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pytorch-image-models/timm/models/vision_transformer.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
`FlexiViT: One Model for All Patch Sizes`
- https://arxiv.org/abs/2212.08013
The official jax code is released and available at
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision
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
* 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 logging
import math
from collections import OrderedDict
from functools import partial
from typing import Optional, List
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD, \
OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, resample_patch_embed, \
resample_abs_pos_embed
from ._builder import build_model_with_cfg
from ._manipulate import named_apply, checkpoint_seq, adapt_input_conv
from ._pretrained import generate_default_cfgs
from ._registry import register_model
__all__ = ['VisionTransformer'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.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.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)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
init_values=None,
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)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class ResPostBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
init_values=None,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm
):
super().__init__()
self.init_values = init_values
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.init_weights()
def init_weights(self):
# NOTE this init overrides that base model init with specific changes for the block type
if self.init_values is not None:
nn.init.constant_(self.norm1.weight, self.init_values)
nn.init.constant_(self.norm2.weight, self.init_values)
def forward(self, x):
x = x + self.drop_path1(self.norm1(self.attn(x)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class ParallelBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
num_parallel=2,
mlp_ratio=4.,
qkv_bias=False,
init_values=None,
drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm
):
super().__init__()
self.num_parallel = num_parallel
self.attns = nn.ModuleList()
self.ffns = nn.ModuleList()
for _ in range(num_parallel):
self.attns.append(nn.Sequential(OrderedDict([
('norm', norm_layer(dim)),
('attn', Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)),
('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
])))
self.ffns.append(nn.Sequential(OrderedDict([
('norm', norm_layer(dim)),
('mlp', Mlp(dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)),
('ls', LayerScale(dim, init_values=init_values) if init_values else nn.Identity()),
('drop_path', DropPath(drop_path) if drop_path > 0. else nn.Identity())
])))
def _forward_jit(self, x):
x = x + torch.stack([attn(x) for attn in self.attns]).sum(dim=0)
x = x + torch.stack([ffn(x) for ffn in self.ffns]).sum(dim=0)
return x
@torch.jit.ignore
def _forward(self, x):
x = x + sum(attn(x) for attn in self.attns)
x = x + sum(ffn(x) for ffn in self.ffns)
return x
def forward(self, x):
if torch.jit.is_scripting() or torch.jit.is_tracing():
return self._forward_jit(x)
else:
return self._forward(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
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='token',
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=True,
init_values=None,
class_token=True,
no_embed_class=False,
pre_norm=False,
fc_norm=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
weight_init='',
embed_layer=PatchEmbed,
norm_layer=None,
act_layer=None,
block_fn=Block,
):
"""
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
global_pool (str): type of global pooling for final sequence (default: 'token')
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
init_values: (float): layer-scale init values
class_token (bool): use class token
fc_norm (Optional[bool]): pre-fc norm after pool, set if global_pool == 'avg' if None (default: None)
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
weight_init (str): weight init scheme
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
act_layer: (nn.Module): MLP activation layer
"""
super().__init__()
assert global_pool in ('', 'avg', 'token')
assert class_token or global_pool != 'token'
use_fc_norm = global_pool == 'avg' if fc_norm is None else fc_norm
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.no_embed_class = no_embed_class
self.grad_checkpointing = False
self.patch_embed = embed_layer(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
bias=not pre_norm, # disable bias if pre-norm is used (e.g. CLIP)
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
embed_len = num_patches if no_embed_class else num_patches + self.num_prefix_tokens
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
self.pos_drop = nn.Dropout(p=drop_rate)
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.Sequential(*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
init_values=init_values,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer
)
for i in range(depth)])
self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
# Classifier Head
self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode, head_bias), self)
def _init_weights(self, m):
