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"""Multi-Scale TRAM — Robust token selection combining multiple strategies.
Unlike standard TRAM which relies solely on attention centrality, MultiScaleTRAM
combines three complementary selection strategies:
1. Attention-based (70%) — discriminative regions via ViT attention
2. Uniform spatial (20%) — guaranteed spatial coverage
3. Random sampling (10%) — regularization to prevent selection bias
This design makes token selection more robust to:
- Domain shift (attention bias from different distributions)
- Training instability (poor early-stage attention)
- Over-concentration (selecting only high-attention regions)
"""
import math
import torch
import torch.nn as nn
class MultiScaleTRAM(nn.Module):
"""Multi-scale token selection for robust feature extraction.
Parameters
----------
num_tokens : int
Total number of tokens to select (K). Default 30 for fingerprints.
attention_ratio : float
Fraction of tokens selected via attention centrality (0-1).
uniform_ratio : float
Fraction of tokens selected via uniform spatial sampling (0-1).
random_ratio : float
Fraction of tokens selected randomly (0-1).
Note: attention_ratio + uniform_ratio + random_ratio must equal 1.0
grid_size : tuple[int, int]
Spatial grid dimensions (H, W) for uniform sampling.
Default (14, 14) for 224px images with 16px patches.
method : str
Attention aggregation method: "incoming_sum" (default) | "eigenvector".
"""
def __init__(
self,
num_tokens: int = 30,
attention_ratio: float = 0.7,
uniform_ratio: float = 0.2,
random_ratio: float = 0.1,
grid_size: tuple[int, int] = (14, 14),
method: str = "incoming_sum",
):
super().__init__()
# Validate ratios sum to 1
total_ratio = attention_ratio + uniform_ratio + random_ratio
if not math.isclose(total_ratio, 1.0, abs_tol=1e-6):
raise ValueError(
f"Ratios must sum to 1.0, got {total_ratio:.4f} "
f"({attention_ratio} + {uniform_ratio} + {random_ratio})"
)
self.num_tokens = num_tokens
self.attention_ratio = attention_ratio
self.uniform_ratio = uniform_ratio
self.random_ratio = random_ratio
self.grid_size = grid_size
self.method = method
# Compute number of tokens per strategy
self.k_attention = max(1, int(num_tokens * attention_ratio))
self.k_uniform = max(1, int(num_tokens * uniform_ratio))
self.k_random = max(0, num_tokens - self.k_attention - self.k_uniform)
# Adjust if rounding causes mismatch
total_k = self.k_attention + self.k_uniform + self.k_random
if total_k != num_tokens:
# Give extra tokens to attention strategy
self.k_attention += (num_tokens - total_k)
# ------------------------------------------------------------------
def forward(
self,
patch_tokens: torch.Tensor,
attn_maps: list[torch.Tensor],
num_prefix_tokens: int = 1,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Select K tokens using multi-scale strategy.
Args:
patch_tokens: (B, P, D) patch features from ViT.
attn_maps: List of L tensors, each (B, H, N, N) where N = P + num_prefix_tokens.
num_prefix_tokens: Number of prefix tokens (CLS + registers) to skip.
Returns:
selected_tokens: (B, K, D) features of selected tokens.
selected_indices: (B, K) indices into the P patch tokens (sorted).
centrality_scores: (B, P) attention centrality score for every patch.
"""
B, P, D = patch_tokens.shape
device = patch_tokens.device
# 1. Attention-based selection
centrality, attention_indices = self._select_by_attention(
attn_maps, num_prefix_tokens, B, P, device
)
# 2. Uniform spatial selection
uniform_indices = self._select_uniform_spatial(B, P, device)
# 3. Random selection (avoid already selected)
random_indices = self._select_random(
attention_indices, uniform_indices, B, P, device
)
# 4. Combine all indices and sort to preserve spatial order
all_selected = torch.cat([attention_indices, uniform_indices, random_indices], dim=1)
all_selected_sorted, _ = all_selected.sort(dim=-1)
# 5. Gather selected token features (WITH gradient for end-to-end training)
selected_tokens = torch.gather(
patch_tokens,
dim=1,
index=all_selected_sorted.unsqueeze(-1).expand(-1, -1, D),
)
return selected_tokens, all_selected_sorted, centrality
# ------------------------------------------------------------------
def _select_by_attention(
self,
attn_maps: list[torch.Tensor],
num_prefix_tokens: int,
B: int,
P: int,
device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Select tokens based on attention centrality.
Returns:
centrality: (B, P) centrality scores.
indices: (B, k_attention) selected token indices.
