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CAD-GUI Benchmark 统一评测引擎 (benchmark_evaluator.py)
========================================================
整合了项目中所有任务的评测逻辑，服务器上直接用这一个文件即可。
包含：
1. 几何判定引擎（点/线[圆滑]/多边形/椭圆/避让区/拖拽起终点）
2. 模型输出解析（point / bbox / action / 坐标 / 方向 / 输入内容）
3. 坐标转换（0-1000 归一化 <-> 像素）
4. 各任务评测：定位(Point/BBox) / 动作类型+参数 / 多步规划(Teacher Forcing)
5. LLM-as-Judge 意图一致性判定
依赖: pip install shapely numpy scipy openai
"""
import json
import math
import re
import numpy as np
from shapely.geometry import Point, Polygon, LineString
from scipy.interpolate import splprep, splev
TOLERANCE = 5.0 # 像素容差（基于真实CAD鼠标点击物理容差）
# ============================================================
# 1. 几何判定引擎
# ============================================================
def smooth_trajectory(kps):
"""对 >=3 个点的轨迹做 B-spline 圆滑插值，返回 LineString。
注意：标注点顺序必须沿曲线走向，否则会过冲/交叉。"""
if len(kps) < 3:
return LineString(kps)
try:
pts = np.array(kps)
k = min(3, len(kps) - 1)
tck, u = splprep([pts[:, 0], pts[:, 1]], s=0, k=k)
u_new = np.linspace(0, 1, max(len(kps) * 10, 100))
x_new, y_new = splev(u_new, tck)
return LineString(list(zip(x_new.tolist(), y_new.tolist())))
except Exception:
return LineString(kps)
def is_point_in_ellipse(px, py, el):
"""判断点是否在旋转椭圆内"""
cx, cy, rx, ry = el['cx'], el['cy'], el['rx'], el['ry']
angle = math.radians(el.get('rotation', 0))
cos_a, sin_a = math.cos(angle), math.sin(angle)
dx, dy = px - cx, py - cy
rx2 = cos_a * dx + sin_a * dy
ry2 = -sin_a * dx + cos_a * dy
if rx == 0 or ry == 0:
return False
return (rx2**2 / rx**2) + (ry2**2 / ry**2) <= 1
def hit_region(px, py, region, tol=TOLERANCE):
"""判断像素点 (px,py) 是否命中一个区域 (含 polygons/keypoints/ellipses/arc_trajectory)"""
p = Point(px, py)
for poly in region.get("polygons", []):
if len(poly) >= 3 and Polygon(poly).buffer(tol).contains(p):
return True
for el in region.get("ellipses", []):
if is_point_in_ellipse(px, py, el):
return True
kps = region.get("keypoints", [])
if len(kps) == 1:
if p.distance(Point(kps[0][0], kps[0][1])) <= tol:
return True
elif len(kps) >= 2:
traj = smooth_trajectory(kps) if len(kps) >= 3 else LineString(kps)
if traj.distance(p) <= tol:
return True
arc = region.get("arc_trajectory", [])
if len(arc) >= 2:
traj = smooth_trajectory(arc) if len(arc) >= 3 else LineString(arc)
if traj.distance(p) <= tol:
return True
return False
def evaluate_prediction(px, py, ground_truth, tol=TOLERANCE):
"""核心判定：像素坐标是否命中目标且避开禁区。
先判 exclude（命中即False），再判 include（命中即True）。"""
exclude = ground_truth.get("exclude", {})
if hit_region(px, py, exclude, tol):
return False
include = ground_truth.get("include", {})
return hit_region(px, py, include, tol)
def compute_iou(box1, box2):
"""两个 bbox [xmin,ymin,xmax,ymax] 的 IoU"""
x1, y1 = max(box1[0], box2[0]), max(box1[1], box2[1])
x2, y2 = min(box1[2], box2[2]), min(box1[3], box2[3])
inter = max(0, x2 - x1) * max(0, y2 - y1)
a1 = (box1[2] - box1[0]) * (box1[3] - box1[1])
a2 = (box2[2] - box2[0]) * (box2[3] - box2[1])
union = a1 + a2 - inter
return inter / union if union > 0 else 0
# ============================================================
# 2. 坐标转换
# ============================================================
def norm_to_pixel(nx, ny, w, h):
"""0-1000 归一化 -> 像素"""
return nx / 1000.0 * w, ny / 1000.0 * h
def pixel_bbox_to_norm(bbox_pixels, resolution):
"""像素 bbox -> 0-1000 归一化（用于 IoU 评测时把 GT 转到归一化空间）"""
w, h = resolution
return [bbox_pixels[0]/w*1000, bbox_pixels[1]/h*1000,
bbox_pixels[2]/w*1000, bbox_pixels[3]/h*1000]
def smart_resize(h, w, factor=28, min_pixels=3136, max_pixels=12845056):
"""Qwen2-VL/2.5-VL 的 smart_resize：返回 (resized_h, resized_w)。
像素坐标模型(UI-Venus/Holo2/InfiGUI)输出的是 resize 后空间的像素，
需按各模型的 min/max_pixels 反算回原图。"""
import math
h_bar = round(h / factor) * factor
w_bar = round(w / factor) * factor
if h_bar * w_bar > max_pixels:
beta = math.sqrt((h * w) / max_pixels)
h_bar = math.floor(h / beta / factor) * factor
w_bar = math.floor(w / beta / factor) * factor
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (h * w))
h_bar = math.ceil(h * beta / factor) * factor
w_bar = math.ceil(w * beta / factor) * factor
return h_bar, w_bar
# 各像素坐标模型的 smart_resize 参数（min_pixels, max_pixels）
SMART_RESIZE_PARAMS = {
"ui-venus": (2000000, 4800000), # 官方 README
"holo2": (3136, 12845056), # Qwen2.5-VL 默认（Holo2 用默认 smart_resize）
