run evaluation

.gitignore

@@ -1,3 +1,6 @@
+results/png/
+results/svg/
+results/*.json
 unsloth_compiled_cache/
 *.ipynb
 star-vector/

app.py

@@ -0,0 +1,71 @@
+import streamlit as st
+import base64
+from ml import MLModel
+from dl import DLModel
+
+st.set_page_config(page_title="Drawing with LLM", page_icon="🎨", layout="wide")
+
+@st.cache_resource
+def load_ml_model():
+    return MLModel(device="cuda" if st.session_state.get("use_gpu", True) else "cpu")
+
+@st.cache_resource
+def load_dl_model():
+    return DLModel(device="cuda" if st.session_state.get("use_gpu", True) else "cpu")
+
+def render_svg(svg_content):
+    b64 = base64.b64encode(svg_content.encode("utf-8")).decode("utf-8")
+    return f'<img src="data:image/svg+xml;base64,{b64}" width="100%" height="auto"/>'
+
+st.title("Drawing with LLM 🎨")
+
+with st.sidebar:
+    st.header("Settings")
+    model_type = st.selectbox("Model Type", ["ML Model (vtracer)", "DL Model (starvector)"])
+    use_gpu = st.checkbox("Use GPU", value=True)
+    st.session_state["use_gpu"] = use_gpu
+    
+    if model_type == "ML Model (vtracer)":
+        st.subheader("ML Model Settings")
+        simplify = st.checkbox("Simplify SVG", value=True)
+        color_precision = st.slider("Color Precision", 1, 10, 6)
+        filter_speckle = st.slider("Filter Speckle", 0, 10, 4)
+        path_precision = st.slider("Path Precision", 1, 10, 8)
+
+prompt = st.text_area("Enter your description", "A cat sitting on a windowsill at sunset")
+
+if st.button("Generate SVG"):
+    with st.spinner("Generating SVG..."):
+        if model_type == "ML Model (vtracer)":
+            model = load_ml_model()
+            svg_content = model.predict(
+                prompt,
+                simplify=simplify,
+                color_precision=color_precision,
+                filter_speckle=filter_speckle,
+                path_precision=path_precision
+            )
+        else:
+            model = load_dl_model()
+            svg_content = model.predict(prompt)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.subheader("Generated SVG")
+            st.markdown(render_svg(svg_content), unsafe_allow_html=True)
+        
+        with col2:
+            st.subheader("SVG Code")
+            st.code(svg_content, language="xml")
+            
+            # Download button for SVG
+            st.download_button(
+                label="Download SVG",
+                data=svg_content,
+                file_name="generated_svg.svg",
+                mime="image/svg+xml"
+            )
+
+st.markdown("---")
+st.markdown("This app uses Stable Diffusion to generate images from text and converts them to SVG.")

