pytorch-image-models/timm/data/loader.py

""" Loader Factory, Fast Collate, CUDA Prefetcher

Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf

Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from contextlib import suppress
from functools import partial
from itertools import repeat
from typing import Callable, Optional, Tuple, Union

import torch
import torch.utils.data
import numpy as np

from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .dataset import IterableImageDataset, ImageDataset
from .distributed_sampler import OrderedDistributedSampler, RepeatAugSampler
from .random_erasing import RandomErasing
from .mixup import FastCollateMixup
from .transforms_factory import create_transform

_logger = logging.getLogger(__name__)


def fast_collate(batch):
    """ A fast collation function optimized for uint8 images (np array or torch) and int64 targets (labels)"""
    assert isinstance(batch[0], tuple)
    batch_size = len(batch)
    if isinstance(batch[0][0], tuple):
        # This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position
        # such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position
        inner_tuple_size = len(batch[0][0])
        flattened_batch_size = batch_size * inner_tuple_size
        targets = torch.zeros(flattened_batch_size, dtype=torch.int64)
        tensor = torch.zeros((flattened_batch_size, *batch[0][0][0].shape), dtype=torch.uint8)
        for i in range(batch_size):
            assert len(batch[i][0]) == inner_tuple_size  # all input tensor tuples must be same length
            for j in range(inner_tuple_size):
                targets[i + j * batch_size] = batch[i][1]
                tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j])
        return tensor, targets
    elif isinstance(batch[0][0], np.ndarray):
        targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
        assert len(targets) == batch_size
        tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
        for i in range(batch_size):
            tensor[i] += torch.from_numpy(batch[i][0])
        return tensor, targets
    elif isinstance(batch[0][0], torch.Tensor):
        targets = torch.tensor([b[1] for b in batch], dtype=torch.int64)
        assert len(targets) == batch_size
        tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)
        for i in range(batch_size):
            tensor[i].copy_(batch[i][0])
        return tensor, targets
    else:
        assert False


def adapt_to_chs(x, n):
    if not isinstance(x, (tuple, list)):
        x = tuple(repeat(x, n))
    elif len(x) != n:
        x_mean = np.mean(x).item()
        x = (x_mean,) * n
        _logger.warning(f'Pretrained mean/std different shape than model, using avg value {x}.')
    else:
        assert len(x) == n, 'normalization stats must match image channels'
    return x


class PrefetchLoader:

    def __init__(
            self,
            loader,
            mean=IMAGENET_DEFAULT_MEAN,
            std=IMAGENET_DEFAULT_STD,
            channels=3,
            device=torch.device('cuda'),
            img_dtype=torch.float32,
            fp16=False,
            re_prob=0.,
            re_mode='const',
            re_count=1,
            re_num_splits=0):

        mean = adapt_to_chs(mean, channels)
        std = adapt_to_chs(std, channels)
        normalization_shape = (1, channels, 1, 1)

        self.loader = loader
        self.device = device
        if fp16:
            # fp16 arg is deprecated, but will override dtype arg if set for bwd compat
            img_dtype = torch.float16
        self.img_dtype = img_dtype
        self.mean = torch.tensor(
            [x * 255 for x in mean], device=device, dtype=img_dtype).view(normalization_shape)
        self.std = torch.tensor(
            [x * 255 for x in std], device=device, dtype=img_dtype).view(normalization_shape)
        if re_prob > 0.:
            self.random_erasing = RandomErasing(
                probability=re_prob,
                mode=re_mode,
                max_count=re_count,
                num_splits=re_num_splits,
                device=device,
            )
        else:
            self.random_erasing = None
        self.is_cuda = device.type == 'cuda' and torch.cuda.is_available()
        self.is_npu = device.type == 'npu' and torch.npu.is_available()

    def __iter__(self):
        first = True
        if self.is_cuda:
            stream = torch.cuda.Stream()
            stream_context = partial(torch.cuda.stream, stream=stream)
        elif self.is_npu:
            stream = torch.npu.Stream()
            stream_context = partial(torch.npu.stream, stream=stream)
        else:
            stream = None
            stream_context = suppress

        for next_input, next_target in self.loader:

            with stream_context():
                next_input = next_input.to(device=self.device, non_blocking=True)
                next_target = next_target.to(device=self.device, non_blocking=True)
                next_input = next_input.to(self.img_dtype).sub_(self.mean).div_(self.std)
                if self.random_erasing is not None:
                    next_input = self.random_erasing(next_input)

            if not first:
                yield input, target
            else:
                first = False

            if stream is not None:
                if self.is_cuda:
                    torch.cuda.current_stream().wait_stream(stream)
                elif self.is_npu:
                    torch.npu.current_stream().wait_stream(stream)

            input = next_input
            target = next_target

        yield input, target

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

    @property
    def sampler(self):
        return self.loader.sampler

    @property
    def dataset(self):
        return self.loader.dataset

    @property
    def mixup_enabled(self):
        if isinstance(self.loader.collate_fn, FastCollateMixup):
            return self.loader.collate_fn.mixup_enabled
        else:
            return False

