vit_timm0412.py

# Copyright (c) 2022 Robert Bosch GmbH
# SPDX-License-Identifier: AGPL-3.0
# This source code is adapted from pytorch-image-models/timm
# (https://github.com/huggingface/pytorch-image-model)
# Hacked together by / Copyright 2021 Ross Wightman
# This source code is licensed under the Apache2.0 license found in the
# 3rd-party-licenses.txt file in the root directory of this source tree.
# Vision Transformer (ViT) in PyTorch
#### Original license, copyright and acknowledgements ####
# A PyTorch implement of Vision Transformers as described in:
# 'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
#     - https://arxiv.org/abs/2010.11929
# `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
#     - https://arxiv.org/abs/2106.10270
# The official jax code is released and available at https://github.com/google-research/vision_transformer
# DeiT model defs and weights from https://github.com/facebookresearch/deit,
# paper `DeiT: Data-efficient Image Transformers` - https://arxiv.org/abs/2012.12877
# Acknowledgments:
# * The paper authors for releasing code and weights, thanks!
# * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
# for some einops/einsum fun
# * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
# * Bert reference code checks against Huggingface Transformers and Tensorflow Bert
# Hacked together by / Copyright 2021 Ross Wightman

import math
import logging
from functools import partial
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.models.helpers import build_model_with_cfg
from timm_future_imports import named_apply, adapt_input_conv, PatchEmbed, Mlp, lecun_normal_
from timm.models.layers import DropPath, trunc_normal_
from timm.models.registry import register_model

_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 = {
    # patch models (weights from official Google JAX impl)
    'vit_tiny_patch16_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
    'vit_tiny_patch16_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_small_patch32_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
    'vit_small_patch32_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_small_patch16_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
    'vit_small_patch16_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_base_patch32_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz'),
    'vit_base_patch32_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_base_patch16_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
    'vit_base_patch16_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_large_patch32_224': _cfg(
        url='',  # no official model weights for this combo, only for in21k
    ),
    'vit_large_patch32_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_large_patch16_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
    'vit_large_patch16_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),

    # patch models, imagenet21k (weights from official Google JAX impl)
    'vit_tiny_patch16_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843),
    'vit_small_patch32_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843),
    'vit_small_patch16_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843),
    'vit_base_patch32_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843),
    'vit_base_patch16_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
        num_classes=21843),
    'vit_large_patch32_224_in21k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
        num_classes=21843),
    'vit_large_patch16_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz',
        num_classes=21843),
    'vit_huge_patch14_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
        hf_hub='timm/vit_huge_patch14_224_in21k',
        num_classes=21843),

    # deit models (FB weights)
    'deit_tiny_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
    'deit_small_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
    'deit_base_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
    'deit_base_patch16_384': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0),
    'deit_tiny_distilled_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
    'deit_small_distilled_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
    'deit_base_distilled_patch16_224': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
    'deit_base_distilled_patch16_384': _cfg(
        url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0,
        classifier=('head', 'head_dist')),

    # ViT ImageNet-21K-P pretraining by MILL
    'vit_base_patch16_224_miil_in21k': _cfg(
        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
    ),
    'vit_base_patch16_224_miil': _cfg(
        url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm'
            '/vit_base_patch16_224_1k_miil_84_4.pth',
        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
    ),
}


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., args=None):
        super().__init__()
        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.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.return_attention = args.return_attention
        self.return_z = args.return_z
        self.linprobe_after_residuals = args.linprobe_after_residuals
        self.return_qkv = args.return_qkv
        self.attention_type = args.attention_type
        self.qkv_grad_plot = args.qkv_grad_plot

    def forward(self, x, scale=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[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
        attn = (q @ k.transpose(-2, -1))

        if scale is None:
            attn = attn * self.scale
        else:
            attn = attn * scale
        if self.attention_type =='softmax':
            attn = attn.softmax(dim=-1)
        elif self.attention_type == 'norm_softmax' or self.attention_type == 'norm_softmax_std':
            #undo scaling
            if scale is None: 
                attn = attn / self.scale
                scale = self.scale
            else:
                attn = attn / scale
            if self.attention_type == 'norm_softmax':
                t = attn - attn.mean(dim=-1, keepdim=True)
                s = (t ** 2).mean(dim=-1, keepdim=True)
            elif self.attention_type == 'norm_softmax_std':
                s = torch.std(attn, dim=-1, keepdim=True)           
            attn = (attn / torch.minimum(torch.ones_like(s) * 1/scale, s)).softmax(dim=-1)