eval.py

@@ -0,0 +1,370 @@
+import ast
+import argparse
+import logging
+import numpy as np
+import pandas as pd
+import json
+from datetime import datetime
+import os
+from PIL import Image
+from ml import MLModel
+from dl import DLModel
+from naive import NaiveModel
+import cairosvg
+import io
+from typing import Dict, Any, List, Tuple
+from tqdm import tqdm
+from metric import harmonic_mean, VQAEvaluator, AestheticEvaluator
+import gc
+import torch
+
+# Setup logging
+os.makedirs("logs", exist_ok=True)
+log_file = f"logs/eval_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler(log_file),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger(__name__)
+
+# Custom JSON encoder to handle NumPy types
+class NumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            return float(obj)
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return super(NumpyEncoder, self).default(obj)
+
+def svg_to_png(svg_code: str, size: tuple = (384, 384)) -> Image.Image:
+    """Converts SVG code to a PNG image.
+    
+    Args:
+        svg_code (str): SVG code to convert
+        size (tuple, optional): Output image size. Defaults to (384, 384).
+        
+    Returns:
+        PIL.Image.Image: The converted PNG image
+    """
+    try:
+        png_data = cairosvg.svg2png(bytestring=svg_code.encode('utf-8'), output_width=size[0], output_height=size[1])
+        return Image.open(io.BytesIO(png_data))
+    except Exception as e:
+        logger.error(f"Error converting SVG to PNG: {e}")
+        # Return a default red circle if conversion fails
+        default_svg = """<svg width="384" height="384" viewBox="0 0 256 256"><circle cx="128" cy="128" r="64" fill="red" /></svg>"""
+        png_data = cairosvg.svg2png(bytestring=default_svg.encode('utf-8'), output_width=size[0], output_height=size[1])
+        return Image.open(io.BytesIO(png_data))
+
+def load_evaluation_data(eval_csv_path: str, descriptions_csv_path: str, index: int = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """Load evaluation data from CSV files.
+    
+    Args:
+        eval_csv_path (str): Path to the evaluation CSV
+        descriptions_csv_path (str): Path to the descriptions CSV
+        index (int, optional): Specific index to load. Defaults to None (load all).
+        
+    Returns:
+        Tuple[pd.DataFrame, pd.DataFrame]: Loaded evaluation and descriptions dataframes
+    """
+    logger.info(f"Loading evaluation data from {eval_csv_path} and {descriptions_csv_path}")
+    
+    with tqdm(total=2, desc="Loading data files") as pbar:
+        eval_df = pd.read_csv(eval_csv_path)
+        pbar.update(1)
+        
+        descriptions_df = pd.read_csv(descriptions_csv_path)
+        pbar.update(1)
+    
+    if index is not None:
+        eval_df = eval_df.iloc[[index]]
+        descriptions_df = descriptions_df.iloc[[index]]
+        logger.info(f"Selected description at index {index}: {descriptions_df.iloc[0]['description']}")
+    
+    return eval_df, descriptions_df
+
+def generate_svg(model: Any, description: str, eval_data: pd.Series, 
+                results_dir: str = "results") -> Dict[str, Any]:
+    """Generate SVG using the model and save it.
+    
+    Args:
+        model (Any): The model to evaluate (MLModel, DLModel, or NaiveModel)
+        description (str): Text description to generate SVG from
+        eval_data (pd.Series): Evaluation data with questions, choices, and answers
+        results_dir (str): Directory to save results to
+        
+    Returns:
+        Dict[str, Any]: Generation results
+    """
+    # Create output directories
+    os.makedirs(results_dir, exist_ok=True)
+    os.makedirs(f"{results_dir}/svg", exist_ok=True)
+    os.makedirs(f"{results_dir}/png", exist_ok=True)
+    
+    model_name = model.__class__.__name__
+    results = {
+        "description": description,
+        "model_type": model_name,
+        "id": eval_data.get('id', '0'),
+        "category": description.split(',')[-1] if ',' in description else "unknown",
+        "timestamp": datetime.now().isoformat(),
+    }
+    
+    # Generate SVG
+    logger.info(f"Generating SVG for description: {description}")
+    start_time = datetime.now()
+    svg = model.predict(description)
+    generation_time = (datetime.now() - start_time).total_seconds()
+    results["svg"] = svg
+    results["generation_time_seconds"] = generation_time
+    
+    # Convert SVG to PNG for visual evaluation
+    image = svg_to_png(svg)
+    results["image_width"] = image.width
+    results["image_height"] = image.height
+    
+    # Save the SVG and PNG for inspection
+    output_filename = f"{results['id']}_{model_name}"
+    with open(f"{results_dir}/svg/{output_filename}.svg", "w") as f:
+        f.write(svg)
+    image.save(f"{results_dir}/png/{output_filename}.png")
+    
+    logger.info(f"Generated SVG for model {model_name} in {generation_time:.2f} seconds")
+    
+    return results
+
+def evaluate_results(results_list: List[Dict[str, Any]], 
+                    vqa_evaluator, aesthetic_evaluator,
+                    results_dir: str = "results") -> List[Dict[str, Any]]:
+    """Evaluate generated SVGs.
+    
+    Args:
+        results_list (List[Dict[str, Any]]): List of generation results
+        vqa_evaluator: VQA evaluation model
+        aesthetic_evaluator: Aesthetic evaluation model
+        results_dir (str): Directory with saved results
+        
+    Returns:
+        List[Dict[str, Any]]: Evaluation results
+    """
+    evaluated_results = []
+    
+    for result in tqdm(results_list, desc="Evaluating results"):
+        model_name = result["model_type"]
+        output_filename = f"{result['id']}_{model_name}"
+        
+        # Load the PNG image
+        image = Image.open(f"{results_dir}/png/{output_filename}.png").convert('RGB')
+        
+        try:
+            # Parse evaluation data
+            questions = result.get("questions")
+            choices = result.get("choices")
+            answers = result.get("answers")
+            
+            if not all([questions, choices, answers]):
+                logger.warning(f"Missing evaluation data for {output_filename}")
+                continue
+                
+            # Calculate scores
+            logger.info(f"Calculating VQA score for model: {model_name}")
+            vqa_score = vqa_evaluator.score(questions, choices, answers, image)
+            
+            logger.info(f"Calculating aesthetic score for model: {model_name}")
+            aesthetic_score = aesthetic_evaluator.score(image)
+            
+            # Calculate final fidelity score using harmonic mean
+            instance_score = harmonic_mean(vqa_score, aesthetic_score, beta=0.5)
+            
+            # Add scores to results
+            result["vqa_score"] = vqa_score
+            result["aesthetic_score"] = aesthetic_score
+            result["fidelity_score"] = instance_score
+            
+            logger.info(f"VQA Score: {vqa_score:.4f}")
+            logger.info(f"Aesthetic Score: {aesthetic_score:.4f}")
+            logger.info(f"Final Fidelity Score: {instance_score:.4f}")
+        except Exception as e:
+            logger.error(f"Error during evaluation: {e}")
+            result["error"] = str(e)
+        
+        evaluated_results.append(result)
+    
+    return evaluated_results
+
+def create_model(model_type: str, device: str = "cuda") -> Any:
+    """Create a model instance based on model type.
+    
+    Args:
+        model_type (str): Type of model ('ml', 'dl', or 'naive')
+        device (str, optional): Device to run model on. Defaults to "cuda".
+        
+    Returns:
+        Any: Model instance
+    """
+    logger.info(f"Creating {model_type.upper()} model on {device}")
+    with tqdm(total=1, desc=f"Loading {model_type.upper()} model") as pbar:
+        if model_type.lower() == 'ml':
+            model = MLModel(device=device)
+        elif model_type.lower() == 'dl':
+            model = DLModel(device=device)
+        elif model_type.lower() == 'naive':
+            model = NaiveModel(device=device)
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+        pbar.update(1)
+    
+    return model
+
+def main():
+    parser = argparse.ArgumentParser(description='Evaluate SVG generation models')
+    # dl is not working and takes too long, so we don't evaluate it by default
+    parser.add_argument('--models', nargs='+', choices=['ml', 'dl', 'naive'], default=['ml', 'naive'], 
+                        help='Models to evaluate (ml, dl, naive)')
+    parser.add_argument('--index', type=int, default=None,
+                        help='Index of the description to evaluate (default: None, evaluate all)')
+    parser.add_argument('--device', type=str, default='cuda',
+                        help='Device to run models on (default: cuda)')
+    parser.add_argument('--eval-csv', type=str, default='data/eval.csv',
+                        help='Path to evaluation CSV (default: data/eval.csv)')
+    parser.add_argument('--descriptions-csv', type=str, default='data/descriptions.csv',
+                        help='Path to descriptions CSV (default: data/descriptions.csv)')
+    parser.add_argument('--results-dir', type=str, default='results',
+                        help='Directory to save results (default: results)')
+    parser.add_argument('--generate-only', action='store_true',
+                        help='Only generate SVGs without evaluation')
+    parser.add_argument('--evaluate-only', action='store_true',
+                        help='Only evaluate previously generated SVGs')
+    
+    args = parser.parse_args()
+    
+    # Create results directory
+    os.makedirs(args.results_dir, exist_ok=True)
+    
+    # Load evaluation data
+    eval_df, descriptions_df = load_evaluation_data(args.eval_csv, args.descriptions_csv, args.index)
+    
+    # Load cached results or initialize new results
+    cached_results_file = f"{args.results_dir}/cached_results.json"
+    if os.path.exists(cached_results_file) and args.evaluate_only:
+        with open(cached_results_file, 'r') as f:
+            results = json.load(f)
+        logger.info(f"Loaded {len(results)} cached results from {cached_results_file}")
