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"""
Voting Predictor — Ensemble inference engine for image classification.
Combines three models via weighted soft voting:
- M1: DINOv3 ViT-Large (weight 4/7) — best single model, self-supervised features
- M2: XGBoost on ResNet50 features (weight 1/7) — uncorrelated with neural nets
- M3: EfficientNet-B0 (weight 2/7) — lightweight CNN for diversity
XGBoost probabilities are "sharpened" (p^3 / sum(p^3)) to prevent
its flat distributions from diluting confident predictions from DINOv3.
"""
import torch
import torch.nn.functional as F
import xgboost as xgb
import timm, joblib, numpy as np
from pathlib import Path
from torchvision import models
import torch.nn as nn
from src.features.build_features import preprocess_image
class VotingPredictor:
"""Loads 3 image models and returns fused top-5 predictions."""
def __init__(self, models_dir):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.mdir = Path(models_dir)
self.loaded = False
def load_models(self):
"""Load all three models + label encoder into memory (lazy, once)."""
if self.loaded:
return
# M1 — DINOv3 ViT-Large fine-tuned on 27 Rakuten classes
self.m1 = timm.create_model(
'vit_large_patch14_reg4_dinov2.lvd142m', pretrained=False, num_classes=27
)
self.m1.load_state_dict(
torch.load(self.mdir / "M1_IMAGE_DeepLearning_DINOv3.pth", map_location=self.device)
)
# M2 — XGBoost classifier on ResNet50 features (2048-dim)
self.has_xgboost = False
xgb_path = self.mdir / "M2_IMAGE_Classic_XGBoost.json"
if xgb_path.exists():
self.m2 = xgb.XGBClassifier()
self.m2.load_model(str(xgb_path))
self.le = joblib.load(self.mdir / "M2_IMAGE_XGBoost_Encoder.pkl")
# ResNet50 feature extractor (headless) — feeds M2
res = models.resnet50(weights=None)
self.ext = nn.Sequential(*list(res.children())[:-1])
self.ext.to(self.device).eval()
self.has_xgboost = True
# M3 — EfficientNet-B0 with custom 27-class head
self.m3 = models.efficientnet_b0(weights=None)
self.m3.classifier[1] = nn.Linear(1280, 27)
self.m3.load_state_dict(
torch.load(self.mdir / "M3_IMAGE_Classic_EfficientNetB0.pth", map_location=self.device)
)
# Move all torch models to device and set eval mode
for m in [self.m1, self.m3]:
m.to(self.device).eval()
self.loaded = True
def predict(self, img_p):
"""
Run voting inference on a single image.
Args:
img_p: Path to the image file.
Returns:
List of top-5 dicts: [{"label": str, "confidence": float}, ...]
"""
if not self.loaded:
self.load_models()
with torch.no_grad():
# M1 — DINOv3 (518x518 input)
p1 = F.softmax(
self.m1(preprocess_image(img_p, "dino").to(self.device)), dim=1
).cpu().numpy()[0]
# Shared 224x224 input for M2 and M3
i2 = preprocess_image(img_p, "standard").to(self.device)
# M3 — EfficientNet-B0
p3 = F.softmax(self.m3(i2), dim=1).cpu().numpy()[0]
if self.has_xgboost:
# M2 — XGBoost on ResNet50 features
f = self.ext(i2).squeeze().cpu().numpy().reshape(1, -1)
raw_p2 = self.m2.predict_proba(f)[0]
# Sharpening: raise to power 3, then renormalize.
# This forces XGBoost to commit to a class instead of
# spreading probability uniformly across all 27 classes.
sharp_p2 = np.power(raw_p2, 3)
p2 = sharp_p2 / sharp_p2.sum()
# Weighted soft vote: DINOv3=4, EfficientNet=2, XGBoost=1
f_p = (4.0 * p1 + 1.0 * p2 + 2.0 * p3) / 7.0
else:
# Fallback: DINOv3 + EfficientNet only (weights 4:2)
f_p = (4.0 * p1 + 2.0 * p3) / 6.0
# Return top-5 predictions with original Rakuten category codes
ids = np.argsort(f_p)[-5:][::-1]
# Use label encoder if available (XGBoost), otherwise use class index
if self.has_xgboost:
labels = [str(self.le.inverse_transform([i])[0]) for i in ids]
else:
# Load category mapping to get actual Rakuten codes
import json
mapping_path = self.mdir / "category_mapping.json"
if mapping_path.exists():
with open(mapping_path, 'r') as mf:
cat_map = json.load(mf)
code_list = sorted(cat_map.keys(), key=int)
labels = [code_list[i] if i < len(code_list) else str(i) for i in ids]
else:
labels = [str(i) for i in ids]
return [
{"label": labels[j], "confidence": float(f_p[ids[j]])}
for j in range(len(ids))
]
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