File size: 11,815 Bytes
0e61117 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 |
"""
heatmap.py
----------
Grad-ECLIP visual explanations for CLIP/PaintingCLIP models.
Generates heatmap overlays showing which image regions contribute to image-text similarity.
Based on "Gradient-based Visual Explanation for Transformer-based CLIP"
by Zhao et al. (ICML 2024)
Public entry point:
------------------
generate_heatmap(
image, # str | PIL.Image.Image
sentence, # caption text
model, # CLIPModel or PEFT-wrapped model
processor, # CLIPProcessor
device, # torch.device
*,
layer_idx: int = -1, # which visual transformer block to explain
alpha: float = 0.45, # overlay opacity
colormap: int = cv2.COLORMAP_JET,
resize: Optional[Tuple[int, int]] = None,
) -> PIL.Image.Image # RGB overlay for display
"""
from __future__ import annotations
from typing import Any, Dict, Optional, Tuple, Union
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from transformers import CLIPModel, CLIPProcessor
# ============================================================================ #
# Core Grad-ECLIP Implementation #
# ============================================================================ #
class _GradECLIPHooks:
"""
Context manager for forward/backward hooks to capture Grad-ECLIP components.
"""
def __init__(self, model: CLIPModel, layer_idx: int):
self.model = model
self.layer_idx = layer_idx
self.captures: Dict[str, Any] = {}
self.handles = []
def __enter__(self):
# Get target layer
vision_layers = self.model.vision_model.encoder.layers
if self.layer_idx < 0:
self.layer_idx = len(vision_layers) + self.layer_idx
self.target_layer = vision_layers[self.layer_idx]
# Register hooks
self._register_forward_hook()
self._register_backward_hook()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
# Clean up hooks
for handle in self.handles:
handle.remove()
self.handles.clear()
def _register_forward_hook(self):
"""Register forward hook to capture Q, K, V and attention weights."""
def forward_hook(module, input, output):
if len(input) > 0:
hidden_states = input[0]
# Get attention inputs
x = hidden_states
if hasattr(module.self_attn, "layer_norm"):
x = module.self_attn.layer_norm(x)
# Compute Q, K, V
if hasattr(module.self_attn, "q_proj"):
batch_size, seq_len, hidden_dim = x.shape
Q = module.self_attn.q_proj(x)
K = module.self_attn.k_proj(x)
V = module.self_attn.v_proj(x)
# Store raw projections
self.captures["V"] = V
self.captures["hidden_states_pre"] = hidden_states
# Compute attention for head-averaged weights
head_dim = hidden_dim // module.self_attn.num_heads
num_heads = module.self_attn.num_heads
# Reshape for multi-head attention
Q_heads = Q.view(
batch_size, seq_len, num_heads, head_dim
).transpose(1, 2)
K_heads = K.view(
batch_size, seq_len, num_heads, head_dim
).transpose(1, 2)
# Compute attention weights
scale = head_dim**-0.5
attn_weights = (
torch.matmul(Q_heads, K_heads.transpose(-2, -1)) * scale
)
attn_weights = torch.softmax(attn_weights, dim=-1)
# Store for later use
self.captures["Q"] = Q_heads
self.captures["K"] = K_heads
self.captures["attn_weights"] = attn_weights.mean(
dim=1
) # Average over heads
handle = self.target_layer.register_forward_hook(forward_hook)
self.handles.append(handle)
def _register_backward_hook(self):
"""Register backward hook to capture gradients."""
def backward_hook(module, grad_input, grad_output):
if len(grad_output) > 0:
self.captures["grad_attn"] = grad_output[0]
handle = self.target_layer.register_full_backward_hook(backward_hook)
self.handles.append(handle)
def get_captures(self) -> Dict[str, torch.Tensor]:
"""Return captured tensors."""
return self.captures
def _compute_gradeclip_importance(
captures: Dict[str, torch.Tensor],
use_k_similarity: bool = True,
device: torch.device = None,
) -> torch.Tensor:
"""
Compute Grad-ECLIP importance scores from captured tensors.