        attn = self.attn_drop(attn)

        z = (attn @ v)
        if self.qkv_grad_plot and q.requires_grad:
            gr_q = torch.autograd.grad(z.sum(), q, retain_graph=True)[0].clone().detach()
            gr_k = torch.autograd.grad(z.sum(), k, retain_graph=True)[0].clone().detach()
            gr_v = torch.autograd.grad(z.sum(), v, retain_graph=True)[0].clone().detach()

        x = z.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        returns = {'x': x}
        if self.return_attention:
            returns['attn'] = attn
        if self.return_z:
            returns['z'] = z.detach().clone()
        if self.return_qkv:
            returns['q'] = q.detach().clone()
            returns['k'] = k.detach().clone()
            returns['v'] = v.detach().clone()
        if self.qkv_grad_plot and q.requires_grad:
            returns['grad_q'] = gr_q
            returns['grad_k'] = gr_k
            returns['grad_v'] = gr_v

        return returns


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, args=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=drop, args=args)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, 
                       drop=drop)
        self.return_z = args.return_z
        self.return_qkv = args.return_qkv
        self.return_intermed_x = args.return_intermed_x
        self.linprobe_after_residuals = args.linprobe_after_residuals
        self.qkv_grad_plot = args.qkv_grad_plot

    def forward(self, x):
        if isinstance(x, dict):
            x_dict = x
            scale = x['scale']
            x = x['x']
        else:
            x_dict = None
            scale = None

        att_out = self.attn(self.norm1(x), scale)
        if self.linprobe_after_residuals:
            att_out['z'] = att_out['z'] + x.reshape(
                x.shape[0], x.shape[1], -1, att_out['z'].shape[-1]).permute(0, 2, 1, 3)
        if self.return_z:
            att_out['z'] = att_out['z'].unsqueeze(1)
        if self.return_qkv:
            att_out['q'] = att_out['q'].unsqueeze(1)
            att_out['k'] = att_out['k'].unsqueeze(1)
            att_out['v'] = att_out['v'].unsqueeze(1)
        if self.qkv_grad_plot and 'grad_q' in att_out.keys():
            att_out['grad_q'] = att_out['grad_q'].unsqueeze(1)
            att_out['grad_k'] = att_out['grad_k'].unsqueeze(1)
            att_out['grad_v'] = att_out['grad_v'].unsqueeze(1)
        if self.return_intermed_x:
            att_out['x_intermed'] = att_out['x'].clone().detach().unsqueeze(1)

        x = x + self.drop_path(att_out['x'])
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        att_out['x'] = x
        att_out['scale'] = scale
        if not x_dict is None:
            if 'attn' in x_dict.keys():
                att_out['attn'] = torch.cat([x_dict['attn'], att_out['attn']], dim=1)
            if 'z' in x_dict.keys():
                att_out['z'] = torch.cat([x_dict['z'], att_out['z']], dim=1)
            if 'q' in x_dict.keys():
                att_out['q'] = torch.cat([x_dict['q'], att_out['q']], dim=1)
                att_out['k'] = torch.cat([x_dict['k'], att_out['k']], dim=1)
                att_out['v'] = torch.cat([x_dict['v'], att_out['v']], dim=1)
            if 'grad_q' in x_dict.keys():
                att_out['grad_q'] = torch.cat([x_dict['grad_q'], att_out['grad_q']], dim=1)
                att_out['grad_k'] = torch.cat([x_dict['grad_k'], att_out['grad_k']], dim=1)
                att_out['grad_v'] = torch.cat([x_dict['grad_v'], att_out['grad_v']], dim=1)
            if 'x_intermed' in x_dict.keys():
                att_out['x_intermed'] = torch.cat([x_dict['x_intermed'], 
                                                   att_out['x_intermed']], dim=1)
        return att_out