    @mixup_enabled.setter
    def mixup_enabled(self, x):
        if isinstance(self.loader.collate_fn, FastCollateMixup):
            self.loader.collate_fn.mixup_enabled = x


def _worker_init(worker_id, worker_seeding='all'):
    worker_info = torch.utils.data.get_worker_info()
    assert worker_info.id == worker_id
    if isinstance(worker_seeding, Callable):
        seed = worker_seeding(worker_info)
        random.seed(seed)
        torch.manual_seed(seed)
        np.random.seed(seed % (2 ** 32 - 1))
    else:
        assert worker_seeding in ('all', 'part')
        # random / torch seed already called in dataloader iter class w/ worker_info.seed
        # to reproduce some old results (same seed + hparam combo), partial seeding is required (skip numpy re-seed)
        if worker_seeding == 'all':
            np.random.seed(worker_info.seed % (2 ** 32 - 1))


def create_loader(
        dataset: Union[ImageDataset, IterableImageDataset],
        input_size: Union[int, Tuple[int, int], Tuple[int, int, int]],
        batch_size: int,
        is_training: bool = False,
        no_aug: bool = False,
        re_prob: float = 0.,
        re_mode: str = 'const',
        re_count: int = 1,
        re_split: bool = False,
        train_crop_mode: Optional[str] = None,
        scale: Optional[Tuple[float, float]] = None,
        ratio: Optional[Tuple[float, float]] = None,
        hflip: float = 0.5,
        vflip: float = 0.,
        color_jitter: float = 0.4,
        color_jitter_prob: Optional[float] = None,
        grayscale_prob: float = 0.,
        gaussian_blur_prob: float = 0.,
        auto_augment: Optional[str] = None,
        num_aug_repeats: int = 0,
        num_aug_splits: int = 0,
        interpolation: str = 'bilinear',
        mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
        std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
        num_workers: int = 1,
        distributed: bool = False,
        crop_pct: Optional[float] = None,
        crop_mode: Optional[str] = None,
        crop_border_pixels: Optional[int] = None,
        collate_fn: Optional[Callable] = None,
        pin_memory: bool = False,
        fp16: bool = False,  # deprecated, use img_dtype
        img_dtype: torch.dtype = torch.float32,
        device: torch.device = torch.device('cuda'),
        use_prefetcher: bool = True,
        use_multi_epochs_loader: bool = False,
        persistent_workers: bool = True,
        worker_seeding: str = 'all',
        tf_preprocessing: bool = False,
):
    """

    Args:
        dataset: The image dataset to load.
        input_size: Target input size (channels, height, width) tuple or size scalar.
        batch_size: Number of samples in a batch.
        is_training: Return training (random) transforms.
        no_aug: Disable augmentation for training (useful for debug).
        re_prob: Random erasing probability.
        re_mode: Random erasing fill mode.
        re_count: Number of random erasing regions.
        re_split: Control split of random erasing across batch size.
        scale: Random resize scale range (crop area, < 1.0 => zoom in).
        ratio: Random aspect ratio range (crop ratio for RRC, ratio adjustment factor for RKR).
        hflip: Horizontal flip probability.
        vflip: Vertical flip probability.
        color_jitter: Random color jitter component factors (brightness, contrast, saturation, hue).
            Scalar is applied as (scalar,) * 3 (no hue).
        color_jitter_prob: Apply color jitter with this probability if not None (for SimlCLR-like aug
        grayscale_prob: Probability of converting image to grayscale (for SimCLR-like aug).
        gaussian_blur_prob: Probability of applying gaussian blur (for SimCLR-like aug).
        auto_augment: Auto augment configuration string (see auto_augment.py).
        num_aug_repeats: Enable special sampler to repeat same augmentation across distributed GPUs.
        num_aug_splits: Enable mode where augmentations can be split across the batch.
        interpolation: Image interpolation mode.
        mean: Image normalization mean.
        std: Image normalization standard deviation.
        num_workers: Num worker processes per DataLoader.
        distributed: Enable dataloading for distributed training.
        crop_pct: Inference crop percentage (output size / resize size).
        crop_mode: Inference crop mode. One of ['squash', 'border', 'center']. Defaults to 'center' when None.
        crop_border_pixels: Inference crop border of specified # pixels around edge of original image.
        collate_fn: Override default collate_fn.
        pin_memory: Pin memory for device transfer.
        fp16: Deprecated argument for half-precision input dtype. Use img_dtype.
        img_dtype: Data type for input image.
        device: Device to transfer inputs and targets to.
        use_prefetcher: Use efficient pre-fetcher to load samples onto device.
        use_multi_epochs_loader:
        persistent_workers: Enable persistent worker processes.
        worker_seeding: Control worker random seeding at init.
        tf_preprocessing: Use TF 1.0 inference preprocessing for testing model ports.