+    else:
+        results = []
+        
+    # Step 1: Generate SVGs if not in evaluate-only mode
+    if not args.evaluate_only:
+        # Process one model at a time to avoid loading/unloading models repeatedly
+        for model_type in args.models:
+            logger.info(f"Processing all descriptions with model: {model_type}")
+            model = create_model(model_type, args.device)
+            
+            # Process all descriptions with the current model
+            for idx, (_, desc_row) in enumerate(descriptions_df.iterrows()):
+                description = desc_row['description']
+                eval_data = eval_df.iloc[idx]
+                
+                logger.info(f"Processing description {idx}: {description}")
+                
+                # Generate SVG and save
+                result = generate_svg(model, description, eval_data, args.results_dir)
+                
+                # Add questions, choices and answers to the result
+                try:
+                    result["questions"] = ast.literal_eval(eval_data['question'])
+                    result["choices"] = ast.literal_eval(eval_data['choices'])
+                    result["answers"] = ast.literal_eval(eval_data['answer'])
+                except Exception as e:
+                    logger.error(f"Error parsing evaluation data: {e}")
+                
+                results.append(result)
+                logger.info(f"Completed SVG generation for description {idx}")
+            
+            # Free up memory after processing all descriptions with this model
+            logger.info(f"Completed all SVG generations for model: {model_type}")
+            del model
+            if args.device == 'cuda':
+                torch.cuda.empty_cache()
+            gc.collect()
+        
+        # Save the results for later evaluation
+        with open(cached_results_file, 'w') as f:
+            # Remove image data from results to avoid large JSON files
+            clean_results = []
+            for result in results:
+                clean_result = {k: v for k, v in result.items() if k not in ['image', 'svg']}
+                clean_results.append(clean_result)
+            json.dump(clean_results, f, indent=2, cls=NumpyEncoder)
+        logger.info(f"Saved {len(results)} results to {cached_results_file}")
+    
+    # Exit if only generating
+    if args.generate_only:
+        logger.info("Generation completed. Skipping evaluation as requested.")
+        return
+    
+    # Step 2: Evaluate the generated SVGs
+    logger.info("Starting evaluation phase")
+    
+    # Initialize evaluators
+    logger.info("Initializing VQA evaluator...")
+    vqa_evaluator = VQAEvaluator()
+    
+    logger.info("Initializing Aesthetic evaluator...")
+    aesthetic_evaluator = AestheticEvaluator()
+    
+    # Evaluate all results
+    evaluated_results = evaluate_results(results, vqa_evaluator, aesthetic_evaluator, args.results_dir)
+    
+    # Save final results
+    results_file = f"{args.results_dir}/results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
+    with open(results_file, 'w') as f:
+        # Remove image data from results to avoid large JSON files
+        clean_results = []
+        for result in evaluated_results:
+            clean_result = {k: v for k, v in result.items() if k not in ['image', 'svg']}
+            clean_results.append(clean_result)
+        json.dump(clean_results, f, indent=2, cls=NumpyEncoder)
+    
+    # Create a summary CSV
+    summary_data = []
+    for result in evaluated_results:
+        summary_data.append({
+            'model': result['model_type'],
+            'description': result['description'],
+            'id': result['id'],
+            'category': result['category'],
+            'vqa_score': result.get('vqa_score', float('nan')),
+            'aesthetic_score': result.get('aesthetic_score', float('nan')),
+            'fidelity_score': result.get('fidelity_score', float('nan')),
+            'generation_time': result.get('generation_time_seconds', float('nan')),
+            'timestamp': result['timestamp']
+        })
+    
+    summary_df = pd.DataFrame(summary_data)
+    summary_file = f"{args.results_dir}/summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
+    summary_df.to_csv(summary_file, index=False)
+    
+    # Print summary
+    logger.info("\nEvaluation Summary:")
+    for result in evaluated_results:
+        logger.info(f"Model: {result['model_type']}")
+        logger.info(f"Description: {result['description']}")
+        logger.info(f"VQA Score: {result.get('vqa_score', 'N/A')}")
+        logger.info(f"Aesthetic Score: {result.get('aesthetic_score', 'N/A')}")
+        logger.info(f"Fidelity Score: {result.get('fidelity_score', 'N/A')}")
+        logger.info(f"Generation Time: {result.get('generation_time_seconds', 'N/A')} seconds")
+        logger.info("---")
+    
+    logger.info(f"Results saved to: {results_file}")
+    logger.info(f"Summary saved to: {summary_file}")
+    logger.info(f"Log file: {log_file}")
+
+if __name__ == "__main__":
+    main()

