""" Relative Position Vision Transformer (ViT) in PyTorch NOTE: these models are experimental / WIP, expect changes Hacked together by / Copyright 2022, Ross Wightman """ import logging import math from functools import partial from typing import Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.checkpoint import checkpoint from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_ from ._builder import build_model_with_cfg from ._registry import register_model __all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint fn to this _logger = logging.getLogger(__name__) 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': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, 'first_conv': 'patch_embed.proj', 'classifier': 'head', **kwargs } default_cfgs = { 'vit_relpos_base_patch32_plus_rpn_256': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth', input_size=(3, 256, 256)), 'vit_relpos_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240)), 'vit_relpos_small_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth'), 'vit_relpos_medium_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth'), 'vit_relpos_base_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'), 'vit_srelpos_small_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth'), 'vit_srelpos_medium_patch16_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth'), 'vit_relpos_medium_patch16_cls_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth'), 'vit_relpos_base_patch16_cls_224': _cfg( url=''), 'vit_relpos_base_patch16_clsgap_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth'), 'vit_relpos_small_patch16_rpn_224': _cfg(url=''), 'vit_relpos_medium_patch16_rpn_224': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth'), 'vit_relpos_base_patch16_rpn_224': _cfg(url=''), } def gen_relative_position_index( q_size: Tuple[int, int], k_size: Tuple[int, int] = None, class_token: bool = False) -> torch.Tensor: # Adapted with significant modifications from Swin / BeiT codebases # get pair-wise relative position index for each token inside the window q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1) # 2, Wh, Ww if k_size is None: k_coords = q_coords k_size = q_size else: # different q vs k sizes is a WIP k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1) relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2 _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0) if class_token: # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias # NOTE not intended or tested with MLP log-coords max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1])) num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3 relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0]) 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.contiguous() def gen_relative_log_coords( win_size: Tuple[int, int], pretrained_win_size: Tuple[int, int] = (0, 0), mode='swin', ): assert mode in ('swin', 'cr', 'rw') # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32) relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32) relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2 if mode == 'swin': if pretrained_win_size[0] > 0: relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1) relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1) else: relative_coords_table[:, :, 0] /= (win_size[0] - 1) relative_coords_table[:, :, 1] /= (win_size[1] - 1) relative_coords_table *= 8 # normalize to -8, 8 relative_coords_table = torch.sign(relative_coords_table) * torch.log2( 1.0 + relative_coords_table.abs()) / math.log2(8) else: if mode == 'rw': # cr w/ window size normalization -> [-1,1] log coords relative_coords_table[:, :, 0] /= (win_size[0] - 1) relative_coords_table[:, :, 1] /= (win_size[1] - 1) relative_coords_table *= 8 # scale to -8, 8 relative_coords_table = torch.sign(relative_coords_table) * torch.log2( 1.0 + relative_coords_table.abs()) relative_coords_table /= math.log2(9) # -> [-1, 1] else: # mode == 'cr' relative_coords_table = torch.sign(relative_coords_table) * torch.log( 1.0 + relative_coords_table.abs()) return relative_coords_table class RelPosMlp(nn.Module): def __init__( self, window_size, num_heads=8, hidden_dim=128, prefix_tokens=0, mode='cr', pretrained_window_size=(0, 0) ): super().__init__() self.window_size = window_size self.window_area = self.window_size[0] * self.window_size[1] self.prefix_tokens = prefix_tokens self.num_heads = num_heads self.bias_shape = (self.window_area,) * 2 + (num_heads,) if mode == 'swin': self.bias_act = nn.Sigmoid() self.bias_gain = 16 mlp_bias = (True, False) elif mode == 'rw': self.bias_act = nn.Tanh() self.bias_gain = 4 mlp_bias = True else: self.bias_act = nn.Identity() self.bias_gain = None mlp_bias = True self.mlp = Mlp( 2, # x, y hidden_features=hidden_dim, out_features=num_heads, act_layer=nn.ReLU, bias=mlp_bias, drop=(0.125, 0.) ) self.register_buffer( "relative_position_index", gen_relative_position_index(window_size), persistent=False) # get relative_coords_table self.register_buffer( "rel_coords_log", gen_relative_log_coords(window_size, pretrained_window_size, mode=mode), persistent=False) def get_bias(self) -> torch.Tensor: relative_position_bias = self.mlp(self.rel_coords_log) if self.relative_position_index is not None: relative_position_bias = relative_position_bias.view(-1, self.num_heads)[ self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.view(self.bias_shape) relative_position_bias = relative_position_bias.permute(2, 0, 1) relative_position_bias = self.bias_act(relative_position_bias) if self.bias_gain is not None: relative_position_bias = self.bias_gain * relative_position_bias if self.prefix_tokens: relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0]) return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() class RelPosBias(nn.Module): def __init__(self, window_size, num_heads, prefix_tokens=0): super().