"infigui": (3136, 12845056), # 实测接近原图，用默认
"ui-tars": (3136, 12845056), # UI-TARS-1.5: Qwen2.5-VL 默认 smart_resize，输出绝对像素
"opencua": (3136, 12845056), # OpenCUA: Qwen2.5-VL 默认 smart_resize，输出绝对像素
}
# ============================================================
# 3. 模型输出解析
# ============================================================
def parse_point(text):
"""解析 'Point: [x, y]' 或任意 [x,y]，返回 0-1000 范围的 [x,y] 或 None"""
for m in re.findall(r'\[(\d+\.?\d*),\s*(\d+\.?\d*)\]', text):
x, y = float(m[0]), float(m[1])
if 0 <= x <= 1000 and 0 <= y <= 1000:
return [x, y]
return None
def parse_bbox(text):
"""解析 'Box: [xmin,ymin,xmax,ymax]'，返回 0-1000 的 4 元组或 None"""
for m in re.findall(r'\[(\d+\.?\d*),\s*(\d+\.?\d*),\s*(\d+\.?\d*),\s*(\d+\.?\d*)\]', text):
v = [float(x) for x in m]
if all(0 <= x <= 1000 for x in v):
return v
return None
def parse_action(text):
"""解析 'Action: xxx'，返回小写动作名或 None"""
m = re.search(r'Action:\s*([a-z_]+)', text, re.IGNORECASE)
return m.group(1).strip().lower() if m else None
def parse_coords_list(text):
"""解析 Params 行里的所有 [x,y]，返回列表（用于拖拽/框选的起终点）"""
pm = re.search(r'Params:\s*(.*)', text, re.IGNORECASE | re.DOTALL)
seg = pm.group(1) if pm else text
return [[float(a), float(b)] for a, b in re.findall(r'\[(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\]', seg)]
def parse_direction(text):
"""解析方向，归一化为英文 (up/down/left/right/left_up/...)"""
pm = re.search(r'Params:\s*(.*)', text, re.IGNORECASE)
seg = (pm.group(1).lower() if pm else text.lower())
for key in ['left_up', 'left_down', 'right_up', 'right_down',
'向左上', '向右上', '向左下', '向右下',
'上', '下', '左', '右', 'up', 'down', 'left', 'right']:
if key in seg:
return {'上': 'up', '下': 'down', '左': 'left', '右': 'right',
'向左上': 'left_up', '向右上': 'right_up',
'向左下': 'left_down', '向右下': 'right_down'}.get(key, key)
return None
def parse_thought(text):
"""解析 'Thought: ...'（到 Action 之前）"""
m = re.search(r'Thought:\s*(.*?)(?=Action:|$)', text, re.IGNORECASE | re.DOTALL)
return m.group(1).strip() if m else ""
def parse_type_value(text):
"""解析 type_enter 的输入内容"""
pm = re.search(r'Params:\s*(.*)', text, re.IGNORECASE)
return pm.group(1).strip().strip('[]"\'') if pm else None
# ---- 专用模型坐标解析器（各模型原生输出格式不同）----
def parse_paren_point(text):
"""UGround: 输出 '(x, y)' 圆括号，0-1000。取最后一个括号对。"""
ms = re.findall(r'\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\)', text)
if ms:
x, y = float(ms[-1][0]), float(ms[-1][1])
return [x, y]
return None
def parse_uitars_point(text):
"""UI-TARS: 'click(start_box='(x,y)')' 或 '(x,y)'，0-1000。"""
m = re.search(r"start_box\s*=\s*['\"]?\(?\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)", text)
if m:
return [float(m.group(1)), float(m.group(2))]
return parse_paren_point(text)
def parse_osatlas_point(text):
"""OS-Atlas: 可能输出 '(x1,y1),(x2,y2)' 圆括号 或 '[[x1,y1,x2,y2]]' 方括号，0-1000。
出框时取中心点；出点时直接用。会先复述指令文字，正则只抓坐标。"""
# 先试方括号 bbox [[x1,y1,x2,y2]] 或 [x1,y1,x2,y2]
m = re.search(r'\[+\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', text)
if m:
v = [float(m.group(i)) for i in range(1, 5)]
return [(v[0]+v[2])/2, (v[1]+v[3])/2]
# 再试圆括号点对
pts = re.findall(r'\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\)', text)
if len(pts) >= 2:
return [(float(pts[0][0])+float(pts[1][0]))/2, (float(pts[0][1])+float(pts[1][1]))/2]
if len(pts) == 1:
return [float(pts[0][0]), float(pts[0][1])]
# 最后试方括号点 [x,y]
m2 = re.search(r'\[\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', text)
if m2:
return [float(m2.group(1)), float(m2.group(2))]
return None
def parse_osatlas_bbox(text):
"""OS-Atlas 出框：支持 '[[x1,y1,x2,y2]]' 方括号 或 '(x1,y1),(x2,y2)' 圆括号，0-1000。"""
m = re.search(r'\[+\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', text)
if m:
v = [float(m.group(i)) for i in range(1, 5)]
return [min(v[0], v[2]), min(v[1], v[3]), max(v[0], v[2]), max(v[1], v[3])]
pts = re.findall(r'\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\)', text)
if len(pts) >= 2:
x1, y1 = float(pts[0][0]), float(pts[0][1])
x2, y2 = float(pts[1][0]), float(pts[1][1])
return [min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)]
return None
def parse_json_point(text):
"""InfiGUI-G1: 去掉<think>后 JSON [{"point_2d":[x,y]}]；像素坐标。
兼容 ```json 围栏与多候选（取第一个）。"""
seg = text.split("</think>")[-1]
seg = seg.replace("```json", "").replace("```", "")
