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Wrapper for ViT Base submodel.
"""
import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from PIL import Image
from torchvision import transforms
try:
import timm
TIMM_AVAILABLE = True
except ImportError:
TIMM_AVAILABLE = False
from app.core.errors import InferenceError, ConfigurationError
from app.core.logging import get_logger
from app.models.wrappers.base_wrapper import BaseSubmodelWrapper
from app.services.explainability import attention_rollout, heatmap_to_base64, compute_focus_summary
logger = get_logger(__name__)
class ViTWithMLPHead(nn.Module):
"""
ViT model wrapper matching the training checkpoint format.
The checkpoint was saved with:
- self.vit = timm ViT backbone (num_classes=0)
- self.fc1 = Linear(768, hidden)
- self.fc2 = Linear(hidden, num_classes)
"""
def __init__(self, arch: str = "vit_base_patch16_224", num_classes: int = 2, hidden_dim: int = 512):
super().__init__()
# Create backbone without classification head
self.vit = timm.create_model(arch, pretrained=False, num_classes=0)
embed_dim = self.vit.embed_dim # 768 for ViT-Base
self.fc1 = nn.Linear(embed_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
features = self.vit(x) # [B, embed_dim]
x = F.relu(self.fc1(features))
logits = self.fc2(x)
return logits
class ViTBaseWrapper(BaseSubmodelWrapper):
"""
Wrapper for ViT Base model (Vision Transformer).
Model expects 224x224 RGB images with ImageNet normalization.
"""
def __init__(
self,
repo_id: str,
config: Dict[str, Any],
local_path: str
):
super().__init__(repo_id, config, local_path)
self._model: Optional[nn.Module] = None
self._transform: Optional[transforms.Compose] = None
self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self._threshold = config.get("threshold", 0.5)
logger.info(f"Initialized ViTBaseWrapper for {repo_id}")
def load(self) -> None:
"""Load the ViT Base model with trained weights."""
if not TIMM_AVAILABLE:
raise ConfigurationError(
message="timm package not installed. Run: pip install timm",
details={"repo_id": self.repo_id}
)
weights_path = Path(self.local_path) / "deepfake_vit_finetuned_wildfake.pth"
preprocess_path = Path(self.local_path) / "preprocess.json"
if not weights_path.exists():
raise ConfigurationError(
message=f"deepfake_vit_finetuned_wildfake.pth not found in {self.local_path}",
details={"repo_id": self.repo_id, "expected_path": str(weights_path)}
)
try:
# Load preprocessing config
preprocess_config = {}
if preprocess_path.exists():
with open(preprocess_path, "r") as f:
preprocess_config = json.load(f)
# Build transform pipeline
input_size = preprocess_config.get("input_size", 224)
if isinstance(input_size, list):
input_size = input_size[0]
normalize_config = preprocess_config.get("normalize", {})
mean = normalize_config.get("mean", [0.485, 0.456, 0.406])
std = normalize_config.get("std", [0.229, 0.224, 0.225])
# Use bicubic interpolation as specified
interpolation = preprocess_config.get("interpolation", "bicubic")
interp_mode = transforms.InterpolationMode.BICUBIC if interpolation == "bicubic" else transforms.InterpolationMode.BILINEAR
self._transform = transforms.Compose([
transforms.Resize((input_size, input_size), interpolation=interp_mode),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
# Create model architecture matching the training checkpoint format
arch = self.config.get("arch", "vit_base_patch16_224")
num_classes = self.config.get("num_classes", 2)
# MLP hidden dim is 512 per training notebook (fc1: 768->512, fc2: 512->2)
# Note: config.hidden_dim (768) is ViT embedding dim, not MLP hidden dim
mlp_hidden_dim = self.config.get("mlp_hidden_dim", 512)
# Use custom wrapper that matches checkpoint structure (vit.* + fc1/fc2)
self._model = ViTWithMLPHead(arch=arch, num_classes=num_classes, hidden_dim=mlp_hidden_dim)
# Load trained weights
checkpoint = torch.load(weights_path, map_location=self._device, weights_only=False)
# Handle training checkpoint format (has "model", "optimizer_state", "epoch" keys)
if isinstance(checkpoint, dict) and "model" in checkpoint:
state_dict = checkpoint["model"]
else:
state_dict = checkpoint
self._model.load_state_dict(state_dict)
self._model.to(self._device)
self._model.eval()
# Mark as loaded
self._predict_fn = self._run_inference
logger.info(f"Loaded ViT Base model from {self.repo_id}")
except ConfigurationError:
raise
except Exception as e:
logger.error(f"Failed to load ViT Base model: {e}")
raise ConfigurationError(
message=f"Failed to load model: {e}",
details={"repo_id": self.repo_id, "error": str(e)}
)
def _run_inference(
self,
image_tensor: torch.Tensor,
explain: bool = False
) -> Dict[str, Any]:
"""Run model inference on preprocessed tensor."""
