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scripts/inference_pipeline.py
End-to-End Inference Pipeline for Hierarchical OCT Classification.
Connects L1 -> L2 -> L3 into a single callable function.
Takes a raw OCT scan and returns a final diagnosis with confidence scores.
"""
import sys
import os
import json
import logging
from pathlib import Path
from typing import Dict, Any, Optional
import torch
import torch.nn.functional as F
from PIL import Image
# Add parent directory to path so we can import from models, data, and utils
sys.path.append(str(Path(__file__).resolve().parent.parent))
from models.level1_gatekeeper import build_gatekeeper
from models.level2_router import build_router
from models.level3_specialist import build_specialist, SPECIALIST_CONFIGS
from data.transforms import get_transforms
from utils.gradcam import GradCAM
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
logger = logging.getLogger(__name__)
class OCTInferencePipeline:
def __init__(
self,
l1_ckpt: Optional[str] = None,
l2_ckpt: Optional[str] = None,
l3_ckpts: Optional[Dict[str, str]] = None,
device: str = "auto",
):
"""
Initializes the entire L1 -> L2 -> L3 inference pipeline.
Args:
l1_ckpt: Path to Level 1 (Gatekeeper) checkpoint.
l2_ckpt: Path to Level 2 (Router) checkpoint.
l3_ckpts: Dict mapping specialist names ('Macular', etc.) to checkpoint paths.
device: 'cuda', 'mps', 'cpu', or 'auto'.
"""
if device == "auto":
if torch.backends.mps.is_available():
self.device = torch.device("mps")
elif torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
else:
self.device = torch.device(device)
logger.info(f"Initialising OCT Inference Pipeline on device: {self.device}")
# 1. Load Transforms (we use 'val' split for deterministic preprocessing)
self.transform_l1_l2 = get_transforms("level1", "val")
# For L3, we can just use level3_macular's val transform as all L3 val transforms are identical (384px)
self.transform_l3 = get_transforms("level3_macular", "val")
# 2. Build Models
logger.info("Building Level 1 Gatekeeper...")
self.l1_model = build_gatekeeper(pretrained=True).to(self.device)
self._load_ckpt(self.l1_model, l1_ckpt)
self.l1_model.eval()
logger.info("Building Level 2 Router...")
self.l2_model = build_router(pretrained=True).to(self.device)
self._load_ckpt(self.l2_model, l2_ckpt)
self.l2_model.eval()
self.l3_models = {}
l3_ckpts = l3_ckpts or {}
for spec_name in SPECIALIST_CONFIGS.keys():
logger.info(f"Building Level 3 Specialist: {spec_name}...")
model = build_specialist(spec_name, pretrained=True).to(self.device)
self._load_ckpt(model, l3_ckpts.get(spec_name))
model.eval()
self.l3_models[spec_name] = model
# 3. Label Mappings
self.l1_mapping = {0: "NORMAL", 1: "ABNORMAL"}
self.l2_mapping = {
0: "Macular",
1: "Diabetic",
2: "Vascular",
3: "Fluid",
4: "Structural"
}
def _load_ckpt(self, model: torch.nn.Module, ckpt_path: Optional[str]):
"""Helper to load state dict if path is provided."""
if ckpt_path and os.path.exists(ckpt_path):
state = torch.load(ckpt_path, map_location=self.device)
if "model_state_dict" in state:
model.load_state_dict(state["model_state_dict"])
else:
model.load_state_dict(state)
logger.info(f" -> Loaded weights from {ckpt_path}")
else:
logger.warning(f" -> No checkpoint provided for {model.__class__.__name__}. Using random initialization!")
def _get_heatmap_base64(self, img_pil, cam_array):
"""Helper to generate a base64 encoded overlay image."""
import base64
import io
overlay = GradCAM.overlay_cam(img_pil, cam_array, alpha=0.5)
buffered = io.BytesIO()
overlay.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
return f"data:image/jpeg;base64,{img_str}"
def predict(
self,
image_path: str,
gradcam: bool = False,
output_dir: str = "output/explanations"
) -> Dict[str, Any]:
"""
Runs the end-to-end inference pipeline on a single image.
