feat: lora fine tune

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+SAM2 (Segment Anything Model 2) evaluation project for crack segmentation on the Crack500 dataset. The project evaluates SAM2's performance using different prompting strategies: bounding box prompts and point prompts (1/3/5 points).
+
+## Environment Setup
+
+This project uses Pixi for dependency management:
+
+```bash
+# Install dependencies
+pixi install
+
+# Activate environment
+pixi shell
+```
+
+Key dependencies are defined in `pixi.toml`. SAM2 is installed as an editable package from `/home/dustella/projects/sam2`.
+
+## Running Evaluations
+
+### Bounding Box Prompt Evaluation
+```bash
+# Full pipeline (inference + evaluation + visualization)
+python run_bbox_evaluation.py
+
+# With custom parameters
+python run_bbox_evaluation.py \
+    --data_root ./crack500 \
+    --test_file ./crack500/test.txt \
+    --expand_ratio 0.05 \
+    --output_dir ./results/bbox_prompt \
+    --num_vis 20
+
+# Skip specific steps
+python run_bbox_evaluation.py --skip_inference      # Use existing predictions
+python run_bbox_evaluation.py --skip_evaluation     # Skip metrics calculation
+python run_bbox_evaluation.py --skip_visualization  # Skip visualization
+```
+
+### Point Prompt Evaluation
+```bash
+# Evaluate with 1, 3, and 5 points
+python run_point_evaluation.py \
+    --data_root ./crack500 \
+    --test_file ./crack500/test.txt \
+    --point_configs 1 3 5 \
+    --per_component
+```
+
+## Architecture
+
+### Core Modules
+
+**src/bbox_prompt.py**: Bounding box prompting implementation
+- `extract_bboxes_from_mask()`: Extracts bounding boxes from GT masks using connected component analysis
+- `predict_with_bbox_prompt()`: Runs SAM2 inference with bbox prompts
+- `process_test_set()`: Batch processes the test set
+
+**src/point_prompt.py**: Point prompting implementation
+- Similar structure to bbox_prompt.py but uses point coordinates as prompts
+- Supports multiple points per connected component
+
+**src/evaluation.py**: Metrics computation
+- `compute_iou()`: Intersection over Union
+- `compute_dice()`: Dice coefficient
+- `compute_precision_recall()`: Precision and Recall
+- `compute_skeleton_iou()`: Skeleton IoU for thin crack structures
+
+**src/visualization.py**: Visualization utilities
+- `create_overlay_visualization()`: Creates TP/FP/FN overlays (yellow/red/green)
+- `create_comparison_figure()`: 4-panel comparison (original, GT, prediction, overlay)
+- `create_metrics_distribution_plot()`: Plots metric distributions
+
+### Data Format
+
+Test set file (`crack500/test.txt`) format:
+```
+testcrop/image_name.jpg testcrop/mask_name.png
+```
+
+Images are in `crack500/testcrop/`, masks in `crack500/testdata/`.
+
+### Prompting Strategies
+
+**Bounding Box**: Extracts bboxes from GT masks via connected components, optionally expands by `expand_ratio` (default 5%) to simulate imprecise annotations.
+
+**Point Prompts**: Samples N points (1/3/5) from each connected component in GT mask. When `--per_component` is used, points are sampled per component; otherwise, globally.
+
+## Model Configuration
+
+Default model: `sam2.1_hiera_small.pt`
+- Located at: `../sam2/checkpoints/sam2.1_hiera_small.pt`
+- Config: `configs/sam2.1/sam2.1_hiera_s.yaml`
+
+Alternative models:
+- `sam2.1_hiera_tiny.pt`: Fastest, lower accuracy
+- `sam2.1_hiera_large.pt`: Highest accuracy, slower
+
+## Results Structure
+
+```
+results/
+└── bbox_prompt/
+    ├── predictions/         # Predicted masks (.png)
+    ├── visualizations/      # Comparison figures
+    ├── evaluation_results.csv
+    └── evaluation_summary.json
+```
+
+## Performance Benchmarks
+
+Current SAM2 results on Crack500 (from note.md):
+- Bbox prompt: 39.60% IoU, 53.58% F1
+- 1 point: 8.43% IoU, 12.70% F1
+- 3 points: 33.35% IoU, 45.94% F1
+- 5 points: 38.50% IoU, 51.89% F1
+
+Compared to specialized models (CrackSegMamba: 57.4% IoU, 72.9% F1), SAM2 underperforms without fine-tuning.
+
+## Key Implementation Details
+
+- Connected component analysis uses `cv2.connectedComponentsWithStats()` with 8-connectivity
+- Minimum component area threshold: 10 pixels (filters noise)
+- SAM2 inference uses `multimask_output=False` for single mask per prompt
+- Multiple bboxes/points per image are combined with logical OR
+- Masks are binary (0 or 255)
+
+## LoRA Fine-tuning
+
+The project includes LoRA (Low-Rank Adaptation) fine-tuning to improve SAM2 performance on crack segmentation.
+
+### Quick Start
+
+```bash
+# Verify dataset
+python scripts/prepare_data.py
+
+# Train with Strategy B (recommended)
+python train_lora.py \
+    --lora_strategy B \
+    --epochs 50 \
+    --batch_size 4 \
+    --gradient_accumulation_steps 4 \
+    --use_skeleton_loss \
+    --use_wandb
+
+# Evaluate fine-tuned model
+python evaluate_lora.py \
+    --lora_checkpoint ./results/lora_training/checkpoints/best_model.pt \
+    --lora_strategy B
+```
+
+### LoRA Strategies
+
+**Strategy A: Decoder-Only** (Fast, ~2-3 hours)
+- Targets only mask decoder attention layers
+- ~2-3M trainable parameters
+- Expected improvement: 45-50% IoU
+```bash
+python train_lora.py --lora_strategy A --epochs 30 --batch_size 8 --gradient_accumulation_steps 2
+```
+
+**Strategy B: Decoder + Encoder** (Recommended, ~4-6 hours)
+- Targets mask decoder + image encoder attention layers
+- ~5-7M trainable parameters
+- Expected improvement: 50-55% IoU
+```bash
+python train_lora.py --lora_strategy B --epochs 50 --batch_size 4 --gradient_accumulation_steps 4
+```
+
+**Strategy C: Full Model** (Maximum, ~8-12 hours)
+- Targets all attention + FFN layers
+- ~10-15M trainable parameters
+- Expected improvement: 55-60% IoU
+```bash
+python train_lora.py --lora_strategy C --epochs 80 --batch_size 2 --gradient_accumulation_steps 8 --learning_rate 3e-5
+```
+
+### Training Features
+
+- **Gradient Accumulation**: Simulates larger batch sizes with limited GPU memory
+- **Mixed Precision**: Automatic mixed precision (AMP) for memory efficiency
+- **Early Stopping**: Stops training when validation IoU plateaus (patience=10)
+- **Skeleton Loss**: Additional loss for thin crack structures
+- **W&B Logging**: Track experiments with Weights & Biases (optional)
+
+### Key Files
+
+**Training Infrastructure:**
+- `train_lora.py`: Main training script
+- `evaluate_lora.py`: Evaluation script for fine-tuned models
+- `src/dataset.py`: Dataset with automatic bbox prompt generation
+- `src/losses.py`: Combined Dice + Focal loss, Skeleton loss
+- `src/lora_utils.py`: Custom LoRA implementation (no PEFT dependency)
+
+**Configuration:**
+- `configs/lora_configs.py`: Three LoRA strategies (A, B, C)
+- `configs/train_config.yaml`: Default hyperparameters
+
+### Training Process
+
+1. **Data Preparation**: Automatically generates bbox prompts from GT masks during training
+2. **Forward Pass**: Keeps all data on GPU (no CPU-GPU transfers during training)
+3. **Loss Computation**: Combined Dice + Focal loss for class imbalance, optional Skeleton loss
+4. **Optimization**: AdamW optimizer with cosine annealing schedule
+5. **Validation**: Computes IoU, Dice, F1 on validation set every epoch
+6. **Checkpointing**: Saves best model based on validation IoU
+
+### Expected Results
+
+**Baseline (No Fine-tuning):**
+- Bbox prompt: 39.60% IoU, 53.58% F1
+
+**After Fine-tuning:**
+- Strategy A: 45-50% IoU (modest improvement)
+- Strategy B: 50-55% IoU (significant improvement)
+- Strategy C: 55-60% IoU (approaching SOTA)
+
+**SOTA Comparison:**
+- CrackSegMamba: 57.4% IoU, 72.9% F1
+
+### Important Notes
+
+- Uses official Meta SAM2 implementation (not transformers library)
+- Custom LoRA implementation to avoid dependency issues
+- All training data stays on GPU for efficiency
+- LoRA weights can be saved separately (~10-50MB vs full model ~200MB)
+- Compatible with existing evaluation infrastructure

