataset) for dataset in datasets])
def __len__(self):
return self._cum_sizes[-1]
def __repr__(self):
# remove uselessly long transform
return ' + '.join(repr(dataset).replace(',transform=Compose( ToTensor() Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))', '') for dataset in self.datasets)
def set_epoch(self, epoch):
for dataset in self.datasets:
dataset.set_epoch(epoch)
def __getitem__(self, idx):
other = None
if isinstance(idx, tuple):
idx, other = idx
if not (0 <= idx < len(self)):
raise IndexError()
db_idx = np.searchsorted(self._cum_sizes, idx, 'right')
dataset = self.datasets[db_idx]
new_idx = idx - (self._cum_sizes[db_idx - 1] if db_idx > 0 else 0)
if other is not None:
new_idx = (new_idx, other)
return dataset[new_idx]
@property
def _resolutions(self):
resolutions = self.datasets[0]._resolutions
for dataset in self.datasets[1:]:
assert tuple(dataset._resolutions) == tuple(resolutions)
return resolutions
|