# this fn left here for compat with downstream users
init_weights_vit_timm(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'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|pos_embed|patch_embed', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes: int, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def _pos_embed(self, x):
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
x = x + self.pos_embed
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
x = x + self.pos_embed
return self.pos_drop(x)
def forward_features(self, x):
x = self.patch_embed(x)
x = self._pos_embed(x)
x = self.norm_pre(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.blocks, x)
else:
x = self.blocks(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def init_weights_vit_timm(module: nn.Module, name: str = ''):
""" ViT weight initialization, original timm impl (for reproducibility) """
if isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def init_weights_vit_jax(module: nn.Module, name: str = '', head_bias: float = 0.):
""" ViT weight initialization, matching JAX (Flax) impl """
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.normal_(module.bias, std=1e-6) if 'mlp' in name else nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def init_weights_vit_moco(module: nn.Module, name: str = ''):
""" ViT weight initialization, matching moco-v3 impl minus fixed PatchEmbed """
if isinstance(module, nn.Linear):
if 'qkv' in name:
# treat the weights of Q, K, V separately
val = math.sqrt(6. / float(module.weight.shape[0] // 3 + module.weight.shape[1]))
nn.init.uniform_(module.weight, -val, val)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
module.init_weights()
def get_init_weights_vit(mode='jax', head_bias: float = 0.):
if 'jax' in mode:
return partial(init_weights_vit_jax, head_bias=head_bias)
elif 'moco' in mode:
return init_weights_vit_moco
else:
return init_weights_vit_timm
def resize_pos_embed(
posemb,
posemb_new,
num_prefix_tokens=1,
gs_new=(),
interpolation='bicubic',
antialias=False,
):
""" Rescale the grid of position embeddings when loading from state_dict.
*DEPRECATED* This function is being deprecated in favour of resample_abs_pos_embed
Adapted from:
https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
"""
ntok_new = posemb_new.shape[1]
if num_prefix_tokens:
posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
ntok_new -= num_prefix_tokens
else:
posemb_prefix, 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(f'Resized position embedding: {posemb.shape} ({[gs_old, gs_old]}) to {posemb_new.shape} ({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=interpolation, antialias=antialias, 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_prefix, posemb_grid], dim=1)
return posemb
@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)
interpolation = 'bilinear'
antialias = False
big_vision = False
if not prefix:
if 'opt/target/embedding/kernel' in w:
prefix = 'opt/target/'
elif 'params/embedding/kernel' in w:
prefix = 'params/'
big_vision = True
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']))
if embed_conv_w.shape[-2:] != model.patch_embed.proj.weight.shape[-2:]:
embed_conv_w = resample_patch_embed(
embed_conv_w,
model.patch_embed.proj.weight.shape[-2:],
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
model.patch_embed.proj.weight.copy_(embed_conv_w)
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
if model.cls_token is not None:
model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
if big_vision:
pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False)
else:
pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
if pos_embed_w.shape != model.pos_embed.shape:
old_shape = pos_embed_w.shape
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
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']))
# NOTE representation layer has been removed, not used in latest 21k/1k pretrained weights
# 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']))
mha_sub, b_sub, ln1_sub = (0, 0, 1) if big_vision else (1, 3, 2)
for i, block in enumerate(model.blocks.children()):
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_{mha_sub}/'
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_{b_sub}/Dense_{r}/kernel']))
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias']))
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/scale']))
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/bias']))
def _convert_openai_clip(state_dict, model):
out_dict = {}
swaps = [
('visual.', ''), ('conv1', 'patch_embed.proj'), ('positional_embedding', 'pos_embed'),
('transformer.resblocks.', 'blocks.'), ('ln_pre', 'norm_pre'), ('ln_post', 'norm'), ('ln_', 'norm'),
('in_proj_', 'qkv.'), ('out_proj', 'proj'), ('mlp.c_fc', 'mlp.fc1'), ('mlp.c_proj', 'mlp.fc2'),
]
for k, v in state_dict.items():
if not k.startswith('visual.'):
continue
for sp in swaps:
k = k.replace(sp[0], sp[1])
if k == 'proj':
k = 'head.weight'
v = v.transpose(0, 1)
out_dict['head.bias'] = torch.zeros(v.shape[0])
elif k == 'class_embedding':
k = 'cls_token'
v = v.unsqueeze(0).unsqueeze(1)
elif k == 'pos_embed':
v = v.unsqueeze(0)
if v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
v = resize_pos_embed(
v,
model.pos_embed,
0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
model.patch_embed.grid_size
)
out_dict[k] = v
return out_dict
def checkpoint_filter_fn(
state_dict,
model,
adapt_layer_scale=False,