configs/lora_configs.py

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+"""
+LoRA configuration strategies for SAM2 fine-tuning
+Defines three strategies: Decoder-only, Decoder+Encoder, Full Model
+"""
+
+from dataclasses import dataclass
+from typing import List
+
+
+@dataclass
+class LoRAConfig:
+    """LoRA configuration for a specific strategy"""
+    name: str
+    description: str
+    target_modules: List[str]  # Regex patterns for module names
+    r: int  # LoRA rank
+    lora_alpha: int  # Scaling factor
+    lora_dropout: float
+
+
+# Strategy A: Decoder-Only (Fast Training)
+STRATEGY_A = LoRAConfig(
+    name="decoder_only",
+    description="Fast training, decoder adaptation only (~2-3M params, 2-3 hours)",
+    target_modules=[
+        # Mask decoder transformer attention layers
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.self_attn",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_token_to_image",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_image_to_token",
+        r"sam_mask_decoder\.transformer\.final_attn_token_to_image",
+    ],
+    r=8,
+    lora_alpha=16,
+    lora_dropout=0.05
+)
+
+
+# Strategy B: Decoder + Encoder (Balanced) [RECOMMENDED]
+STRATEGY_B = LoRAConfig(
+    name="decoder_encoder",
+    description="Balanced training, best performance/cost ratio (~5-7M params, 4-6 hours)",
+    target_modules=[
+        # Mask decoder attention (from Strategy A)
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.self_attn",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_token_to_image",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_image_to_token",
+        r"sam_mask_decoder\.transformer\.final_attn_token_to_image",
+        # Image encoder attention
+        r"image_encoder\.trunk\.blocks\.\d+\.attn",
+    ],
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05
+)
+
+
+# Strategy C: Full Model (Maximum Adaptation)
+STRATEGY_C = LoRAConfig(
+    name="full_model",
+    description="Maximum adaptation, highest compute cost (~10-15M params, 8-12 hours)",
+    target_modules=[
+        # All attention from Strategy B
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.self_attn",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_token_to_image",
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.cross_attn_image_to_token",
+        r"sam_mask_decoder\.transformer\.final_attn_token_to_image",
+        r"image_encoder\.trunk\.blocks\.\d+\.attn",
+        # FFN layers
+        r"sam_mask_decoder\.transformer\.layers\.\d+\.mlp",
+        r"image_encoder\.trunk\.blocks\.\d+\.mlp",
+    ],
+    r=32,
+    lora_alpha=64,
+    lora_dropout=0.1
+)
+
+
+# Strategy registry
+STRATEGIES = {
+    'A': STRATEGY_A,
+    'B': STRATEGY_B,
+    'C': STRATEGY_C
+}
+
+
+def get_strategy(strategy_name: str) -> LoRAConfig:
+    """Get LoRA strategy by name"""
+    if strategy_name not in STRATEGIES:
+        raise ValueError(f"Unknown strategy: {strategy_name}. Choose from {list(STRATEGIES.keys())}")
+    return STRATEGIES[strategy_name]

configs/train_config.yaml

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+# SAM2 LoRA Fine-tuning Configuration
+
+model:
+  checkpoint: ../sam2/checkpoints/sam2.1_hiera_small.pt
+  config: sam2.1_hiera_s.yaml
+
+data:
+  root: ./crack500
+  train_file: ./crack500/train.txt
+  val_file: ./crack500/val.txt
+  test_file: ./crack500/test.txt
+  expand_ratio: 0.05  # Bbox expansion ratio
+
+training:
+  epochs: 50
+  batch_size: 4
+  learning_rate: 0.0001  # 1e-4
+  weight_decay: 0.01
+  gradient_accumulation_steps: 4
+
+  # Early stopping
+  patience: 10
+
+  # Loss weights
+  dice_weight: 0.5
+  focal_weight: 0.5
+  use_skeleton_loss: true
+  skeleton_weight: 0.2
+
+lora:
+  strategy: B  # A (decoder-only), B (decoder+encoder), C (full)
+
+system:
+  num_workers: 4
+  use_amp: true  # Mixed precision training
+  save_freq: 5  # Save checkpoint every N epochs
+
+wandb:
+  use_wandb: false
+  project: sam2-crack-lora
+  entity: null
+
+# Strategy-specific recommendations:
+# Strategy A: batch_size=8, gradient_accumulation_steps=2, lr=1e-4, epochs=30
+# Strategy B: batch_size=4, gradient_accumulation_steps=4, lr=5e-5, epochs=50
+# Strategy C: batch_size=2, gradient_accumulation_steps=8, lr=3e-5, epochs=80

