[validation] Upload validation examples - 2026-03-12T21:57:46.931478

validation_examples/all_epochs_metrics.json

@@ -0,0 +1,26 @@
+{
+  "2": {
+    "epoch": 2,
+    "num_samples": 5,
+    "psnr": {
+      "mean": 8.42457007356943,
+      "std": 2.0490775580319913,
+      "min": 5.580395004140195,
+      "max": 10.949018083124695
+    },
+    "ssim": {
+      "mean": 0.19272444483279338,
+      "std": 0.06094948968742925,
+      "min": 0.13446655088904022,
+      "max": 0.27528859925476507
+    },
+    "lpips": {
+      "mean": 0.7371994853019714,
+      "std": 0.040124533781707016
+    },
+    "clip_similarity": {
+      "mean": 0.29375,
+      "std": 0.019739128114254125
+    }
+  }
+}

validation_examples/epoch_2/metrics.json

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+{
+  "epoch": 2,
+  "num_samples": 5,
+  "psnr": {
+    "mean": 8.42457007356943,
+    "std": 2.0490775580319913,
+    "min": 5.580395004140195,
+    "max": 10.949018083124695
+  },
+  "ssim": {
+    "mean": 0.19272444483279338,
+    "std": 0.06094948968742925,
+    "min": 0.13446655088904022,
+    "max": 0.27528859925476507
+  },
+  "lpips": {
+    "mean": 0.7371994853019714,
+    "std": 0.040124533781707016
+  },
+  "clip_similarity": {
+    "mean": 0.29375,
+    "std": 0.019739128114254125
+  }
+}

validation_examples/test_epoch_validation.py

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+#!/usr/bin/env python3
+"""
+Quick test for validate_epochs.py
+
+Tests basic functionality without full validation:
+1. Import check
+2. Config loading
+3. Dataset loading
+4. Checkpoint detection
+"""
+
+import sys
+from pathlib import Path
+
+print("=" * 70)
+print("🧪 Testing Epoch Validation Pipeline")
+print("=" * 70)
+
+# Test 1: Import check
+print("\n[1/4] Testing imports...")
+try:
+    sys.path.insert(0, str(Path(__file__).parent))
+    sys.path.insert(0, str(Path(__file__).parent / "src"))
+    
+    from validate_epochs import EpochValidator
+    from configs.config import get_default_config
+    from datasets.sketchy_dataset import SketchyDataset
+    print("   ✅ All imports successful")
+except Exception as e:
+    print(f"   ❌ Import failed: {e}")
+    sys.exit(1)
+
+# Test 2: Config loading
+print("\n[2/4] Testing config...")
+try:
+    config = get_default_config()
+    print(f"   ✅ Config loaded")
+    print(f"   - Image size: {config['data'].image_size}")
+    print(f"   - Pretrained model: {config['model'].pretrained_model_name}")
+except Exception as e:
+    print(f"   ❌ Config failed: {e}")
+    sys.exit(1)
+
+# Test 3: Dataset check
+print("\n[3/4] Testing dataset...")
+try:
+    import os
+    dataset_root = "/workspace/sketchy"
+    
+    if not os.path.exists(dataset_root):
+        print(f"   ⚠️  Dataset not found at {dataset_root}")
+        dataset_root = "/root/Dual-Stage-Controllable-Diffusion-with-Adaptive-Modality-Fusion/sketchy"
+    
+    if os.path.exists(dataset_root):
+        dataset = SketchyDataset(
+            root_dir=dataset_root,
+            split='test',
+            image_size=config['data'].image_size,
+            augment=False
+        )
+        print(f"   ✅ Dataset loaded: {len(dataset)} samples")
+    else:
+        print(f"   ⚠️  Dataset not available (will be needed for actual validation)")
+except Exception as e:
+    print(f"   ⚠️  Dataset check skipped: {e}")
+
+# Test 4: Checkpoint detection
+print("\n[4/4] Testing checkpoint detection...")
+try:
+    # Check local checkpoints
+    local_checkpoint_dir = Path("/root/checkpoints/stage1")
+    if local_checkpoint_dir.exists():
+        checkpoints = list(local_checkpoint_dir.glob("epoch_*.pt"))
+        if checkpoints:
+            print(f"   ✅ Found {len(checkpoints)} local checkpoints:")
+            for cp in sorted(checkpoints)[:5]:  # Show first 5
+                print(f"      - {cp.name}")
+            if len(checkpoints) > 5:
+                print(f"      ... and {len(checkpoints) - 5} more")
+        else:
+            print(f"   ⚠️  No epoch checkpoints found locally")
+    else:
+        print(f"   ⚠️  Local checkpoint directory not found")
+    
+    # Try HuggingFace detection
+    print("\n   Testing HuggingFace checkpoint detection...")
+    try:
+        from huggingface_hub import list_repo_files
+        
+        # Replace with actual repo ID
+        hf_repo_id = "DrRORAL/ragaf-diffusion-checkpoints"
+        
+        files = list_repo_files(hf_repo_id)
+        epoch_files = [f for f in files if 'epoch' in f.lower() and f.endswith('.pt')]
+        
+        if epoch_files:
+            print(f"   ✅ Found {len(epoch_files)} checkpoint files on HuggingFace:")
+            for f in sorted(epoch_files)[:5]:
+                print(f"      - {f}")
+            if len(epoch_files) > 5:
+                print(f"      ... and {len(epoch_files) - 5} more")
+        else:
+            print(f"   ⚠️  No epoch checkpoints found on HuggingFace")
+            
+    except Exception as e:
+        print(f"   ⚠️  HuggingFace check skipped: {e}")
+        
+except Exception as e:
+    print(f"   ⚠️  Checkpoint detection failed: {e}")
+
+# Summary
+print("\n" + "=" * 70)
+print("📊 Test Summary")
+print("=" * 70)
+print("""
+✅ Basic functionality working!
+
+To run actual validation:
+  python validate_epochs.py --num_samples 20
+
+Options:
+  --hf_repo         HuggingFace repo ID (default: DrRORAL/ragaf-diffusion-checkpoints)
+  --dataset_root    Path to Sketchy dataset (default: /workspace/sketchy)
+  --epochs          Specific epochs to validate (default: all)
+  --num_samples     Samples per epoch (default: 50)
+  --output_dir      Output directory (default: validation_results)
+  --guidance_scale  Guidance scale (default: 2.5)
+
+Quick start:
+  python validate_epochs.py --num_samples 10  # Quick test
+
+See docs/EPOCH_VALIDATION_GUIDE.md for complete documentation.
+""")
+print("=" * 70)

