Upload ViT_new.py

ViT_new.py

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+""" Vision Transformer (ViT) in PyTorch
+
+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 2020, Ross Wightman
+"""
+import math
+import logging
+from functools import partial
+from collections import OrderedDict
+from copy import deepcopy
+
+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, named_apply, adapt_input_conv
+from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_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_base_patch8_224': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/'
+            'B_8-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_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),
+
+    'vit_huge_patch14_224': _cfg(url=''),
+    'vit_giant_patch14_224': _cfg(url=''),
+    'vit_gigantic_patch14_224': _cfg(url=''),
+
+    # 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_base_patch8_224_in21k': _cfg(
+        url='https://storage.googleapis.com/vit_models/augreg/B_8-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),
+
+    # SAM trained models (https://arxiv.org/abs/2106.01548)
+    'vit_base_patch32_sam_224': _cfg(
+        url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz'),
+    'vit_base_patch16_sam_224': _cfg(
+        url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz'),
+
+    # 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.):
+        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.attn_gradients = None
+        self.attention_map = None
+
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+
+    def get_attn_gradients(self):
+        return self.attn_gradients
+
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+
+    def get_attention_map(self):
+        return self.attention_map
+
+    def forward(self, x, register_hook=False):
+        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
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        self.save_attention_map(attn)
+        if register_hook:
+            attn.register_hook(self.save_attn_gradients)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, 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 = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        # 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)
+
+    def forward(self, x, register_hook=False):
+        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+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:
+            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
+        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
+        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 = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList([Block(
+                dim=embed_dim, num_heads=num_heads, 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)
+            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)
+        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)
+
+    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.
+        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, register_hook=False):
+        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)
+        # x = self.blocks(x)
+        for blk in self.blocks:
+            x = blk(x, register_hook=register_hook)
+        x = self.norm(x)
+        if self.dist_token is None:
+            return self.pre_logits(x[:, 0])
+        else:
+            return x[:, 0], x[:, 1]
+
+    def forward(self, x, register_hook=False):
+        x = self.forward_features(x, register_hook=register_hook)
+        if self.head_dist is not None:
+            x, x_dist = self.head(x[0]), self.head_dist(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
+                return x, x_dist
+            else:
+                return (x + x_dist) / 2
+        else:
+            x = self.head(x)
+        return x
+
+
+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='bicubic', align_corners=False)
+    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
+
+
+@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).
+    ImageNet-1k weights fine-tuned from in21k, 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_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_base_patch8_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/8) 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=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch8_224', 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_base_patch16_sam_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/16) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
+    """
+    # NOTE original SAM weights release worked with representation_size=768
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
+    model = _create_vision_transformer('vit_base_patch16_sam_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_base_patch32_sam_224(pretrained=False, **kwargs):
+    """ ViT-Base (ViT-B/32) w/ SAM pretrained weights. Paper: https://arxiv.org/abs/2106.01548
+    """
+    # NOTE original SAM weights release worked with representation_size=768
+    model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=0, **kwargs)
+    model = _create_vision_transformer('vit_base_patch32_sam_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_huge_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_giant_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Giant model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs)
+    return model
+
+
+@register_model
+def vit_gigantic_patch14_224(pretrained=False, **kwargs):
+    """ ViT-Gigantic model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
+    """
+    model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs)
+    model = _create_vision_transformer('vit_gigantic_patch14_224', 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_base_patch8_224_in21k(pretrained=False, **kwargs):
+    """ ViT-Base model (ViT-B/8) 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=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
+    model = _create_vision_transformer('vit_base_patch8_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