interpolation='bicubic',
antialias=True,
):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
import re
out_dict = {}
if 'model' in state_dict:
# For deit models
state_dict = state_dict['model']
if 'visual.class_embedding' in state_dict:
return _convert_openai_clip(state_dict, model)
for k, v in state_dict.items():
if 'patch_embed.proj.weight' in k:
O, I, H, W = model.patch_embed.proj.weight.shape
if 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)
if v.shape[-1] != W or v.shape[-2] != H:
v = resample_patch_embed(
v,
(H, W),
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1)
v = resample_abs_pos_embed(
v,
new_size=model.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
elif adapt_layer_scale and 'gamma_' in k:
# remap layer-scale gamma into sub-module (deit3 models)
k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
elif 'pre_logits' in k:
# NOTE representation layer removed as not used in latest 21k/1k pretrained weights
continue
out_dict[k] = v
return out_dict
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 = generate_default_cfgs({
# re-finetuned augreg 21k FT on in1k weights
'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg(
hf_hub_id='timm/'),
'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg(),
'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg(
hf_hub_id='timm/'),
# How to train your ViT (augreg) weights, pretrained on 21k FT on in1k
'vit_tiny_patch16_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_tiny_patch16_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch32_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch32_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch16_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch16_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch32_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch32_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch16_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch16_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch8_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_large_patch16_224.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True),
'vit_large_patch16_384.augreg_in21k_ft_in1k': _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',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
# patch models (weights from official Google JAX impl) pretrained on in21k FT on in1k
'vit_base_patch16_224.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
hf_hub_id='timm/'),
'vit_base_patch16_384.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch32_384.orig_in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=1.0),
# How to train your ViT (augreg) weights trained on in1k only
'vit_small_patch16_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_small_patch16_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch32_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch32_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch16_224.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz',
hf_hub_id='timm/',
custom_load=True),
'vit_base_patch16_384.augreg_in1k': _cfg(
url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz',
hf_hub_id='timm/',
custom_load=True, input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch14_224.untrained': _cfg(url=''),
'vit_huge_patch14_224.untrained': _cfg(url=''),
'vit_giant_patch14_224.untrained': _cfg(url=''),
'vit_gigantic_patch14_224.untrained': _cfg(url=''),
# patch models, imagenet21k (weights from official Google JAX impl)
'vit_large_patch32_224.orig_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
hf_hub_id='timm/',
num_classes=21843),
'vit_huge_patch14_224.orig_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
# How to train your ViT (augreg) weights, pretrained on in21k
'vit_tiny_patch16_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_small_patch32_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_small_patch16_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch32_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch16_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_base_patch8_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
'vit_large_patch16_224.augreg_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',
hf_hub_id='timm/',
custom_load=True, num_classes=21843),
# SAM trained models (https://arxiv.org/abs/2106.01548)
'vit_base_patch32_224.sam': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz', custom_load=True,
hf_hub_id='timm/'),
'vit_base_patch16_224.sam': _cfg(
url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz', custom_load=True,
hf_hub_id='timm/'),
# DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only)
'vit_small_patch16_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_small_patch8_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch16_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
'vit_base_patch8_224.dino': _cfg(
url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth',
hf_hub_id='timm/',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0),
# ViT ImageNet-21K-P pretraining by MILL
'vit_base_patch16_224_miil.in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth',
hf_hub_id='timm/',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221),
'vit_base_patch16_224_miil.in21k_ft_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.pth',
hf_hub_id='timm/',
mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'),
# Custom timm variants
'vit_base_patch16_rpn_224.in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth',
hf_hub_id='timm/'),
'vit_medium_patch16_gap_240.in12k': _cfg(
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=11821),
'vit_medium_patch16_gap_256.in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), crop_pct=0.95),
'vit_medium_patch16_gap_384.in12k_ft_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), crop_pct=0.95, crop_mode='squash'),
'vit_base_patch16_gap_224': _cfg(),
# CLIP pretrained image tower and related fine-tuned weights
'vit_base_patch32_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch32_clip_384.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 384, 384)),
'vit_base_patch32_clip_448.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 448, 448)),
'vit_base_patch16_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
'vit_base_patch16_clip_384.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.openai_ft_in12k_in1k': _cfg(
# hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch32_clip_384.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
'vit_base_patch16_clip_224.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95),
'vit_base_patch16_clip_384.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.openai_ft_in12k_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0),
'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg(
hf_hub_id='',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'),
'vit_base_patch32_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD),
'vit_base_patch16_clip_384.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'),
'vit_large_patch14_clip_224.openai_ft_in1k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0),
'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg(
#hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821),
'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
'vit_base_patch32_clip_224.openai_ft_in12k': _cfg(
# hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_base_patch16_clip_224.openai_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821),
'vit_large_patch14_clip_224.openai_ft_in12k': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821),
'vit_base_patch32_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_base_patch16_clip_224.laion2b': _cfg(
# hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512),
'vit_large_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=768),
'vit_huge_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_giant_patch14_clip_224.laion2b': _cfg(
hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K',
hf_hub_filename='open_clip_pytorch_model.bin',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024),
'vit_base_patch32_clip_224.openai': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_base_patch16_clip_224.openai': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
'vit_large_patch14_clip_224.openai': _cfg(
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768),
# experimental (may be removed)
'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95),
'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95),
'vit_small_patch16_36x1_224': _cfg(url=''),
'vit_small_patch16_18x2_224': _cfg(url=''),
'vit_base_patch16_18x2_224': _cfg(url=''),
# EVA fine-tuned weights from MAE style MIM - EVA-CLIP target pretrain
# https://github.com/baaivision/EVA/blob/7ecf2c0a370d97967e86d047d7af9188f78d2df3/eva/README.md#eva-l-learning-better-mim-representations-from-eva-clip
'eva_large_patch14_196.in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_21k_to_1k_ft_88p6.pt',
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 196, 196), crop_pct=1.0),
'eva_large_patch14_336.in22k_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_21k_to_1k_ft_89p2.pt',
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
'eva_large_patch14_196.in22k_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_1k_ft_88p0.pt',
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 196, 196), crop_pct=1.0),
'eva_large_patch14_336.in22k_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_1k_ft_88p65.pt',
hf_hub_id='timm/',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
'flexivit_small.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_small.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_small.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.1000ep_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_1000ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_base.300ep_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_large.1200ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_large.600ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_600ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_large.300ep_in1k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95),
'flexivit_base.patch16_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/vit_b16_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
'flexivit_base.patch30_in21k': _cfg(
url='https://storage.googleapis.com/big_vision/flexivit/vit_b30_i21k_300ep.npz', custom_load=True,
hf_hub_id='timm/',
input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843),
})
def _create_vision_transformer(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
if 'flexi' in variant:
# FIXME Google FlexiViT pretrained models have a strong preference for bilinear patch / embed
# interpolation, other pretrained models resize better w/ anti-aliased bicubic interpolation.
_filter_fn = partial(checkpoint_filter_fn, interpolation='bilinear', antialias=False)
else:
_filter_fn = checkpoint_filter_fn
return build_model_with_cfg(
VisionTransformer, variant, pretrained,
pretrained_filter_fn=_filter_fn,
**kwargs,
)
@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)
model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_small_patch16_224(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_small_patch16_384(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_small_patch8_224(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/8)
"""
model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6)
model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_large_patch16_224(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 @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_large_patch14_224(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/14)
"""
model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16)
model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_giant_patch14_224(pretrained=False, **kwargs):
""" ViT-Giant (little-g) 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)
model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_gigantic_patch14_224(pretrained=False, **kwargs):
""" ViT-Gigantic (big-G) 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)
model = _create_vision_transformer(
'vit_gigantic_patch14_224', pretrained=pretrained, **dict(model_kwargs, **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)
model = _create_vision_transformer(
'vit_base_patch16_224_miil', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_240(pretrained=False, **kwargs):
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 240x240
"""
model_kwargs = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_240', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_256(pretrained=False, **kwargs):
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 256x256
"""
model_kwargs = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_256', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_medium_patch16_gap_384(pretrained=False, **kwargs):
""" ViT-Medium (ViT-M/16) w/o class token, w/ avg-pool @ 384x384
"""
model_kwargs = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False,
global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False)
model = _create_vision_transformer(
'vit_medium_patch16_gap_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_gap_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 256x256
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, global_pool='avg', fc_norm=False)
model = _create_vision_transformer(
'vit_base_patch16_gap_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch32_clip_224(pretrained=False, **kwargs):
""" ViT-B/32 CLIP image tower @ 224x224
"""
model_kwargs = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch32_clip_384(pretrained=False, **kwargs):
""" ViT-B/32 CLIP image tower @ 384x384
"""
model_kwargs = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch32_clip_448(pretrained=False, **kwargs):
""" ViT-B/32 CLIP image tower @ 448x448
"""
model_kwargs = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch32_clip_448', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_clip_224(pretrained=False, **kwargs):
""" ViT-B/16 CLIP image tower
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch16_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_clip_384(pretrained=False, **kwargs):
""" ViT-B/16 CLIP image tower @ 384x384
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_base_patch16_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_large_patch14_clip_224(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/14) CLIP image tower
"""
model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_large_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_large_patch14_clip_336(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/14) CLIP image tower @ 336x336
"""
model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_large_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_224(pretrained=False, **kwargs):
""" ViT-Huge model (ViT-H/14) CLIP image tower.
"""
model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_huge_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_huge_patch14_clip_336(pretrained=False, **kwargs):
""" ViT-Huge model (ViT-H/14) CLIP image tower @ 336x336
"""
model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_huge_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_giant_patch14_clip_224(pretrained=False, **kwargs):
""" ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560
Pretrained weights from CLIP image tower.
"""
model_kwargs = dict(
patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm)
model = _create_vision_transformer(
'vit_giant_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
# Experimental models below
@register_model
def vit_base_patch32_plus_256(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/32+)
"""
model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
model = _create_vision_transformer(
'vit_base_patch32_plus_256', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_plus_240(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16+)
"""
model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5)
model = _create_vision_transformer(
'vit_base_patch16_plus_240', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_rpn_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ residual post-norm
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5,
class_token=False, block_fn=ResPostBlock, global_pool='avg')
model = _create_vision_transformer(
'vit_base_patch16_rpn_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_small_patch16_36x1_224(pretrained=False, **kwargs):
""" ViT-Base w/ LayerScale + 36 x 1 (36 block serial) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5)
model = _create_vision_transformer(
'vit_small_patch16_36x1_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_small_patch16_18x2_224(pretrained=False, **kwargs):
""" ViT-Small w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow.
"""
model_kwargs = dict(
patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock)
model = _create_vision_transformer(
'vit_small_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def vit_base_patch16_18x2_224(pretrained=False, **kwargs):
""" ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove.
Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock)
model = _create_vision_transformer(
'vit_base_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def eva_large_patch14_196(pretrained=False, **kwargs):
""" EVA-large model https://arxiv.org/abs/2211.07636 /via MAE MIM pretrain"""
model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
model = _create_vision_transformer(
'eva_large_patch14_196', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def eva_large_patch14_336(pretrained=False, **kwargs):
""" EVA-large model https://arxiv.org/abs/2211.07636 via MAE MIM pretrain"""
model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg')
model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def flexivit_small(pretrained=False, **kwargs):
""" FlexiViT-Small
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True)
model = _create_vision_transformer('flexivit_small', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def flexivit_base(pretrained=False, **kwargs):
""" FlexiViT-Base
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True)
model = _create_vision_transformer('flexivit_base', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def flexivit_large(pretrained=False, **kwargs):
""" FlexiViT-Large
"""
model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True)
model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
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