m = re.search(r'"point_2d"\s*:\s*\[\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', seg)
if m:
return [float(m.group(1)), float(m.group(2))]
return None
def parse_click_absolute(text):
"""Holo2: JSON {"action":"click_absolute","x":..,"y":..}；像素坐标。"""
mx = re.search(r'"x"\s*:\s*(\d+\.?\d*)', text)
my = re.search(r'"y"\s*:\s*(\d+\.?\d*)', text)
if mx and my:
return [float(mx.group(1)), float(my.group(1))]
return None
def parse_uivenus_bbox(text):
"""UI-Venus: 输出 [x1,y1,x2,y2]（smart_resize 像素），返回原始 4 元组。"""
m = re.search(r'\[\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)\s*\]', text)
if m:
return [float(m.group(i)) for i in range(1, 5)]
return None
def parse_uivenus_point(text):
"""UI-Venus: [x1,y1,x2,y2] -> 中心点（smart_resize 像素）。"""
bb = parse_uivenus_bbox(text)
if bb:
return [(bb[0] + bb[2]) / 2, (bb[1] + bb[3]) / 2]
return None
def parse_pyautogui_point(text):
"""OpenCUA: 'pyautogui.click(x=1443, y=343)'，smart_resize 后绝对像素。
兼容 click/moveTo/dragTo 等，取第一个 x=,y=。"""
m = re.search(r'x\s*=\s*(\d+\.?\d*)\s*,\s*y\s*=\s*(\d+\.?\d*)', text)
if m:
return [float(m.group(1)), float(m.group(2))]
return None
def parse_point_by_style(text, style):
"""按 parse_style 分发到对应解析器，统一返回原始数值对 [x,y] 或 None。
范围/归一化由各模型 cfg 决定，这里只负责从文本里把数提出来。"""
if style == "paren":
return parse_paren_point(text)
if style == "uitars":
return parse_uitars_point(text)
if style == "osatlas":
return parse_osatlas_point(text)
if style == "json_point": # InfiGUI-G1
return parse_json_point(text)
if style == "click_absolute": # Holo2
return parse_click_absolute(text)
if style == "uivenus": # UI-Venus: bbox 中心点
return parse_uivenus_point(text)
if style == "pyautogui": # OpenCUA: pyautogui.click(x=,y=)
return parse_pyautogui_point(text)
return parse_point(text) # bracket 默认
def parse_bbox_by_style(text, style):
"""按 parse_style 分发 bbox 解析。"""
if style == "osatlas":
return parse_osatlas_bbox(text)
if style == "uivenus":
return parse_uivenus_bbox(text)
return parse_bbox(text) # bracket 默认
# ============================================================
# 4. 统一 Prompt 模板
# ============================================================
COORD_SYSTEM = """COORDINATE SYSTEM:
- (0, 0) = top-left corner of the image
- (1000, 1000) = bottom-right corner of the image
- Simply estimate the target's position as a percentage of image width/height, then multiply by 10.
- Do NOT assume any screen resolution (not 1920x1080, not 2560x1440)."""
PROMPT_POINT = """You are a GUI expert. Given a screenshot and an action intent about interacting with a specific UI element, you need to identify the click point (x, y) of the target element, normalized to 0-1000. Note that the X-axis runs horizontally from left (0) to right (999), and the Y-axis runs vertically from top (0) to bottom (999).
""" + COORD_SYSTEM + """
Action Intent: {instruction}
IMPORTANT: Output ONLY the result in the exact format below. Do NOT explain.
Output format:
Point: [x, y]"""
PROMPT_BBOX = """You are a GUI expert. Given a screenshot and an action intent, identify the bounding box [xmin, ymin, xmax, ymax] of the target element, normalized to 0-1000.
""" + COORD_SYSTEM + """
Action Intent: {instruction}
IMPORTANT: Output ONLY the result. Do NOT explain.
Output format:
Box: [xmin, ymin, xmax, ymax]"""
ACTION_SPACE = """Available actions:
- left_click [x,y]
- double_click [x,y]
- right_click [x,y]
- ctrl_click [x,y]
- drag [x1,y1] [x2,y2]
- box_select [x1,y1] [x2,y2]
- middle_drag [direction] # rotate 3D view
- ctrl_middle_drag [direction] # pan view
- shift_middle_drag [direction] # zoom: up=zoom in, down=zoom out
- double_middle_click # reset/fit view
- type_enter [text]
- press_esc
- press_delete"""
PROMPT_ACTION = """You are a SOLIDWORKS operation expert. Given a screenshot and a user's intent, decide the SINGLE next action.
""" + ACTION_SPACE + "\n\n" + COORD_SYSTEM + """
User intent: {instruction}
Output STRICTLY:
Action: <action_name>
Params: <[x,y] or [x1,y1] [x2,y2] or direction or text or NONE>"""
PROMPT_MULTISTEP = """You are a SOLIDWORKS expert doing a multi-step task.
High-level task: {high_level_instruction}
""" + ACTION_SPACE + "\n\n" + COORD_SYSTEM + """
History (already done correctly):
{history}
Based on the CURRENT screenshot, decide the NEXT step.
