305 lines
10 KiB
Python
305 lines
10 KiB
Python
"""DataLoader module for PinaDataset."""
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import itertools
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from functools import partial
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import torch
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from torch.utils.data.sampler import SequentialSampler
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class DummyDataloader:
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"""
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DataLoader that returns the entire dataset in a single batch.
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"""
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def __init__(self, dataset, device=None):
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"""
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Prepare a dataloader object that returns the entire dataset in a single
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batch. Depending on the number of GPUs, the dataset is managed
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as follows:
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- **Distributed Environment** (multiple GPUs): Divides dataset across
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processes using the rank and world size. Fetches only portion of
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data corresponding to the current process.
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- **Non-Distributed Environment** (single GPU): Fetches the entire
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dataset.
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:param PinaDataset dataset: The dataset object to be processed.
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.. note::
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This dataloader is used when the batch size is ``None``.
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"""
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# Handle distributed environment
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if PinaSampler.is_distributed():
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# Get rank and world size
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rank = torch.distributed.get_rank()
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world_size = torch.distributed.get_world_size()
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# Ensure dataset is large enough
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if len(dataset) < world_size:
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raise RuntimeError(
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"Dimension of the dataset smaller than world size."
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" Increase the size of the partition or use a single GPU"
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)
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# Split dataset among processes
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idx, i = [], rank
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while i < len(dataset):
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idx.append(i)
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i += world_size
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else:
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idx = [i for i in range(len(dataset))]
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self.dataset = dataset.getitem_from_list(idx)
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self.device = device
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self.dataset = (
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{k: v.to(self.device) for k, v in self.dataset.items()}
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if self.device
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else self.dataset
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)
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def __iter__(self):
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"""
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Iterate over the dataloader.
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"""
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return self
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def __len__(self):
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"""
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Return the length of the dataloader, which is always 1.
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:return: The length of the dataloader.
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:rtype: int
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"""
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return 1
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def __next__(self):
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"""
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Return the entire dataset as a single batch.
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:return: The entire dataset.
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:rtype: dict
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"""
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return self.dataset
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class PinaSampler:
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"""
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This class is used to create the sampler instance based on the shuffle
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parameter and the environment in which the code is running.
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"""
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def __new__(cls, dataset, shuffle=True):
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"""
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Instantiate and initialize the sampler.
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:param PinaDataset dataset: The dataset from which to sample.
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:return: The sampler instance.
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:rtype: :class:`torch.utils.data.Sampler`
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"""
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if cls.is_distributed():
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sampler = DistributedSampler(dataset, shuffle=shuffle)
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else:
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if shuffle:
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sampler = torch.utils.data.RandomSampler(dataset)
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else:
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sampler = SequentialSampler(dataset)
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return sampler
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@staticmethod
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def is_distributed():
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"""
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Check if the sampler is distributed.
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:return: True if the sampler is distributed, False otherwise.
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:rtype: bool
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"""
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return (
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torch.distributed.is_available()
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and torch.distributed.is_initialized()
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)
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def _collect_items(batch):
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"""
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Helper function to collect items from a batch of graph data samples.
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:param batch: List of graph data samples.
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"""
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to_return = {name: [] for name in batch[0].keys()}
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for sample in batch:
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for k, v in sample.items():
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to_return[k].append(v)
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return to_return
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def collate_fn_custom(batch, dataset):
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"""
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Override the default collate function to handle datasets without automatic
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batching.
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:param batch: List of indices from the dataset.
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:param dataset: The PinaDataset instance (must be provided).
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"""
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return dataset.getitem_from_list(batch)
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def collate_fn_default(batch, stack_fn):
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"""
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Default collate function that simply returns the batch as is.
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:param batch: List of data samples.
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"""
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to_return = _collect_items(batch)
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return {k: stack_fn[k](v) for k, v in to_return.items()}
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class PinaDataLoader:
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"""
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Custom DataLoader for PinaDataset.
