mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
342 lines
12 KiB
Python
342 lines
12 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
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from .naflex_dataset import VariableSeqMapWrapper
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from .transforms_factory import create_transform
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class NaFlexCollator:
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"""Custom collator for batching NaFlex-style variable-resolution images."""
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def __init__(
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self,
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patch_size=16,
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max_seq_len=None,
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):
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self.patch_size = patch_size
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self.max_seq_len = max_seq_len or 576 # Default ViT-B/16 sequence length (577 = 24*24)
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def __call__(self, batch):
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"""
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Args:
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batch: List of tuples (patch_dict, target)
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Returns:
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A tuple of (input_dict, targets) where input_dict contains:
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- patches: Padded tensor of patches
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- patch_coord: Coordinates for each patch (y, x)
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- patch_valid: Valid indicators
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"""
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assert isinstance(batch[0], tuple)
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batch_size = len(batch)
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# FIXME
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# get seq len from sampler schedule
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# resize to final size based on seq_len and patchify
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# Extract targets
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targets = torch.tensor([item[1] for item in batch], dtype=torch.int64)
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# Get patch dictionaries
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patch_dicts = [item[0] for item in batch]
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# If we have a maximum sequence length constraint, ensure we don't exceed it
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if self.max_seq_len is not None:
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max_patches = self.max_seq_len
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else:
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# Find the maximum number of patches in this batch
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max_patches = max(item['patches'].shape[0] for item in patch_dicts)
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# Get patch dimensionality
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patch_dim = patch_dicts[0]['patches'].shape[1]
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# Prepare tensors for the batch
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patches = torch.zeros((batch_size, max_patches, patch_dim), dtype=torch.float32)
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patch_coord = torch.zeros((batch_size, max_patches, 2), dtype=torch.int64) # [B, N, 2] for (y, x)
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patch_valid = torch.zeros((batch_size, max_patches), dtype=torch.bool)
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# Fill in the tensors
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for i, patch_dict in enumerate(patch_dicts):
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num_patches = min(patch_dict['patches'].shape[0], max_patches)
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patches[i, :num_patches] = patch_dict['patches'][:num_patches]
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patch_coord[i, :num_patches] = patch_dict['patch_coord'][:num_patches]
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patch_valid[i, :num_patches] = patch_dict['patch_valid'][:num_patches]
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return {
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'patches': patches,
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'patch_coord': patch_coord,
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'patch_valid': patch_valid,
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'seq_len': max_patches,
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}, targets
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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,
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mean=(0.485, 0.456, 0.406),
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std=(0.229, 0.224, 0.225),
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img_dtype=torch.float32,
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device=torch.device('cuda')
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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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self.mean = torch.tensor([x * 255 for x in mean], device=device, dtype=self.img_dtype).view(1, 1, 3)
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self.std = torch.tensor([x * 255 for x in std], device=device, dtype=self.img_dtype).view(1, 1, 3)
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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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patches = next_input_dict['patches'].view(batch_size, -1, 3) # to [B*N, P*P, C] for normalization
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patches = patches.sub(self.mean).div(self.std)
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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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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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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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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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# patch_size=patch_size,
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# max_seq_len=max(seq_lens) + 1, # +1 for class token
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# use_prefetcher=use_prefetcher
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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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)
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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(
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patch_size=patch_size,
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max_seq_len=max_seq_len,
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)
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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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