Output:
Thought: <what to do now and why>
Action: <action_name>
Params: <[x,y] or [x1,y1] [x2,y2] or text or NONE>"""
# ============================================================
# 5. 单步评测（支持 alternatives 多方案 / 各种动作类型）
# ============================================================
NEED_COORD = {"left_click", "double_click", "right_click", "ctrl_click"}
NEED_DRAG = {"drag", "box_select"}
NO_COORD = {"press_esc", "press_delete", "double_middle_click"}
def eval_one_alternative(pred_action, coords, type_text, gt, w, h):
"""判定模型输出是否匹配单个 GT 方案。返回 (action_ok, param_ok)"""
gt_action = gt["action_type"]
action_ok = (pred_action == gt_action)
if not action_ok:
return False, False
if gt_action in NEED_COORD:
if coords:
px, py = norm_to_pixel(coords[0][0], coords[0][1], w, h)
return True, evaluate_prediction(px, py, gt)
return True, False
elif gt_action in NEED_DRAG:
if len(coords) >= 2:
ps = norm_to_pixel(coords[0][0], coords[0][1], w, h)
pe = norm_to_pixel(coords[1][0], coords[1][1], w, h)
# 兼容4.4格式: drag_start/end 可能是 {"polygon":[[x,y],...]} 需转成 {"polygons":[[[x,y],...]]}
ds = gt.get("drag_start") or {}
de = gt.get("drag_end") or {}
if "polygon" in ds and "polygons" not in ds:
ds = {"polygons": [ds["polygon"]], "keypoints": [], "ellipses": []}
if "polygon" in de and "polygons" not in de:
de = {"polygons": [de["polygon"]], "keypoints": [], "ellipses": []}
s_ok = hit_region(ps[0], ps[1], ds)
e_ok = hit_region(pe[0], pe[1], de)
return True, (s_ok and e_ok)
return True, False
elif gt_action == "type_enter":
tv = gt.get("type_value", "")
return True, (tv in (type_text or "")) if tv else True
else: # NO_COORD 或视图方向类
if "direction" in gt:
return True, True # 方向需调用方单独判
return True, True
def eval_step(reply, gt, w, h):
"""评测一步。支持 gt 带 alternatives（多等价方案，命中任一即对）。
返回 dict: thought, action, action_ok, param_ok, step_ok"""
thought = parse_thought(reply)
action = parse_action(reply)
coords = parse_coords_list(reply)
type_text = parse_type_value(reply)
direction = parse_direction(reply)
alts = gt["alternatives"] if "alternatives" in gt else [gt]
best = (False, False)
for alt in alts:
a_ok, p_ok = eval_one_alternative(action, coords, type_text, alt, w, h)
# 方向类单独判
if a_ok and "direction" in alt:
p_ok = (direction == alt["direction"])
if a_ok and p_ok:
best = (True, True); break
if a_ok and not best[0]:
best = (True, False)
action_ok, param_ok = best
return {
"thought": thought, "action": action,
"action_ok": action_ok, "param_ok": param_ok,
"step_ok": action_ok and param_ok,
}
# ============================================================
# 6. LLM-as-Judge 意图一致性
# ============================================================
def judge_thought(client, gt_thought, pred_thought, high_instr, judge_model="qwen-plus"):
"""用 LLM 判断模型 thought 与 GT thought 语义是否一致。返回 True/False"""
prompt = f"""你是评判员。在SolidWorks多步操作任务中，判断模型的"当前步骤意图"和标准答案意图是否在语义上一致（想做的事相同即可，措辞不同没关系）。
高层任务: {high_instr}
标准意图: {gt_thought}
模型意图: {pred_thought}
只回答一个词: 一致 或 不一致"""
try:
r = client.chat.completions.create(
model=judge_model,
messages=[{"role": "user", "content": prompt}],
max_tokens=10, timeout=60)
reply = (r.choices[0].message.content or "").strip().strip('"\'“”‘’。.!！ \t\r\n')
return reply == "一致"
except Exception:
return False
def attribute_verdict(action_ok, param_ok, intent_ok):
"""归因分类"""
if action_ok and param_ok:
return "完全正确"
if action_ok and not param_ok and intent_ok:
return "懂意图懂动作_点歪了"
if action_ok and not param_ok and not intent_ok:
return "动作蒙对_没真懂"
if not action_ok and intent_ok:
return "懂意图_动作选错"
return "不懂该做什么"
# ============================================================
# 9. 分组统计与能力分级
# ============================================================
def grade(rate):
"""能力分级：掌握>70% / 部分30-70% / 缺失<30%"""
if rate >= 70: return "掌握"
if rate >= 30: return "部分"
return "缺失"
def summarize_groups(results, groups):
"""按分组汇总定位结果。
results: run_localization 返回的逐题列表（每条含 id/hits/tries，可选 avg_iou/acc50）
groups: [(组名, set(id集合)), ...]