evaluate_lora.py

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+#!/usr/bin/env python3
+"""
+Evaluate LoRA fine-tuned SAM2 model on Crack500 test set
+Uses existing evaluation infrastructure from bbox_prompt.py and evaluation.py
+"""
+
+import torch
+import argparse
+from pathlib import Path
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from src.lora_utils import apply_lora_to_model, load_lora_weights
+from src.bbox_prompt import process_test_set
+from src.evaluation import evaluate_test_set
+from src.visualization import visualize_test_set
+from configs.lora_configs import get_strategy
+
+
+def load_lora_model(args):
+    """Load SAM2 model with LoRA weights"""
+    print(f"Loading base SAM2 model: {args.model_cfg}")
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # Load base model
+    sam2_model = build_sam2(
+        args.model_cfg,
+        args.base_checkpoint,
+        device=device,
+        mode="eval"
+    )
+
+    # Apply LoRA configuration
+    lora_config = get_strategy(args.lora_strategy)
+    print(f"\nApplying LoRA strategy: {lora_config.name}")
+
+    sam2_model = apply_lora_to_model(
+        sam2_model,
+        target_modules=lora_config.target_modules,
+        r=lora_config.r,
+        lora_alpha=lora_config.lora_alpha,
+        lora_dropout=lora_config.lora_dropout
+    )
+
+    # Load LoRA weights
+    print(f"\nLoading LoRA weights from: {args.lora_checkpoint}")
+    checkpoint = torch.load(args.lora_checkpoint, map_location=device)
+
+    if 'model_state_dict' in checkpoint:
+        sam2_model.load_state_dict(checkpoint['model_state_dict'])
+        print(f"Loaded checkpoint from epoch {checkpoint.get('epoch', 'unknown')}")
+        if 'metrics' in checkpoint:
+            print(f"Checkpoint metrics: {checkpoint['metrics']}")
+    else:
+        # Load LoRA weights only
+        load_lora_weights(sam2_model, args.lora_checkpoint)
+
+    sam2_model.eval()
+    return sam2_model
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Evaluate LoRA fine-tuned SAM2')
+
+    # Model parameters
+    parser.add_argument('--lora_checkpoint', type=str, required=True,
+                        help='Path to LoRA checkpoint')
+    parser.add_argument('--base_checkpoint', type=str,
+                        default='../sam2/checkpoints/sam2.1_hiera_small.pt')
+    parser.add_argument('--model_cfg', type=str, default='configs/sam2.1/sam2.1_hiera_s.yaml')
+    parser.add_argument('--lora_strategy', type=str, default='B', choices=['A', 'B', 'C'])
+
+    # Data parameters
+    parser.add_argument('--data_root', type=str, default='./crack500')
+    parser.add_argument('--test_file', type=str, default='./crack500/test.txt')
+    parser.add_argument('--expand_ratio', type=float, default=0.05)
+
+    # Output parameters
+    parser.add_argument('--output_dir', type=str, default='./results/lora_eval')
+    parser.add_argument('--num_vis', type=int, default=20,
+                        help='Number of samples to visualize')
+    parser.add_argument('--vis_all', action='store_true',
+                        help='Visualize all samples')
+
+    # Workflow control
+    parser.add_argument('--skip_inference', action='store_true')
+    parser.add_argument('--skip_evaluation', action='store_true')
+    parser.add_argument('--skip_visualization', action='store_true')
+
+    args = parser.parse_args()
+
+    # Create output directory
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Load model
+    if not args.skip_inference:
+        print("\n" + "="*60)
+        print("Loading LoRA model...")
+        print("="*60)
+        model = load_lora_model(args)
+        predictor = SAM2ImagePredictor(model)
+
+        # Run inference
+        print("\n" + "="*60)
+        print("Running inference on test set...")
+        print("="*60)
+        process_test_set(
+            data_root=args.data_root,
+            test_file=args.test_file,
+            predictor=predictor,
+            output_dir=str(output_dir),
+            expand_ratio=args.expand_ratio
+        )
+
+    # Evaluate
+    if not args.skip_evaluation:
+        print("\n" + "="*60)
+        print("Evaluating predictions...")
+        print("="*60)
+        pred_dir = output_dir / 'predictions'
+        evaluate_test_set(
+            data_root=args.data_root,
+            test_file=args.test_file,
+            pred_dir=str(pred_dir),
+            output_dir=str(output_dir)
+        )
+
+    # Visualize
+    if not args.skip_visualization:
+        print("\n" + "="*60)
+        print("Creating visualizations...")
+        print("="*60)
+        pred_dir = output_dir / 'predictions'
+        visualize_test_set(
+            data_root=args.data_root,
+            test_file=args.test_file,
+            pred_dir=str(pred_dir),
+            output_dir=str(output_dir),
+            num_vis=args.num_vis if not args.vis_all else None
+        )
+
+    print("\n" + "="*60)
+    print("Evaluation complete!")
+    print(f"Results saved to: {output_dir}")
+    print("="*60)
+
+
+if __name__ == '__main__':
+    main()

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+      - pypi: https://mirror.nju.edu.cn/pypi/web/packages/5a/87/b70ad306ebb6f9b585f114d0ac2137d792b48be34d732d60e597c2f8465a/pydantic-2.12.5-py3-none-any.whl
+      - pypi: https://mirror.nju.edu.cn/pypi/web/packages/0d/76/941cc9f73529988688a665a5c0ecff1112b3d95ab48f81db5f7606f522d3/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/8b/40/2614036cdd416452f5bf98ec037f38a1afb17f327cb8e6b652d4729e0af8/pyparsing-3.3.1-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl
@@ -110,8 +116,10 @@ environments:
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/79/2e/415119c9ab3e62249e18c2b082c07aff907a273741b3f8160414b0e9193c/scipy-1.16.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl
+      - pypi: https://mirror.nju.edu.cn/pypi/web/packages/c2/90/f66c0f1d87c5d00ecae5774398e5d636c76bdf84d8b7d0e8182c82c37cd1/simsimd-6.5.12-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl
+      - pypi: https://mirror.nju.edu.cn/pypi/web/packages/47/09/f51053ba053427da5d5f640aba40d9e7ac2d053ac6ab2665f0b695011765/stringzilla-4.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/1b/fe/e59859aa1134fac065d36864752daf13215c98b379cb5d93f954dc0ec830/tifffile-2025.12.20-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/d0/c6/dc3a0db5a6766416c32c034286d7c2d406da1f498e4de04ab1b8959edd00/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
@@ -124,6 +132,7 @@ environments:
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/6a/6b/2f416568b3c4c91c96e5a365d164f8a4a4a88030aa8ab4644181fdadce97/transformers-4.57.3-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/f5/3a/e991574f3102147b642e49637e0281e9bb7c4ba254edb2bab78247c85e01/triton-3.5.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl
+      - pypi: https://mirror.nju.edu.cn/pypi/web/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/c7/b0/003792df09decd6849a5e39c28b513c06e84436a54440380862b5aeff25d/tzdata-2025.3-py2.py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/6d/b9/4095b668ea3678bf6a0af005527f39de12fb026516fb3df17495a733b7f8/urllib3-2.6.2-py3-none-any.whl
       - pypi: https://mirror.nju.edu.cn/pypi/web/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl
@@ -150,6 +159,41 @@ packages:
   purls: []
   size: 23621
   timestamp: 1650670423406
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/0a/e2/91f145e1f32428e9e1f21f46a7022ffe63d11f549ee55c3b9265ff5207fc/albucore-0.0.24-py3-none-any.whl
+  name: albucore
+  version: 0.0.24
+  sha256: adef6e434e50e22c2ee127b7a3e71f2e35fa088bcf54431e18970b62d97d0005
+  requires_dist:
+  - numpy>=1.24.4
+  - typing-extensions>=4.9.0 ; python_full_version < '3.10'
+  - stringzilla>=3.10.4
+  - simsimd>=5.9.2
+  - opencv-python-headless>=4.9.0.80
+  requires_python: '>=3.9'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/8e/64/013409c451a44b61310fb757af4527f3de57fc98a00f40448de28b864290/albumentations-2.0.8-py3-none-any.whl
+  name: albumentations
+  version: 2.0.8
+  sha256: c4c4259aaf04a7386ad85c7fdcb73c6c7146ca3057446b745cc035805acb1017
+  requires_dist:
+  - numpy>=1.24.4
+  - scipy>=1.10.0
+  - pyyaml
+  - typing-extensions>=4.9.0 ; python_full_version < '3.10'
+  - pydantic>=2.9.2
+  - albucore==0.0.24
+  - eval-type-backport ; python_full_version < '3.10'
+  - opencv-python-headless>=4.9.0.80
+  - huggingface-hub ; extra == 'hub'
+  - torch ; extra == 'pytorch'
+  - pillow ; extra == 'text'
+  requires_python: '>=3.9'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl
+  name: annotated-types
+  version: 0.7.0
+  sha256: 1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53
+  requires_dist:
+  - typing-extensions>=4.0.0 ; python_full_version < '3.9'
+  requires_python: '>=3.8'
 - pypi: https://mirror.nju.edu.cn/pypi/web/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz
   name: antlr4-python3-runtime
   version: 4.9.3
@@ -1223,6 +1267,14 @@ packages:
   - numpy<2.0 ; python_full_version < '3.9'
   - numpy>=2,<2.3.0 ; python_full_version >= '3.9'
   requires_python: '>=3.6'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/89/53/e19c21e0c4eb1275c3e2c97b081103b6dfb3938172264d283a519bf728b9/opencv_python_headless-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+  name: opencv-python-headless
+  version: 4.12.0.88
+  sha256: 236c8df54a90f4d02076e6f9c1cc763d794542e886c576a6fee46ec8ff75a7a9
+  requires_dist:
+  - numpy<2.0 ; python_full_version < '3.9'
+  - numpy>=2,<2.3.0 ; python_full_version >= '3.9'
+  requires_python: '>=3.6'
 - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.0-h26f9b46_0.conda
   sha256: a47271202f4518a484956968335b2521409c8173e123ab381e775c358c67fe6d
   md5: 9ee58d5c534af06558933af3c845a780
@@ -1466,6 +1518,25 @@ packages:
   sha256: 1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0
   requires_dist:
   - pytest ; extra == 'tests'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/5a/87/b70ad306ebb6f9b585f114d0ac2137d792b48be34d732d60e597c2f8465a/pydantic-2.12.5-py3-none-any.whl
+  name: pydantic
+  version: 2.12.5
+  sha256: e561593fccf61e8a20fc46dfc2dfe075b8be7d0188df33f221ad1f0139180f9d
+  requires_dist:
+  - annotated-types>=0.6.0
+  - pydantic-core==2.41.5
+  - typing-extensions>=4.14.1
+  - typing-inspection>=0.4.2
+  - email-validator>=2.0.0 ; extra == 'email'
+  - tzdata ; python_full_version >= '3.9' and sys_platform == 'win32' and extra == 'timezone'
+  requires_python: '>=3.9'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/0d/76/941cc9f73529988688a665a5c0ecff1112b3d95ab48f81db5f7606f522d3/pydantic_core-2.41.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  name: pydantic-core
+  version: 2.41.5
+  sha256: eceb81a8d74f9267ef4081e246ffd6d129da5d87e37a77c9bde550cb04870c1c
+  requires_dist:
+  - typing-extensions>=4.14.1
+  requires_python: '>=3.9'
 - pypi: https://mirror.nju.edu.cn/pypi/web/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl
   name: pygments
   version: 2.19.2
@@ -1836,6 +1907,10 @@ packages:
   - importlib-metadata>=7.0.2 ; python_full_version < '3.10' and extra == 'type'
   - jaraco-develop>=7.21 ; sys_platform != 'cygwin' and extra == 'type'
   requires_python: '>=3.9'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/c2/90/f66c0f1d87c5d00ecae5774398e5d636c76bdf84d8b7d0e8182c82c37cd1/simsimd-6.5.12-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
+  name: simsimd
+  version: 6.5.12
+  sha256: 9d7213a87303563b7a82de1c597c604bf018483350ddab93c9c7b9b2b0646b70
 - pypi: https://mirror.nju.edu.cn/pypi/web/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl
   name: six
   version: 1.17.0
@@ -1854,6 +1929,11 @@ packages:
   - pygments ; extra == 'tests'
   - littleutils ; extra == 'tests'
   - cython ; extra == 'tests'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/47/09/f51053ba053427da5d5f640aba40d9e7ac2d053ac6ab2665f0b695011765/stringzilla-4.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl
+  name: stringzilla
+  version: 4.5.1
+  sha256: a0ecb1806fb8565f5a497379a6e5c53c46ab730e120a6d6c8444cb030547ce31
+  requires_python: '>=3.8'
 - pypi: https://mirror.nju.edu.cn/pypi/web/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl
   name: sympy
   version: 1.14.0
@@ -2506,6 +2586,13 @@ packages:
   version: 4.15.0
   sha256: f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
   requires_python: '>=3.9'
+- pypi: https://mirror.nju.edu.cn/pypi/web/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl
+  name: typing-inspection
+  version: 0.4.2
+  sha256: 4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7
+  requires_dist:
+  - typing-extensions>=4.12.0
+  requires_python: '>=3.9'
 - pypi: https://mirror.nju.edu.cn/pypi/web/packages/c7/b0/003792df09decd6849a5e39c28b513c06e84436a54440380862b5aeff25d/tzdata-2025.3-py2.py3-none-any.whl
   name: tzdata
   version: '2025.3'

