lib/pymafx/utils/data_loader.py

from __future__ import division

import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import Sampler


class RandomSampler(Sampler):
    def __init__(self, data_source, checkpoint):
        self.data_source = data_source
        if checkpoint is not None and checkpoint['dataset_perm'] is not None:
            self.dataset_perm = checkpoint['dataset_perm']
            self.perm = self.dataset_perm[checkpoint['batch_size'] * checkpoint['batch_idx']:]
        else:
            self.dataset_perm = torch.randperm(len(self.data_source)).tolist()
            self.perm = torch.randperm(len(self.data_source)).tolist()

    def __iter__(self):
        return iter(self.perm)

    def __len__(self):
        return len(self.perm)


class SequentialSampler(Sampler):
    def __init__(self, data_source, checkpoint):
        self.data_source = data_source
        if checkpoint is not None and checkpoint['dataset_perm'] is not None:
            self.dataset_perm = checkpoint['dataset_perm']
            self.perm = self.dataset_perm[checkpoint['batch_size'] * checkpoint['batch_idx']:]
        else:
            self.dataset_perm = list(range(len(self.data_source)))
            self.perm = self.dataset_perm

    def __iter__(self):
        return iter(self.perm)

    def __len__(self):
        return len(self.perm)


class CheckpointDataLoader(DataLoader):
    """
    Extends torch.utils.data.DataLoader to handle resuming training from an arbitrary point within an epoch.
    """
    def __init__(
        self,
        dataset,
        checkpoint=None,
        batch_size=1,
        shuffle=False,
        num_workers=0,
        pin_memory=False,
        drop_last=True,
        timeout=0,
        worker_init_fn=None
    ):

        if shuffle:
            sampler = RandomSampler(dataset, checkpoint)
        else:
            sampler = SequentialSampler(dataset, checkpoint)
        if checkpoint is not None:
            self.checkpoint_batch_idx = checkpoint['batch_idx']
        else:
            self.checkpoint_batch_idx = 0

        super(CheckpointDataLoader, self).__init__(
            dataset,
            sampler=sampler,
            shuffle=False,
            batch_size=batch_size,
            num_workers=num_workers,
            drop_last=drop_last,
            pin_memory=pin_memory,
            timeout=timeout,
            worker_init_fn=None
        )