返回每组的命中率、best-of-N、能力分级、（若有）平均IoU/Acc@0.5
"""
by_id = {r["id"]: r for r in results}
table = []
for gname, ids in groups:
grp = [by_id[i] for i in ids if i in by_id]
if not grp:
continue
total_hits = sum(r["hits"] for r in grp)
total_tries = sum(r["tries"] for r in grp)
best_of_n = sum(1 for r in grp if r["hits"] > 0)
hit_rate = total_hits / total_tries * 100 if total_tries else 0
best_rate = best_of_n / len(grp) * 100 if grp else 0
row = {
"group": gname, "n_items": len(grp),
"hit_rate": round(hit_rate, 1),
"best_of_n_rate": round(best_rate, 1),
"grade": grade(hit_rate),
}
ious = [r["avg_iou"] for r in grp if "avg_iou" in r]
if ious:
row["avg_iou"] = round(sum(ious)/len(ious), 4)
acc50 = [r["acc50"] for r in grp if "acc50" in r]
if acc50:
row["acc50"] = round(sum(acc50)/len(acc50)*100, 1)
table.append(row)
return table
def make_groups_every_n(results, n=10):
"""便捷：每 n 个 id 一组，组名 Group1/Group2..."""
ids = sorted(r["id"] for r in results)
groups = []
for g, start in enumerate(range(0, len(ids), n), 1):
chunk = set(ids[start:start+n])
groups.append((f"Group{g}", chunk))
return groups
def make_groups_by_direction(results):
"""便捷：按 id 末位分'从左到右'(1-5)和'从右到左'(6-0)两组（任务3.1方位）"""
l2r = {r["id"] for r in results if r["id"] % 10 in (1, 2, 3, 4, 5)}
r2l = {r["id"] for r in results if r["id"] % 10 in (6, 7, 8, 9, 0)}
return [("从左到右", l2r), ("从右到左", r2l)]
def print_group_table(table, title=""):
"""打印分组汇总表"""
if title:
print(f"\n{'='*70}\n=== {title} ===\n{'='*70}")
header = f"{'组别':<18}{'题数':>5}{'命中率':>9}{'Best-of-N':>11}{'分级':>7}"
has_iou = any("avg_iou" in r for r in table)
if has_iou:
header += f"{'Acc@0.5':>9}{'平均IoU':>9}"
print(header)
print("-" * len(header))
for r in table:
line = f"{r['group']:<18}{r['n_items']:>5}{r['hit_rate']:>8.1f}%{r['best_of_n_rate']:>10.1f}%{r['grade']:>7}"
if has_iou and "avg_iou" in r:
line += f"{r.get('acc50',0):>8.1f}%{r['avg_iou']:>9.4f}"
print(line)
# ============================================================
# 10. 模型坐标格式适配（不同模型坐标约定不同）
# ============================================================
# 用法：根据模型名选择对应的解析+转换策略
MODEL_COORD_CONFIG = {
# type: specialized=专用GUI模型(原生模板,不加CoT) / general=通用大模型(GUI expert prompt)
# prompt: 用哪个模板 parse: 用哪个解析器 norm/order: 坐标转换
# point_only: 只能出点(任务1/2的IoU列留空)
"default": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
# ---- 专用 GUI 模型：各自原生模板 + 各自解析 ----
# UI-TARS-1.5: 输出 smart_resize 后绝对像素坐标(不是0-1000!)，需反算回原图
"ui-tars": {"type": "specialized", "prompt": "uitars", "parse": "uitars", "norm": "smart_resize", "resize": "ui-tars", "order": "xy", "point_only": True},
# OS-Atlas: 输出 [[x1,y1,x2,y2]]方括号 或 (x1,y1),(x2,y2)圆括号 0-1000，会复述指令文字需大token
"os-atlas": {"type": "specialized", "prompt": "osatlas", "parse": "osatlas", "norm": 1000, "order": "xy", "max_tokens": 640},
"uground": {"type": "specialized", "prompt": "uground", "parse": "paren", "norm": 1000, "order": "xy", "point_only": True},
# OpenCUA: 官方system + pyautogui.click(x=,y=)，smart_resize绝对像素(默认参数)，point_only
"opencua": {"type": "specialized", "prompt": "opencua", "parse": "pyautogui", "norm": "smart_resize", "resize": "opencua", "order": "xy", "system": "opencua", "point_only": True, "max_tokens": 512},
# UI-Venus: 官方 prompt 出 [x1,y1,x2,y2]，坐标在 smart_resize(min2M,max4.8M)像素空间，取中心点
"ui-venus": {"type": "specialized", "prompt": "uivenus", "parse": "uivenus", "norm": "smart_resize", "resize": "ui-venus", "order": "xy"},
"gui-owl": {"type": "specialized", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
# InfiGUI-G1: 像素坐标 + system prompt 告知分辨率 + <think> + JSON point_2d（实测≈原图像素）
"infigui": {"type": "specialized", "prompt": "infigui", "parse": "json_point", "norm": "smart_resize", "resize": "infigui", "order": "xy", "system": "infigui", "point_only": True},
# Holo2: 实测输出 0-1000 归一化(非smart_resize像素)，JSON click_absolute，话痨需大max_tokens
"holo2": {"type": "specialized", "prompt": "holo2", "parse": "click_absolute", "norm": 1000, "order": "xy", "point_only": True, "max_tokens": 640},
# GELab-Zero(StepGUI): 0-999 归一化
"gelab": {"type": "specialized", "prompt": "point", "parse": "bracket", "norm": 999, "order": "xy"},
# ---- 通用大模型：统一 GUI expert prompt，thinking 模型自带 CoT ----
"qwen3-vl": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
"glm-4.5v": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
"glm-4.6v": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy", "max_tokens": 1024},