validation_examples/test_validation_quick.py

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+#!/usr/bin/env python3
+"""
+Quick validation test - simplified version for debugging
+"""
+
+import torch
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+
+from models.stage1_diffusion import Stage1SketchGuidedDiffusion, Stage1DiffusionPipeline
+from datasets.sketchy_dataset import SketchyDataset
+from configs.config import get_default_config
+
+print("=" * 80)
+print("Quick Validation Test")
+print("=" * 80)
+
+# Load config
+print("\n1. Loading config...")
+config = get_default_config()
+print(f"   Dataset root: {config['data'].sketchy_root}")
+print(f"   Image size: {config['data'].image_size}")
+
+# Load dataset
+print("\n2. Loading dataset...")
+dataset = SketchyDataset(
+    root_dir=config['data'].sketchy_root,
+    split='test',
+    image_size=config['data'].image_size,
+    augment=False
+)
+print(f"   Total samples: {len(dataset)}")
+
+# Test loading one sample
+print("\n3. Loading one sample...")
+data = dataset[0]
+print(f"   Sketch shape: {data['sketch'].shape}")
+print(f"   Photo shape: {data['photo'].shape}")
+print(f"   Prompt: {data['text_prompt']}")
+print(f"   Category: {data['category']}")
+
+# Load model
+print("\n4. Loading model...")
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"   Device: {device}")
+
+model_config = config['model']
+print(f"   Model name: {model_config.pretrained_model_name}")
+print(f"   Sketch encoder channels: {model_config.sketch_encoder_channels}")
+
+try:
+    model = Stage1SketchGuidedDiffusion(
+        pretrained_model_name=model_config.pretrained_model_name,
+        sketch_encoder_channels=model_config.sketch_encoder_channels,
+        freeze_base_unet=model_config.freeze_stage1_unet,
+        use_lora=model_config.use_lora,
+        lora_rank=model_config.lora_rank
+    ).to(device)
+    print("   ✅ Model created successfully")
+except Exception as e:
+    print(f"   ❌ Model creation failed: {e}")
+    sys.exit(1)
+
+# Load checkpoint
+print("\n5. Loading checkpoint...")
+checkpoint_path = "/root/checkpoints/stage1/final.pt"
+try:
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    model.eval()
+    print(f"   ✅ Checkpoint loaded")
+except Exception as e:
+    print(f"   ❌ Checkpoint loading failed: {e}")
+    sys.exit(1)
+
+# Create pipeline
+print("\n6. Creating pipeline...")
+try:
+    pipeline = Stage1DiffusionPipeline(
+        model=model,
+        num_inference_steps=20,  # Reduced for testing
+        guidance_scale=2.5,
+        device=device
+    )
+    print("   ✅ Pipeline created")
+except Exception as e:
+    print(f"   ❌ Pipeline creation failed: {e}")
+    sys.exit(1)
+
+# Test generation
+print("\n7. Testing generation...")
+sketch = data['sketch'].unsqueeze(0)  # Add batch dimension
+prompt = data['text_prompt']
+img_size = config['data'].image_size  # Use configured image size
+
+print(f"   Sketch input shape: {sketch.shape}")
+print(f"   Prompt: {prompt}")
+print(f"   Image size: {img_size}")
+
+try:
+    with torch.no_grad():
+        generated = pipeline.generate(
+            sketch=sketch,
+            text_prompt=prompt,
+            height=img_size,  # Use dataset image size
+            width=img_size,   # Use dataset image size
+            seed=42
+        )
+    print(f"   ✅ Generation successful!")
+    print(f"   Generated shape: {generated.shape}")
+except Exception as e:
+    print(f"   ❌ Generation failed: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)
+
+print("\n" + "=" * 80)
+print("✅ All tests passed!")
+print("=" * 80)