"""
logger.info(f"Processing image: {image_path}")
# Load Image
try:
img = Image.open(image_path).convert("RGB")
except Exception as e:
return {"error": f"Failed to load image: {e}"}
# Prepare Tensors
tensor_224 = self.transform_l1_l2(img).unsqueeze(0).to(self.device)
tensor_384 = self.transform_l3(img).unsqueeze(0).to(self.device)
results = {
"Level1": {},
"Level2": {},
"Level3": {},
"Final_Diagnosis": None,
"Path": [],
"gradcams": {}
}
if gradcam:
# Need gradients enabled for Grad-CAM
grad_context = torch.enable_grad()
# Also require gradients for input tensors
tensor_224.requires_grad = True
tensor_384.requires_grad = True
else:
grad_context = torch.no_grad()
with grad_context:
# --- LEVEL 1: Gatekeeper ---
if gradcam:
l1_cam_gen = GradCAM(self.l1_model, self.l1_model.features[-1])
logits_l1 = self.l1_model(tensor_224)
probs_l1 = F.softmax(logits_l1, dim=1).squeeze(0)
pred_l1_idx = torch.argmax(probs_l1).item()
pred_l1_label = self.l1_mapping[pred_l1_idx]
conf_l1 = probs_l1[pred_l1_idx].item()
results["Level1"] = {
"prediction": pred_l1_label,
"confidence": conf_l1,
"probs": {self.l1_mapping[i]: probs_l1[i].item() for i in range(2)}
}
results["Path"].append(f"L1: {pred_l1_label}")
if gradcam:
heatmap = l1_cam_gen.generate_cam(tensor_224, pred_l1_idx)
results["gradcams"]["L1"] = self._get_heatmap_base64(img, heatmap)
# Cleanup gradcam to free hooks
l1_cam_gen.target_layer._forward_hooks.clear()
l1_cam_gen.target_layer._backward_hooks.clear()
if pred_l1_label == "NORMAL":
results["Final_Diagnosis"] = "NORMAL"
logger.info("Pipeline terminated at Level 1 (NORMAL)")
# return results # Disabled for Grad-CAM testing
# --- LEVEL 2: Disease Router ---
if gradcam:
l2_cam_gen = GradCAM(self.l2_model, self.l2_model.features[-1])
logits_l2 = self.l2_model(tensor_224)
probs_l2 = F.softmax(logits_l2, dim=1).squeeze(0)
pred_l2_idx = torch.argmax(probs_l2).item()
pred_l2_label = self.l2_mapping[pred_l2_idx]
conf_l2 = probs_l2[pred_l2_idx].item()
results["Level2"] = {
"prediction": pred_l2_label,
"confidence": conf_l2,
"probs": {self.l2_mapping[i]: probs_l2[i].item() for i in range(5)}
}
results["Path"].append(f"L2: {pred_l2_label}")
if gradcam:
heatmap = l2_cam_gen.generate_cam(tensor_224, pred_l2_idx)
results["gradcams"]["L2"] = self._get_heatmap_base64(img, heatmap)
l2_cam_gen.target_layer._forward_hooks.clear()
l2_cam_gen.target_layer._backward_hooks.clear()
# --- LEVEL 3: Specialist ---
specialist_model = self.l3_models[pred_l2_label]
spec_config = SPECIALIST_CONFIGS[pred_l2_label]
l3_classes_map = spec_config["classes"]
if gradcam:
l3_cam_gen = GradCAM(specialist_model, specialist_model.features[-1])
logits_l3 = specialist_model(tensor_384)
probs_l3 = F.softmax(logits_l3, dim=1).squeeze(0)
pred_l3_idx = torch.argmax(probs_l3).item()
pred_l3_label = l3_classes_map[pred_l3_idx]
conf_l3 = probs_l3[pred_l3_idx].item()
results["Level3"] = {
"specialist_used": spec_config["specialist_name"],
"prediction": pred_l3_label,
"confidence": conf_l3,
"probs": {l3_classes_map[i]: probs_l3[i].item() for i in range(len(l3_classes_map))}
}
results["Path"].append(f"L3: {pred_l3_label}")
results["Final_Diagnosis"] = pred_l3_label
if gradcam:
heatmap = l3_cam_gen.generate_cam(tensor_384, pred_l3_idx)
results["gradcams"]["L3"] = self._get_heatmap_base64(img, heatmap)
l3_cam_gen.target_layer._forward_hooks.clear()
l3_cam_gen.target_layer._backward_hooks.clear()
return results
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Run OCT Hierarchical Inference")
parser.add_argument("--image", type=str, required=True, help="Path to raw OCT image")
parser.add_argument("--l1_ckpt", type=str, default=None, help="L1 model checkpoint path")
parser.add_argument("--l2_ckpt", type=str, default=None, help="L2 model checkpoint path")
parser.add_argument("--gradcam", action="store_true", help="Generate Grad-CAM heatmaps")
parser.add_argument("--output-dir", type=str, default="output/explanations", help="Output directory for heatmaps")
args = parser.parse_args()
pipeline = OCTInferencePipeline(
l1_ckpt=args.l1_ckpt,
l2_ckpt=args.l2_ckpt,
)
res = pipeline.predict(args.image, gradcam=args.gradcam, output_dir=args.output_dir)
print("\n--- INFERENCE RESULTS ---")
print(json.dumps(res, indent=4))
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