mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
309 lines
10 KiB
Python
309 lines
10 KiB
Python
import math
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from contextlib import suppress
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from functools import partial
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from typing import Callable, List, Optional, Tuple, Union
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import torch
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from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from .loader import _worker_init, adapt_to_chs
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from .naflex_dataset import VariableSeqMapWrapper, NaFlexCollator
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from .naflex_random_erasing import PatchRandomErasing
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from .transforms_factory import create_transform
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class NaFlexPrefetchLoader:
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"""Data prefetcher for NaFlex format which normalizes patches."""
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def __init__(
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self,
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loader: torch.utils.data.DataLoader,
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mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
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std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
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channels: int = 3,
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device: torch.device = torch.device('cuda'),
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img_dtype: Optional[torch.dtype] = None,
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re_prob: float = 0.,
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re_mode: str = 'const',
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re_count: int = 1,
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re_num_splits: int = 0,
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):
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self.loader = loader
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self.device = device
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self.img_dtype = img_dtype or torch.float32
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# Create mean/std tensors for normalization (will be applied to patches)
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mean = adapt_to_chs(mean, channels)
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std = adapt_to_chs(std, channels)
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normalization_shape = (1, 1, channels)
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self.channels = channels
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self.mean = torch.tensor(
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[x * 255 for x in mean], device=device, dtype=self.img_dtype).view(normalization_shape)
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self.std = torch.tensor(
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[x * 255 for x in std], device=device, dtype=self.img_dtype).view(normalization_shape)
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if re_prob > 0.:
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self.random_erasing = PatchRandomErasing(
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erase_prob=re_prob,
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mode=re_mode,
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max_count=re_count,
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num_splits=re_num_splits,
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device=device,
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)
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else:
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self.random_erasing = None
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# Check for CUDA/NPU availability
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self.is_cuda = device.type == 'cuda' and torch.cuda.is_available()
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self.is_npu = device.type == 'npu' and torch.npu.is_available()
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def __iter__(self):
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first = True
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if self.is_cuda:
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stream = torch.cuda.Stream()
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stream_context = partial(torch.cuda.stream, stream=stream)
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elif self.is_npu:
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stream = torch.npu.Stream()
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stream_context = partial(torch.npu.stream, stream=stream)
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else:
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stream = None
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stream_context = suppress
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for next_input_dict, next_target in self.loader:
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with stream_context():
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# Move all tensors in input_dict to device
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for k, v in next_input_dict.items():
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if isinstance(v, torch.Tensor):
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dtype = self.img_dtype if k == 'patches' else None
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next_input_dict[k] = next_input_dict[k].to(
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device=self.device,
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non_blocking=True,
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dtype=dtype,
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)
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next_target = next_target.to(device=self.device, non_blocking=True)
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# Normalize patch values (assuming patches are in format [B, N, P*P*C])
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batch_size, num_patches, patch_pixels = next_input_dict['patches'].shape
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# To [B*N, P*P, C] for normalization and erasing
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patches = next_input_dict['patches'].view(batch_size, num_patches, -1, self.channels)
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patches = patches.sub(self.mean).div(self.std)
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if self.random_erasing is not None:
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patches = self.random_erasing(
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patches,
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patch_coord=next_input_dict['patch_coord'],
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patch_valid=next_input_dict.get('patch_valid', None),
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)
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# Reshape back
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next_input_dict['patches'] = patches.reshape(batch_size, num_patches, patch_pixels)
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if not first:
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yield input_dict, target
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else:
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first = False
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if stream is not None:
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if self.is_cuda:
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torch.cuda.current_stream().wait_stream(stream)
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elif self.is_npu:
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torch.npu.current_stream().wait_stream(stream)
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input_dict = next_input_dict
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target = next_target
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yield input_dict, target
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def __len__(self):
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return len(self.loader)
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@property
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def sampler(self):
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return self.loader.sampler
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@property
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def dataset(self):
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return self.loader.dataset
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def create_naflex_loader(
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dataset,
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patch_size: Union[Tuple[int, int], int] = 16,
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train_seq_lens: List[int] = (128, 256, 576, 784, 1024), # Training sequence lengths
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max_seq_len: int = 576, # Fixed sequence length for validation
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batch_size: int = 32, # Used for max_seq_len and max(train_seq_lens)
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is_training: bool = False,
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mixup_fn: Optional[Callable] = None,
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no_aug: bool = False,
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re_prob: float = 0.,
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re_mode: str = 'const',
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re_count: int = 1,
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re_split: bool = False,
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train_crop_mode: Optional[str] = None,
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scale: Optional[Tuple[float, float]] = None,
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ratio: Optional[Tuple[float, float]] = None,
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hflip: float = 0.5,
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vflip: float = 0.,
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color_jitter: float = 0.4,
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color_jitter_prob: Optional[float] = None,
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grayscale_prob: float = 0.,
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gaussian_blur_prob: float = 0.,
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auto_augment: Optional[str] = None,
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num_aug_repeats: int = 0,
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num_aug_splits: int = 0,
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interpolation: str = 'bilinear',
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mean: Tuple[float, ...] = IMAGENET_DEFAULT_MEAN,
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std: Tuple[float, ...] = IMAGENET_DEFAULT_STD,
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crop_pct: Optional[float] = None,
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crop_mode: Optional[str] = None,
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crop_border_pixels: Optional[int] = None,
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num_workers: int = 4,
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distributed: bool = False,
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rank: int = 0,
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world_size: int = 1,
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seed: int = 42,
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epoch: int = 0,
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use_prefetcher: bool = True,
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pin_memory: bool = True,
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img_dtype: torch.dtype = torch.float32,
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device: Union[str, torch.device] = torch.device('cuda'),
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persistent_workers: bool = True,
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worker_seeding: str = 'all',
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):
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"""Create a data loader with dynamic sequence length sampling for training.