Args:
captures: Dictionary with captured tensors from hooks
use_k_similarity: Whether to use Q-K similarity weighting
device: Computation device
Returns:
Importance scores for each patch (excluding CLS token)
"""
# Extract captured tensors
V = captures.get("V")
grad_attn = captures.get("grad_attn")
attn_weights = captures.get("attn_weights")
if V is None or grad_attn is None:
raise ValueError("Missing required captures for Grad-ECLIP computation")
# 1. Channel importance: gradients at CLS token
grad_cls = grad_attn[0, 0, :] # Shape: (hidden_dim,)
# 2. Extract patch values (exclude CLS token)
V_patches = V[0, 1:, :] # Shape: (num_patches, hidden_dim)
num_patches = V_patches.shape[0]
# 3. Get spatial attention weights
if attn_weights is not None:
# Use captured attention from CLS to patches
cls_attn = attn_weights[0, 0, 1 : num_patches + 1]
else:
# Fallback: uniform weights
cls_attn = torch.ones(num_patches, device=device or V.device) / num_patches
# 4. Optional: Apply Q-K similarity normalization
if use_k_similarity and "Q" in captures and "K" in captures:
Q = captures["Q"]
K = captures["K"]
# Get CLS token query (average over heads)
q_cls = Q[:, :, 0:1, :].mean(dim=1) # Shape: (1, 1, head_dim)
k_patches = K[:, :, 1:, :].mean(dim=1) # Shape: (1, num_patches, head_dim)
# Normalize and compute cosine similarity
q_cls = F.normalize(q_cls, dim=-1)
k_patches = F.normalize(k_patches, dim=-1)
k_similarity = torch.matmul(q_cls, k_patches.transpose(-2, -1)).squeeze()
# Normalize to [0, 1]
k_similarity = (k_similarity - k_similarity.min()) / (
k_similarity.max() - k_similarity.min() + 1e-8
)
# Apply K-similarity weighting
cls_attn = cls_attn * k_similarity[:num_patches]
# 5. Compute importance: ReLU(Σ_c grad_c * v_i,c * attn_i)
importance = (grad_cls * V_patches).sum(dim=-1) # Channel-wise importance
importance = importance * cls_attn # Spatial weighting
importance = torch.relu(importance) # ReLU activation
return importance
# ============================================================================ #
# Public API #
# ============================================================================ #
def generate_heatmap(
image: Union[str, Image.Image],
sentence: str,
model: CLIPModel,
processor: CLIPProcessor,
device: torch.device,
*,
layer_idx: int = -1,
alpha: float = 0.45,
colormap: int = cv2.COLORMAP_JET,
resize: Optional[Tuple[int, int]] = None,
) -> Image.Image:
"""
Generate Grad-ECLIP heatmap overlay for image-text pair.
Parameters
----------
image : str or PIL.Image
Input image path or PIL Image object
sentence : str
Text description to explain
model : CLIPModel
Pre-loaded CLIP model (possibly with LoRA adapter)
processor : CLIPProcessor
CLIP processor for preprocessing
device : torch.device
Computation device
layer_idx : int, optional
Which vision transformer layer to analyze (default: -1 for last layer)
alpha : float, optional
Heatmap overlay opacity (default: 0.45)
colormap : int, optional
OpenCV colormap for visualization (default: COLORMAP_JET)
resize : tuple, optional
Target (width, height) for output image
Returns
-------
PIL.Image
RGB image with heatmap overlay
"""
# Load image if path provided
if isinstance(image, str):
pil_image = Image.open(image).convert("RGB")
else:
pil_image = image.convert("RGB")
# Store original size
orig_size = pil_image.size # (width, height)
# Apply resize if requested
if resize:
display_image = pil_image.resize(resize, Image.Resampling.BICUBIC)
else:
display_image = pil_image
# Prepare inputs
inputs = processor(
images=pil_image, text=sentence, return_tensors="pt", padding=True
)
inputs = {k: v.to(device) for k, v in inputs.items()}
# Temporarily enable gradients
model_requires_grad = [p.requires_grad for p in model.parameters()]
for param in model.parameters():
param.requires_grad = True
try:
# Forward and backward pass with hooks
with torch.set_grad_enabled(True):
with _GradECLIPHooks(model, layer_idx) as hooks:
# Forward pass
outputs = model(**inputs, output_attentions=False)
# Get normalized embeddings
image_embeds = F.normalize(outputs.image_embeds, dim=-1)
text_embeds = F.normalize(outputs.text_embeds, dim=-1)
# Compute similarity
similarity = (image_embeds @ text_embeds.T).squeeze()
# Backward pass
model.zero_grad()
similarity.backward(retain_graph=False)
# Get captured tensors
captures = hooks.get_captures()
# Compute Grad-ECLIP importance
importance = _compute_gradeclip_importance(
captures, use_k_similarity=True, device=device
)
# Reshape to 2D grid
num_patches = importance.shape[0]
grid_size = int(np.sqrt(num_patches))
importance_map = importance.reshape(grid_size, grid_size)
# Convert to numpy and normalize
saliency_map = importance_map.detach().cpu().numpy()
saliency_map = saliency_map - saliency_map.min()
saliency_map = saliency_map / (saliency_map.max() + 1e-8)
# Resize saliency map to match display image
saliency_resized = cv2.resize(
saliency_map,
display_image.size, # (width, height)
interpolation=cv2.INTER_CUBIC,
)
# Apply colormap
heatmap_uint8 = (saliency_resized * 255).astype(np.uint8)
heatmap_bgr = cv2.applyColorMap(heatmap_uint8, colormap)
heatmap_rgb = cv2.cvtColor(heatmap_bgr, cv2.COLOR_BGR2RGB)
# Blend with original image
img_array = np.array(display_image).astype(np.float32)
overlay = (1 - alpha) * img_array + alpha * heatmap_rgb
overlay = np.clip(overlay, 0, 255).astype(np.uint8)
return Image.fromarray(overlay, mode="RGB")
finally:
# Restore original gradient settings
for param, requires_grad in zip(model.parameters(), model_requires_grad):
param.requires_grad = requires_grad
__all__ = ["generate_heatmap"]
|