    Returns:
        DataLoader
    """
    re_num_splits = 0
    if re_split:
        # apply RE to second half of batch if no aug split otherwise line up with aug split
        re_num_splits = num_aug_splits or 2
    dataset.transform = create_transform(
        input_size,
        is_training=is_training,
        no_aug=no_aug,
        train_crop_mode=train_crop_mode,
        scale=scale,
        ratio=ratio,
        hflip=hflip,
        vflip=vflip,
        color_jitter=color_jitter,
        color_jitter_prob=color_jitter_prob,
        grayscale_prob=grayscale_prob,
        gaussian_blur_prob=gaussian_blur_prob,
        auto_augment=auto_augment,
        interpolation=interpolation,
        mean=mean,
        std=std,
        crop_pct=crop_pct,
        crop_mode=crop_mode,
        crop_border_pixels=crop_border_pixels,
        re_prob=re_prob,
        re_mode=re_mode,
        re_count=re_count,
        re_num_splits=re_num_splits,
        tf_preprocessing=tf_preprocessing,
        use_prefetcher=use_prefetcher,
        separate=num_aug_splits > 0,
    )

    if isinstance(dataset, IterableImageDataset):
        # give Iterable datasets early knowledge of num_workers so that sample estimates
        # are correct before worker processes are launched
        dataset.set_loader_cfg(num_workers=num_workers)

    sampler = None
    if distributed and not isinstance(dataset, torch.utils.data.IterableDataset):
        if is_training:
            if num_aug_repeats:
                sampler = RepeatAugSampler(dataset, num_repeats=num_aug_repeats)
            else:
                sampler = torch.utils.data.distributed.DistributedSampler(dataset)
        else:
            # This will add extra duplicate entries to result in equal num
            # of samples per-process, will slightly alter validation results
            sampler = OrderedDistributedSampler(dataset)
    else:
        assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"

    if collate_fn is None:
        collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate

    loader_class = torch.utils.data.DataLoader
    if use_multi_epochs_loader:
        loader_class = MultiEpochsDataLoader

    loader_args = dict(
        batch_size=batch_size,
        shuffle=not isinstance(dataset, torch.utils.data.IterableDataset) and sampler is None and is_training,
        num_workers=num_workers,
        sampler=sampler,
        collate_fn=collate_fn,
        pin_memory=pin_memory,
        drop_last=is_training,
        worker_init_fn=partial(_worker_init, worker_seeding=worker_seeding),
        persistent_workers=persistent_workers
    )
    try:
        loader = loader_class(dataset, **loader_args)
    except TypeError as e:
        loader_args.pop('persistent_workers')  # only in Pytorch 1.7+
        loader = loader_class(dataset, **loader_args)
    if use_prefetcher:
        prefetch_re_prob = re_prob if is_training and not no_aug else 0.
        loader = PrefetchLoader(
            loader,
            mean=mean,
            std=std,
            channels=input_size[0],
            device=device,
            fp16=fp16,  # deprecated, use img_dtype
            img_dtype=img_dtype,
            re_prob=prefetch_re_prob,
            re_mode=re_mode,
            re_count=re_count,
            re_num_splits=re_num_splits
        )

    return loader


class MultiEpochsDataLoader(torch.utils.data.DataLoader):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._DataLoader__initialized = False
        if self.batch_sampler is None:
            self.sampler = _RepeatSampler(self.sampler)
        else:
            self.batch_sampler = _RepeatSampler(self.batch_sampler)
        self._DataLoader__initialized = True
        self.iterator = super().__iter__()

    def __len__(self):
        return len(self.sampler) if self.batch_sampler is None else len(self.batch_sampler.sampler)

    def __iter__(self):
        for i in range(len(self)):
            yield next(self.iterator)


class _RepeatSampler(object):
    """ Sampler that repeats forever.

    Args:
        sampler (Sampler)
    """

    def __init__(self, sampler):
        self.sampler = sampler

    def __iter__(self):
        while True:
            yield from iter(self.sampler)