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inference.py

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+
+# Simple inference script for wildlife ensemble detector
+import torch
+import cv2
+import numpy as np
+from detectron2 import model_zoo
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+
+def load_ensemble_models(inception_path, resnet_path, class_names):
+    """Load both ensemble models for inference"""
+    
+    # You'll need to copy the model_architecture.py content here
+    # and register the backbones before loading
+    
+    # Setup configs (simplified)
+    cfg_inception = get_cfg()
+    cfg_inception.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
+    cfg_inception.MODEL.BACKBONE.NAME = "InceptionBackboneWrapper"
+    cfg_inception.MODEL.ROI_HEADS.NUM_CLASSES = len(class_names)
+    cfg_inception.MODEL.WEIGHTS = inception_path
+    cfg_inception.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+    
+    cfg_resnet = get_cfg()
+    cfg_resnet.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
+    cfg_resnet.MODEL.BACKBONE.NAME = "ResNetBackboneWrapper"
+    cfg_resnet.MODEL.ROI_HEADS.NUM_CLASSES = len(class_names)
+    cfg_resnet.MODEL.WEIGHTS = resnet_path
+    cfg_resnet.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+    
+    predictor_inception = DefaultPredictor(cfg_inception)
+    predictor_resnet = DefaultPredictor(cfg_resnet)
+    
+    return predictor_inception, predictor_resnet
+
+def predict_ensemble(image_path, predictor_inception, predictor_resnet, class_names):
+    """Run ensemble inference on an image"""
+    
+    img = cv2.imread(image_path)
+    
+    # Get predictions from both models
+    outputs_inc = predictor_inception(img)
+    outputs_res = predictor_resnet(img)
+    
+    # Combine predictions (simplified)
+    # Add your ensemble logic here
+    
+    return outputs_inc, outputs_res