"glm-5v": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy", "max_tokens": 1024},
"gpt-5": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy", "max_tokens": 2048},
"claude": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
"o3": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy"},
# Gemini: point 输出 xy(正常)，但 bbox 是 [ymin,xmin,ymax,xmax] → bbox_order=yx
"gemini-2.5": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy", "bbox_order": "yx", "max_tokens": 8192},
"gemini": {"type": "general", "prompt": "point", "parse": "bracket", "norm": 1000, "order": "xy", "bbox_order": "yx", "max_tokens": 1024},
# Qwen2.5-VL: 输出像素坐标，需告知真实分辨率，转换时 norm=None
"qwen2.5-vl": {"type": "general", "prompt": "point", "parse": "bracket", "norm": None, "order": "xy"},
}
def get_model_cfg(model_name):
"""按模型名（子串匹配）取配置，找不到用 default。"""
key = model_name.lower()
for name, cfg in MODEL_COORD_CONFIG.items():
if name != "default" and name in key:
return cfg
return MODEL_COORD_CONFIG["default"]
def normalize_coord(coord, cfg, w, h):
"""根据模型配置把模型输出的坐标转成原图像素 [x,y]。
coord: 模型输出的原始数值对; cfg: MODEL_COORD_CONFIG[xxx]
norm 取值:
- 数值(1000/999): 归一化基数, 坐标/norm*原图
- "smart_resize": 输出是 resize 后像素, 按 cfg['resize'] 参数反算回原图
- "pixel"/None: 已是原图像素, 直接用"""
if cfg["order"] == "yx":
coord = [coord[1], coord[0]]
norm = cfg.get("norm")
if isinstance(norm, (int, float)) and norm:
return coord[0]/norm*w, coord[1]/norm*h
if norm == "smart_resize":
params = SMART_RESIZE_PARAMS.get(cfg.get("resize"), (3136, 12845056))
rh, rw = smart_resize(h, w, min_pixels=params[0], max_pixels=params[1])
return coord[0]/rw*w, coord[1]/rh*h
return coord[0], coord[1] # 已是原图像素（norm == "pixel" / None）
# ============================================================
# 12. 专用模型原生模板 + CoT 模板
# ============================================================
# UGround-V1：输出单个 "(x, y)" 0-1000（官方模板，末尾 Answer:）
PROMPT_UGROUND = """Your task is to help the user identify the precise coordinates (x, y) of a specific area/element/object on the screen based on a description.
- Your response should aim to point to the center or a representative point within the described area/element/object as accurately as possible.
- If the description is unclear or ambiguous, infer the most relevant area or element based on its likely context or purpose.
- Your answer should be a single string (x, y) corresponding to the point of the interest.
Description: {instruction}
Answer:"""
# OS-Atlas：输出 "<|box_start|>(x1,y1),(x2,y2)<|box_end|>" 0-1000，可出框/点
PROMPT_OSATLAS_POINT = """In the screenshot of this page, please give me the coordinates of the element I want to click on according to my instructions(with point).
"{instruction}\""""
# UI-TARS-1.5：grounding 模式，输出 click(start_box='(x,y)') 0-1000
PROMPT_UITARS = """Output only the coordinate of one point in your response. What element matches the following instruction: {instruction}"""
# InfiGUI-G1：像素坐标 + system prompt 告知分辨率 + <think> + JSON
# system prompt 与 user prompt 在 call 时分别传入
INFIGUI_SYSTEM = ('You FIRST think about the reasoning process as an internal monologue '
'and then provide the final answer.\n'
'The reasoning process MUST BE enclosed within <think> </think> tags.')
PROMPT_INFIGUI = ("The screen's resolution is {w}x{h}.\n"
'Locate the UI element(s) for "{instruction}", '
'output the coordinates using JSON format: [{{"point_2d": [x, y]}}, ...]')
# Holo2：像素坐标(smart_resize) + JSON click_absolute
PROMPT_HOLO2 = ('Localize an element on the GUI image according to the provided target '
'and output a click position. You must output a valid JSON following the format: '
'{{"action": "click_absolute", "x": <int px from left>, "y": <int px from top>}}\n'
'Your target is: {instruction}')
# UI-Venus：官方 grounding prompt，输出 [x1,y1,x2,y2]（smart_resize 像素空间）
PROMPT_UIVENUS = ('Outline the position corresponding to the instruction: {instruction}. '
'The output should be only [x1,y1,x2,y2].')
# OpenCUA：官方 system + 直接给 instruction，输出 pyautogui.click(x=,y=)，smart_resize 绝对像素
OPENCUA_SYSTEM = ('You are a GUI agent. You are given a task and a screenshot of the screen. '
'You need to perform a series of pyautogui actions to complete the task.')
PROMPT_OPENCUA = "{instruction}"
# 任务1.3/2.3 CoT 对照（通用大模型）
PROMPT_POINT_COT = """You are a GUI expert. Analyze step by step.
