use cifar10 as dataset and update dataloader format

.gitignore

@@ -159,3 +159,6 @@ coverage.xml
 # ignore testmon and coverage files
 .coverage
 .testmondata*
+
+# ignore data files
+datasets

dit/models/utils/ckpt_utils.py

@@ -0,0 +1,21 @@
+import logging
+
+import torch.distributed as dist
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format="[\033[34m%(asctime)s\033[0m] %(message)s",
+            datefmt="%Y-%m-%d %H:%M:%S",
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")],
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger

dit/models/utils/data_utils.py

@@ -0,0 +1,110 @@
+import random
+from typing import Iterator, Optional
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.distributed import ProcessGroup
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.utils.data import DataLoader, Dataset, DistributedSampler
+from torch.utils.data.distributed import DistributedSampler
+
+
+class StatefulDistributedSampler(DistributedSampler):
+    def __init__(
+        self,
+        dataset: Dataset,
+        num_replicas: Optional[int] = None,
+        rank: Optional[int] = None,
+        shuffle: bool = True,
+        seed: int = 0,
+        drop_last: bool = False,
+    ) -> None:
+        super().__init__(dataset, num_replicas, rank, shuffle, seed, drop_last)
+        self.start_index: int = 0
+
+    def __iter__(self) -> Iterator:
+        iterator = super().__iter__()
+        indices = list(iterator)
+        indices = indices[self.start_index :]
+        return iter(indices)
+
+    def __len__(self) -> int:
+        return self.num_samples - self.start_index
+
+    def set_start_index(self, start_index: int) -> None:
+        self.start_index = start_index
+
+
+def prepare_dataloader(
+    dataset,
+    batch_size,
+    shuffle=False,
+    seed=1024,
+    drop_last=False,
+    pin_memory=False,
+    num_workers=0,
+    process_group: Optional[ProcessGroup] = None,
+    **kwargs,
+):
+    r"""
+    Prepare a dataloader for distributed training. The dataloader will be wrapped by
+    `torch.utils.data.DataLoader` and `StatefulDistributedSampler`.
+
+
+    Args:
+        dataset (`torch.utils.data.Dataset`): The dataset to be loaded.
+        shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False.
+        seed (int, optional): Random worker seed for sampling, defaults to 1024.
+        add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True.
+        drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
+            is not divisible by the batch size. If False and the size of dataset is not divisible by
+            the batch size, then the last batch will be smaller, defaults to False.
+        pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False.
+        num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0.
+        kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in
+                `DataLoader <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataloader.html#DataLoader>`_.
+
+    Returns:
+        :class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing.
+    """
+    _kwargs = kwargs.copy()
+    process_group = process_group or _get_default_group()
+    sampler = StatefulDistributedSampler(
+        dataset, num_replicas=process_group.size(), rank=process_group.rank(), shuffle=shuffle
+    )
+
+    # Deterministic dataloader
+    def seed_worker(worker_id):
+        worker_seed = seed
+        np.random.seed(worker_seed)
+        torch.manual_seed(worker_seed)
+        random.seed(worker_seed)
+
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        worker_init_fn=seed_worker,
+        drop_last=drop_last,
+        pin_memory=pin_memory,
+        num_workers=num_workers,
+        **_kwargs,
+    )
+
+
+def center_crop_arr(pil_image, image_size):
+    """
+    Center cropping implementation from ADM.
+    https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
+    """
+    while min(*pil_image.size) >= 2 * image_size:
+        pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size), resample=Image.BOX)
+
+    scale = image_size / min(*pil_image.size)
+    pil_image = pil_image.resize(tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC)
+
+    arr = np.array(pil_image)
+    crop_y = (arr.shape[0] - image_size) // 2
+    crop_x = (arr.shape[1] - image_size) // 2
+    return Image.fromarray(arr[crop_y : crop_y + image_size, crop_x : crop_x + image_size])