validation_examples/upload_summary.json

@@ -0,0 +1,40 @@
+{
+  "timestamp": "2026-03-12T21:51:05.217893",
+  "repo_id": "DrRORAL/ragaf-diffusion-checkpoints",
+  "subfolder": "validation_examples",
+  "local_source": "/root/Dual-Stage-Controllable-Diffusion-with-Adaptive-Modality-Fusion/test_validation",
+  "files": [
+    {
+      "path": "all_epochs_metrics.json",
+      "size_mb": 0.0
+    },
+    {
+      "path": "epoch_2/metrics.json",
+      "size_mb": 0.0
+    },
+    {
+      "path": "epoch_2/sample_0000.png",
+      "size_mb": 1.07
+    },
+    {
+      "path": "metrics_across_epochs.png",
+      "size_mb": 0.31
+    },
+    {
+      "path": "test_epoch_validation.py",
+      "size_mb": 0.0
+    },
+    {
+      "path": "test_validation_quick.py",
+      "size_mb": 0.0
+    },
+    {
+      "path": "upload_summary.json",
+      "size_mb": 0.0
+    },
+    {
+      "path": "validation_report.html",
+      "size_mb": 0.0
+    }
+  ]
+}

validation_examples/validation_report.html

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+
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <title>Stage-1 Validation Report</title>
+            <style>
+                body { font-family: Arial, sans-serif; margin: 40px; background: #f5f5f5; }
+                h1 { color: #333; border-bottom: 3px solid #4CAF50; padding-bottom: 10px; }
+                h2 { color: #555; margin-top: 30px; }
+                table { border-collapse: collapse; width: 100%; margin: 20px 0; background: white; }
+                th, td { border: 1px solid #ddd; padding: 12px; text-align: left; }
+                th { background-color: #4CAF50; color: white; }
+                tr:nth-child(even) { background-color: #f2f2f2; }
+                .best { background-color: #90EE90 !important; font-weight: bold; }
+                .metric-card { background: white; padding: 20px; margin: 20px 0; 
+                              border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }
+                .images { display: flex; flex-wrap: wrap; gap: 10px; }
+                .images img { max-width: 300px; border: 1px solid #ddd; border-radius: 4px; }
+            </style>
+        </head>
+        <body>
+            <h1>🎨 Stage-1 Sketch-Guided Diffusion - Validation Report</h1>
+            <div class="metric-card">
+                <h2>📊 Metrics Across Epochs</h2>
+                <table>
+                    <tr>
+                        <th>Epoch</th>
+                        <th>PSNR (dB)</th>
+                        <th>SSIM</th>
+                        <th>LPIPS</th>
+                        <th>FID</th>
+                        <th>CLIP Sim</th>
+                        <th>Samples</th>
+                    </tr>
+        
+                    <tr>
+                        <td><strong>Epoch 2</strong></td>
+                        <td class="best">8.42 ± 2.05</td>
+                        <td class="best">0.1927 ± 0.0609</td>
+                        <td>0.7371994853019714</td>
+                        <td>N/A</td>
+                        <td>0.29375</td>
+                        <td>5</td>
+                    </tr>
+            
+                </table>
+            </div>
+            
+            <div class="metric-card">
+                <h2>📈 Visualization</h2>
+                <img src="metrics_across_epochs.png" style="max-width: 100%;">
+            </div>
+            
+            <div class="metric-card">
+                <h2>🎯 Recommendations</h2>
+                <ul>
+        <li>⚠️ SSIM is low (&lt;0.5). Consider training for more epochs.</li>
+                </ul>
+            </div>
+        </body>
+        </html>
+