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import math
from functools import partial
from urllib.parse import urlparse
from typing import Union, Callable, Literal, Optional, Type, Set, Tuple
import torch
import torch.nn as nn
from timm.models.vision_transformer import Mlp
from timm.layers import PatchDropout, AttentionPoolLatent
from huggingface_hub import hf_hub_download, load_state_dict_from_file
from spectre.utils import global_pool_nlc, to_3tuple, resample_abs_pos_embed
from spectre.models.vision_transformer import Block
from spectre.models.layers import RotaryPositionEmbedding
class FeatureVisionTransformer(nn.Module):
""" Vision Transformer that accepts flattened patches as input.
"""
def __init__(
self,
grid_size: Optional[Union[int, Tuple[int, int, int]]] = None,
patch_dim: int = 768,
num_classes: int = 1000,
global_pool: Literal['', 'avg', 'avgmax', 'max', 'token', 'map'] = 'token',
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
attn_mode: str = 'mha',
q_proj_dim: Optional[int] = None,
kv_proj_dim: Optional[int] = None,
mlp_ratio: float = 4.,
qkv_bias: bool = True,
qk_norm: bool = False,
proj_bias: bool = True,
init_values: Optional[float] = None,
class_token: bool = True,
pos_embed: str = 'learn',
no_embed_class: bool = False,
rope_kwargs: Optional[dict] = None,
reg_tokens: int = 0,
pre_norm: bool = False,
final_norm: bool = True,
fc_norm: Optional[bool] = None,
dynamic_grid_size: bool = False,
drop_rate: float = 0.,
pos_drop_rate: float = 0.,
patch_drop_rate: float = 0.,
proj_drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
norm_layer: Optional[Union[Callable, Type[torch.nn.Module]]] = None,
act_layer: Optional[Union[Callable, Type[torch.nn.Module]]] = None,
block_fn: Type[nn.Module] = Block,
mlp_layer: Type[nn.Module] = Mlp,
) -> None:
"""
Args:
num_patches: Number of patches in the input.
patch_dim: Dimension of each flattened input patch.
num_classes: Number of classes for classification head.
global_pool: Type of global pooling for final sequence (default: 'token').
embed_dim: Transformer embedding dimension.
depth: Depth of transformer.
num_heads: Number of attention heads.
attn_mode: Attention mode ('mha', 'mqa', 'mla').
q_proj_dim: Query projection dimension for 'mla' mode.
kv_proj_dim: Key, value projection dimension for 'mla' mode.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
qkv_bias: Enable bias for qkv projections if True.
init_values: Layer-scale init values (layer-scale enabled if not None).
class_token: Use class token.
no_embed_class: Don't include position embeddings for class (or reg) tokens.
reg_tokens: Number of register tokens.
pre_norm: Enable norm after embeddings, before transformer blocks (standard in CLIP ViT).
final_norm: Enable norm after transformer blocks, before head (standard in most ViT).
fc_norm: Move final norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
drop_rate: Head dropout rate.
pos_drop_rate: Position embedding dropout rate.
attn_drop_rate: Attention dropout rate.
drop_path_rate: Stochastic depth rate.
weight_init: Weight initialization scheme.
fix_init: Apply weight initialization fix (scaling w/ layer index).
norm_layer: Normalization layer.
act_layer: MLP activation layer.
block_fn: Transformer block layer.
"""
super().__init__()
assert global_pool in ('', 'avg', 'avgmax', 'max', 'token', 'map')
assert class_token or global_pool != 'token'
assert pos_embed in ('', 'none', 'learn', 'rope')
assert attn_mode in ('mha', 'mqa', 'mla')
assert grid_size is not None or pos_embed in ('', 'none', 'rope')
rope_kwargs = {} if rope_kwargs is None else dict(rope_kwargs)
rope_kwargs.setdefault("dtype", torch.float32) # robust with mixed-precision
use_fc_norm = global_pool in ('avg', 'avgmax', 'max') 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.grid_size = None if grid_size is None else to_3tuple(grid_size)
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
self.num_prefix_tokens = 1 if class_token else 0
self.num_prefix_tokens += reg_tokens
self.num_reg_tokens = reg_tokens
self.has_class_token = class_token
self.no_embed_class = no_embed_class # don't embed prefix positions (includes reg)
self.dynamic_grid_size = dynamic_grid_size
self.num_patches = None if grid_size is None else int(math.prod(grid_size))
self.patch_proj = nn.Linear(patch_dim, embed_dim, proj_bias)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
self.reg_token = nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
self.pos_embed, self.rope, self.requires_per_sample_rope = None, None, False
