vision_transformer.py

# copy-pasted from https://github.com/rwightman/pytorch-image-models/blob/78933122c950c431b37c6fdeb14c1402ef22824f/timm/models/vision_transformer.py

""" 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 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_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": 0.9,
        "interpolation": "bicubic",
        "fixed_input_size": True,
        "mean": IMAGENET_INCEPTION_MEAN,
        "std": IMAGENET_INCEPTION_STD,
        "first_conv": "patch_embed.proj",
        "classifier": "head",
        **kwargs,
    }


default_cfgs = {
    "vit_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_12heads_patch16_224": _cfg(),
    "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_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"
    ),
}


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0):
        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)

    def forward(self, x):
        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)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        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 > 0.0 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):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        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=4.0,
        qkv_bias=True,
        representation_size=None,
        distilled=False,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        embed_layer=PatchEmbed,
        norm_layer=None,
        act_layer=None,
        weight_init="",
        use_global_avg_pool=False,
    ):
        """
        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.use_global_avg_pool = use_global_avg_pool

        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.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,
                )
                for i in range(depth)
            ]
        )
        if not self.use_global_avg_pool:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = nn.Identity()

        # 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 0.0
        trunc_normal_(self.pos_embed, std=0.02)
        if self.dist_token is not None:
            trunc_normal_(self.dist_token, std=0.02)
        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=0.02)
            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_feature_map(self, x, norm=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)
        if norm:
            x = self.norm(x)
        return x[:, 1:] if self.dist_token is None else x[:, 2:]

    def forward_features(self, x):
        if self.use_global_avg_pool:
            feature_map = self.forward_feature_map(x, norm=False)
            return torch.mean(feature_map, dim=1)

        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)
        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):
        x = self.forward_features(x)
        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 = 0.0, 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=0.02)
                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_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_12heads_patch16_224(pretrained=False, **kwargs):
    """ MoCo v3's ViT-Small (ViT-S/16)
    """
    model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer(
        "vit_small_12heads_patch16_224", 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_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