pixi.toml

@@ -27,3 +27,4 @@ pandas = ">=2.0.0"
 transformers = ">=4.57.3, <5"
 ipykernel = ">=7.1.0, <8"
 sam-2 = { path = "/home/dustella/projects/sam2", editable = true }
+albumentations = ">=2.0.8, <3"

scripts/prepare_data.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""
+Verify Crack500 dataset structure and data files
+Checks train.txt, val.txt, test.txt and validates all paths
+"""
+
+from pathlib import Path
+import argparse
+
+
+def verify_data_files(data_root, split_files):
+    """Verify dataset files exist and are properly formatted"""
+    data_root = Path(data_root)
+    print(f"Verifying dataset at: {data_root}")
+    print("="*60)
+
+    total_stats = {}
+
+    for split_name, split_file in split_files.items():
+        print(f"\n{split_name.upper()} SET:")
+        print("-"*60)
+
+        if not Path(split_file).exists():
+            print(f"❌ File not found: {split_file}")
+            continue
+
+        # Read file
+        with open(split_file, 'r') as f:
+            lines = [line.strip() for line in f if line.strip()]
+
+        print(f"Total samples: {len(lines)}")
+
+        # Verify paths
+        valid_count = 0
+        missing_images = []
+        missing_masks = []
+
+        for line in lines:
+            parts = line.split()
+            if len(parts) != 2:
+                print(f"⚠️  Invalid format: {line}")
+                continue
+
+            img_rel, mask_rel = parts
+            img_path = data_root / img_rel
+            mask_path = data_root / mask_rel
+
+            if not img_path.exists():
+                missing_images.append(str(img_path))
+            if not mask_path.exists():
+                missing_masks.append(str(mask_path))
+
+            if img_path.exists() and mask_path.exists():
+                valid_count += 1
+
+        print(f"Valid samples: {valid_count}")
+        if missing_images:
+            print(f"❌ Missing images: {len(missing_images)}")
+            if len(missing_images) <= 5:
+                for img in missing_images:
+                    print(f"   - {img}")
+        if missing_masks:
+            print(f"❌ Missing masks: {len(missing_masks)}")
+            if len(missing_masks) <= 5:
+                for mask in missing_masks:
+                    print(f"   - {mask}")
+
+        if valid_count == len(lines):
+            print("✅ All paths valid!")
+
+        total_stats[split_name] = {
+            'total': len(lines),
+            'valid': valid_count,
+            'missing_images': len(missing_images),
+            'missing_masks': len(missing_masks)
+        }
+
+    # Summary
+    print("\n" + "="*60)
+    print("SUMMARY:")
+    print("="*60)
+    for split_name, stats in total_stats.items():
+        print(f"{split_name}: {stats['valid']}/{stats['total']} valid samples")
+
+    return total_stats
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Verify Crack500 dataset')
+    parser.add_argument('--data_root', type=str, default='./crack500')
+    parser.add_argument('--train_file', type=str, default='./crack500/train.txt')
+    parser.add_argument('--val_file', type=str, default='./crack500/val.txt')
+    parser.add_argument('--test_file', type=str, default='./crack500/test.txt')
+
+    args = parser.parse_args()
+
+    split_files = {
+        'train': args.train_file,
+        'val': args.val_file,
+        'test': args.test_file
+    }
+
+    stats = verify_data_files(args.data_root, split_files)
+
+    # Check if all valid
+    all_valid = all(s['valid'] == s['total'] for s in stats.values())
+    if all_valid:
+        print("\n✅ Dataset verification passed!")
+        return 0
+    else:
+        print("\n❌ Dataset verification failed!")
+        return 1
+
+
+if __name__ == '__main__':
+    exit(main())

