pytorch-image-models/timm/models/vision_transformer_relpos.py

""" 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