class VisionTransformer(nn.Module):
    """ Vision Transformer
    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929
    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
        - https://arxiv.org/abs/2012.12877
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None, weight_init='',args=None, **kwargs):
        """
        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
            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
            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
            distilled (bool): model includes a distillation token and head as in DeiT models
            drop_rate (float): dropout rate
            attn_drop_rate (float): attention dropout rate
            drop_path_rate (float): stochastic depth rate
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
            weight_init: (str): weight init scheme
        """
        super().__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 2 if distilled else 1
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(
            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.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        self.pos_embed_type = kwargs['pos_embed_type']
        if self.pos_embed_type == 'learnable' or self.pos_embed_type is None:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        elif self.pos_embed_type == 'sine':
            self.pos_embed = torch.nn.Parameter(self.sinusoidal_embedding(
                                                    num_patches + self.num_tokens, embed_dim), 
                                                requires_grad=False)
        else:
            raise Exception('selected positional embedding not implemented: ' + \
                             str(args.pos_embed_type))
        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth decay rule
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  
        self.blocks = nn.Sequential(*[
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, 
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                act_layer=act_layer, args=args)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        # Representation layer
        if representation_size and not distilled:
            self.num_features = representation_size
            self.pre_logits = nn.Sequential(OrderedDict([
                ('fc', nn.Linear(embed_dim, representation_size)),
                ('act', nn.Tanh())
            ]))
        else:
            self.pre_logits = nn.Identity()

        # Classifier head(s)
        if isinstance(num_classes, list):
            num_classes = num_classes[0]
        self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        self.head_dist = None
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

        self.init_weights(weight_init)

        # compatibility stuff
        self.num_heads = num_heads
        self.depth = depth

    def init_weights(self, mode=''):
        assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
        head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
        if self.pos_embed_type == 'learnable' or self.pos_embed_type is None:
            trunc_normal_(self.pos_embed, std=.02)
        if self.dist_token is not None:
            trunc_normal_(self.dist_token, std=.02)
        if mode.startswith('jax'):
            # leave cls token as zeros to match jax impl
            named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self)
        else:
            trunc_normal_(self.cls_token, std=.02)
            self.apply(_init_vit_weights)

    def _init_weights(self, m):
        # this fn left here for compat with downstream users
        _init_vit_weights(m)

    @torch.jit.ignore()
    def load_pretrained(self, checkpoint_path, prefix=''):
        _load_weights(self, checkpoint_path, prefix)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token', 'dist_token'}

    def get_classifier(self):
        if self.dist_token is None:
            return self.head
        else:
            return self.head, self.head_dist

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        if self.num_tokens == 2:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        if isinstance(x, dict):
            x_dict = x
            x = x['x']
        else:
            x_dict = None
        x = self.patch_embed(x)
        cls_token = self.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
        if self.dist_token is None:
            x = torch.cat((cls_token, x), dim=1)
        else:
            x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
        x = self.pos_drop(x + self.pos_embed)
        if x_dict is None:
            x = self.blocks(x)
        else:
            x = self.blocks({'x': x, "scale": x_dict['scale']})
        x['x'] = self.norm(x['x'])
        if self.dist_token is None:
            x['x'] = self.pre_logits(x['x'][:, 0])
        else:
            x['x'] = (x['x'][:, 0], x['x'][:, 1])

        return x

    def forward(self, x):
        forward_out = self.forward_features(x)

        if self.head_dist is not None:
            x, x_dist = self.head(forward_out['x'][0]), self.head_dist(forward_out['x'][1])  # x must be a tuple
            if self.training and not torch.jit.is_scripting():
                # during inference, return the average of both classifier predictions
                forward_out['x'] = (x, x_dist)
            else:
                forward_out['x'] = (x + x_dist) / 2
        else:
            forward_out['x'] = self.head(forward_out['x'])

        return forward_out

    #taken from compact transformers/transformers.py
    @staticmethod
    def sinusoidal_embedding(n_channels, dim):
        pe = torch.FloatTensor([[p / (10000 ** (2 * (i // 2) / dim)) for i in range(dim)]
                                for p in range(n_channels)])
        pe[:, 0::2] = torch.sin(pe[:, 0::2])
        pe[:, 1::2] = torch.cos(pe[:, 1::2])
        return pe.unsqueeze(0)


