| import json |
| from math import sqrt |
| import re |
| from nltk.translate.bleu_score import sentence_bleu |
|
|
| |
| gold_fn = 'test.json' |
|
|
| pred_fn = 'llava-v1.5-13b.json' |
| gold = json.load(open(gold_fn)) |
| pred = json.load(open(pred_fn)) |
|
|
| sequence_match = 0 |
| action_score = 0 |
| total_click_penalty = 0 |
| total_press_penalty = 0 |
| total_write_penalty = 0 |
| ideal_score = 0 |
| max_click_penalty = 0 |
| max_press_penalty = 0 |
| max_write_penalty = 0 |
|
|
|
|
|
|
| def get_bounds(box: dict(), cx, cy): |
| for i in box: |
| tl = box[i]["top_left"] |
| br = box[i]["bottom_right"] |
| if (tl[0]+br[0])/2 == cx and (tl[1]+br[1])/2 == cy: |
| return (tl,br) |
| |
| assert False |
|
|
| |
| def dynamic_dirichlet_l2_penalty(tl, br, px, py): |
| |
| len_x = br[0] - tl[0] |
| len_y = br[1] - tl[1] |
| |
| cx = ( br[0] - tl[0] ) / 2 |
| cy = ( br[1] - tl[1] ) / 2 |
| |
| dx = abs(cx - px) - (len_x * 0.5) |
| dy = abs(cy - py) - (len_y * 0.5) |
| dist = sqrt((dx * (dx > 0)) ** 2 + (dy * (dy > 0)) ** 2) |
| |
| mu = sqrt( len_x ** 2 + len_y ** 2) |
| |
| score = mu / (dist+mu) |
| penalty = 1 - score |
| return penalty |
|
|
| for idx in gold: |
| |
| gold_script = open(gold[idx]['task']).read().strip().split('\n')[2:] |
| llm_script = pred[idx].strip().split() |
| llm_script = [x for x in llm_script if x.strip().startswith('pyautogui')] |
| |
| sample_weight = (len(gold_script)-0.9) |
|
|
| ideal_score += sample_weight |
| for gold_line in gold_script: |
| action_type = gold_line.split("pyautogui.")[1].split("(")[0] |
| if action_type == 'click' or action_type == 'rightClick' or action_type == 'moveTo' or action_type == 'dragTo': |
| max_click_penalty += sample_weight/len(gold_script) |
| if action_type == 'press' or action_type == 'hotkey': |
| max_press_penalty += sample_weight/len(gold_script) |
| if action_type == 'write': |
| max_write_penalty += sample_weight/len(gold_script) |
| |
| seq_match_flag = 1 |
| click_penalty = 0 |
| press_penalty = 0 |
| write_penalty = 0 |
| |
| |
| |
| if len(llm_script) != len(gold_script): |
| seq_match_flag = 0 |
| if seq_match_flag == 1: |
| for i in range(len(gold_script)): |
| gold_line = gold_script[i].strip() |
| gold_action = gold_line.split('pyautogui.')[1].split('(')[0] |
| pred_line = llm_script[i] |
| if pred_line.startswith('pyautogui.') == False: |
| seq_match_flag = 0 |
| break |
| pred_action = pred_line.split('pyautogui.')[1].split('(')[0] |
| if pred_action != gold_action: |
| seq_match_flag = 0 |
| break |
| |
| |
| box_path = gold[idx]['box'] |
| box_num = box_path.split("_")[-1].split(".json")[0] |
| box_path = "_".join(box_path.split("_")[:-1])+box_num+"_boxes.json" |
| box = json.load(open(box_path)) |
|
|
| for i in range(len(gold_script)): |
| gold_line = gold_script[i].strip() |
| gold_action = gold_line.split('pyautogui.')[1].split('(')[0] |
| |
| if seq_match_flag == 0: |
| if gold_action == 'click' or gold_action == 'rightClick' or gold_action == 'moveTo' or gold_action == 'dragTo': |
| click_penalty += 1/len(gold_script) |
| if gold_action == 'press' or gold_action == 'hotkey': |
| press_penalty += 1/len(gold_script) |
| if gold_action == 'write': |
| write_penalty += 1/len(gold_script) |
| continue |
| pred_line = llm_script[i] |
| pred_action = pred_line.split('pyautogui.')[1].split('(')[0] |
|
|
| |
| |
| if gold_action == 'click' or gold == 'rightClick': |
| |
| gold_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0] |
| gold_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0] |
| tl, br = get_bounds(box, float(gold_cx), float(gold_cy)) |
| |
| |
| pred_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0] |
| pred_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0] |
| |
| click_penalty += (1.0/len(gold_script)) * dynamic_dirichlet_l2_penalty(tl, br, float(pred_cx), float(pred_cy)) |
| |
| |
| if gold_action == 'press': |
| gold_key = gold_line.split("\"")[1] |
| pred_key = (re.split("\"|'", pred_line))[1] |
| if gold_key.strip() != pred_key.strip(): |
| press_penalty += 1/len(gold_script) |
| |
| |
| if gold_action == 'hotkey': |
| gold_keys = gold_line.split("(")[1].split(")")[0].split(",") |
| pred_keys = pred_line.split("(")[1].split(")")[0].split(",") |
| |
| gold_key_set = set([x[1:-1] for x in gold_keys if len(x)>2]) |
| pred_key_set = set([x[1:-1] for x in pred_keys if len(x)>2]) |
| if gold_key_set != pred_key_set: |
| press_penalty += 1/len(gold_script) |
| |
| |
| if gold_action == 'write': |
| reference = [gold_line.split("\"")[1]] |
| candidate = re.split("\"|'", pred_line)[1] |
| write_penalty += (1-sentence_bleu(reference, candidate, weights=(0.5, 0.5))) / len(gold_script) |
| |
| sequence_match += (seq_match_flag) * sample_weight |
| action_score += (max(seq_match_flag - click_penalty - press_penalty - write_penalty, 0)) * sample_weight |
| if seq_match_flag: |
| total_click_penalty += click_penalty * sample_weight |
| total_press_penalty += press_penalty * sample_weight |
| total_write_penalty += write_penalty * sample_weight |
| |
|
|
| print(ideal_score) |
| print(f"Sequence match: {sequence_match/ideal_score}") |
| print(f"Action match: {action_score/ideal_score}") |
| |
| |
| print(total_click_penalty/ideal_score) |
| print(total_press_penalty/ideal_score) |
| print(total_write_penalty/ideal_score) |
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