Create debug_detections.py

debug_detections.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+Quick debug script to test model locally and inspect detections
+Run this in your container or locally to see what the model returns
+"""
+
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+from PIL import Image
+import numpy as np
+from rfdetr import RFDETRSegPreview
+import supervision as sv
+
+# Path to your checkpoint
+CHECKPOINT_PATH = "/tmp/checkpoint_best_total.pth"
+
+print("Loading model...")
+model = RFDETRSegPreview(pretrain_weights=CHECKPOINT_PATH)
+print("Model loaded!")
+
+# Create a test image (or load your own)
+test_img = Image.new("RGB", (640, 480), color=(73, 109, 137))
+print(f"Test image size: {test_img.size}")
+
+# Run prediction
+print("\nRunning prediction with threshold=0.25...")
+detections = model.predict(test_img, threshold=0.25)
+
+print("\n" + "="*60)
+print("DETECTION RESULTS:")
+print("="*60)
+print(f"Type: {type(detections)}")
+print(f"Length: {len(detections)}")
+
+# Check attributes
+attrs = dir(detections)
+print(f"\nAvailable attributes: {[a for a in attrs if not a.startswith('_')]}")
+
+if hasattr(detections, 'confidence'):
+    print(f"\nConfidence scores: {detections.confidence}")
+    print(f"Confidence count: {len(detections.confidence)}")
+    print(f"Confidence type: {type(detections.confidence)}")
+
+if hasattr(detections, 'class_id'):
+    print(f"\nClass IDs: {detections.class_id}")
+    print(f"Class count: {len(detections.class_id)}")
+
+if hasattr(detections, 'masks'):
+    masks = detections.masks
+    print(f"\nMasks present: {masks is not None}")
+    if masks is not None:
+        print(f"Masks type: {type(masks)}")
+        if hasattr(masks, 'shape'):
+            print(f"Masks shape: {masks.shape}")
+        elif hasattr(masks, '__len__'):
+            print(f"Masks length: {len(masks)}")
+            if len(masks) > 0:
+                print(f"First mask type: {type(masks[0])}")
+                if hasattr(masks[0], 'shape'):
+                    print(f"First mask shape: {masks[0].shape}")
+else:
+    print("\nNO MASKS ATTRIBUTE FOUND!")
+
+# Try to annotate
+if len(detections) > 0:
+    print("\n" + "="*60)
+    print("Testing annotation...")
+    print("="*60)
+    try:
+        palette = sv.ColorPalette.from_hex(["#ffff00", "#ff9b00"])
+        mask_annotator = sv.MaskAnnotator(color=palette)
+        annotated = mask_annotator.annotate(test_img, detections)
+        print("✓ Mask annotation successful!")
+        print(f"Annotated image size: {annotated.size}")
+    except Exception as e:
+        print(f"✗ Mask annotation failed: {e}")
+        import traceback
+        traceback.print_exc()
+
+print("\n" + "="*60)
+print("Debug complete!")
+print("="*60)