def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
    """ ViT weight initialization
    * When called without n, head_bias, jax_impl args it will behave exactly the same
      as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
    * When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
    """
    if isinstance(module, nn.Linear):
        if name.startswith('head'):
            nn.init.zeros_(module.weight)
            nn.init.constant_(module.bias, head_bias)
        elif name.startswith('pre_logits'):
            lecun_normal_(module.weight)
            nn.init.zeros_(module.bias)
        else:
            if jax_impl:
                nn.init.xavier_uniform_(module.weight)
                if module.bias is not None:
                    if 'mlp' in name:
                        nn.init.normal_(module.bias, std=1e-6)
                    else:
                        nn.init.zeros_(module.bias)
            else:
                trunc_normal_(module.weight, std=.02)
                if module.bias is not None:
                    nn.init.zeros_(module.bias)
    elif jax_impl and isinstance(module, nn.Conv2d):
        # NOTE conv was left to pytorch default in my original init
        lecun_normal_(module.weight)
        if module.bias is not None:
            nn.init.zeros_(module.bias)
    elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
        nn.init.zeros_(module.bias)
        nn.init.ones_(module.weight)


@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
    """ Load weights from .npz checkpoints for official Google Brain Flax implementation
    """
    import numpy as np

    def _n2p(w, t=True):
        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
            w = w.flatten()
        if t:
            if w.ndim == 4:
                w = w.transpose([3, 2, 0, 1])
            elif w.ndim == 3:
                w = w.transpose([2, 0, 1])
            elif w.ndim == 2:
                w = w.transpose([1, 0])
        return torch.from_numpy(w)

    w = np.load(checkpoint_path)
    if not prefix and 'opt/target/embedding/kernel' in w:
        prefix = 'opt/target/'

    if hasattr(model.patch_embed, 'backbone'):
        # hybrid
        backbone = model.patch_embed.backbone
        stem_only = not hasattr(backbone, 'stem')
        stem = backbone if stem_only else backbone.stem
        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
        stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
        stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
        if not stem_only:
            for i, stage in enumerate(backbone.stages):
                for j, block in enumerate(stage.blocks):
                    bp = f'{prefix}block{i + 1}/unit{j + 1}/'
                    for r in range(3):
                        getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
                        getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
                        getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
                    if block.downsample is not None:
                        block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
                        block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
                        block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
        embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
    else:
        embed_conv_w = adapt_input_conv(
            model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
    model.patch_embed.proj.weight.copy_(embed_conv_w)
    model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
    model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
    pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
    if pos_embed_w.shape != model.pos_embed.shape:
        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
            pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
    model.pos_embed.copy_(pos_embed_w)
    model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
    model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
    if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
        model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
        model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
    if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
        model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
        model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
    for i, block in enumerate(model.blocks.children()):
        block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
        mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
        block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
        block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
        block.attn.qkv.weight.copy_(torch.cat([
            _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
        block.attn.qkv.bias.copy_(torch.cat([
            _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
        block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
        block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
        for r in range(2):
            getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
            getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
        block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
        block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))


def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
    _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
    ntok_new = posemb_new.shape[1]
    if num_tokens:
        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
        ntok_new -= num_tokens
    else:
        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
    gs_old = int(math.sqrt(len(posemb_grid)))
    if not len(gs_new):  # backwards compatibility
        gs_new = [int(math.sqrt(ntok_new))] * 2
    assert len(gs_new) >= 2
    _logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
    posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bilinear')
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
    return posemb


def checkpoint_filter_fn(state_dict, model):
    """ convert patch embedding weight from manual patchify + linear proj to conv"""
    out_dict = {}
    if 'model' in state_dict:
        # For deit models
        state_dict = state_dict['model']
    for k, v in state_dict.items():
        if 'patch_embed.proj.weight' in k and len(v.shape) < 4:
            # For old models that I trained prior to conv based patchification
            O, I, H, W = model.patch_embed.proj.weight.shape
            v = v.reshape(O, -1, H, W)
        elif k == 'pos_embed' and v.shape != model.pos_embed.shape:
            # To resize pos embedding when using model at different size from pretrained weights
            v = resize_pos_embed(
                v, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
        out_dict[k] = v
    return out_dict


def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kwargs):
    default_cfg = default_cfg or default_cfgs[variant]
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')