src/dataset.py

@@ -0,0 +1,164 @@
+"""
+Crack500 Dataset for SAM2 LoRA Fine-tuning
+Loads image-mask pairs and generates bbox prompts from GT masks
+"""
+
+import torch
+from torch.utils.data import Dataset
+import cv2
+import numpy as np
+from pathlib import Path
+from typing import Dict, List
+import albumentations as A
+from albumentations.pytorch import ToTensorV2
+
+
+class Crack500Dataset(Dataset):
+    """Crack500 dataset with automatic bbox prompt generation from GT masks"""
+
+    def __init__(
+        self,
+        data_root: str,
+        split_file: str,
+        transform=None,
+        min_component_area: int = 10,
+        expand_ratio: float = 0.05
+    ):
+        """
+        Args:
+            data_root: Root directory of Crack500 dataset
+            split_file: Path to train.txt or val.txt
+            transform: Albumentations transform pipeline
+            min_component_area: Minimum area for connected components
+            expand_ratio: Bbox expansion ratio (default 5%)
+        """
+        self.data_root = Path(data_root)
+        self.transform = transform
+        self.min_component_area = min_component_area
+        self.expand_ratio = expand_ratio
+
+        # Load image-mask pairs
+        with open(split_file, 'r') as f:
+            self.samples = [line.strip().split() for line in f if line.strip()]
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, idx) -> Dict:
+        img_rel, mask_rel = self.samples[idx]
+
+        # Load image and mask
+        img_path = self.data_root / img_rel
+        mask_path = self.data_root / mask_rel
+
+        image = cv2.imread(str(img_path))
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)
+
+        # Generate bbox prompts from mask BEFORE augmentation
+        bboxes = self._extract_bboxes(mask)
+
+        # Apply augmentation
+        if self.transform:
+            augmented = self.transform(image=image, mask=mask)
+            image = augmented['image']
+            mask = augmented['mask']
+
+        # Convert mask to binary float
+        if isinstance(mask, torch.Tensor):
+            mask_binary = (mask > 0).float()
+        else:
+            mask_binary = torch.from_numpy((mask > 0).astype(np.float32))
+
+        return {
+            'image': image,
+            'mask': mask_binary.unsqueeze(0),  # (1, H, W)
+            'bboxes': torch.from_numpy(bboxes).float(),  # (N, 4)
+            'image_path': str(img_path)
+        }
+
+    def _extract_bboxes(self, mask: np.ndarray) -> np.ndarray:
+        """Extract bounding boxes from mask using connected components"""
+        binary_mask = (mask > 0).astype(np.uint8)
+
+        # Connected component analysis
+        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(
+            binary_mask, connectivity=8
+        )
+
+        bboxes = []
+        for i in range(1, num_labels):  # Skip background (label 0)
+            area = stats[i, cv2.CC_STAT_AREA]
+            if area < self.min_component_area:
+                continue
+
+            x, y, w, h = stats[i][:4]
+            x1, y1 = x, y
+            x2, y2 = x + w, y + h
+
+            # Expand bbox
+            if self.expand_ratio > 0:
+                cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
+                w_new = w * (1 + self.expand_ratio)
+                h_new = h * (1 + self.expand_ratio)
+                x1 = max(0, int(cx - w_new / 2))
+                y1 = max(0, int(cy - h_new / 2))
+                x2 = min(mask.shape[1], int(cx + w_new / 2))
+                y2 = min(mask.shape[0], int(cy + h_new / 2))
+
+            bboxes.append([x1, y1, x2, y2])
+
+        if len(bboxes) == 0:
+            # Return empty bbox if no components found
+            return np.zeros((0, 4), dtype=np.float32)
+
+        return np.array(bboxes, dtype=np.float32)
+
+
+def get_train_transform(augment=True):
+    """Training augmentation pipeline"""
+    transforms = [A.Resize(1024, 1024)]
+
+    if augment:
+        transforms.extend([
+            A.HorizontalFlip(p=0.5),
+            A.VerticalFlip(p=0.5),
+            A.RandomRotate90(p=0.5),
+            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5),
+        ])
+
+    transforms.extend([
+        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ToTensorV2()
+    ])
+
+    return A.Compose(transforms)
+
+
+def get_val_transform():
+    """Validation transform (no augmentation)"""
+    return A.Compose([
+        # Resize to SAM2 input size
+        A.Resize(1024, 1024),
+
+        A.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        ),
+        ToTensorV2()
+    ])
+
+
+def collate_fn(batch):
+    """Custom collate function to handle variable number of bboxes"""
+    images = torch.stack([item['image'] for item in batch])
+    masks = torch.stack([item['mask'] for item in batch])
+    bboxes = [item['bboxes'] for item in batch]  # Keep as list
+    image_paths = [item['image_path'] for item in batch]
+
+    return {
+        'image': images,
+        'mask': masks,
+        'bboxes': bboxes,
+        'image_path': image_paths
+    }

src/lora_utils.py

@@ -0,0 +1,165 @@
+"""
+Custom LoRA (Low-Rank Adaptation) implementation for SAM2
+Implements LoRA layers and utilities to apply LoRA to SAM2 models
+"""
+
+import torch
+import torch.nn as nn
+import re
+from typing import List, Dict
+import math
+
+
+class LoRALayer(nn.Module):
+    """LoRA layer that wraps a Linear layer with low-rank adaptation"""
+
+    def __init__(
+        self,
+        original_layer: nn.Linear,
+        r: int = 8,
+        lora_alpha: int = 16,
+        lora_dropout: float = 0.0
+    ):
+        super().__init__()
+        self.r = r
+        self.lora_alpha = lora_alpha
+
+        # Store original layer (frozen)
+        self.original_layer = original_layer
+        for param in self.original_layer.parameters():
+            param.requires_grad = False
+
+        # LoRA low-rank matrices
+        in_features = original_layer.in_features
+        out_features = original_layer.out_features
+        device = original_layer.weight.device
+        dtype = original_layer.weight.dtype
+
+        self.lora_A = nn.Parameter(torch.zeros(in_features, r, device=device, dtype=dtype))
+        self.lora_B = nn.Parameter(torch.zeros(r, out_features, device=device, dtype=dtype))
+
+        # Scaling factor
+        self.scaling = lora_alpha / r
+
+        # Dropout
+        self.lora_dropout = nn.Dropout(p=lora_dropout) if lora_dropout > 0 else nn.Identity()
+
+        # Initialize
+        nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
+        nn.init.zeros_(self.lora_B)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Original forward pass (frozen)
+        result = self.original_layer(x)
+
+        # LoRA adaptation
+        lora_out = self.lora_dropout(x) @ self.lora_A @ self.lora_B
+        result = result + lora_out * self.scaling
+
+        return result
+
+
+def apply_lora_to_model(
+    model: nn.Module,
+    target_modules: List[str],
+    r: int = 8,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.0
+) -> nn.Module:
+    """
+    Apply LoRA to specified modules in the model
+
+    Args:
+        model: SAM2 model
+        target_modules: List of regex patterns for module names to apply LoRA
+        r: LoRA rank
+        lora_alpha: LoRA alpha (scaling factor)
+        lora_dropout: Dropout probability
+
+    Returns:
+        Model with LoRA applied
+    """
+    # Compile regex patterns
+    patterns = [re.compile(pattern) for pattern in target_modules]
+
+    # Find and replace matching Linear layers
+    replaced_count = 0
+
+    # Iterate through all named modules
+    for name, module in list(model.named_modules()):
+        # Skip if already a LoRA layer
+        if isinstance(module, LoRALayer):
+            continue
+
+        # Check if this module name matches any pattern
+        if any(pattern.search(name) for pattern in patterns):
+            # Check if this module is a Linear layer
+            if isinstance(module, nn.Linear):
+                # Get parent module and attribute name
+                *parent_path, attr_name = name.split('.')
+                parent = model
+                for p in parent_path:
+                    parent = getattr(parent, p)
+
+                # Replace with LoRA layer
+                lora_layer = LoRALayer(
+                    module,
+                    r=r,
+                    lora_alpha=lora_alpha,
+                    lora_dropout=lora_dropout
+                )
+                setattr(parent, attr_name, lora_layer)
+                replaced_count += 1
+                print(f"Applied LoRA to: {name}")
+
+    print(f"\nTotal LoRA layers applied: {replaced_count}")
+    return model
+
+
+def get_lora_parameters(model: nn.Module) -> List[nn.Parameter]:
+    """Get only LoRA parameters (trainable)"""
+    lora_params = []
+    for module in model.modules():
+        if isinstance(module, LoRALayer):
+            lora_params.extend([module.lora_A, module.lora_B])
+    return lora_params
+
+
+def print_trainable_parameters(model: nn.Module):
+    """Print statistics about trainable parameters"""
+    trainable_params = 0
+    all_params = 0
+
+    for param in model.parameters():
+        all_params += param.numel()
+        if param.requires_grad:
+            trainable_params += param.numel()
+
+    print(f"\nTrainable parameters: {trainable_params:,}")
+    print(f"All parameters: {all_params:,}")
+    print(f"Trainable %: {100 * trainable_params / all_params:.2f}%")
+
+
+def save_lora_weights(model: nn.Module, save_path: str):
+    """Save only LoRA weights (not full model)"""
+    lora_state_dict = {}
+    for name, module in model.named_modules():
+        if isinstance(module, LoRALayer):
+            lora_state_dict[f"{name}.lora_A"] = module.lora_A
+            lora_state_dict[f"{name}.lora_B"] = module.lora_B
+
+    torch.save(lora_state_dict, save_path)
+    print(f"Saved LoRA weights to: {save_path}")
+
+
+def load_lora_weights(model: nn.Module, load_path: str):
+    """Load LoRA weights into model"""
+    lora_state_dict = torch.load(load_path)
+
+    for name, module in model.named_modules():
+        if isinstance(module, LoRALayer):
+            if f"{name}.lora_A" in lora_state_dict:
+                module.lora_A.data = lora_state_dict[f"{name}.lora_A"]
+                module.lora_B.data = lora_state_dict[f"{name}.lora_B"]
+
+    print(f"Loaded LoRA weights from: {load_path}")