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"""
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def __init__(
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self,
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dataset_dict,
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batch_size,
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num_workers=0,
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shuffle=False,
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common_batch_size=True,
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separate_conditions=False,
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device=None,
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):
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self.dataset_dict = dataset_dict
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self.batch_size = batch_size
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self.num_workers = num_workers
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self.shuffle = shuffle
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self.separate_conditions = separate_conditions
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self.device = device
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# Batch size None means we want to load the entire dataset in a single
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# batch
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if batch_size is None:
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batch_size_per_dataset = {
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split: None for split in dataset_dict.keys()
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}
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else:
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# Compute batch size per dataset
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if common_batch_size: # all datasets have the same batch size
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# (the sum of the batch sizes is equal to
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# n_conditions * batch_size)
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batch_size_per_dataset = {
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split: batch_size for split in dataset_dict.keys()
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}
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else: # batch size proportional to dataset size (the sum of the
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# batch sizes is equal to the specified batch size)
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batch_size_per_dataset = self._compute_batch_size()
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# Creaete a dataloader per dataset
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self.dataloaders = {
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split: self._create_dataloader(
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dataset, batch_size_per_dataset[split]
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)
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for split, dataset in dataset_dict.items()
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}
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def _compute_batch_size(self):
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"""
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Compute an appropriate batch size for the given dataset.
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"""
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# Compute number of elements per dataset
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elements_per_dataset = {
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dataset_name: len(dataset)
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for dataset_name, dataset in self.dataset_dict.items()
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}
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# Compute the total number of elements
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total_elements = sum(el for el in elements_per_dataset.values())
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# Compute the portion of each dataset
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portion_per_dataset = {
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name: el / total_elements
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for name, el in elements_per_dataset.items()
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}
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# Compute batch size per dataset. Ensure at least 1 element per
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# dataset.
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batch_size_per_dataset = {
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name: max(1, int(portion * self.batch_size))
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for name, portion in portion_per_dataset.items()
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}
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# Adjust batch sizes to match the specified total batch size
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tot_el_per_batch = sum(el for el in batch_size_per_dataset.values())
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if self.batch_size > tot_el_per_batch:
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difference = self.batch_size - tot_el_per_batch
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while difference > 0:
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for k, v in batch_size_per_dataset.items():
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if difference == 0:
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break
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if v > 1:
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batch_size_per_dataset[k] += 1
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difference -= 1
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if self.batch_size < tot_el_per_batch:
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difference = tot_el_per_batch - self.batch_size
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while difference > 0:
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for k, v in batch_size_per_dataset.items():
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if difference == 0:
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break
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if v > 1:
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batch_size_per_dataset[k] -= 1
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difference -= 1
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return batch_size_per_dataset
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def _create_dataloader(self, dataset, batch_size):
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"""
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Create the dataloader for the given dataset.
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"""
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# If batch size is None, use DummyDataloader
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if batch_size is None or batch_size >= len(dataset):
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return DummyDataloader(dataset, device=self.device)
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# Determine the appropriate collate function
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if not dataset.automatic_batching:
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collate_fn = partial(collate_fn_custom, dataset=dataset)
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else:
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collate_fn = partial(collate_fn_default, stack_fn=dataset.stack_fn)
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# Create and return the dataloader
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return DataLoader(
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dataset,
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batch_size=batch_size,
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collate_fn=collate_fn,
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num_workers=self.num_workers,
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sampler=PinaSampler(dataset, shuffle=self.shuffle),
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)
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def __len__(self):
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"""
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Return the length of the dataloader.
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:return: The length of the dataloader.
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:rtype: int
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"""
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# If separate conditions, return sum of lengths of all dataloaders
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# else, return max length among dataloaders
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if self.separate_conditions:
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return sum(len(dl) for dl in self.dataloaders.values())
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return max(len(dl) for dl in self.dataloaders.values())
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def __iter__(self):
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"""
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Iterate over the dataloader.
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:return: Yields batches from the dataloader.
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:rtype: dict
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"""
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if self.separate_conditions:
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for split, dl in self.dataloaders.items():
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for batch in dl:
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yield {split: batch}
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return
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iterators = {
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split: itertools.cycle(dl) for split, dl in self.dataloaders.items()
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}
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for _ in range(len(self)):
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batch_dict = {}
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for split, it in iterators.items():
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# Iterate through each dataloader and get the next batch
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batch = next(it, None)
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# Check if batch is None (in case of uneven lengths)
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if batch is None:
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return
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batch_dict[split] = batch
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yield batch_dict
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