__init__() assert prefix_tokens <= 1 self.window_size = window_size self.window_area = window_size[0] * window_size[1] self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,) num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads)) self.register_buffer( "relative_position_index", gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0), persistent=False, ) self.init_weights() def init_weights(self): trunc_normal_(self.relative_position_bias_table, std=.02) def get_bias(self) -> torch.Tensor: relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] # win_h * win_w, win_h * win_w, num_heads relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1) return relative_position_bias.unsqueeze(0).contiguous() def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): return attn + self.get_bias() class RelPosAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.): super().__init__() assert dim % num_heads == 0, 'dim should be divisible by num_heads' self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim ** -0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) attn = (q @ k.transpose(-2, -1)) * self.scale if self.rel_pos is not None: attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos) elif shared_rel_pos is not None: attn = attn + shared_rel_pos attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x class LayerScale(nn.Module): def __init__(self, dim, init_values=1e-5, inplace=False): super().__init__() self.inplace = inplace self.gamma = nn.Parameter(init_values * torch.ones(dim)) def forward(self, x): return x.mul_(self.gamma) if self.inplace else x * self.gamma class RelPosBlock(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.norm1 = norm_layer(dim) self.attn = RelPosAttention( dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop) self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() # 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) self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop) self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity() self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None): x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos))) x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x)))) return x class ResPostRelPosBlock(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4., qkv_bias=False, rel_pos_cls=None, init_values=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): super().__init__() self.init_values = init_values self.attn = RelPosAttention( dim, num_heads, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, attn_drop=attn_drop, proj_drop=drop) self.norm1 = norm_layer(dim) self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop) self.norm2 = norm_layer(dim) self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.init_weights() def init_weights(self): # NOTE this init overrides that base model init with specific changes for the block type if self.init_values is not None: nn.init.constant_(self.norm1.weight, self.init_values) nn.init.constant_(self.norm2.weight, self.init_values) def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None): x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos))) x = x + self.drop_path2(self.norm2(self.mlp(x))) return x class VisionTransformerRelPos(nn.Module): """ Vision Transformer w/ Relative Position Bias Differing from classic vit, this impl * uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed * defaults to no class token (can be enabled) * defaults to global avg pool for head (can be changed) * layer-scale (residual branch gain) enabled """ def __init__( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg', embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-6, class_token=False, fc_norm=False, rel_pos_type='mlp', rel_pos_dim=None, shared_rel_pos=False, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip', embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock ): """ Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number of input channels num_classes (int): number of classes for classification head global_pool (str): type of global pooling for final sequence (default: 'avg') embed_dim (int): embedding dimension depth (int): depth of transformer num_heads (int): number of attention heads mlp_ratio (int): ratio of mlp hidden dim to embedding dim qkv_bias (bool): enable bias for qkv if True init_values: (float): layer-scale init values class_token (bool): use class token (default: False) fc_norm (bool): use pre classifier norm instead of pre-pool rel_pos_ty pe (str): type of relative position shared_rel_pos (bool): share relative pos across all blocks drop_rate (float): dropout rate attn_drop_rate (float): attention dropout rate drop_path_rate (float): stochastic depth rate weight_init (str): weight init scheme embed_layer (nn.Module): patch embedding layer norm_layer: (nn.Module): normalization layer act_layer: (nn.Module): MLP activation layer """ super().