src/losses.py

@@ -0,0 +1,96 @@
+"""
+Loss functions for SAM2 crack segmentation fine-tuning
+Includes Dice Loss, Focal Loss, and Skeleton Loss for thin structures
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+class CombinedLoss(nn.Module):
+    """Combined Dice + Focal loss for crack segmentation with class imbalance"""
+
+    def __init__(
+        self,
+        dice_weight: float = 0.5,
+        focal_weight: float = 0.5,
+        focal_alpha: float = 0.25,
+        focal_gamma: float = 2.0,
+        smooth: float = 1e-6
+    ):
+        super().__init__()
+        self.dice_weight = dice_weight
+        self.focal_weight = focal_weight
+        self.focal_alpha = focal_alpha
+        self.focal_gamma = focal_gamma
+        self.smooth = smooth
+
+    def dice_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """Dice loss for handling class imbalance"""
+        pred = torch.sigmoid(pred)
+        pred_flat = pred.view(-1)
+        target_flat = target.view(-1)
+
+        intersection = (pred_flat * target_flat).sum()
+        dice = (2. * intersection + self.smooth) / (
+            pred_flat.sum() + target_flat.sum() + self.smooth
+        )
+
+        return 1 - dice
+
+    def focal_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """Focal loss for hard example mining"""
+        bce_loss = F.binary_cross_entropy_with_logits(pred, target, reduction='none')
+        pt = torch.exp(-bce_loss)
+        focal_loss = self.focal_alpha * (1 - pt) ** self.focal_gamma * bce_loss
+
+        return focal_loss.mean()
+
+    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pred: Predicted logits (B, 1, H, W)
+            target: Ground truth binary mask (B, 1, H, W)
+        """
+        dice = self.dice_loss(pred, target)
+        focal = self.focal_loss(pred, target)
+
+        return self.dice_weight * dice + self.focal_weight * focal
+
+
+class SkeletonLoss(nn.Module):
+    """Additional loss for thin crack structures using distance transform"""
+
+    def __init__(self, weight: float = 0.2):
+        super().__init__()
+        self.weight = weight
+
+    def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            pred: Predicted logits (B, 1, H, W)
+            target: Ground truth binary mask (B, 1, H, W)
+        """
+        # Compute skeleton weights emphasizing crack centerlines
+        skeleton_weight = self._compute_skeleton_weight(target)
+
+        # Weighted BCE loss
+        weighted_bce = F.binary_cross_entropy_with_logits(
+            pred, target, weight=skeleton_weight, reduction='mean'
+        )
+
+        return self.weight * weighted_bce
+
+    def _compute_skeleton_weight(self, target: torch.Tensor) -> torch.Tensor:
+        """
+        Compute weights emphasizing pixels near crack centers
+        Uses distance transform to weight pixels closer to crack centerlines more heavily
+        """
+        # Simple weighting: emphasize positive pixels more
+        # For more sophisticated skeleton weighting, use distance transform
+        weight = torch.ones_like(target)
+        weight = weight + target * 0.5  # 1.5x weight for crack pixels
+
+        return weight