""" + COORD_SYSTEM + """
Action Intent: {instruction}
Output:
Observation: <what you see relevant to the intent>
Reasoning: <which element to click and why>
Point: [x, y]"""
# ============================================================
# 11. 模型调用（OpenAI 兼容 API；本地部署模型用 vLLM/sglang 起 OpenAI server 即可）
# ============================================================
import base64
import os
import time
def encode_image(path):
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode("utf-8")
def call_vlm(client, model, image_path, prompt, max_tokens=300, retries=3, temperature=0.0, system=None):
"""调用多模态模型。client 为 openai.OpenAI 实例。
temperature 默认 0.0：任务1/2 每题仅 1 次，必须确定性输出才可复现
（UGround 等官方亦强调 temperature=0）。
system: 可选 system prompt（InfiGUI-G1 等需要）。
"""
b64 = encode_image(image_path)
messages = []
if system:
messages.append({"role": "system", "content": system})
messages.append({"role": "user", "content": [
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}},
{"type": "text", "text": prompt}
]})
# GPT-5 / o3 等推理模型：用 max_completion_tokens，且只支持默认 temperature
is_reasoning = any(k in model.lower() for k in ("gpt-5", "o3", "o4"))
for a in range(retries):
try:
kwargs = dict(model=model, messages=messages, timeout=120)
if is_reasoning:
kwargs["max_completion_tokens"] = max_tokens
else:
kwargs["max_tokens"] = max_tokens
kwargs["temperature"] = temperature
resp = client.chat.completions.create(**kwargs)
return resp.choices[0].message.content or ""
except Exception as e:
if a < retries - 1:
time.sleep(3)
else:
return f"API_ERROR: {e}"
SYSTEM_PROMPTS = {"infigui": INFIGUI_SYSTEM, "opencua": OPENCUA_SYSTEM}
# ============================================================
# 8. 任务级评测函数
# ============================================================
def build_localization_prompt(prompt_style, instruction, mode, cot=False, w=None, h=None):
"""按 prompt_style 选模板生成定位 prompt。
prompt_style: point/uground/osatlas/uitars/infigui/holo2; mode: point/bbox; cot: 意图题用
w/h: 像素模型(infigui)需要把分辨率写进 prompt"""
if cot:
return PROMPT_POINT_COT.format(instruction=instruction)
if prompt_style == "uground":
return PROMPT_UGROUND.format(instruction=instruction)
if prompt_style == "osatlas":
return PROMPT_OSATLAS_POINT.format(instruction=instruction)
if prompt_style == "uitars":
return PROMPT_UITARS.format(instruction=instruction)
if prompt_style == "infigui":
return PROMPT_INFIGUI.format(instruction=instruction, w=w, h=h)
if prompt_style == "holo2":
return PROMPT_HOLO2.format(instruction=instruction)
if prompt_style == "uivenus":
return PROMPT_UIVENUS.format(instruction=instruction)
if prompt_style == "opencua":
return PROMPT_OPENCUA.format(instruction=instruction)
# 通用大模型 / 默认
if mode == "bbox":
return PROMPT_BBOX.format(instruction=instruction)
return PROMPT_POINT.format(instruction=instruction)
def run_localization(client, model, benchmark_file, image_dir, tries=None,
mode="point", cfg=None, cot=False, max_tokens=None):
"""定位任务（任务1/2/3/4.1）。
- cfg: MODEL_COORD_CONFIG 条目；None 时按 model 名自动取（含 prompt/parse/norm/order/point_only）
- tries: 每题测试次数；None 时按数据规模自动（任务1/2 大数据集=1次，任务3/4=10次）
- mode: 'point' 或 'bbox'；point_only 模型强制 point
- cot: 意图题(1.3/2.3) CoT 对照实验时置 True
benchmark 既支持 bbox_pixels（任务1/2）也支持 ground_truth（任务3/4）格式。"""
cfg = cfg or get_model_cfg(model)
data = json.load(open(benchmark_file, "r", encoding="utf-8"))
# point_only 模型不能出框：bbox 模式自动降级为 point（IoU 留空）
if cfg.get("point_only") and mode == "bbox":
mode = "point"
# tries 默认：bbox_pixels 格式(任务1/2)每题1次，ground_truth 格式(任务3/4)每题10次
if tries is None:
tries = 1 if ("bbox_pixels" in data[0]) else 10
# CoT 模式需要更长输出；专用话痨模型(如Holo2)可在cfg里指定 max_tokens
if max_tokens is None:
max_tokens = cfg.get("max_tokens", 512 if cot else 100)
prompt_style = cfg.get("prompt", "point")
parse_style = cfg.get("parse", "bracket")
results = []
consec_quota_err = 0 # 连续配额错误计数，达阈值则中止（避免余额耗尽后空跑）
for item in data:
img = os.path.join(image_dir, item["image_path"])
w, h = item["image_resolution"]
if "ground_truth" in item:
gt = item["ground_truth"]
gt_box_norm = None
else: # bbox_pixels 格式
bp = item["bbox_pixels"]
gt = {"include": {"polygons": [[[bp[0],bp[1]],[bp[2],bp[1]],[bp[2],bp[3]],[bp[0],bp[3]]]],
"keypoints": [], "ellipses": []},
"exclude": {"polygons": [], "keypoints": [], "ellipses": []}}
gt_box_norm = pixel_bbox_to_norm(bp, item["image_resolution"])
prompt = build_localization_prompt(prompt_style, item["instruction"], mode, cot, w, h)
sys_prompt = SYSTEM_PROMPTS.get(cfg.get("system"))
hits = 0
ious = []
parse_fail = 0
trials = []
for _ in range(tries):
started_at = time.perf_counter()
reply = call_vlm(client, model, img, prompt, max_tokens, system=sys_prompt)
latency_s = round(time.perf_counter() - started_at, 4)
# 配额耗尽保护：连续 20 次 429/quota 错误则中止整轮，避免空跑污染结果
if isinstance(reply, str) and reply.startswith("API_ERROR") and \
("429" in reply or "quota" in reply.lower() or "insufficient" in reply.lower()):