if pos_embed == 'learn':
embed_len = self.num_patches if no_embed_class else self.num_patches + self.num_prefix_tokens
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * .02)
if pos_embed == 'rope':
self.rope = RotaryPositionEmbedding(
embed_dim=embed_dim,
num_heads=num_heads,
**rope_kwargs,
)
self.pos_drop = nn.Dropout(p=pos_drop_rate)
if patch_drop_rate > 0:
self.patch_drop = PatchDropout(
patch_drop_rate,
num_prefix_tokens=self.num_prefix_tokens,
)
else:
self.patch_drop = nn.Identity()
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
dpr = [drop_path_rate * i / (depth - 1) if depth > 1 else 0.0 for i in range(depth)] # stochastic depth decay rule
self.blocks = nn.Sequential(*[
block_fn(
dim=embed_dim,
num_heads=num_heads,
attn_mode=attn_mode,
q_proj_dim=q_proj_dim,
kv_proj_dim=kv_proj_dim,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
proj_bias=proj_bias,
init_values=init_values,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
mlp_layer=mlp_layer,
)
for i in range(depth)])
self.feature_info = [
dict(module=f'blocks.{i}', num_chs=embed_dim) for i in range(depth)]
self.norm = norm_layer(embed_dim) if final_norm and not use_fc_norm else nn.Identity()
# Classifier Head
if global_pool == 'map':
self.attn_pool = AttentionPoolLatent(
self.embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
act_layer=act_layer,
)
else:
self.attn_pool = None
self.fc_norm = norm_layer(embed_dim) if final_norm and use_fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.init_weights()
def init_weights(self) -> None:
if self.pos_embed is not None and not self.pos_embed.is_meta:
nn.init.trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None and not self.cls_token.is_meta:
nn.init.normal_(self.cls_token, std=1e-6)
if self.reg_token is not None and not self.reg_token.is_meta:
nn.init.normal_(self.reg_token, std=1e-6)
self.apply(self._init_weights)
def _init_weights(self, m: nn.Module) -> None:
# this fn left here for compat with downstream users
if isinstance(m, nn.Linear):
if not m.weight.is_meta:
nn.init.trunc_normal_(m.weight, std=.02)
if m.bias is not None and not m.bias.is_meta:
nn.init.zeros_(m.bias)
@torch.jit.ignore
def no_weight_decay(self) -> Set:
return {'pos_embed', 'cls_token', 'dist_token'}
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'avgmax', 'max', 'token', 'map')
if global_pool == 'map' and self.attn_pool is None:
assert False, "Cannot currently add attention pooling in reset_classifier()."
elif global_pool != 'map' and self.attn_pool is not None:
self.attn_pool = None # remove attention pooling
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: torch.Tensor,
grid_size: Optional[Union[int, Tuple[int, int, int]]] = None,
):
if self.pos_embed is None and self.rope is None:
x = x.view(x.shape[0], -1, x.shape[-1])
if self.reg_token is not None:
x = torch.cat([self.reg_token.expand(x.shape[0], -1, -1), x], dim=1)
if self.cls_token is not None:
x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
return x, None
if self.dynamic_grid_size or self.rope is not None:
assert grid_size is not None, "grid_size must be provided when using dynamic_grid_size or RoPE."
pos_embed, rope = None, None
if self.pos_embed is not None:
if self.dynamic_grid_size:
H, W, D = to_3tuple(grid_size)
prev_grid_size = self.grid_size
pos_embed = resample_abs_pos_embed(
self.pos_embed,
new_size=(H, W, D),
old_size=prev_grid_size,
num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
)
else:
pos_embed = self.pos_embed
if self.rope is not None:
B = x.shape[0]
H, W, D = to_3tuple(grid_size)
if self.requires_per_sample_rope:
rope = [self.rope(H=H, W=W, D=D) for _ in range(B)]
else:
rope = self.rope(H=H, W=W, D=D)
to_cat = []
if self.cls_token is not None:
to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
if self.reg_token is not None:
to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
if self.no_embed_class:
# deit-3, updated JAX (big vision)
# position embedding does not overlap with class token, add then concat
if pos_embed is not None:
x = x + pos_embed
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
else:
# original timm, JAX, and deit vit impl
# pos_embed has entry for class token, concat then add
if to_cat:
x = torch.cat(to_cat + [x], dim=1)
if pos_embed is not None:
x = x + pos_embed
return self.pos_drop(x), rope
def forward_features(
self,
x: torch.Tensor,
grid_size: Optional[Union[int, Tuple[int, int, int]]] = None,
) -> torch.Tensor:
assert x.ndim == 3, f"Expected input with 3 dimensions (B, N, C), got {x.ndim}."