train_lora.py

@@ -0,0 +1,516 @@
+#!/usr/bin/env python3
+"""
+SAM2 LoRA Fine-tuning for Crack Segmentation
+Main training script with gradient accumulation, mixed precision, early stopping, and W&B logging
+"""
+
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import CosineAnnealingLR
+import argparse
+from pathlib import Path
+from tqdm import tqdm
+import json
+from datetime import datetime
+import sys
+import os
+
+# Add src to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from sam2.build_sam import build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from src.dataset import Crack500Dataset, get_train_transform, get_val_transform, collate_fn
+from src.losses import CombinedLoss, SkeletonLoss
+from src.lora_utils import apply_lora_to_model, print_trainable_parameters, save_lora_weights
+from src.evaluation import compute_iou, compute_dice
+from configs.lora_configs import get_strategy
+
+try:
+    import wandb
+    WANDB_AVAILABLE = True
+except ImportError:
+    WANDB_AVAILABLE = False
+    print("Warning: wandb not installed. Logging disabled.")
+
+
+class SAM2Trainer:
+    def __init__(self, args):
+        self.args = args
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        print(f"Using device: {self.device}")
+
+        # Setup directories
+        self.output_dir = Path(args.output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        self.checkpoint_dir = self.output_dir / 'checkpoints'
+        self.checkpoint_dir.mkdir(exist_ok=True)
+
+        # Initialize model
+        self.setup_model()
+
+        # Initialize datasets
+        self.setup_data()
+
+        # Initialize training components
+        self.setup_training()
+
+        # Initialize logging
+        if args.use_wandb and WANDB_AVAILABLE:
+            wandb.init(
+                project=args.wandb_project,
+                name=f"sam2_lora_{args.lora_strategy}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+                config=vars(args)
+            )
+
+        # Early stopping
+        self.best_iou = 0.0
+        self.patience_counter = 0
+
+    def setup_model(self):
+        """Initialize SAM2 model with LoRA"""
+        print(f"\nLoading SAM2 model: {self.args.model_cfg}")
+        sam2_model = build_sam2(
+            self.args.model_cfg,
+            self.args.checkpoint,
+            device=self.device,
+            mode="eval"  # Start in eval mode, will set to train later
+        )
+
+        # Get LoRA configuration
+        lora_config = get_strategy(self.args.lora_strategy)
+        print(f"\nApplying LoRA strategy: {lora_config.name}")
+        print(f"Description: {lora_config.description}")
+
+        # Apply LoRA
+        self.model = apply_lora_to_model(
+            sam2_model,
+            target_modules=lora_config.target_modules,
+            r=lora_config.r,
+            lora_alpha=lora_config.lora_alpha,
+            lora_dropout=lora_config.lora_dropout
+        )
+
+        # Print trainable parameters
+        print_trainable_parameters(self.model)
+
+        # Set to training mode
+        self.model.train()
+
+        # Create predictor wrapper
+        self.predictor = SAM2ImagePredictor(self.model)
+
+    def setup_data(self):
+        """Initialize dataloaders"""
+        print(f"\nLoading datasets...")
+        train_dataset = Crack500Dataset(
+            data_root=self.args.data_root,
+            split_file=self.args.train_file,
+            transform=get_train_transform(augment=self.args.use_augmentation),
+            expand_ratio=self.args.expand_ratio
+        )
+
+        val_dataset = Crack500Dataset(
+            data_root=self.args.data_root,
+            split_file=self.args.val_file,
+            transform=get_val_transform(),
+            expand_ratio=self.args.expand_ratio
+        )
+
+        self.train_loader = DataLoader(
+            train_dataset,
+            batch_size=self.args.batch_size,
+            shuffle=True,
+            num_workers=self.args.num_workers,
+            pin_memory=True,
+            collate_fn=collate_fn
+        )
+
+        self.val_loader = DataLoader(
+            val_dataset,
+            batch_size=self.args.batch_size,
+            shuffle=False,
+            num_workers=self.args.num_workers,
+            pin_memory=True,
+            collate_fn=collate_fn
+        )
+
+        print(f"Train samples: {len(train_dataset)}")
+        print(f"Val samples: {len(val_dataset)}")
+        print(f"Train batches: {len(self.train_loader)}")
+        print(f"Val batches: {len(self.val_loader)}")
+
+    def setup_training(self):
+        """Initialize optimizer, scheduler, and loss"""
+        # Loss function
+        self.criterion = CombinedLoss(
+            dice_weight=self.args.dice_weight,
+            focal_weight=self.args.focal_weight
+        )
+
+        if self.args.use_skeleton_loss:
+            self.skeleton_loss = SkeletonLoss(weight=self.args.skeleton_weight)
+        else:
+            self.skeleton_loss = None
+
+        # Optimizer - only optimize LoRA parameters
+        trainable_params = [p for p in self.model.parameters() if p.requires_grad]
+        self.optimizer = AdamW(
+            trainable_params,
+            lr=self.args.learning_rate,
+            weight_decay=self.args.weight_decay
+        )
+
+        # Learning rate scheduler
+        self.scheduler = CosineAnnealingLR(
+            self.optimizer,
+            T_max=self.args.epochs,
+            eta_min=self.args.learning_rate * 0.01
+        )
+
+        # Mixed precision training
+        self.scaler = torch.cuda.amp.GradScaler() if self.args.use_amp else None
+
+    def train_epoch(self, epoch):
+        """Train for one epoch"""
+        self.model.train()
+        total_loss = 0
+        self.optimizer.zero_grad()
+
+        pbar = tqdm(self.train_loader, desc=f"Epoch {epoch}/{self.args.epochs}")
+
+        for batch_idx, batch in enumerate(pbar):
+            images = batch['image'].to(self.device)  # (B, 3, H, W)
+            masks = batch['mask'].to(self.device)  # (B, 1, H, W)
+            bboxes = [bbox.to(self.device) for bbox in batch['bboxes']]  # List of (N, 4) tensors
+
+            # Process each image in batch
+            batch_loss = 0
+            valid_samples = 0
+
+            for i in range(len(images)):
+                image = images[i]  # (3, H, W)
+                mask = masks[i]  # (1, H, W)
+                bbox_list = bboxes[i]  # (N, 4)
+
+                if len(bbox_list) == 0:
+                    continue  # Skip if no bboxes
+
+                # Forward pass with mixed precision - keep everything on GPU
+                with torch.cuda.amp.autocast(enabled=self.args.use_amp):
+                    # Get image embeddings
+                    image_input = image.unsqueeze(0)  # (1, 3, H, W)
+                    backbone_out = self.model.forward_image(image_input)
+
+                    # Extract image embeddings from backbone output
+                    _, vision_feats, vision_pos_embeds, feat_sizes = self.model._prepare_backbone_features(backbone_out)
+                    image_embeddings = vision_feats[-1].permute(1, 2, 0).view(1, -1, *feat_sizes[-1])
+                    high_res_feats = [
+                        feat.permute(1, 2, 0).view(1, -1, *feat_size)
+                        for feat, feat_size in zip(vision_feats[:-1], feat_sizes[:-1])
+                    ]
+
+                    # Process each bbox prompt
+                    masks_pred = []
+                    for bbox in bbox_list:
+                        # Encode bbox prompt
+                        bbox_input = bbox.unsqueeze(0)  # (1, 4)
+                        sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
+                            points=None,
+                            boxes=bbox_input,
+                            masks=None
+                        )
+
+                        # Decode mask
+                        low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder(
+                            image_embeddings=image_embeddings,
+                            image_pe=self.model.sam_prompt_encoder.get_dense_pe(),
+                            sparse_prompt_embeddings=sparse_embeddings,
+                            dense_prompt_embeddings=dense_embeddings,
+                            multimask_output=False,
+                            repeat_image=False,
+                            high_res_features=high_res_feats if self.model.use_high_res_features_in_sam else None
+                        )
+                        masks_pred.append(low_res_masks)
+
+                    # Combine predictions (max pooling)
+                    if len(masks_pred) > 0:
+                        combined_mask = torch.cat(masks_pred, dim=0).max(dim=0, keepdim=True)[0]
+
+                        # Resize to match GT mask size
+                        combined_mask = torch.nn.functional.interpolate(
+                            combined_mask,
+                            size=mask.shape[-2:],
+                            mode='bilinear',
+                            align_corners=False
+                        )
+
+                        # Compute loss
+                        loss = self.criterion(combined_mask, mask.unsqueeze(0))
+
+                        if self.skeleton_loss is not None:
+                            loss += self.skeleton_loss(combined_mask, mask.unsqueeze(0))
+
+                        batch_loss += loss
+                        valid_samples += 1
+
+            if valid_samples > 0:
+                # Normalize by valid samples and gradient accumulation
+                batch_loss = batch_loss / (valid_samples * self.args.gradient_accumulation_steps)
+
+                # Backward pass
+                if self.args.use_amp:
+                    self.scaler.scale(batch_loss).backward()
+                else:
+                    batch_loss.backward()
+
+                # Gradient accumulation
+                if (batch_idx + 1) % self.args.gradient_accumulation_steps == 0:
+                    if self.args.use_amp:
+                        self.scaler.step(self.optimizer)
+                        self.scaler.update()
+                    else:
+                        self.optimizer.step()
+                    self.optimizer.zero_grad()
+
+                total_loss += batch_loss.item() * self.args.gradient_accumulation_steps
+                pbar.set_postfix({'loss': batch_loss.item() * self.args.gradient_accumulation_steps})
+
+                # Log to wandb
+                if self.args.use_wandb and WANDB_AVAILABLE and batch_idx % 10 == 0:
+                    wandb.log({
+                        'train_loss': batch_loss.item() * self.args.gradient_accumulation_steps,
+                        'learning_rate': self.optimizer.param_groups[0]['lr']
+                    })
+
+        self.scheduler.step()
+        return total_loss / len(self.train_loader)
+
+    @torch.no_grad()
+    def validate(self, epoch):
+        """Validate the model"""
+        self.model.eval()
+        total_loss = 0
+        total_iou = 0
+        total_dice = 0
+        num_samples = 0
+
+        for batch in tqdm(self.val_loader, desc="Validation"):
+            images = batch['image'].to(self.device)
+            masks = batch['mask'].to(self.device)
+            bboxes = [bbox.to(self.device) for bbox in batch['bboxes']]
+
+            for i in range(len(images)):
+                image = images[i]
+                mask = masks[i]
+                bbox_list = bboxes[i]
+
+                if len(bbox_list) == 0:
+                    continue
+
+                # Forward pass - keep on GPU
+                image_input = image.unsqueeze(0)
+                backbone_out = self.model.forward_image(image_input)
+
+                # Extract image embeddings
+                _, vision_feats, vision_pos_embeds, feat_sizes = self.model._prepare_backbone_features(backbone_out)
+                image_embeddings = vision_feats[-1].permute(1, 2, 0).view(1, -1, *feat_sizes[-1])
+                high_res_feats = [
+                    feat.permute(1, 2, 0).view(1, -1, *feat_size)
+                    for feat, feat_size in zip(vision_feats[:-1], feat_sizes[:-1])
+                ]
+
+                masks_pred = []
+                for bbox in bbox_list:
+                    bbox_input = bbox.unsqueeze(0)
+                    sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
+                        points=None,
+                        boxes=bbox_input,
+                        masks=None
+                    )
+
+                    low_res_masks, _, _, _ = self.model.sam_mask_decoder(
+                        image_embeddings=image_embeddings,
+                        image_pe=self.model.sam_prompt_encoder.get_dense_pe(),
+                        sparse_prompt_embeddings=sparse_embeddings,
+                        dense_prompt_embeddings=dense_embeddings,
+                        multimask_output=False,
+                        repeat_image=False,
+                        high_res_features=high_res_feats if self.model.use_high_res_features_in_sam else None
+                    )
+                    masks_pred.append(low_res_masks)
+
+                if len(masks_pred) > 0:
+                    combined_mask = torch.cat(masks_pred, dim=0).max(dim=0, keepdim=True)[0]
+                    combined_mask = torch.nn.functional.interpolate(
+                        combined_mask,
+                        size=mask.shape[-2:],
+                        mode='bilinear',
+                        align_corners=False
+                    )
+
+                    # Compute loss
+                    loss = self.criterion(combined_mask, mask.unsqueeze(0))
+                    total_loss += loss.item()
+
+                    # Compute metrics - convert to numpy only for metric computation
+                    pred_binary = (torch.sigmoid(combined_mask) > 0.5).squeeze().cpu().numpy() * 255
+                    mask_np = mask.squeeze().cpu().numpy() * 255
+
+                    iou = compute_iou(pred_binary, mask_np)
+                    dice = compute_dice(pred_binary, mask_np)
+
+                    total_iou += iou
+                    total_dice += dice
+                    num_samples += 1
+
+        avg_loss = total_loss / num_samples if num_samples > 0 else 0
+        avg_iou = total_iou / num_samples if num_samples > 0 else 0
+        avg_dice = total_dice / num_samples if num_samples > 0 else 0
+
+        print(f"\nValidation - Loss: {avg_loss:.4f}, IoU: {avg_iou:.4f}, Dice: {avg_dice:.4f}")
+
+        if self.args.use_wandb and WANDB_AVAILABLE:
+            wandb.log({
+                'val_loss': avg_loss,
+                'val_iou': avg_iou,
+                'val_dice': avg_dice,
+                'epoch': epoch
+            })
+
+        return avg_loss, avg_iou, avg_dice
+
+    def save_checkpoint(self, epoch, metrics, is_best=False):
+        """Save model checkpoint"""
+        checkpoint = {
+            'epoch': epoch,
+            'model_state_dict': self.model.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict(),
+            'scheduler_state_dict': self.scheduler.state_dict(),
+            'metrics': metrics,
+            'args': vars(self.args)
+        }
+
+        # Save latest
+        checkpoint_path = self.checkpoint_dir / f'checkpoint_epoch_{epoch}.pt'
+        torch.save(checkpoint, checkpoint_path)
+
+        # Save LoRA weights only
+        lora_path = self.checkpoint_dir / f'lora_weights_epoch_{epoch}.pt'
+        save_lora_weights(self.model, str(lora_path))
+
+        # Save best
+        if is_best:
+            best_path = self.checkpoint_dir / 'best_model.pt'
+            torch.save(checkpoint, best_path)
+            best_lora_path = self.checkpoint_dir / 'best_lora_weights.pt'
+            save_lora_weights(self.model, str(best_lora_path))
+            print(f"Saved best model with IoU: {metrics['iou']:.4f}")
+
+    def train(self):
+        """Main training loop"""
+        print(f"\n{'='*60}")
+        print("Starting training...")
+        print(f"{'='*60}\n")
+
+        for epoch in range(1, self.args.epochs + 1):
+            print(f"\n{'='*60}")
+            print(f"Epoch {epoch}/{self.args.epochs}")
+            print(f"{'='*60}")
+
+            # Train
+            train_loss = self.train_epoch(epoch)
+            print(f"Train Loss: {train_loss:.4f}")
+
+            # Validate
+            val_loss, val_iou, val_dice = self.validate(epoch)
+
+            # Save checkpoint
+            metrics = {
+                'train_loss': train_loss,
+                'val_loss': val_loss,
+                'iou': val_iou,
+                'dice': val_dice
+            }
+
+            is_best = val_iou > self.best_iou
+            if is_best:
+                self.best_iou = val_iou
+                self.patience_counter = 0
+            else:
+                self.patience_counter += 1
+
+            if epoch % self.args.save_freq == 0 or is_best:
+                self.save_checkpoint(epoch, metrics, is_best)
+
+            # Early stopping
+            if self.patience_counter >= self.args.patience:
+                print(f"\nEarly stopping triggered after {epoch} epochs")
+                print(f"Best IoU: {self.best_iou:.4f}")
+                break
+
+        print(f"\nTraining completed! Best IoU: {self.best_iou:.4f}")
+
+        # Save final summary
+        summary = {
+            'best_iou': self.best_iou,
+            'final_epoch': epoch,
+            'strategy': self.args.lora_strategy,
+            'args': vars(self.args)
+        }
+        with open(self.output_dir / 'training_summary.json', 'w') as f:
+            json.dump(summary, f, indent=2)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='SAM2 LoRA Fine-tuning for Crack Segmentation')
+
+    # Data parameters
+    parser.add_argument('--data_root', type=str, default='./crack500')
+    parser.add_argument('--train_file', type=str, default='./crack500/train.txt')
+    parser.add_argument('--val_file', type=str, default='./crack500/val.txt')
+    parser.add_argument('--expand_ratio', type=float, default=0.05)
+
+    # Model parameters
+    parser.add_argument('--checkpoint', type=str, default='../sam2/checkpoints/sam2.1_hiera_small.pt')
+    parser.add_argument('--model_cfg', type=str, default='configs/sam2.1/sam2.1_hiera_s.yaml')
+    parser.add_argument('--lora_strategy', type=str, default='B', choices=['A', 'B', 'C'])
+
+    # Training parameters
+    parser.add_argument('--epochs', type=int, default=50)
+    parser.add_argument('--batch_size', type=int, default=4)
+    parser.add_argument('--learning_rate', type=float, default=1e-4)
+    parser.add_argument('--weight_decay', type=float, default=0.01)
+    parser.add_argument('--gradient_accumulation_steps', type=int, default=4)
+    parser.add_argument('--use_augmentation', action='store_true', default=True, help='Enable data augmentation (flipping, rotation, brightness/contrast)')
+
+    # Early stopping
+    parser.add_argument('--patience', type=int, default=10)
+
+    # Loss parameters
+    parser.add_argument('--dice_weight', type=float, default=0.5)
+    parser.add_argument('--focal_weight', type=float, default=0.5)
+    parser.add_argument('--use_skeleton_loss', action='store_true')
+    parser.add_argument('--skeleton_weight', type=float, default=0.2)
+
+    # System parameters
+    parser.add_argument('--num_workers', type=int, default=4)
+    parser.add_argument('--use_amp', action='store_true', default=True)
+    parser.add_argument('--output_dir', type=str, default='./results/lora_training')
+    parser.add_argument('--save_freq', type=int, default=5)
+
+    # Logging
+    parser.add_argument('--use_wandb', action='store_true')
+    parser.add_argument('--wandb_project', type=str, default='sam2-crack-lora')
+
+    args = parser.parse_args()
+
+    # Create trainer and start training
+    trainer = SAM2Trainer(args)
+    trainer.train()
+
+
+if __name__ == '__main__':
+    main()