heatmap = None
if explain:
# Collect attention weights from all blocks
attentions: List[torch.Tensor] = []
handles = []
def get_attention_hook(module, input, output):
# For timm ViT, the attention forward returns (attn @ v)
# We need to hook into the softmax to get raw attention weights
# Alternative: access module's internal attn variable if available
pass
# Hook into attention modules to capture weights
# timm ViT blocks structure: blocks[i].attn
# We'll use a forward hook that computes attention manually
def create_attn_hook():
stored_attn = []
def hook(module, inputs, outputs):
# Get q, k from the module's forward computation
# inputs[0] is x of shape [B, N, C]
x = inputs[0]
B, N, C = x.shape
# Access the attention module's parameters
qkv = module.qkv(x) # [B, N, 3*dim]
qkv = qkv.reshape(B, N, 3, module.num_heads, C // module.num_heads)
qkv = qkv.permute(2, 0, 3, 1, 4) # [3, B, heads, N, dim_head]
q, k, v = qkv[0], qkv[1], qkv[2]
# Compute attention weights
scale = (C // module.num_heads) ** -0.5
attn = (q @ k.transpose(-2, -1)) * scale
attn = attn.softmax(dim=-1) # [B, heads, N, N]
# Average over heads
attn_avg = attn.mean(dim=1) # [B, N, N]
stored_attn.append(attn_avg.detach())
return hook, stored_attn
all_stored_attns = []
for block in self._model.vit.blocks:
hook_fn, stored = create_attn_hook()
all_stored_attns.append(stored)
handle = block.attn.register_forward_hook(hook_fn)
handles.append(handle)
try:
with torch.no_grad():
logits = self._model(image_tensor)
probs = F.softmax(logits, dim=1)
prob_fake = probs[0, 1].item()
pred_int = 1 if prob_fake >= self._threshold else 0
# Get attention from hooks
attention_list = [stored[0] for stored in all_stored_attns if len(stored) > 0]
if attention_list:
# Stack: [num_layers, B, N, N]
attention_stack = torch.stack(attention_list, dim=0)
# Compute rollout - returns (grid_size, grid_size) heatmap
attention_map = attention_rollout(
attention_stack[:, 0], # [num_layers, N, N]
head_fusion="mean", # Already averaged
discard_ratio=0.0,
num_prefix_tokens=1 # ViT has 1 CLS token
) # Returns (14, 14) for ViT-Base
# Resize to image size
from PIL import Image as PILImage
heatmap_img = PILImage.fromarray(
(attention_map * 255).astype(np.uint8)
).resize((224, 224), PILImage.BILINEAR)
heatmap = np.array(heatmap_img).astype(np.float32) / 255.0
finally:
for handle in handles:
handle.remove()
else:
with torch.no_grad():
logits = self._model(image_tensor)
probs = F.softmax(logits, dim=1)
prob_fake = probs[0, 1].item()
pred_int = 1 if prob_fake >= self._threshold else 0
result = {
"logits": logits[0].cpu().numpy().tolist(),
"prob_fake": prob_fake,
"pred_int": pred_int
}
if heatmap is not None:
result["heatmap"] = heatmap
return result
def predict(
self,
image: Optional[Image.Image] = None,
image_bytes: Optional[bytes] = None,
explain: bool = False,
**kwargs
) -> Dict[str, Any]:
"""
Run prediction on an image.
Args:
image: PIL Image object
image_bytes: Raw image bytes (will be converted to PIL Image)
explain: If True, compute attention rollout heatmap
Returns:
Standardized prediction dictionary with optional heatmap
"""
if self._model is None or self._transform is None:
raise InferenceError(
message="Model not loaded",
details={"repo_id": self.repo_id}
)
try:
# Convert bytes to PIL Image if needed
if image is None and image_bytes is not None:
import io
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
elif image is not None:
image = image.convert("RGB")
else:
raise InferenceError(
message="No image provided",
details={"repo_id": self.repo_id}
)
# Preprocess
image_tensor = self._transform(image).unsqueeze(0).to(self._device)
# Run inference
result = self._run_inference(image_tensor, explain=explain)
# Standardize output
labels = self.config.get("labels", {"0": "real", "1": "fake"})
pred_int = result["pred_int"]
output = {
"pred_int": pred_int,
"pred": labels.get(str(pred_int), "unknown"),
"prob_fake": result["prob_fake"],
"meta": {
"model": self.name,
"threshold": self._threshold,
"logits": result["logits"]
}
}
# Add heatmap if requested
if explain and "heatmap" in result:
heatmap = result["heatmap"]
output["heatmap_base64"] = heatmap_to_base64(heatmap)
output["explainability_type"] = "attention_rollout"
output["focus_summary"] = compute_focus_summary(heatmap)
return output
except InferenceError:
raise
except Exception as e:
logger.error(f"Prediction failed for {self.repo_id}: {e}")
raise InferenceError(
message=f"Prediction failed: {e}",
details={"repo_id": self.repo_id, "error": str(e)}
)
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