Add scripts, results, and visualization archive

results/few_shot_results.md

@@ -0,0 +1,18 @@
+# All Categories - Few-shot Protocol Results
+
+**Last Updated:** 2025-08-18 03:35:23
+
+**Protocol:** Few-shot
+**Available Categories:** ----------, breakfast_box, juice_bottle, pushpins, screw_bag, splicing_connectors
+
+## Summary Table
+
+| Category | K-shots | F1-Max (Image) | AUROC (Image) | F1-Max (Logical) | AUROC (Logical) | F1-Max (Structural) | AUROC (Structural) |
+|----------|---------|----------------|---------------|------------------|-----------------|---------------------|-------------------|
+| ---------- | --------- | ---------------- | --------------- | ------------------ | ----------------- | --------------------- | ------------------- |
+| breakfast_box | 4 | 89.91 | 94.36 | 92.41 | 97.08 | 85.03 | 91.85 |
+| juice_bottle | 4 | 88.16 | 84.26 | 83.54 | 85.46 | 77.31 | 82.46 |
+| pushpins | 4 | 81.36 | 82.89 | 78.82 | 84.28 | 70.81 | 81.32 |
+| screw_bag | 4 | 82.95 | 81.29 | 77.14 | 79.72 | 78.21 | 83.91 |
+| splicing_connectors | 4 | 84.70 | 88.60 | 84.49 | 89.21 | 82.80 | 87.82 |
+| **Average** | 4 | 85.42 | 86.28 | 83.28 | 87.15 | 78.83 | 85.47 |

results/full_data_results.md

@@ -0,0 +1,18 @@
+# All Categories - Full-data Protocol Results
+
+**Last Updated:** 2025-08-18 15:14:57
+
+**Protocol:** Full-data
+**Available Categories:** ----------, breakfast_box, juice_bottle, pushpins, screw_bag, splicing_connectors
+
+## Summary Table
+
+| Category | K-shots | F1-Max (Image) | AUROC (Image) | F1-Max (Logical) | AUROC (Logical) | F1-Max (Structural) | AUROC (Structural) |
+|----------|---------|----------------|---------------|------------------|-----------------|---------------------|-------------------|
+| ---------- | --------- | ---------------- | --------------- | ------------------ | ----------------- | --------------------- | ------------------- |
+| breakfast_box | 4 | 91.95 | 95.69 | 90.45 | 97.14 | 90.48 | 94.35 |
+| juice_bottle | 4 | 93.94 | 95.23 | 91.91 | 94.80 | 94.12 | 95.88 |
+| pushpins | 4 | 81.34 | 83.95 | 79.53 | 84.68 | 73.58 | 83.14 |
+| screw_bag | 4 | 85.22 | 83.20 | 77.85 | 80.20 | 86.03 | 88.22 |
+| splicing_connectors | 4 | 91.26 | 93.48 | 86.57 | 90.34 | 93.57 | 97.47 |
+| **Average** | 4 | 88.74 | 90.31 | 85.26 | 89.43 | 87.56 | 91.81 |

