""" UIPress Step 1b: Compute evaluation metrics on generated HTML. Uses Design2Code's official metrics + our custom token analysis. Usage: # Evaluate a single model's predictions python scripts/step1_eval_metrics.py --pred_dir results/qwen2_5_vl_7b/html_predictions # Evaluate all models in results/ python scripts/step1_eval_metrics.py --all # Quick CLIP-only evaluation (much faster, no browser needed) python scripts/step1_eval_metrics.py --all --clip_only """ # ---- HuggingFace 镜像 (必须在其他 import 之前) ---- import os os.environ["HF_ENDPOINT"] = os.environ.get("HF_ENDPOINT", "https://hf-mirror.com") os.environ["HF_HOME"] = os.environ.get("HF_HOME", "/root/rivermind-data/huggingface") import argparse import json import sys import subprocess import tempfile from pathlib import Path import torch from PIL import Image from tqdm import tqdm PROJECT_ROOT = Path(__file__).parent.parent # ============================================================ # CLIP Score (standalone, no Design2Code repo needed) # ============================================================ class CLIPScorer: """Compute CLIP similarity between reference screenshot and generated HTML rendering.""" def __init__(self, device="cuda"): import open_clip self.device = device self.model, _, self.preprocess = open_clip.create_model_and_transforms( "ViT-B-32", pretrained="openai" # ViT-B/32 matches Design2Code's metric ) self.model = self.model.to(device).eval() @torch.no_grad() def score_images(self, img1: Image.Image, img2: Image.Image) -> float: """Compute CLIP cosine similarity between two images.""" t1 = self.preprocess(img1).unsqueeze(0).to(self.device) t2 = self.preprocess(img2).unsqueeze(0).to(self.device) f1 = self.model.encode_image(t1) f2 = self.model.encode_image(t2) f1 = f1 / f1.norm(dim=-1, keepdim=True) f2 = f2 / f2.norm(dim=-1, keepdim=True) return float((f1 * f2).sum()) # ============================================================ # HTML to Screenshot Rendering # ============================================================ def render_html_to_image(html_path: str, output_path: str, width: int = 1280, height: int = 1024) -> bool: """Render HTML file to screenshot. Tries Playwright first, falls back to Selenium.""" # Try Playwright (Design2Code official uses this) if _render_playwright(html_path, output_path, width, height): return True # Fallback to Selenium return _render_selenium(html_path, output_path, width, height) def _render_playwright(html_path: str, output_path: str, width: int, height: int) -> bool: """Render using Playwright (recommended, matches Design2Code pipeline).""" try: from playwright.sync_api import sync_playwright abs_path = os.path.abspath(html_path) with sync_playwright() as p: browser = p.chromium.launch(headless=True) page = browser.new_page(viewport={"width": width, "height": height}) page.goto(f"file://{abs_path}", wait_until="networkidle") page.screenshot(path=output_path, full_page=False) browser.close() return True except ImportError: return False except Exception as e: print(f" [WARN] Playwright render failed for {html_path}: {e}") return False def _render_selenium(html_path: str, output_path: str, width: int, height: int) -> bool: """Fallback: render using Selenium + Chrome.""" try: from selenium import webdriver from selenium.webdriver.chrome.options import Options options = Options() options.add_argument("--headless=new") options.add_argument("--no-sandbox") options.add_argument("--disable-dev-shm-usage") options.add_argument(f"--window-size={width},{height}") options.add_argument("--disable-gpu") options.add_argument("--force-device-scale-factor=1") driver = webdriver.Chrome(options=options) driver.set_window_size(width, height) abs_path = os.path.abspath(html_path) driver.get(f"file://{abs_path}") import time time.sleep(1) driver.save_screenshot(output_path) driver.quit() return True except Exception as e: print(f" [WARN] Selenium render failed for {html_path}: {e}") return False # ============================================================ # Evaluation Pipeline # ============================================================ def evaluate_predictions(pred_dir: str, ref_dir: str, clip_only: bool = False): """ Evaluate HTML predictions against reference screenshots. Args: pred_dir: Directory containing predicted .html files ref_dir: Directory containing reference .png screenshots (and .html ground truth) clip_only: If True, only compute CLIP score (faster) """ pred_path = Path(pred_dir) ref_path = Path(ref_dir) pred_files = sorted(pred_path.glob("*.html")) if not pred_files: print(f"No HTML files found in {pred_dir}") return None print(f"Found {len(pred_files)} predictions in {pred_dir}") # Initialize CLIP scorer device = "cuda" if torch.cuda.is_available() else "cpu" clip_scorer = CLIPScorer(device=device) results = {} clip_scores = [] with tempfile.TemporaryDirectory() as tmp_dir: for pred_file in tqdm(pred_files, desc="Evaluating"): sample_id = pred_file.stem # Find reference screenshot ref_img_path = ref_path / f"{sample_id}.png" if not ref_img_path.exists(): # Try numeric matching for ext in [".png", ".jpg", ".jpeg"]: candidate = ref_path / f"{sample_id}{ext}" if candidate.exists(): ref_img_path = candidate break if not ref_img_path.exists(): continue ref_img = Image.open(ref_img_path).convert("RGB") # Render predicted HTML to image pred_img_path = os.path.join(tmp_dir, f"{sample_id}.png") success = render_html_to_image(str(pred_file), pred_img_path) if success and os.path.exists(pred_img_path): pred_img = Image.open(pred_img_path).convert("RGB") clip_score = clip_scorer.score_images(ref_img, pred_img) else: clip_score = 0.0 clip_scores.append(clip_score) results[sample_id] = {"clip_score": round(clip_score, 4)} # Summary avg_clip = sum(clip_scores) / len(clip_scores) if clip_scores else 0 summary = { "n_evaluated": len(clip_scores), "avg_clip_score": round(avg_clip, 4), "min_clip_score": round(min(clip_scores), 4) if clip_scores else 0, "max_clip_score": round(max(clip_scores), 4) if clip_scores else 0, "per_sample": results, } print(f"\n{'='*50}") print(f"CLIP Score: {avg_clip:.4f} (n={len(clip_scores)})") print(f" Min: {summary['min_clip_score']:.4f}") print(f" Max: {summary['max_clip_score']:.4f}") print(f"{'='*50}") return summary def run_design2code_official_eval(pred_dir: str): """ Run Design2Code's official evaluation script if available. This provides Block-Match, Text, Position, Color metrics. """ eval_script = PROJECT_ROOT / "repos" / "Design2Code" / "metrics" / "multi_processing_eval.py" if not eval_script.exists(): print("[INFO] Design2Code official eval not available.") print(" Clone the repo first: git clone https://github.com/NoviScl/Design2Code repos/Design2Code") print(" Only CLIP score was computed.") return None print(f"\nRunning Design2Code official evaluation...") print(f" Script: {eval_script}") print(f" Predictions: {pred_dir}") # Note: The official script requires manual configuration of prediction directories # We print instructions for the user print(f"\n To run manually:") print(f" 1. Edit {eval_script} line ~54") print(f" 2. Set prediction directory to: {pred_dir}") print(f" 3. Run: python {eval_script}") return None # ============================================================ # CLI # ============================================================ def main(): parser = argparse.ArgumentParser(description="UIPress: Evaluate generated HTML") parser.add_argument("--pred_dir", type=str, help="Directory with predicted .html files") parser.add_argument("--ref_dir", type=str, default=None, help="Directory with reference .png screenshots") parser.add_argument("--all", action="store_true", help="Evaluate all models in results/") parser.add_argument("--clip_only", action="store_true", help="Only compute CLIP score (skip Design2Code metrics)") parser.add_argument("--output", type=str, default=None, help="Output JSON path") args = parser.parse_args() # Find reference directory if args.ref_dir is None: candidates = [ PROJECT_ROOT / "data" / "testset_final", PROJECT_ROOT / "repos" / "Design2Code" / "testset_final", ] for c in candidates: if c.exists(): args.ref_dir = str(c) break # Fallback: extract reference images from HF dataset if args.ref_dir is None: hf_path = PROJECT_ROOT / "data" / "design2code" if hf_path.exists(): ref_tmp = PROJECT_ROOT / "data" / "ref_screenshots" ref_tmp.mkdir(parents=True, exist_ok=True) if not any(ref_tmp.iterdir()): print("Extracting reference screenshots from HF dataset...") from datasets import load_from_disk ds = load_from_disk(str(hf_path)) if hasattr(ds, 'keys'): ds = ds[list(ds.keys())[0]] for i, item in enumerate(ds): img = item.get("image") or item.get("screenshot") if img is not None: if not isinstance(img, Image.Image): img = Image.open(img).convert("RGB") img.save(str(ref_tmp / f"{i}.png")) print(f" Extracted {len(list(ref_tmp.glob('*.png')))} screenshots") args.ref_dir = str(ref_tmp) if args.ref_dir is None: print("Cannot find reference data. Specify --ref_dir or run download_data.py") sys.exit(1) if args.all: # Evaluate all models results_dir = PROJECT_ROOT / "results" all_results = {} for model_dir in sorted(results_dir.iterdir()): pred_html = model_dir / "html_predictions" if pred_html.exists(): print(f"\n{'='*60}") print(f"Evaluating: {model_dir.name}") print(f"{'='*60}") summary = evaluate_predictions( str(pred_html), args.ref_dir, args.clip_only ) if summary: all_results[model_dir.name] = summary # Print comparison if all_results: print(f"\n{'='*70}") print("FINAL COMPARISON") print(f"{'='*70}") print(f"{'Model':<30} {'CLIP Score':>12} {'N Samples':>12}") print("-" * 55) for name, s in sorted(all_results.items(), key=lambda x: -x[1]["avg_clip_score"]): print(f"{name:<30} {s['avg_clip_score']:>12.4f} {s['n_evaluated']:>12}") # Save out = results_dir / "step1_metrics_comparison.json" with open(out, "w") as f: json.dump(all_results, f, indent=2) print(f"\nSaved to: {out}") elif args.pred_dir: summary = evaluate_predictions(args.pred_dir, args.ref_dir, args.clip_only) if summary and args.output: with open(args.output, "w") as f: json.dump(summary, f, indent=2) # Also try official eval if not args.clip_only: run_design2code_official_eval(args.pred_dir) else: parser.print_help() if __name__ == "__main__": main()