consec_quota_err += 1
if consec_quota_err >= 20:
raise RuntimeError(
f"[配额耗尽中止] 连续{consec_quota_err}次429/quota错误，"
f"已完成的结果已落盘，请充值后重跑(断点续跑会跳过已完成)。")
else:
consec_quota_err = 0
if mode == "point":
pt = parse_point_by_style(reply, parse_style)
if pt is None:
parse_fail += 1
trials.append({
"parse_ok": False,
"hit": False,
"latency_s": latency_s,
})
continue
px, py = normalize_coord(pt, cfg, w, h)
hit = evaluate_prediction(px, py, gt)
if hit:
hits += 1
trials.append({
"parse_ok": True,
"raw_point": [round(float(pt[0]), 4), round(float(pt[1]), 4)],
"point_px": [round(float(px), 4), round(float(py), 4)],
"hit": bool(hit),
"latency_s": latency_s,
})
else: # bbox
bb = parse_bbox_by_style(reply, parse_style)
if bb is None:
parse_fail += 1
trials.append({
"parse_ok": False,
"latency_s": latency_s,
})
continue
if gt_box_norm:
# 把模型 bbox 的两个角点经 normalize_coord 转成原图像素，
# 再转 0-1000 归一化（统一处理 yx 顺序 / smart_resize / 归一化基数）
# bbox 顺序可能与 point 不同(如 Gemini point=xy 但 bbox=yx)，用 bbox_order 覆盖
bcfg = dict(cfg, order=cfg.get("bbox_order", cfg["order"]))
p1 = normalize_coord([bb[0], bb[1]], bcfg, w, h)
p2 = normalize_coord([bb[2], bb[3]], bcfg, w, h)
bb_norm = [p1[0]/w*1000, p1[1]/h*1000, p2[0]/w*1000, p2[1]/h*1000]
bb_norm = [min(bb_norm[0], bb_norm[2]), min(bb_norm[1], bb_norm[3]),
max(bb_norm[0], bb_norm[2]), max(bb_norm[1], bb_norm[3])]
iou = compute_iou(bb_norm, gt_box_norm)
ious.append(iou)
trials.append({
"parse_ok": True,
"raw_bbox": [round(float(value), 4) for value in bb],
"bbox_norm": [round(float(value), 4) for value in bb_norm],
"iou": round(float(iou), 6),
"latency_s": latency_s,
})
else:
trials.append({
"parse_ok": True,
"raw_bbox": [round(float(value), 4) for value in bb],
"latency_s": latency_s,
})
rec = {
"id": item["id"],
"hits": hits,
"tries": tries,
"parse_fail": parse_fail,
"trials": trials,
}
if mode == "bbox" and ious:
rec["avg_iou"] = sum(ious)/len(ious)
rec["acc50"] = sum(1 for v in ious if v >= 0.5)/len(ious)
rec["acc75"] = sum(1 for v in ious if v >= 0.75)/len(ious)
results.append(rec)
return results
def run_multistep(client, model, benchmark_file, image_base, judge_model="qwen-plus", judge_client=None):
"""多步规划评测（任务5），Teacher Forcing + 三层归因。
image_base: 多步截图目录；支持数据中保存的相对路径或 record/raw_images 布局。"""
judge_client = judge_client or client
data = json.load(open(benchmark_file, "r", encoding="utf-8"))
task_results = []
for task in data:
tid = task["task_id"]
instr = task["high_level_instruction"]
history = []
step_results = []
died_at = None
for s in task["steps"]:
rel_image = s["image_path"].replace("\\", "/")
img = os.path.join(image_base, *rel_image.split("/"))
if not os.path.exists(img):
img = os.path.join(image_base, f"record_{tid:03d}", "raw_images", os.path.basename(rel_image))
if not os.path.exists(img):
img = os.path.join(image_base, *[part for part in rel_image.split("/") if part != "raw_images"])
w, h = s["image_resolution"]
gt = s["ground_truth"]
hist_txt = "\n".join(history) if history else "(none, this is the first step)"
prompt = PROMPT_MULTISTEP.format(high_level_instruction=instr, history=hist_txt)
reply = call_vlm(client, model, img, prompt, 400)
ev = eval_step(reply, gt, w, h)
if ev["step_ok"]:
verdict = "完全正确"; intent_ok = None
elif "thought" in s:
intent_ok = judge_thought(judge_client, s["thought"], ev["thought"], instr, judge_model)
verdict = attribute_verdict(ev["action_ok"], ev["param_ok"], intent_ok)
else:
intent_ok = None
verdict = "动作或参数错误"
if not ev["step_ok"] and died_at is None:
died_at = s["step"]
step_results.append({"step": s["step"], "verdict": verdict, **ev, "intent_ok": intent_ok})
history.append(f"Step {s['step']}: [action]{gt['action_type']}")
correct = sum(1 for r in step_results if r["step_ok"])
task_results.append({
"task_id": tid, "total_steps": len(task["steps"]),
"correct_steps": correct, "task_success": correct == len(task["steps"]),
"died_at_step": died_at if died_at else "全对",
"steps": step_results,
})
return task_results
# ============================================================
# 使用示例
# ============================================================
if __name__ == "__main__":
from openai import OpenAI
# 本地部署模型：用 vLLM / sglang 起一个 OpenAI 兼容 server
# 例: python -m vllm.entrypoints.openai.api_server --model <path> --port 8000
client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
MODEL = "your-model-name"
# 定位任务
res = run_localization(client, MODEL,
"任务3.1基础任务.json", "任务3截图", tries=10, mode="point")
total_hits = sum(r["hits"] for r in res)
total_tries = sum(r["tries"] for r in res)
print(f"命中率: {total_hits}/{total_tries} = {total_hits/total_tries*100:.1f}%")
# 多步规划
# task_res = run_multistep(client, MODEL, "任务5_多步规划.json", "dataset_records")
# for t in task_res:
# print(f"Task{t['task_id']}: {'成功' if t['task_success'] else '失败'} "
# f"{t['correct_steps']}/{t['total_steps']} 死在第{t['died_at_step']}步")
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