__init__() assert global_pool in ('', 'avg', 'token') assert class_token or global_pool != 'token' norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) act_layer = act_layer or nn.GELU 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 if class_token else 0 self.grad_checkpointing = False self.patch_embed = embed_layer( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) feat_size = self.patch_embed.grid_size rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens) if rel_pos_type.startswith('mlp'): if rel_pos_dim: rel_pos_args['hidden_dim'] = rel_pos_dim # FIXME experimenting with different relpos log coord configs if 'swin' in rel_pos_type: rel_pos_args['mode'] = 'swin' elif 'rw' in rel_pos_type: rel_pos_args['mode'] = 'rw' rel_pos_cls = partial(RelPosMlp, **rel_pos_args) else: rel_pos_cls = partial(RelPosBias, **rel_pos_args) self.shared_rel_pos = None if shared_rel_pos: self.shared_rel_pos = rel_pos_cls(num_heads=num_heads) # NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both... rel_pos_cls = None self.cls_token = nn.Parameter(torch.zeros(1, self.num_prefix_tokens, embed_dim)) if class_token else None dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList([ block_fn( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, rel_pos_cls=rel_pos_cls, init_values=init_values, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer) for i in range(depth)]) self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity() # Classifier Head self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity() self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() if weight_init != 'skip': self.init_weights(weight_init) def init_weights(self, mode=''): assert mode in ('jax', 'moco', '') if self.cls_token is not None: nn.init.normal_(self.cls_token, std=1e-6) # FIXME weight init scheme using PyTorch defaults curently #named_apply(get_init_weights_vit(mode, head_bias), self) @torch.jit.ignore def no_weight_decay(self): return {'cls_token'} @torch.jit.ignore def group_matcher(self, coarse=False): return dict( stem=r'^cls_token|patch_embed', # stem and embed blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))] ) @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable @torch.jit.ignore def get_classifier(self): return self.head def reset_classifier(self, num_classes: int, global_pool=None): self.num_classes = num_classes if global_pool is not None: assert global_pool in ('', 'avg', 'token') 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) if self.cls_token is not None: x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1) shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos 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=shared_rel_pos) else: x = blk(x, shared_rel_pos=shared_rel_pos) 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) 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 _create_vision_transformer_relpos(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') model = build_model_with_cfg(VisionTransformerRelPos, variant, pretrained, **kwargs) return model @register_model def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs): """ ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token """ model_kwargs = dict( patch_size=32, embed_dim=896, depth=12, num_heads=14, block_fn=ResPostRelPosBlock, **kwargs) model = _create_vision_transformer_relpos( 'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token """ model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, **kwargs) model = _create_vision_transformer_relpos('vit_relpos_base_patch16_plus_240', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_small_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token """ model_kwargs = dict( patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=True, **kwargs) model = _create_vision_transformer_relpos('vit_relpos_small_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_medium_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=True, **kwargs) model = _create_vision_transformer_relpos('vit_relpos_medium_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, no class token """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, **kwargs) model = _create_vision_transformer_relpos('vit_relpos_base_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_srelpos_small_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token """ model_kwargs = dict( patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=False, rel_pos_dim=384, shared_rel_pos=True, **kwargs) model = _create_vision_transformer_relpos('vit_srelpos_small_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False, rel_pos_dim=512, shared_rel_pos=True, **kwargs) model = _create_vision_transformer_relpos( 'vit_srelpos_medium_patch16_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs): """ ViT-Base (ViT-M/16) w/ relative log-coord position, class token present """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False, rel_pos_dim=256, class_token=True, global_pool='token', **kwargs) model = _create_vision_transformer_relpos( 'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, class_token=True, global_pool='token', **kwargs) model = _create_vision_transformer_relpos('vit_relpos_base_patch16_cls_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position, class token present NOTE this config is a bit of a mistake, class token was enabled but global avg-pool w/ fc-norm was not disabled Leaving here for comparisons w/ a future re-train as it performs quite well. """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True, **kwargs) model = _create_vision_transformer_relpos('vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_small_patch16_rpn_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token """ model_kwargs = dict( patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) model = _create_vision_transformer_relpos( 'vit_relpos_small_patch16_rpn_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_medium_patch16_rpn_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) model = _create_vision_transformer_relpos( 'vit_relpos_medium_patch16_rpn_224', pretrained=pretrained, **model_kwargs) return model @register_model def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token """ model_kwargs = dict( patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock, **kwargs) model = _create_vision_transformer_relpos( 'vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs) return model