x = self.patch_proj(x)
x, rope = self._pos_embed(x, grid_size)
x = self.patch_drop(x)
x = self.norm_pre(x)
for blk in self.blocks:
x = blk(x, rope=rope)
x = self.norm(x)
return x
def pool(self, x: torch.Tensor, pool_type: Optional[str] = None) -> torch.Tensor:
if self.attn_pool is not None:
x = self.attn_pool(x)
return x
pool_type = self.global_pool if pool_type is None else pool_type
x = global_pool_nlc(x, pool_type=pool_type, num_prefix_tokens=self.num_prefix_tokens)
return x
def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
x = self.pool(x)
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(
self,
x: torch.Tensor,
grid_size: Optional[Union[int, Tuple[int, int, int]]] = None,
) -> torch.Tensor:
x = self.forward_features(x, grid_size)
x = self.forward_head(x)
return x
@classmethod
def from_pretrained(
cls,
checkpoint_path_or_url: Union[str, os.PathLike],
verbose: bool = True,
**kwargs
) -> 'FeatureVisionTransformer':
"""Load pretrained model weights from a local path or a URL."""
model = cls(**kwargs)
def _is_url(path: str) -> bool:
try:
parsed = urlparse(str(path))
return parsed.scheme in ('http', 'https')
except Exception:
return False
def _is_hf_url(path: str) -> bool:
try:
parsed = urlparse(str(path))
return 'huggingface.co' in parsed.netloc
except Exception:
return False
if _is_hf_url(checkpoint_path_or_url):
if verbose:
print(f"Downloading pretrained weights from Hugging Face URL: {checkpoint_path_or_url}")
# Extract repo_id and filename from the URL
parsed = urlparse(checkpoint_path_or_url)
parts = parsed.path.strip('/').split('/')
repo_id = '/'.join(parts[:2]) # e.g., 'cclaess/SPECTRE'
filename = parts[-1] # e.g., 'spectre_backbone_vit_large_patch16_128.pt'
local_path = hf_hub_download(repo_id=repo_id, filename=filename)
state_dict = load_state_dict_from_file(local_path, map_location='cpu')
elif _is_url(checkpoint_path_or_url):
if verbose:
print(f"Downloading pretrained weights from URL: {checkpoint_path_or_url}")
state_dict = torch.hub.load_state_dict_from_url(
checkpoint_path_or_url, map_location='cpu', weights_only=False, progress=verbose)
else:
local_path = os.fspath(checkpoint_path_or_url)
if not os.path.exists(local_path):
raise FileNotFoundError(f"Checkpoint file not found: {local_path}")
if verbose:
print(f"Loading checkpoint from local path: {local_path}")
state_dict = torch.load(local_path, map_location='cpu', weights_only=False)
msg = model.load_state_dict(state_dict, strict=False)
if verbose:
print(f"Loaded pretrained weights with msg: {msg}")
return model
def feat_vit_tiny(
patch_dim,
checkpoint_path_or_url: Optional[str] = None,
**kwargs,
) -> FeatureVisionTransformer:
"""Feature ViT-Tiny model.
"""
kwargs = dict(
patch_dim=patch_dim,
embed_dim=192,
depth=2,
num_heads=2,
mlp_ratio=4,
qkv_bias=True,
norm_layer=nn.LayerNorm,
**kwargs,
)
if checkpoint_path_or_url is not None:
return FeatureVisionTransformer.from_pretrained(checkpoint_path_or_url, **kwargs)
return FeatureVisionTransformer(**kwargs)
def feat_vit_small(
patch_dim,
checkpoint_path_or_url: Optional[str] = None,
**kwargs,
) -> FeatureVisionTransformer:
"""Feature ViT-Small model.
"""
kwargs = dict(
patch_dim=patch_dim,
embed_dim=384,
depth=2,
num_heads=4,
mlp_ratio=4,
qkv_bias=True,
norm_layer=nn.LayerNorm,
**kwargs,
)
if checkpoint_path_or_url is not None:
return FeatureVisionTransformer.from_pretrained(checkpoint_path_or_url, **kwargs)
return FeatureVisionTransformer(**kwargs)
def feat_vit_base(
patch_dim,
checkpoint_path_or_url: Optional[str] = None,
**kwargs,
) -> FeatureVisionTransformer:
"""Feature ViT-Base model.
"""
kwargs = dict(
patch_dim=patch_dim,
embed_dim=768,
depth=2,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
norm_layer=nn.LayerNorm,
**kwargs,
)
if checkpoint_path_or_url is not None:
return FeatureVisionTransformer.from_pretrained(checkpoint_path_or_url, **kwargs)
return FeatureVisionTransformer(**kwargs)
def feat_vit_large(
patch_dim,
checkpoint_path_or_url: Optional[str] = None,
**kwargs,
) -> FeatureVisionTransformer:
"""Feature ViT-Large model.
"""
kwargs = dict(
patch_dim=patch_dim,
embed_dim=1080,
depth=4,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
norm_layer=nn.LayerNorm,
**kwargs,
)
if checkpoint_path_or_url is not None:
return FeatureVisionTransformer.from_pretrained(checkpoint_path_or_url, **kwargs)
return FeatureVisionTransformer(**kwargs)
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