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eva02 non-CLIP weights on HF hub, add initial eva02 clip model configs w/ postnorm variant & attn LN

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Ross Wightman 2023-03-30 23:43:59 -07:00
parent ac67098147
commit 0737bd3ec8
3 changed files with 320 additions and 41 deletions
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12
timm/models/_hub.py
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@ -316,9 +316,17 @@ def generate_readme(model_card: dict, model_name: str):
readme_text += f"license: {model_card.get('license', 'apache-2.0')}\n"
if 'details' in model_card and 'Dataset' in model_card['details']:
readme_text += 'datasets:\n'
readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
if isinstance(model_card['details']['Dataset'], (tuple, list)):
for d in model_card['details']['Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Dataset'].lower()}\n"
if 'Pretrain Dataset' in model_card['details']:
readme_text += f"- {model_card['details']['Pretrain Dataset'].lower()}\n"
if isinstance(model_card['details']['Pretrain Dataset'], (tuple, list)):
for d in model_card['details']['Pretrain Dataset']:
readme_text += f"- {d.lower()}\n"
else:
readme_text += f"- {model_card['details']['Pretrain Dataset'].lower()}\n"
readme_text += "---\n"
readme_text += f"# Model card for {model_name}\n"
if 'description' in model_card:

341
timm/models/eva.py
View File

@ -55,7 +55,20 @@ class EvaAttention(nn.Module):
attn_drop: float = 0.,
proj_drop: float = 0.,
attn_head_dim: Optional[int] = None,
norm_layer: Optional[Callable] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
attn_drop:
proj_drop:
attn_head_dim:
norm_layer:
"""
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
@ -82,6 +95,7 @@ class EvaAttention(nn.Module):
self.q_bias = self.k_bias = self.v_bias = None
self.attn_drop = nn.Dropout(attn_drop)
self.norm = norm_layer(all_head_dim) if norm_layer is not None else nn.Identity()
self.proj = nn.Linear(all_head_dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
@ -124,6 +138,7 @@ class EvaAttention(nn.Module):
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.norm(x)
x = self.proj(x)
x = self.proj_drop(x)
return x
@ -138,16 +153,36 @@ class EvaBlock(nn.Module):
qkv_bias: bool = True,
qkv_fused: bool = True,
mlp_ratio: float = 4.,
scale_mlp: bool = False,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
init_values: Optional[float] = None,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
norm_layer: Callable = LayerNorm,
attn_head_dim: Optional[int] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
proj_drop:
attn_drop:
drop_path:
init_values:
act_layer:
norm_layer:
attn_head_dim:
"""
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = EvaAttention(
@ -158,6 +193,7 @@ class EvaBlock(nn.Module):
attn_drop=attn_drop,
proj_drop=proj_drop,
attn_head_dim=attn_head_dim,
norm_layer=norm_layer if scale_attn_inner else None,
)
self.gamma_1 = nn.Parameter(init_values * torch.ones(dim)) if init_values is not None else None
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
@ -204,6 +240,96 @@ class EvaBlock(nn.Module):
return x
class EvaBlockPostNorm(nn.Module):
""" EVA block w/ post-norm and support for swiglu, MLP norm scale, ROPE. """
def __init__(
self,
dim: int,
num_heads: int,
qkv_bias: bool = True,
qkv_fused: bool = True,
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
proj_drop: float = 0.,
attn_drop: float = 0.,
drop_path: float = 0.,
init_values: Optional[float] = None, # ignore for post-norm
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
attn_head_dim: Optional[int] = None,
):
"""
Args:
dim:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
proj_drop:
attn_drop:
drop_path:
init_values:
act_layer:
norm_layer:
attn_head_dim:
"""
super().__init__()
self.attn = EvaAttention(
dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qkv_fused=qkv_fused,
attn_drop=attn_drop,
proj_drop=proj_drop,
attn_head_dim=attn_head_dim,
norm_layer=norm_layer if scale_attn_inner else None,
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
hidden_features = int(dim * mlp_ratio)
if swiglu_mlp:
if scale_mlp:
# when norm in SwiGLU used, an impl with separate fc for gate & x is used
self.mlp = SwiGLU(
in_features=dim,
hidden_features=hidden_features,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
else:
# w/o any extra norm, an impl with packed fc1 weights is used, matches existing GluMLP
self.mlp = GluMlp(
in_features=dim,
hidden_features=hidden_features * 2,
norm_layer=norm_layer if scale_mlp else None,
act_layer=nn.SiLU,
gate_last=False,
drop=proj_drop,
)
else:
self.mlp = Mlp(
in_features=dim,
hidden_features=hidden_features,
act_layer=act_layer,
norm_layer=norm_layer if scale_mlp else None,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, rope: Optional[torch.Tensor] = None, attn_mask: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.norm1(self.attn(x, rope=rope, attn_mask=attn_mask)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class Eva(nn.Module):
""" Eva Vision Transformer w/ Abs & Rotary Pos Embed
@ -227,6 +353,7 @@ class Eva(nn.Module):
mlp_ratio: float = 4.,
swiglu_mlp: bool = False,
scale_mlp: bool = False,
scale_attn_inner: bool = False,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