    # NOTE this extra code to support handling of repr size for in21k pretrained models
    default_num_classes = default_cfg['num_classes']
    num_classes = kwargs.get('num_classes', default_num_classes)
    repr_size = kwargs.pop('representation_size', None)
    if repr_size is not None and num_classes != default_num_classes:
        # Remove representation layer if fine-tuning. This may not always be the desired action,
        # but I feel better than doing nothing by default for fine-tuning. Perhaps a better interface?
        _logger.warning("Removing representation layer for fine-tuning.")
        repr_size = None

    model = build_model_with_cfg(
        VisionTransformer, variant, pretrained,
        default_cfg=default_cfg,
        representation_size=repr_size,
        pretrained_filter_fn=checkpoint_filter_fn,
        pretrained_custom_load='npz' in default_cfg['url'],
        **kwargs)
    return model


def deit_mnist_patch7_28(pretrained=False,args=None, **kwargs):
    if kwargs['num_heads'] is None:
        num_heads = 3
        embed_dim = kwargs['embedding_dim_per_head']
    else:
        num_heads = kwargs['num_heads']
        embed_dim = int(kwargs['embedding_dim_per_head'] * num_heads)

    mlp_ratio = kwargs['mlp_ratio']
    del kwargs['mlp_ratio']
    qkv_bias = kwargs['qkv_bias']
    del kwargs['qkv_bias']

    del kwargs['num_heads']
    norm_layer = partial(nn.LayerNorm, eps=args.__getattribute__('layer_norm_eps'))
    model = VisionTransformer(
        patch_size=args.patch_size, embed_dim=embed_dim, depth=kwargs['mnist_deit_depth'], 
        num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
        norm_layer=norm_layer, args=args, **kwargs)
    model.default_cfg = _cfg()
    return model


@register_model
def vit_tiny_patch16_224(pretrained=False, **kwargs):
    """ ViT-Tiny (Vit-Ti/16)
    """
    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_tiny_patch16_384(pretrained=False, **kwargs):
    """ ViT-Tiny (Vit-Ti/16) @ 384x384.
    """
    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch32_224(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/32)
    """
    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch32_384(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/32) at 384x384.
    """
    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch16_224(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/16)
    NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch16_384(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/16)
    NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch32_224(pretrained=False, **kwargs):
    """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
    """
    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch32_384(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch16_224(pretrained=False, **kwargs):
    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch16_384(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch32_224(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
    """
    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch32_384(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch16_224(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch16_384(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
    """
    model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
    """ ViT-Tiny (Vit-Ti/16).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/16)
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
    """ ViT-Small (ViT-S/16)
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(
        patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
    """
    model_kwargs = dict(
        patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
    model = _create_vision_transformer('vit_large_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
    """
    model_kwargs = dict(
        patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
    model = _create_vision_transformer('vit_huge_patch14_224_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def deit_tiny_patch16_224(pretrained=False, **kwargs):
    """ DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer('deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def deit_small_patch16_224(pretrained=False, **kwargs):
    """ DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer('deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def deit_base_patch16_224(pretrained=False, **kwargs):
    """ DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def deit_base_patch16_384(pretrained=False, **kwargs):
    """ DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer('deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
    """ DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer(
        'deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
    return model


@register_model
def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
    """ DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer(
        'deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
    return model


@register_model
def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
    """ DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer(
        'deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
    return model


@register_model
def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
    """ DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
    ImageNet-1k weights from https://github.com/facebookresearch/deit.
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer(
        'deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
    return model


@register_model
def vit_base_patch16_224_miil_in21k(pretrained=False, **kwargs):
    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224_miil_in21k', pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_patch16_224_miil(pretrained=False, **kwargs):
    """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
    """
    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs)
    model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs)
    return model