scripts/compute_averages.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+"""
+Compute Average Metrics Script for LogSAD Results
+
+This script automatically detects results MD files in the results/ directory,
+calculates average metrics across all categories, and appends them to the files
+if not already present.
+"""
+
+import os
+import re
+import glob
+from pathlib import Path
+
+
+def has_average_metrics(file_path):
+    """Check if the file already contains average metrics row."""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+            return "| **Average**" in content or "| Average" in content
+    except Exception as e:
+        print(f"Error reading {file_path}: {e}")
+        return False
+
+
+def parse_results_table(file_path):
+    """Parse the results table from MD file and extract metrics."""
+    try:
+        with open(file_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        lines = content.split('\n')
+        categories_data = []
+        
+        # Find table data lines
+        for line in lines:
+            if '|' in line and line.count('|') >= 8:
+                parts = [p.strip() for p in line.split('|')]
+                # Skip header, separator, average row, and empty lines
+                if (len(parts) >= 8 and 
+                    parts[1] not in ['Category', '----------', '-----', '', '**Average**', 'Average'] and
+                    parts[1] != '----------' and not parts[1].startswith('**Average')):
+                    try:
+                        category_name = parts[1]
+                        f1_image = float(parts[3])
+                        auroc_image = float(parts[4])
+                        f1_logical = float(parts[5])
+                        auroc_logical = float(parts[6])
+                        f1_structural = float(parts[7])
+                        auroc_structural = float(parts[8])
+                        
+                        categories_data.append({
+                            'category': category_name,
+                            'f1_image': f1_image,
+                            'auroc_image': auroc_image,
+                            'f1_logical': f1_logical,
+                            'auroc_logical': auroc_logical,
+                            'f1_structural': f1_structural,
+                            'auroc_structural': auroc_structural
+                        })
+                    except (ValueError, IndexError):
+                        continue
+        
+        return categories_data
+    except Exception as e:
+        print(f"Error parsing {file_path}: {e}")
+        return []
+
+
+def calculate_averages(categories_data):
+    """Calculate average metrics across all categories."""
+    if not categories_data:
+        return None
+    
+    n = len(categories_data)
+    averages = {
+        'f1_image': sum(cat['f1_image'] for cat in categories_data) / n,
+        'auroc_image': sum(cat['auroc_image'] for cat in categories_data) / n,
+        'f1_logical': sum(cat['f1_logical'] for cat in categories_data) / n,
+        'auroc_logical': sum(cat['auroc_logical'] for cat in categories_data) / n,
+        'f1_structural': sum(cat['f1_structural'] for cat in categories_data) / n,
+        'auroc_structural': sum(cat['auroc_structural'] for cat in categories_data) / n
+    }
+    
+    return averages
+
+
+def append_averages_to_file(file_path, averages):
+    """Append average metrics as the last row of the existing table."""
+    try:
+        # Read the current file content
+        with open(file_path, 'r', encoding='utf-8') as f:
+            content = f.read()
+        
+        # Find the table end and insert average row before it
+        lines = content.split('\n')
+        new_lines = []
+        table_found = False
+        
+        for i, line in enumerate(lines):
+            new_lines.append(line)
+            # Check if this is a table row (has appropriate number of |)
+            if '|' in line and line.count('|') >= 8:
+                table_found = True
+                # Check if this might be the last data row (next line is empty or doesn't have table format)
+                if (i + 1 >= len(lines) or 
+                    lines[i + 1].strip() == '' or 
+                    '|' not in lines[i + 1] or 
+                    lines[i + 1].count('|') < 8):
+                    # Add average row after this line
+                    average_row = f"| **Average** | 4 | {averages['f1_image']:.2f} | {averages['auroc_image']:.2f} | {averages['f1_logical']:.2f} | {averages['auroc_logical']:.2f} | {averages['f1_structural']:.2f} | {averages['auroc_structural']:.2f} |"
+                    new_lines.append(average_row)
+        
+        # Write the updated content back to file
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write('\n'.join(new_lines))
+        
+        print(f"✓ Added average metrics row to {file_path}")
+        return True
+    except Exception as e:
+        print(f"✗ Error appending to {file_path}: {e}")
+        return False
+
+
+def process_results_file(file_path):
+    """Process a single results file."""
+    print(f"Processing: {file_path}")
+    
+    # Check if averages already exist
+    if has_average_metrics(file_path):
+        print(f"  → Average metrics already exist, skipping")
+        return
+    
+    # Parse the results table
+    categories_data = parse_results_table(file_path)
+    if not categories_data:
+        print(f"  → No valid data found, skipping")
+        return
+    
+    print(f"  → Found {len(categories_data)} categories")
+    
+    # Calculate averages
+    averages = calculate_averages(categories_data)
+    if not averages:
+        print(f"  → Failed to calculate averages, skipping")
+        return
+    
+    # Append averages to file
+    append_averages_to_file(file_path, averages)
+
+
+def main():
+    """Main function to process all results files."""
+    print("LogSAD Average Metrics Computation")
+    print("=" * 50)
+    
+    # Find all results MD files
+    results_pattern = "results/*_results.md"
+    results_files = glob.glob(results_pattern)
+    
+    if not results_files:
+        print("No results files found matching pattern:", results_pattern)
+        return
+    
+    print(f"Found {len(results_files)} results file(s):")
+    for file_path in results_files:
+        print(f"  - {file_path}")
+    
+    print("\nProcessing files...")
+    print("-" * 30)
+    
+    # Process each file
+    for file_path in results_files:
+        process_results_file(file_path)
+        print()
+    
+    print("Average metrics computation completed!")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_few_shot.sh

@@ -0,0 +1,49 @@
+#!/bin/bash
+
+# Few-shot Protocol Evaluation Script for LogSAD
+# This script runs evaluation for all categories in the MVTEC LOCO dataset
+
+# Dataset path
+DATASET_PATH="/root/autodl-tmp/datasets/mvtec_loco_anomaly_detection"
+
+# Categories to evaluate
+CATEGORIES=("breakfast_box" "juice_bottle" "pushpins" "screw_bag" "splicing_connectors")
+
+echo "Starting Few-shot Protocol Evaluation..."
+echo "Dataset path: $DATASET_PATH"
+echo "Categories: ${CATEGORIES[@]}"
+echo "=================================="
+
+# Main loop to iterate through all categories
+for i in "${!CATEGORIES[@]}"; do
+    category="${CATEGORIES[$i]}"
+    current=$((i + 1))
+    total=${#CATEGORIES[@]}
+    
+    echo "[$current/$total] Evaluating category: $category"
+    echo "Command: python evaluation.py --module_path model_ensemble_few_shot --category $category --dataset_path $DATASET_PATH --viz"
+    
+    python evaluation.py --module_path model_ensemble_few_shot --category "$category" --dataset_path "$DATASET_PATH" --viz
+    
+    if [ $? -eq 0 ]; then
+        echo "✓ Successfully completed evaluation for $category"
+    else
+        echo "✗ Failed to evaluate $category"
+        exit 1
+    fi
+    
+    echo "=================================="
+done
+
+echo "All categories evaluation completed!"
+echo ""
+echo "Results saved in results/ directory:"
+echo "  - Combined results: results/few_shot_results.md"
+
+echo "=================================="
+echo "Computing average metrics across all categories..."
+
+# Run the average computation script
+python scripts/compute_averages.py
+
+echo "✓ Average metrics computation completed"

