Upload eval.py
#3
by
Yash-Butala
- opened
eval.py
ADDED
@@ -0,0 +1,167 @@
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1 |
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import json
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from math import sqrt
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import re
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from nltk.translate.bleu_score import sentence_bleu
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# gold label file
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gold_fn = 'test.json'
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pred_fn = 'llava-v1.5-13b.json'
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gold = json.load(open(gold_fn))
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pred = json.load(open(pred_fn))
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sequence_match = 0
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action_score = 0
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total_click_penalty = 0
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total_press_penalty = 0
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total_write_penalty = 0
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ideal_score = 0
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max_click_penalty = 0
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max_press_penalty = 0
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max_write_penalty = 0
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def get_bounds(box: dict(), cx, cy):
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for i in box:
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tl = box[i]["top_left"]
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br = box[i]["bottom_right"]
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if (tl[0]+br[0])/2 == cx and (tl[1]+br[1])/2 == cy:
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return (tl,br)
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assert False
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def dynamic_dirichlet_l2_penalty(tl, br, px, py):
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len_x = br[0] - tl[0]
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len_y = br[1] - tl[1]
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cx = ( br[0] - tl[0] ) / 2
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cy = ( br[1] - tl[1] ) / 2
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dx = abs(cx - px) - (len_x * 0.5)
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dy = abs(cy - py) - (len_y * 0.5)
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dist = sqrt((dx * (dx > 0)) ** 2 + (dy * (dy > 0)) ** 2)
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mu = sqrt( len_x ** 2 + len_y ** 2)
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score = mu / (dist+mu)
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penalty = 1 - score
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return penalty
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for idx in gold:
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gold_script = open(gold[idx]['task']).read().strip().split('\n')[2:]
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llm_script = pred[idx].strip().split()
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llm_script = [x for x in llm_script if x.strip().startswith('pyautogui')]
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#find extreme case values
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sample_weight = (len(gold_script)-0.9)
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ideal_score += sample_weight
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for gold_line in gold_script:
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action_type = gold_line.split("pyautogui.")[1].split("(")[0]
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if action_type == 'click' or action_type == 'rightClick' or action_type == 'moveTo' or action_type == 'dragTo':
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max_click_penalty += sample_weight/len(gold_script)
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if action_type == 'press' or action_type == 'hotkey':
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max_press_penalty += sample_weight/len(gold_script)
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if action_type == 'write':
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max_write_penalty += sample_weight/len(gold_script)
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seq_match_flag = 1
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click_penalty = 0
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press_penalty = 0
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write_penalty = 0
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# if length doesn't seq match is 0
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# llm_script = llm_script[:len(gold_script)]
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if len(llm_script) != len(gold_script):
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seq_match_flag = 0
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if seq_match_flag == 1:
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for i in range(len(gold_script)):
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gold_line = gold_script[i].strip()
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gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
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pred_line = llm_script[i]
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if pred_line.startswith('pyautogui.') == False:
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seq_match_flag = 0
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break
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pred_action = pred_line.split('pyautogui.')[1].split('(')[0]
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if pred_action != gold_action:
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seq_match_flag = 0
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break
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# find penalties for correct and wrong sequences
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box_path = gold[idx]['box']
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box_num = box_path.split("_")[-1].split(".json")[0]
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box_path = "_".join(box_path.split("_")[:-1])+box_num+"_boxes.json"
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box = json.load(open(box_path))
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for i in range(len(gold_script)):
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gold_line = gold_script[i].strip()
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gold_action = gold_line.split('pyautogui.')[1].split('(')[0]
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# just add the penalties
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if seq_match_flag == 0:
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if gold_action == 'click' or gold_action == 'rightClick' or gold_action == 'moveTo' or gold_action == 'dragTo':
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click_penalty += 1/len(gold_script)
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if gold_action == 'press' or gold_action == 'hotkey':
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press_penalty += 1/len(gold_script)
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if gold_action == 'write':
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write_penalty += 1/len(gold_script)
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continue
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pred_line = llm_script[i]
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pred_action = pred_line.split('pyautogui.')[1].split('(')[0]
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# l2 penalty for click
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if gold_action == 'click' or gold == 'rightClick':
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# get original box bounds
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gold_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
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gold_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
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tl, br = get_bounds(box, float(gold_cx), float(gold_cy))
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# get predicted point
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pred_cx = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[0]
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pred_cy = gold_line.split("pyautogui.")[1].split('(')[1].split(',')[1].split(')')[0]
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click_penalty += (1.0/len(gold_script)) * dynamic_dirichlet_l2_penalty(tl, br, float(pred_cx), float(pred_cy))
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# penalty for press
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if gold_action == 'press':
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gold_key = gold_line.split("\"")[1]
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pred_key = (re.split("\"|'", pred_line))[1]
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if gold_key.strip() != pred_key.strip():
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press_penalty += 1/len(gold_script)
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# penalty for hotkey
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if gold_action == 'hotkey':
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gold_keys = gold_line.split("(")[1].split(")")[0].split(",")
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pred_keys = pred_line.split("(")[1].split(")")[0].split(",")
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gold_key_set = set([x[1:-1] for x in gold_keys if len(x)>2])
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pred_key_set = set([x[1:-1] for x in pred_keys if len(x)>2])
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if gold_key_set != pred_key_set:
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press_penalty += 1/len(gold_script)
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if gold_action == 'write':
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reference = [gold_line.split("\"")[1]]
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candidate = re.split("\"|'", pred_line)[1]
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write_penalty += (1-sentence_bleu(reference, candidate, weights=(0.5, 0.5))) / len(gold_script)
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sequence_match += (seq_match_flag) * sample_weight
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action_score += (max(seq_match_flag - click_penalty - press_penalty - write_penalty, 0)) * sample_weight
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if seq_match_flag:
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total_click_penalty += click_penalty * sample_weight
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total_press_penalty += press_penalty * sample_weight
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total_write_penalty += write_penalty * sample_weight
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print(ideal_score)
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print(f"Sequence match: {sequence_match/ideal_score}")
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print(f"Action match: {action_score/ideal_score}")
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print(total_click_penalty/ideal_score)
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print(total_press_penalty/ideal_score)
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print(total_write_penalty/ideal_score)
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