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# Copyright (c) OpenMMLab. All rights reserved.
from mmpretrain.utils.dependency import WITH_MULTIMODAL
from .attention import (BEiTAttention, ChannelMultiheadAttention,
CrossMultiheadAttention, LeAttention,
MultiheadAttention, PromptMultiheadAttention,
ShiftWindowMSA, WindowMSA, WindowMSAV2)
from .batch_augments import CutMix, Mixup, RandomBatchAugment, ResizeMix
from .batch_shuffle import batch_shuffle_ddp, batch_unshuffle_ddp
from .channel_shuffle import channel_shuffle
from .clip_generator_helper import QuickGELU, build_clip_model
from .data_preprocessor import (ClsDataPreprocessor,
MultiModalDataPreprocessor,
SelfSupDataPreprocessor,
TwoNormDataPreprocessor, VideoDataPreprocessor)
from .ema import CosineEMA
from .embed import (HybridEmbed, PatchEmbed, PatchMerging, resize_pos_embed,
resize_relative_position_bias_table)
from .helpers import is_tracing, to_2tuple, to_3tuple, to_4tuple, to_ntuple
from .inverted_residual import InvertedResidual
from .layer_scale import LayerScale
from .make_divisible import make_divisible
from .norm import GRN, LayerNorm2d, build_norm_layer
from .position_encoding import (ConditionalPositionEncoding,
PositionEncodingFourier, RotaryEmbeddingFast,
build_2d_sincos_position_embedding)
from .res_layer_extra_norm import ResLayerExtraNorm
from .se_layer import SELayer
from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
from .vector_quantizer import NormEMAVectorQuantizer
__all__ = [
'channel_shuffle',
'make_divisible',
'InvertedResidual',
'SELayer',
'to_ntuple',
'to_2tuple',
'to_3tuple',
'to_4tuple',
'PatchEmbed',
'PatchMerging',
'HybridEmbed',
'RandomBatchAugment',
'ShiftWindowMSA',
'is_tracing',
'MultiheadAttention',
'ConditionalPositionEncoding',
'resize_pos_embed',
'resize_relative_position_bias_table',
'ClsDataPreprocessor',
'Mixup',
'CutMix',
'ResizeMix',
'BEiTAttention',
'LayerScale',
'WindowMSA',
'WindowMSAV2',
'ChannelMultiheadAttention',
'PositionEncodingFourier',
'LeAttention',
'GRN',
'LayerNorm2d',
'build_norm_layer',
'CrossMultiheadAttention',
'build_2d_sincos_position_embedding',
'PromptMultiheadAttention',
'NormEMAVectorQuantizer',
'build_clip_model',
'batch_shuffle_ddp',
'batch_unshuffle_ddp',
'SelfSupDataPreprocessor',
'TwoNormDataPreprocessor',
'VideoDataPreprocessor',
'CosineEMA',
'ResLayerExtraNorm',
'MultiModalDataPreprocessor',
'QuickGELU',
'SwiGLUFFN',
'SwiGLUFFNFused',
'RotaryEmbeddingFast',
]
if WITH_MULTIMODAL:
from .huggingface import (no_load_hf_pretrained_model, register_hf_model,
register_hf_tokenizer)
from .tokenizer import (Blip2Tokenizer, BlipTokenizer, FullTokenizer,
OFATokenizer)
__all__.extend([
'BlipTokenizer', 'OFATokenizer', 'Blip2Tokenizer', 'register_hf_model',
'register_hf_tokenizer', 'no_load_hf_pretrained_model', 'FullTokenizer'
])
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