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# Copyright (c) 2023-2024, Zexin He
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# 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.
import math
from functools import partial
import torch
__all__ = ['MixerDataset']
class MixerDataset(torch.utils.data.Dataset):
def __init__(self,
split: str,
subsets: list[dict],
**dataset_kwargs,
):
self.subsets = [
self._dataset_fn(subset, split)(**dataset_kwargs)
for subset in subsets
]
self.virtual_lens = [
math.ceil(subset_config['sample_rate'] * len(subset_obj))
for subset_config, subset_obj in zip(subsets, self.subsets)
]
@staticmethod
def _dataset_fn(subset_config: dict, split: str):
name = subset_config['name']
dataset_cls = None
if name == "objaverse":
from .objaverse import ObjaverseDataset
dataset_cls = ObjaverseDataset
elif name == 'gobjaverse_delete_tb':
from .gobjaverse import GobjaverseDataset
dataset_cls = GobjaverseDataset
# elif name == 'mvimgnet':
# from .mvimgnet import MVImgNetDataset
# dataset_cls = MVImgNetDataset
else:
raise NotImplementedError(f"Dataset {name} not implemented")
return partial(
dataset_cls,
root_dirs=subset_config['root_dirs'],
meta_path=subset_config['meta_path'][split],
)
def __len__(self):
return sum(self.virtual_lens)
def __getitem__(self, idx):
subset_idx = 0
virtual_idx = idx
while virtual_idx >= self.virtual_lens[subset_idx]:
virtual_idx -= self.virtual_lens[subset_idx]
subset_idx += 1
real_idx = virtual_idx % len(self.subsets[subset_idx])
return self.subsets[subset_idx][real_idx]
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