Delete debug_detections.py

debug_detections.py

@@ -1,83 +0,0 @@
-#!/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)