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"""
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if is_training:
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# For training, use the dynamic sequence length mechanism
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assert num_aug_repeats == 0, 'Augmentation repeats not currently supported in NaFlex loader'
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transform_factory = partial(
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create_transform,
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is_training=True,
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no_aug=no_aug,
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train_crop_mode=train_crop_mode,
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scale=scale,
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ratio=ratio,
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hflip=hflip,
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vflip=vflip,
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color_jitter=color_jitter,
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color_jitter_prob=color_jitter_prob,
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grayscale_prob=grayscale_prob,
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gaussian_blur_prob=gaussian_blur_prob,
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auto_augment=auto_augment,
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interpolation=interpolation,
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mean=mean,
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std=std,
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crop_pct=crop_pct,
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crop_mode=crop_mode,
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crop_border_pixels=crop_border_pixels,
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re_prob=re_prob,
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re_mode=re_mode,
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re_count=re_count,
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use_prefetcher=use_prefetcher,
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naflex=True,
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)
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max_train_seq_len = max(train_seq_lens)
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max_tokens_per_batch = batch_size * max_train_seq_len
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if isinstance(dataset, torch.utils.data.IterableDataset):
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assert False, "IterableDataset Wrapper is a WIP"
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naflex_dataset = VariableSeqMapWrapper(
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dataset,
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transform_factory=transform_factory,
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patch_size=patch_size,
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seq_lens=train_seq_lens,
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max_tokens_per_batch=max_tokens_per_batch,
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mixup_fn=mixup_fn,
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seed=seed,
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distributed=distributed,
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rank=rank,
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world_size=world_size,
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shuffle=True,
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epoch=epoch,
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)
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# NOTE: Collation is handled by the dataset wrapper for training
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# Create the collator (handles fixed-size collation)
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# collate_fn = NaFlexCollator(
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# max_seq_len=max(seq_lens) + 1, # +1 for class token
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# )
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loader = torch.utils.data.DataLoader(
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naflex_dataset,
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batch_size=None,
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shuffle=False,
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num_workers=num_workers,
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sampler=None,
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#collate_fn=collate_fn,
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pin_memory=pin_memory,
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worker_init_fn=partial(_worker_init, worker_seeding=worker_seeding),
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persistent_workers=persistent_workers
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)
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if use_prefetcher:
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loader = NaFlexPrefetchLoader(
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loader,
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mean=mean,
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std=std,
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img_dtype=img_dtype,
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device=device,
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re_prob=re_prob,
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re_mode=re_mode,
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re_count=re_count,
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)
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else:
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# For validation, use fixed sequence length (unchanged)
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dataset.transform = create_transform(
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is_training=False,
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interpolation=interpolation,
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mean=mean,
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std=std,
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# FIXME add crop args when sequence transforms support crop modes
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use_prefetcher=use_prefetcher,
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naflex=True,
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patch_size=patch_size,
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max_seq_len=max_seq_len,
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patchify=True,
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)
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# Create the collator
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collate_fn = NaFlexCollator(max_seq_len=max_seq_len)
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# Handle distributed training
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sampler = None
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if distributed and not isinstance(dataset, torch.utils.data.IterableDataset):
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# For validation, use OrderedDistributedSampler
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from timm.data.distributed_sampler import OrderedDistributedSampler
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sampler = OrderedDistributedSampler(dataset)
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loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=num_workers,
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sampler=sampler,
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collate_fn=collate_fn,
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pin_memory=pin_memory,
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drop_last=False,
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)
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if use_prefetcher:
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loader = NaFlexPrefetchLoader(
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loader,
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mean=mean,
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std=std,
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img_dtype=img_dtype,
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device=device,
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)
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return loader
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