""" BEiT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) Model from official source: https://github.com/microsoft/unilm/tree/master/beit @inproceedings{beit, title={{BEiT}: {BERT} Pre-Training of Image Transformers}, author={Hangbo Bao and Li Dong and Songhao Piao and Furu Wei}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=p-BhZSz59o4} } BEiT-v2 from https://github.com/microsoft/unilm/tree/master/beit2 @article{beitv2, title={{BEiT v2}: Masked Image Modeling with Vector-Quantized Visual Tokenizers}, author={Zhiliang Peng and Li Dong and Hangbo Bao and Qixiang Ye and Furu Wei}, year={2022}, eprint={2208.06366}, archivePrefix={arXiv}, primaryClass={cs.CV} } At this point only the 1k fine-tuned classification weights and model configs have been added, see original source above for pre-training models and procedure. Modifications by / Copyright 2021 Ross Wightman, original copyrights below """ # -------------------------------------------------------- # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) # Github source: https://github.com/microsoft/unilm/tree/master/beit # Copyright (c) 2021 Microsoft # Licensed under The MIT License [see LICENSE for details] # By Hangbo Bao # Based on timm and DeiT code bases # https://github.com/rwightman/pytorch-image-models/tree/master/timm # https://github.com/facebookresearch/deit/ # https://github.com/facebookresearch/dino # --------------------------------------------------------' import math from typing import Callable, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.checkpoint import checkpoint from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.layers import PatchEmbed, Mlp, SwiGLU, LayerNorm, DropPath, trunc_normal_, use_fused_attn from ._builder import build_model_with_cfg from ._registry import generate_default_cfgs, register_model from .vision_transformer import checkpoint_filter_fn __all__ = ['Beit'] def gen_relative_position_index(window_size: Tuple[int, int]) -> torch.Tensor: num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 # cls to token & token 2 cls & cls to cls # get pair-wise relative position index for each token inside the window window_area = window_size[0] * window_size[1] coords = torch.stack(torch.meshgrid( [torch.arange(window_size[0]), torch.arange(window_size[1])])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += window_size[1] - 1 relative_coords[:, :, 0] *= 2 * window_size[1] - 1 relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype) relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww relative_position_index[0, 0:] = num_relative_distance - 3 relative_position_index[0:, 0] = num_relative_distance - 2 relative_position_index[0, 0] = num_relative_distance - 1 return relative_position_index class Attention(nn.Module): fused_attn: torch.jit.Final[bool] def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, attn_drop: float = 0., proj_drop: float = 0., window_size: Optional[Tuple[int, int]] = None, attn_head_dim: Optional[int] = None, ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads if attn_head_dim is not None: head_dim = attn_head_dim all_head_dim = head_dim * self.num_heads self.scale = head_dim ** -0.5 self.fused_attn = use_fused_attn() self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) if qkv_bias: self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) self.register_buffer('k_bias', torch.zeros(all_head_dim), persistent=False) self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) else: self.q_bias = None self.k_bias = None self.v_bias = None if window_size: self.window_size = window_size self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 self.relative_position_bias_table = nn.Parameter( torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH self.register_buffer("relative_position_index", gen_relative_position_index(window_size)) else: self.window_size = None self.relative_position_bias_table = None self.relative_position_index = None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(all_head_dim, dim) self.proj_drop = nn.Dropout(proj_drop) def _get_rel_pos_bias(self): relative_position_bias = self.relative_position_bias_table[ self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1] + 1, self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww return relative_position_bias.unsqueeze(0) def forward(self, x, shared_rel_pos_bias: Optional[torch.Tensor] = None): B, N, C = x.shape qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # B, num_heads, N, head_dim if self.fused_attn: rel_pos_bias = None if self.relative_position_bias_table is not None: rel_pos_bias = self._get_rel_pos_bias() if shared_rel_pos_bias is not None: rel_pos_bias = rel_pos_bias + shared_rel_pos_bias elif shared_rel_pos_bias is not None: rel_pos_bias = shared_rel_pos_bias x = F.scaled_dot_product_attention( q, k, v, attn_mask=rel_pos_bias, dropout_p=self.attn_drop.p, ) else: q = q * self.scale attn = (q @ k.transpose(-2, -1)) if self.relative_position_bias_table is not None: attn = attn + self._get_rel_pos_bias() if shared_rel_pos_bias is not None: attn = attn + shared_rel_pos_bias attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = attn @ v x = x.transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__( self, dim: int, num_heads: int, qkv_bias: bool = False, mlp_ratio: float = 4., scale_mlp: bool = False, swiglu_mlp: bool = False, proj_drop: float = 0., attn_drop: float = 0., drop_path: float = 0., init_values: Optional[float] = None, act_layer: Callable = nn.GELU, norm_layer: Callable = LayerNorm, window_size: Optional[Tuple[int, int]] = None, attn_head_dim: Optional[int] = None, ): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=proj_drop, window_size=window_size, attn_head_dim=attn_head_dim, ) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = norm_layer(dim) if swiglu_mlp: self.mlp = SwiGLU( in_features=dim, hidden_features=int(dim * mlp_ratio), norm_layer=norm_layer if scale_mlp else None, drop=proj_drop, ) else: self.mlp = Mlp( in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, norm_layer=norm_layer if scale_mlp else None, drop=proj_drop, ) self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() if init_values: self.gamma_1 = nn.Parameter(init_values * torch.ones(dim)) self.gamma_2 = nn.Parameter(init_values * torch.ones(dim)) else: self.gamma_1, self.gamma_2 = None, None def forward(self, x, shared_rel_pos_bias: Optional[torch.Tensor] = None): if self.gamma_1 is None: x = x + self.drop_path1(self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias)) x = x + self.drop_path2(self.mlp(self.norm2(x))) else: x = x + self.drop_path1(self.gamma_1 * self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias)) x = x + self.drop_path2(self.gamma_2 * self.mlp(self.norm2(x))) return x class RelativePositionBias(nn.Module): def __init__(self, window_size, num_heads): super().__init__() self.window_size = window_size self.window_area = window_size[0] * window_size[1] num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads)) # trunc_normal_(self.relative_position_bias_table, std=.02) self.register_buffer("relative_position_index", gen_relative_position_index(window_size)) def forward(self): relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_area + 1, self.window_area + 1, -1) # Wh*Ww,Wh*Ww,nH return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww class Beit(nn.Module): """ Vision Transformer with support for patch or hybrid CNN input stage """ def __init__( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int, int]] = 16, in_chans: int = 3, num_classes: int = 1000, global_pool: str = 'avg', embed_dim: int = 768, depth: int = 12, num_heads: int = 12, qkv_bias: bool = True, mlp_ratio: float = 4., swiglu_mlp: bool = False, scale_mlp: bool = False, drop_rate: float = 0., pos_drop_rate: float = 0., proj_drop_rate: float = 0., attn_drop_rate: float = 0., drop_path_rate: float = 0., norm_layer: Callable = LayerNorm, init_values: Optional[float] = None, use_abs_pos_emb: bool = True, use_rel_pos_bias: bool = False, use_shared_rel_pos_bias: bool = False, head_init_scale: float = 0.001, ): super().__init__() self.num_classes = num_classes self.global_pool = global_pool self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.num_prefix_tokens = 1 self.grad_checkpointing = False self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) if use_abs_pos_emb else None self.pos_drop = nn.Dropout(p=pos_drop_rate) if use_shared_rel_pos_bias: self.rel_pos_bias = RelativePositionBias( window_size=self.patch_embed.grid_size, num_heads=num_heads, ) else: self.rel_pos_bias = None dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, qkv_bias=qkv_bias, mlp_ratio=mlp_ratio, scale_mlp=scale_mlp, swiglu_mlp=swiglu_mlp, proj_drop=proj_drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, init_values=init_values, window_size=self.patch_embed.grid_size if use_rel_pos_bias else None, ) for i in range(depth)]) use_fc_norm = self.global_pool == 'avg' self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim) self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity() self.head_drop = nn.Dropout(drop_rate) self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.apply(self._init_weights) if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.fix_init_weight() if isinstance(self.head, nn.Linear): trunc_normal_(self.head.weight, std=.02) self.head.weight.data.mul_(head_init_scale) self.head.bias.data.mul_(head_init_scale) def fix_init_weight(self): def rescale(param, layer_id): param.div_(math.sqrt(2.0 * layer_id)) for layer_id, layer in enumerate(self.blocks): rescale(layer.attn.proj.weight.data, layer_id + 1) rescale(layer.mlp.fc2.weight.data, layer_id + 1) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): nwd = {'pos_embed', 'cls_token'} for n, _ in self.named_parameters(): if 'relative_position_bias_table' in n: nwd.add(n) return nwd @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable @torch.jit.ignore def group_matcher(self, coarse=False): matcher = dict( stem=r'^cls_token|pos_embed|patch_embed|rel_pos_bias', # stem and embed blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))], ) return matcher @torch.jit.ignore def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=None): self.num_classes = num_classes if global_pool is not None: self.global_pool = global_pool self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): x = self.patch_embed(x) x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) if self.pos_embed is not None: x = x + self.pos_embed x = self.pos_drop(x) rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None for blk in self.blocks: if self.grad_checkpointing and not torch.jit.is_scripting(): x = checkpoint(blk, x, shared_rel_pos_bias=rel_pos_bias) else: x = blk(x, shared_rel_pos_bias=rel_pos_bias) x = self.norm(x) return x def forward_head(self, x, pre_logits: bool = False): if self.global_pool: x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0] x = self.fc_norm(x) x = self.head_drop(x) return x if pre_logits else self.head(x) def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), 'first_conv': 'patch_embed.proj', 'classifier': 'head', **kwargs } default_cfgs = generate_default_cfgs({ 'beit_base_patch16_224.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth', hf_hub_id='timm/'), 'beit_base_patch16_384.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth', hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=1.0, ), 'beit_base_patch16_224.in22k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth', hf_hub_id='timm/', num_classes=21841, ), 'beit_large_patch16_224.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth', hf_hub_id='timm/'), 'beit_large_patch16_384.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth', hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=1.0, ), 'beit_large_patch16_512.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth', hf_hub_id='timm/', input_size=(3, 512, 512), crop_pct=1.0, ), 'beit_large_patch16_224.in22k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth', hf_hub_id='timm/', num_classes=21841, ), 'beitv2_base_patch16_224.in1k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth', hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_base_patch16_224.in1k_ft_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft1k.pth', hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_base_patch16_224.in1k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth', hf_hub_id='timm/', num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_large_patch16_224.in1k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21kto1k.pth', hf_hub_id='timm/', crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_large_patch16_224.in1k_ft_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft1k.pth', hf_hub_id='timm/', crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_large_patch16_224.in1k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth', hf_hub_id='timm/', num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), }) def _beit_checkpoint_filter_fn(state_dict, model): if 'module' in state_dict: # beit v2 didn't strip module state_dict = state_dict['module'] return checkpoint_filter_fn(state_dict, model) def _create_beit(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for BEiT models.') model = build_model_with_cfg( Beit, variant, pretrained, # FIXME an updated filter fn needed to interpolate rel pos emb if fine tuning to diff model sizes pretrained_filter_fn=_beit_checkpoint_filter_fn, **kwargs) return model @register_model def beit_base_patch16_224(pretrained=False, **kwargs) -> Beit: model_args = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1) model = _create_beit('beit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beit_base_patch16_384(pretrained=False, **kwargs) -> Beit: model_args = dict( img_size=384, patch_size=16, embed_dim=768, depth=12, num_heads=12, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=0.1) model = _create_beit('beit_base_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beit_large_patch16_224(pretrained=False, **kwargs) -> Beit: model_args = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5) model = _create_beit('beit_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beit_large_patch16_384(pretrained=False, **kwargs) -> Beit: model_args = dict( img_size=384, patch_size=16, embed_dim=1024, depth=24, num_heads=16, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5) model = _create_beit('beit_large_patch16_384', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beit_large_patch16_512(pretrained=False, **kwargs) -> Beit: model_args = dict( img_size=512, patch_size=16, embed_dim=1024, depth=24, num_heads=16, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5) model = _create_beit('beit_large_patch16_512', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beitv2_base_patch16_224(pretrained=False, **kwargs) -> Beit: model_args = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5) model = _create_beit('beitv2_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model @register_model def beitv2_large_patch16_224(pretrained=False, **kwargs) -> Beit: model_args = dict( patch_size=16, embed_dim=1024, depth=24, num_heads=16, use_abs_pos_emb=False, use_rel_pos_bias=True, init_values=1e-5) model = _create_beit('beitv2_large_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model