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"""
GUI Task Evaluation Scripts for GUI-Shift.
Evaluates on:
- AndroidControl (Low / High)
- GUI Odyssey
- ScreenSpot-v2
- ScreenSpot-Pro
Metrics:
- TM: Type Match (correct action type)
- EM: Exact Match (correct type + all parameters)
From: GUI-Shift paper Section 4 (arXiv:2505.12493)
"""
import argparse
import json
import os
from pathlib import Path
from typing import Dict, Any, List, Tuple, Optional
from collections import defaultdict
import torch
from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image
def load_model(model_path: str, device: str = "cuda"):
"""Load trained GUI-Shift model."""
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForVision2Seq.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map=device,
trust_remote_code=True,
)
model.eval()
return model, processor
def parse_predicted_action(text: str) -> Optional[Dict[str, Any]]:
"""Parse action from model output."""
import re
match = re.search(r'<answer>(.*?)</answer>', text, re.DOTALL)
if not match:
return None
content = match.group(1).strip()
try:
return json.loads(content)
except json.JSONDecodeError:
# Fallback regex
action_type_match = re.search(r'"action_type"\s*:\s*"([^"]+)"', content)
if action_type_match:
action = {"action_type": action_type_match.group(1)}
for key in ["x", "y", "direction", "app_name", "text"]:
match = re.search(rf'"{key}"\s*:\s*(?:"([^"]+)"|(\d+))', content)
if match:
val = match.group(1) or int(match.group(2))
if key in ["x", "y"]:
val = int(val)
action[key] = val
return action
return None
def type_match(pred: Optional[Dict], gt: Dict) -> bool:
"""Check if predicted action type matches ground truth."""
if not pred:
return False
return pred.get("action_type") == gt.get("action_type")
def exact_match(pred: Optional[Dict], gt: Dict) -> bool:
"""Check if predicted action exactly matches ground truth."""
if not type_match(pred, gt):
return False
action_type = gt.get("action_type", "")
if action_type in ["click", "long_press"]:
bbox = gt.get("bbox", [0, 0, 0, 0])
x = pred.get("x", 0)
y = pred.get("y", 0)
if bbox and len(bbox) >= 4:
return bbox[0] <= x <= bbox[2] and bbox[1] <= y <= bbox[3]
if "x" in gt and "y" in gt:
tolerance = 20
return abs(x - gt["x"]) <= tolerance and abs(y - gt["y"]) <= tolerance
return False
elif action_type == "scroll":
return pred.get("direction") == gt.get("direction")
elif action_type == "open_app":
return pred.get("app_name") == gt.get("app_name")
elif action_type == "input_text":
return pred.get("text") == gt.get("text")
elif action_type in ["navigate_back", "navigate_home", "wait"]:
return True
return False
def evaluate_sample(
model,
processor,
sample: Dict[str, Any],
device: str = "cuda",
) -> Tuple[bool, bool, str]:
"""Evaluate a single sample. Returns (type_match, exact_match, prediction_text)."""
image_paths = sample.get("image_path", sample.get("image", []))
problem = sample.get("problem", sample.get("instruction", ""))
ground_truth = sample.get("ground_truth_action", sample.get("action", {}))
# Load images
images = []
for img_path in image_paths:
if isinstance(img_path, str) and os.path.exists(img_path):
images.append(Image.open(img_path).convert("RGB"))
# Build prompt
messages = [
{"role": "user", "content": [{"type": "image"} for _ in images] + [{"type": "text", "text": problem}]}
]
text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=text, images=images, return_tensors="pt", padding=True).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=False,
)
generated_text = processor.batch_decode(
outputs[:, inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)[0]
pred_action = parse_predicted_action(generated_text)
tm = type_match(pred_action, ground_truth)
em = exact_match(pred_action, ground_truth)
return tm, em, generated_text
def evaluate_dataset(
model,
processor,
dataset_path: str,
device: str = "cuda",
output_path: Optional[str] = None,
) -> Dict[str, float]:
"""Evaluate a full dataset and compute metrics."""
samples = []
with open(dataset_path, "r") as f:
for line in f:
if line.strip():
samples.append(json.loads(line))
results = {
"total": len(samples),
"type_match": 0,
"exact_match": 0,
"by_type": defaultdict(lambda: {"total": 0, "tm": 0, "em": 0}),
"details": [],
}
for i, sample in enumerate(samples):
print(f" Evaluating {i+1}/{len(samples)}...", end="\r")
tm, em, pred_text = evaluate_sample(model, processor, sample, device)
gt = sample.get("ground_truth_action", sample.get("action", {}))
action_type = gt.get("action_type", "unknown")
results["type_match"] += int(tm)
results["exact_match"] += int(em)
results["by_type"][action_type]["total"] += 1
results["by_type"][action_type]["tm"] += int(tm)
results["by_type"][action_type]["em"] += int(em)
results["details"].append({
"id": sample.get("id", i),
"type_match": tm,
"exact_match": em,
"predicted": pred_text,
"ground_truth": gt,
})
# Compute percentages
results["type_match_pct"] = 100.0 * results["type_match"] / len(samples) if samples else 0
results["exact_match_pct"] = 100.0 * results["exact_match"] / len(samples) if samples else 0
for action_type, counts in results["by_type"].items():
counts["tm_pct"] = 100.0 * counts["tm"] / counts["total"] if counts["total"] else 0
counts["em_pct"] = 100.0 * counts["em"] / counts["total"] if counts["total"] else 0
print(f"\n TM: {results['type_match_pct']:.1f}% | EM: {results['exact_match_pct']:.1f}%")
if output_path:
with open(output_path, "w") as f:
json.dump(results, f, indent=2)
print(f" Results saved to {output_path}")
return results
def main():
parser = argparse.ArgumentParser(description="Evaluate GUI-Shift model on GUI benchmarks")
parser.add_argument("--model_path", type=str, required=True, help="Path to trained model")
parser.add_argument("--dataset", type=str, required=True, help="Path to evaluation dataset (JSONL)")
parser.add_argument("--output", type=str, default="evaluation_results.json", help="Output results file")
parser.add_argument("--device", type=str, default="cuda", help="Device for inference")
parser.add_argument("--benchmark", type=str, default="androidcontrol",
choices=["androidcontrol_low", "androidcontrol_high", "gui_odyssey",
"screenspot_v2", "screenspot_pro"],
help="Benchmark name")
args = parser.parse_args()
print(f"Loading model from {args.model_path}...")
model, processor = load_model(args.model_path, args.device)
print(f"Evaluating on {args.benchmark}...")
results = evaluate_dataset(model, processor, args.dataset, args.device, args.output)
print("\n=== Final Results ===")
print(f"Benchmark: {args.benchmark}")
print(f"Total samples: {results['total']}")
print(f"Type Match (TM): {results['type_match']}/{results['total']} = {results['type_match_pct']:.2f}%")
print(f"Exact Match (EM): {results['exact_match']}/{results['total']} = {results['exact_match_pct']:.2f}%")
print("\nPer-action breakdown:")
for action_type, counts in sorted(results["by_type"].items()):
print(f" {action_type:20s}: TM={counts['tm_pct']:.1f}% EM={counts['em_pct']:.1f}% ({counts['tm']}/{counts['total']})")
if __name__ == "__main__":
main()
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