"""
centrality = torch.zeros(B, P, device=device)
if self.method == "incoming_sum":
# Aggregate incoming attention across all layers
for attn in attn_maps:
# attn: (B, H, N, N)
a = attn.max(dim=1).values # (B, N, N) - max across heads
a_pp = a[:, num_prefix_tokens:, num_prefix_tokens:] # (B, P, P)
# Incoming attention: sum over source dimension
centrality = centrality + a_pp.sum(dim=1) # (B, P)
elif self.method in ("eigenvector", "tram"):
# Original TRAM paper centrality (Marchetti et al.)
L = len(attn_maps)
for idx, attn in enumerate(attn_maps):
a = attn.max(dim=1).values # (B, N, N)
a_pp = a[:, num_prefix_tokens:, num_prefix_tokens:] # (B, P, P)
# In-degree: how much each token is attended to
d_in = a_pp.sum(dim=1) # (B, P)
# Rescale: weight rows by source's in-degree
a_rescaled = a_pp * d_in.unsqueeze(-1) # (B, P, P)
# Weighted in-degree for this layer
centrality_l = a_rescaled.sum(dim=1) # (B, P)
# Accumulate with linear layer weighting
layer_weight = (idx + 1) / L
centrality = centrality_l * layer_weight + centrality
else:
raise ValueError(f"Unknown method: {self.method}")
# Select top-k by centrality
_, top_indices = centrality.topk(self.k_attention, dim=-1)
return centrality, top_indices
# ------------------------------------------------------------------
def _select_uniform_spatial(
self,
B: int,
P: int,
device: torch.device,
) -> torch.Tensor:
"""Select tokens uniformly from spatial grid.
Strategy: Divide grid into regions and sample 1 token per region.
Returns:
indices: (B, k_uniform) selected token indices.
"""
grid_h, grid_w = self.grid_size
expected_P = grid_h * grid_w
if P != expected_P:
# Fallback: random sampling if grid size mismatch
indices = torch.randint(0, P, (B, self.k_uniform), device=device)
return indices
# Compute step size for uniform sampling
# Want sqrt(k_uniform) regions per dimension
n_regions_per_dim = max(1, int(math.sqrt(self.k_uniform) + 0.5))
step_h = max(1, grid_h // n_regions_per_dim)
step_w = max(1, grid_w // n_regions_per_dim)
# Generate uniform grid indices
uniform_indices_flat: list[int] = []
for i in range(0, grid_h, step_h):
for j in range(0, grid_w, step_w):
if len(uniform_indices_flat) < self.k_uniform:
idx = i * grid_w + j
uniform_indices_flat.append(idx)
# Pad if needed
while len(uniform_indices_flat) < self.k_uniform:
uniform_indices_flat.append(P // 2) # Center token as fallback
uniform_indices_flat = uniform_indices_flat[:self.k_uniform]
uniform_indices = torch.tensor(
uniform_indices_flat, device=device, dtype=torch.long
).unsqueeze(0).expand(B, -1)
return uniform_indices
# ------------------------------------------------------------------
def _select_random(
self,
attention_indices: torch.Tensor,
uniform_indices: torch.Tensor,
B: int,
P: int,
device: torch.device,
) -> torch.Tensor:
"""Select random tokens, avoiding already selected ones.
Returns:
indices: (B, k_random) selected token indices.
"""
if self.k_random == 0:
return torch.zeros(B, 0, dtype=torch.long, device=device)
# Build mask of already selected tokens
selected_mask = torch.zeros(B, P, dtype=torch.bool, device=device)
selected_mask.scatter_(1, attention_indices, True)
selected_mask.scatter_(1, uniform_indices, True)
# Sample from remaining tokens
random_indices_list: list[torch.Tensor] = []
all_indices = torch.arange(P, device=device)
for b in range(B):
available = all_indices[~selected_mask[b]]
if len(available) >= self.k_random:
# Sample without replacement
perm = torch.randperm(len(available), device=device)[:self.k_random]
random_idx = available[perm]
else:
# Not enough unique tokens - sample with replacement from all tokens
random_idx = all_indices[
torch.randint(0, P, (self.k_random,), device=device)
]
random_indices_list.append(random_idx)
random_indices = torch.stack(random_indices_list, dim=0)
return random_indices
# ------------------------------------------------------------------
def extra_repr(self) -> str:
"""String representation for debugging."""
return (
f"num_tokens={self.num_tokens}, "
f"attention={self.k_attention}, "
f"uniform={self.k_uniform}, "
f"random={self.k_random}, "
f"grid_size={self.grid_size}, "
f"method={self.method}"
)
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