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omniact / eval.py

melisa

Upload eval.py (#3)

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	import json
	from math import sqrt
	import re
	from nltk.translate.bleu_score import sentence_bleu

	# gold label file
	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')]
	#find extreme case values
	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 length doesn't seq match is 0
	# llm_script = llm_script[:len(gold_script)]
	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

	# find penalties for correct and wrong sequences
	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]
	# just add the penalties
	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]

	# l2 penalty for click

	if gold_action == 'click' or gold == 'rightClick':
	# get original box bounds
	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))

	# get predicted point
	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))

	# penalty for press
	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)

	# penalty for hotkey
	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)