@ -234,9 +361,38 @@ class Eva(nn.Module):
init_values: Optional[float] = None,
use_abs_pos_emb: bool = True,
use_rot_pos_emb: bool = False,
use_post_norm: bool = False,
ref_feat_shape: Optional[Union[Tuple[int, int], int]] = None,
head_init_scale: float = 0.001,
):
"""
Args:
img_size:
patch_size:
in_chans:
num_classes:
global_pool:
embed_dim:
depth:
num_heads:
qkv_bias:
qkv_fused:
mlp_ratio:
swiglu_mlp:
scale_mlp:
scale_attn_inner:
drop_rate:
attn_drop_rate:
drop_path_rate:
norm_layer:
init_values:
use_abs_pos_emb:
use_rot_pos_emb:
use_post_norm:
ref_feat_shape:
head_init_scale:
"""
super().__init__()
self.num_classes = num_classes
self.global_pool = global_pool
@ -268,15 +424,17 @@ class Eva(nn.Module):
self.rope = None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
block_fn = EvaBlockPostNorm if use_post_norm else EvaBlock
self.blocks = nn.ModuleList([
EvaBlock(
block_fn(
dim=embed_dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
qkv_fused=qkv_fused,
mlp_ratio=mlp_ratio,
scale_mlp=scale_mlp,
swiglu_mlp=swiglu_mlp,
scale_mlp=scale_mlp,
scale_attn_inner=scale_attn_inner,
proj_drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
@ -387,10 +545,24 @@ def checkpoint_filter_fn(
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('module', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
no_qkv = 'blocks.0.attn.q_proj.weight' in state_dict
mim_weights = 'mask_token' in state_dict
# prefix for loading OpenCLIP compatible weights
if 'visual.trunk.pos_embed' in state_dict:
prefix = 'visual.trunk.'
elif 'visual.pos_embed' in state_dict:
prefix = 'visual.'
else:
prefix = ''
mim_weights = prefix + 'mask_token' in state_dict
no_qkv = prefix + 'blocks.0.attn.q_proj.weight' in state_dict
len_prefix = len(prefix)
for k, v in state_dict.items():
if prefix:
if k.startswith(prefix):
k = k[len_prefix:]
else:
continue
if 'rope' in k:
# fixed embedding no need to load buffer from checkpoint
continue
@ -418,6 +590,7 @@ def checkpoint_filter_fn(
)
k = k.replace('mlp.ffn_ln', 'mlp.norm')
k = k.replace('attn.inner_attn_ln', 'attn.norm')
k = k.replace('mlp.w12', 'mlp.fc1')
k = k.replace('mlp.w1', 'mlp.fc1_g')
k = k.replace('mlp.w2', 'mlp.fc1_x')
@ -457,12 +630,13 @@ def _cfg(url='', **kwargs):
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': OPENAI_CLIP_MEAN, 'std': OPENAI_CLIP_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
# EVA 01 CLIP fine-tuned on imagenet-1k
'eva_giant_patch14_224.clip_ft_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt',
hf_hub_id='timm/',
@ -471,6 +645,8 @@ default_cfgs = generate_default_cfgs({
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),
# MIM EVA 01 pretrain, ft on in22k -> in1k
'eva_giant_patch14_336.m30m_ft_in22k_in1k': _cfg(
# hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt',
hf_hub_id='timm/',
@ -482,79 +658,113 @@ default_cfgs = generate_default_cfgs({
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD,
input_size=(3, 560, 560), crop_pct=1.0, crop_mode='squash'),
# in22k or m38m MIM pretrain w/ intermediate in22k fine-tune and final in1k fine-tune
'eva02_base_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_B_pt_in21k_medft_in21k_ft_in1k_p14.pt',
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_B_pt_in21k_medft_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
'eva02_large_patch14_448.mim_in22k_ft_in22k_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_in21k_medft_in21k_ft_in1k_p14.pt',
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_in21k_medft_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
'eva02_large_patch14_448.mim_m38m_ft_in22k_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_m38m_medft_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k_to_in1k/eva02_L_pt_m38m_medft_in21k_ft_in1k_p14.pt',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash',
),
# in22k or m3m MIM pretrain w/ in1k fine-tune
'eva02_tiny_patch14_336.mim_in22k_ft_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in1k/eva02_Ti_pt_in21k_ft_in1k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_Ti_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0,
),
'eva02_small_patch14_336.mim_in22k_ft_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in1k/eva02_S_pt_in21k_ft_in1k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_S_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 336, 336), crop_pct=1.0,
),
'eva02_base_patch14_448.mim_in22k_ft_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in1k/eva02_B_pt_in21k_ft_in1k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_B_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
'eva02_large_patch14_448.mim_in22k_ft_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in1k/eva02_L_pt_in21k_ft_in1k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_in21k_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
'eva02_large_patch14_448.mim_m38m_ft_in1k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02',
hf_hub_filename='eva02/cls/in1k/eva02_L_pt_m38m_ft_in1k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in1k/eva02_L_pt_m38m_ft_in1k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0,
),
# in22k or m3m MIM pretrain w/ in22k fine-tune
'eva02_base_patch14_448.mim_in22k_ft_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_B_pt_in21k_medft_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_B_pt_in21k_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