ml.py

@@ -25,8 +25,8 @@ class MLModel:
         self.constraints = svg_constraints.SVGConstraints()
         self.timeout_seconds = 90
     
-    def predict(self, description, simplify=True, color_precision=6, 
-                gradient_step=10, filter_speckle=4, path_precision=8):
+    def predict(self, description, simplify=True, color_precision=6,
+                filter_speckle=4, path_precision=8):
         """
         Generate an SVG from a text description.
         
@@ -34,7 +34,6 @@ class MLModel:
             description (str): The text description to generate an image from.
             simplify (bool): Whether to simplify the SVG paths.
             color_precision (int): Color quantization precision.
-            gradient_step (int): Gradient step for color quantization (not used by vtracer).
             filter_speckle (int): Filter speckle size.
             path_precision (int): Path fitting precision.
             

requirements.txt

@@ -1,28 +1,28 @@
-kagglehub
-polars
-grpcio
-numpy
-pandas
-pyarrow
-protobuf
-defusedxml
-keras
-cairosvg
-bitsandbytes
-opencv-python
-matplotlib
-transformers
-accelerate
-openai
-tqdm
-dotenv
-diffusers
-safetensors
-xformers
-unsloth
-tf-keras
-vtracer
-deepspeed
+kagglehub==0.3.11
+polars==1.27.1
+grpcio==1.71.0
+numpy==1.26.4
+pandas==2.2.3
+pyarrow==19.0.1
+protobuf==3.20.3
+defusedxml==0.7.1
+keras==3.9.2
+cairosvg==2.7.1
+bitsandbytes==0.45.5
+opencv-python==4.11.0.86
+matplotlib==3.10.1
+transformers==4.49.0
+accelerate==1.6.0
+openai==1.75.0
+tqdm==4.67.1
+python-dotenv==1.1.0
+diffusers==0.33.1
+safetensors==0.5.3
+xformers==0.0.29.post1
+unsloth==2025.3.19
+tf_keras==2.19.0
+vtracer==0.6.11
+deepspeed==0.16.7
 torch==2.5.1
 torchvision==0.20.1
 

results/summary_20250421_230054.csv

@@ -0,0 +1,331 @@
+model,description,id,category,vqa_score,aesthetic_score,fidelity_score,generation_time,timestamp
+MLModel,a purple forest at dusk,0,landscape,0.7965157047470643,0.4092556476593018,0.6697625248703778,1.829399,2025-04-21T22:17:01.077821
+MLModel,gray wool coat with a faux fur collar,1,fashion,0.9997133342662794,0.5574348449707032,0.8628010638773217,1.423806,2025-04-21T22:17:02.987075
+MLModel,a lighthouse overlooking the ocean,2,landscape,0.6023924871331942,0.4810472011566162,0.5734611050875327,1.538955,2025-04-21T22:17:04.659418
+MLModel,burgundy corduroy pants with patch pockets and silver buttons,3,fashion,0.6826508174401045,0.5389467239379883,0.6480896514709603,1.365247,2025-04-21T22:17:06.470169
+MLModel,orange corduroy overalls,4,fashion,0.9839363047480356,0.5193052768707276,0.8345917023390731,1.432364,2025-04-21T22:17:07.988716
+MLModel,a purple silk scarf with tassel trim,5,fashion,0.8060851594621122,0.467965030670166,0.7043080172060463,1.478425,2025-04-21T22:17:09.724826
+MLModel,a green lagoon under a cloudy sky,6,landscape,0.4998784426701023,0.5254996299743653,0.5048008426887317,1.449805,2025-04-21T22:17:11.523415
+MLModel,crimson rectangles forming a chaotic grid,7,abstract,0.2566971593494377,0.4509098529815674,0.2808941024411687,1.609664,2025-04-21T22:17:13.361187
+MLModel,purple pyramids spiraling around a bronze cone,8,abstract,0.6468521381523591,0.4969790935516357,0.6100573689357699,1.530329,2025-04-21T22:17:15.551838
+MLModel,magenta trapezoids layered on a transluscent silver sheet,9,abstract,0.6140338651199525,0.4398252487182617,0.5689622690845557,1.642648,2025-04-21T22:17:17.360547
+MLModel,a snowy plain,10,landscape,0.4944889519808084,0.5234058856964111,0.5000138680253938,1.473855,2025-04-21T22:17:19.207617
+MLModel,black and white checkered pants,11,fashion,0.6736965771670621,0.5163840293884278,0.635006572777092,1.355961,2025-04-21T22:17:20.908803
+MLModel,a starlit night over snow-covered peaks,12,landscape,0.7445561775474455,0.4793485641479492,0.670376747173093,1.3432,2025-04-21T22:17:22.432653
+MLModel,khaki triangles and azure crescents,13,abstract,0.3159141906744145,0.4868715286254882,0.339775568149336,1.664099,2025-04-21T22:17:23.825105
+MLModel,a maroon dodecahedron interwoven with teal threads,14,abstract,0.4916230094367899,0.4375280380249023,0.479759729109473,1.654378,2025-04-21T22:17:25.765334
+MLModel,a bright coral beach at midday,15,landscapes,0.9258050661866246,0.5120710372924805,0.7970136842649892,1.476848,2025-04-21T22:17:27.767277
+MLModel,a misty morning over a tranquil fjord,16,landscapes,0.48771226491878,0.5394378662109375,0.4972482906358094,1.398932,2025-04-21T22:17:29.494188
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