scripts/run_full_data.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+
+# Full-data Protocol Evaluation Script for LogSAD
+# This script first computes coreset and then runs evaluation for all categories in the MVTEC LOCO dataset
+
+# Dataset path
+DATASET_PATH="/root/autodl-tmp/datasets/mvtec_loco_anomaly_detection"
+
+# Categories to evaluate
+# CATEGORIES=("breakfast_box" "juice_bottle" "pushpins" "screw_bag" "splicing_connectors")
+CATEGORIES=("splicing_connectors")
+
+echo "Starting Full-data Protocol Evaluation..."
+echo "Dataset path: $DATASET_PATH"
+echo "Categories: ${CATEGORIES[@]}"
+echo "=================================="
+
+# Main loop to iterate through all categories
+for i in "${!CATEGORIES[@]}"; do
+    category="${CATEGORIES[$i]}"
+    current=$((i + 1))
+    total=${#CATEGORIES[@]}
+    
+    echo "[$current/$total] Processing category: $category"
+    
+    # Step 1: Compute coreset for full-data scenarios
+    echo "Step 1: Computing coreset for $category"
+    echo "Command: python compute_coreset.py --module_path model_ensemble --category $category --dataset_path $DATASET_PATH"
+    
+    python compute_coreset.py --module_path model_ensemble --category "$category" --dataset_path "$DATASET_PATH"
+    
+    if [ $? -eq 0 ]; then
+        echo "✓ Successfully computed coreset for $category"
+    else
+        echo "✗ Failed to compute coreset for $category"
+        exit 1
+    fi
+    
+    # Step 2: Run evaluation for full-data protocol
+    echo "Step 2: Running evaluation for $category"
+    echo "Command: python evaluation.py --module_path model_ensemble --category $category --dataset_path $DATASET_PATH --viz"
+    
+    python evaluation.py --module_path model_ensemble --category "$category" --dataset_path "$DATASET_PATH" --viz
+    
+    if [ $? -eq 0 ]; then
+        echo "✓ Successfully completed evaluation for $category"
+    else
+        echo "✗ Failed to evaluate $category"
+        exit 1
+    fi
+    
+    echo "=================================="
+done
+
+echo "All categories processing completed!"
+echo ""
+echo "Results saved in results/ directory:"
+echo "  - Combined results: results/full_data_results.md"
+
+echo "=================================="
+echo "Computing average metrics across all categories..."
+
+# Run the average computation script
+python scripts/compute_averages.py
+
+echo "✓ Average metrics computation completed"

scripts/setup_environment.sh

@@ -0,0 +1,41 @@
+#!/bin/bash
+
+# LogSAD Environment Setup Script
+# This script creates and configures the complete environment for LogSAD
+# Run this script to set up all dependencies and requirements
+
+set -e  # Exit on any error
+
+echo "LogSAD Environment Setup"
+echo "======================="
+echo "Setting up conda environment and dependencies..."
+
+# Create conda environment
+echo "Creating conda environment 'logsad' with Python 3.10..."
+conda create -n logsad python=3.10 -y
+
+# Activate environment
+echo "Activating environment..."
+conda activate logsad
+
+# Install PyTorch with CUDA support
+echo "Installing PyTorch with CUDA 12.1 support..."
+pip install torch==2.1.2+cu121 torchvision -f https://mirrors.aliyun.com/pytorch-wheels/cu121/
+
+# Install project requirements
+echo "Installing project requirements..."
+pip install -r requirements.txt
+
+# Install specific numpy version for compatibility
+echo "Installing compatible numpy version..."
+pip install numpy==1.23.1
+
+echo ""
+echo "✓ Environment setup completed successfully!"
+echo ""
+echo "To activate the environment, run:"
+echo "  conda activate logsad"
+echo ""
+echo "To run evaluations, use:"
+echo "  bash scripts/run_few_shot.sh      # For few-shot protocol"
+echo "  bash scripts/run_full_data.sh     # For full-data protocol"

visualization.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e4f5793a0c62e806d2e9e9067717b5ccb4e366af2a746e665f99f9ec564de99
+size 838311086