'eva02_large_patch14_448.mim_in22k_ft_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_in21k_medft_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_in21k_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
'eva02_large_patch14_448.mim_m38m_ft_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_m38m_medft_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/cls/in21k/eva02_L_pt_m38m_medft_in21k_p14.pt',
hf_hub_id='timm/',
input_size=(3, 448, 448), crop_pct=1.0, crop_mode='squash', num_classes=21841,
),
# in22k or m38m MIM pretrain
'eva02_tiny_patch14_224.mim_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_Ti_pt_in21k_p14.pt',
# hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_Ti_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_small_patch14_224.mim_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_S_pt_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_S_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_base_patch14_224.mim_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_B_pt_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_B_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_large_patch14_224.mim_in22k': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_in21k_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_in21k_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
'eva02_large_patch14_224.mim_m38m': _cfg(
hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_m38m_p14.pt',
#hf_hub_id='Yuxin-CV/EVA-02', hf_hub_filename='eva02/pt/eva02_L_pt_m38m_p14.pt',
hf_hub_id='timm/',
num_classes=0,
),
# EVA01 and EVA02 CLIP image towers
'eva_giant_patch14_224.clip': _cfg(
#hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA01_CLIP_g_14_plus_psz14_s11B.pt',
num_classes=1024,
),
'eva02_base_patch14_clip_224.clip': _cfg(
#hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
num_classes=512,
),
'eva02_large_patch14_clip_224.clip': _cfg(
#hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_L_psz14_s4B.pt',
num_classes=768,
),
'eva02_enormous_patch14_clip_224.clip': _cfg(
#hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_CLIP_E_psz14_plus_s9B.pt',
num_classes=1024,
),
'eva02_enormous_patch14_clip_224.pretrain': _cfg(
#hf_hub_id='QuanSun/EVA-CLIP', hf_hub_filename='EVA02_E_psz14.pt',
num_classes=0,
),
@ -632,8 +842,8 @@ def eva02_base_patch14_224(pretrained=False, **kwargs):
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
scale_mlp=True,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
@ -651,8 +861,8 @@ def eva02_large_patch14_224(pretrained=False, **kwargs):
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
scale_mlp=True,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
@ -704,8 +914,8 @@ def eva02_base_patch14_448(pretrained=False, **kwargs):
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
scale_mlp=True,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
@ -723,10 +933,71 @@ def eva02_large_patch14_448(pretrained=False, **kwargs):
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
scale_mlp=True,
swiglu_mlp=True,
scale_mlp=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
)
model = _create_eva('eva02_large_patch14_448', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def eva02_base_patch16_clip_224(pretrained=False, **kwargs):
# A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_base
model_kwargs = dict(
img_size=224,
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
qkv_fused=False,
mlp_ratio=4 * 2 / 3,
swiglu_mlp=True,
scale_mlp=True,
scale_attn_inner=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
global_pool='token',
)
model = _create_eva('eva02_base_patch16_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def eva02_large_patch14_clip_224(pretrained=False, **kwargs):
# A EVA-CLIP specific variant that adds additional attn scale layernorm to eva02_large
model_kwargs = dict(
img_size=224,
patch_size=14,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4 * 2 / 3,
qkv_fused=False,
swiglu_mlp=True,
scale_mlp=True,
scale_attn_inner=True,
use_rot_pos_emb=True,
ref_feat_shape=(16, 16), # 224/14
global_pool='token',
)
model = _create_eva('eva02_large_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model
@register_model
def eva02_enormous_patch14_clip_224(pretrained=False, **kwargs):
# A EVA-CLIP specific variant that uses residual post-norm in blocks
model_kwargs = dict(
img_size=224,
patch_size=14,
embed_dim=1792,
depth=64,
num_heads=16,
mlp_ratio=15360 / 1792,
use_post_norm=True,
global_pool='token',
)
model = _create_eva('eva02_enormous_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs))
return model

8
timm/models/vision_transformer.py
View File

@ -1216,22 +1216,22 @@ default_cfgs = generate_default_cfgs({
# 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/',
hf_hub_id='timm/', license='mit',
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/',
hf_hub_id='timm/', license='mit',
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/',
hf_hub_id='timm/', license='mit',
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/',
hf_hub_id='timm/', license='mit',
mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD,
input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'),