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# Copyright 2025 Bytedance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
import json
import os
import argparse
from collections import defaultdict
def calculate_wiscore(consistency, realism, aesthetic_quality):
return 0.7 * consistency + 0.2 * realism + 0.1 * aesthetic_quality
def cal_culture(file_path):
all_scores = []
total_objects = 0
has_9_9 = False
with open(file_path, 'r') as file:
for line in file:
total_objects += 1
data = json.loads(line)
if 9.9 in [data['consistency'], data['realism'], data['aesthetic_quality']]:
has_9_9 = True
wiscore = calculate_wiscore(data['consistency'], data['realism'], data['aesthetic_quality'])
all_scores.append(wiscore)
if has_9_9 or total_objects < 400:
print(f"Skipping file {file_path}: Contains 9.9 or has less than 400 objects.")
return None
total_score = sum(all_scores)
avg_score = total_score / (len(all_scores)*2) if len(all_scores) > 0 else 0
score = {
'total': total_score,
'average': avg_score
}
print(f" Cultural - Total: {score['total']:.2f}, Average: {score['average']:.2f}")
return avg_score
def cal_space_time(file_path):
categories = defaultdict(list)
total_objects = 0
has_9_9 = False
with open(file_path, 'r') as file:
for line in file:
total_objects += 1
data = json.loads(line)
if 9.9 in [data['consistency'], data['realism'], data['aesthetic_quality']]:
has_9_9 = True
subcategory = data['Subcategory']
wiscore = calculate_wiscore(data['consistency'], data['realism'], data['aesthetic_quality'])
if subcategory in ['Longitudinal time', 'Horizontal time']:
categories['Time'].append(wiscore)
else:
categories['Space'].append(wiscore)
if has_9_9 or total_objects < 300:
print(f"Skipping file {file_path}: Contains 9.9 or has less than 400 objects.")
return None
total_scores = {category: sum(scores) for category, scores in categories.items()}
avg_scores = {category: sum(scores) / (len(scores) * 2 )if len(scores) > 0 else 0 for category, scores in categories.items()}
scores = {
'total': total_scores,
'average': avg_scores
}
print(f" Time - Total: {scores['total'].get('Time', 0):.2f}, Average: {scores['average'].get('Time', 0):.2f}")
print(f" Space - Total: {scores['total'].get('Space', 0):.2f}, Average: {scores['average'].get('Space', 0):.2f}")
return avg_scores
def cal_science(file_path):
categories = defaultdict(list)
total_objects = 0
has_9_9 = False
with open(file_path, 'r') as file:
for line in file:
total_objects += 1
data = json.loads(line)
if 9.9 in [data['consistency'], data['realism'], data['aesthetic_quality']]:
has_9_9 = True
prompt_id = data.get('prompt_id', 0)
if 701 <= prompt_id <= 800:
category = 'Biology'
elif 801 <= prompt_id <= 900:
category = 'Physics'
elif 901 <= prompt_id <= 1000:
category = 'Chemistry'
else:
category = "?"
wiscore = calculate_wiscore(data['consistency'], data['realism'], data['aesthetic_quality'])
categories[category].append(wiscore)
if has_9_9 or total_objects < 300:
print(f"Skipping file {file_path}: Contains 9.9 or has less than 300 objects.")
return None
total_scores = {category: sum(scores) for category, scores in categories.items()}
avg_scores = {category: sum(scores) / (len(scores)*2) if len(scores) > 0 else 0 for category, scores in categories.items()}
scores = {
'total': total_scores,
'average': avg_scores
}
for category in ['Biology', 'Physics', 'Chemistry']:
print(f" {category} - Total: {scores['total'].get(category, 0):.2f}, Average: {scores['average'].get(category, 0):.2f}")
return avg_scores
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Image Quality Assessment Tool')
parser.add_argument('--output_dir', required=True,
help='Path to the output directory')
args = parser.parse_args()
avg_score = dict()
score = cal_culture(
os.path.join(args.output_dir, "cultural_common_sense_scores.jsonl")
)
avg_score['Cultural'] = score
scores = cal_space_time(
os.path.join(args.output_dir, "spatio-temporal_reasoning_scores.jsonl")
)
avg_score.update(scores)
scores = cal_science(
os.path.join(args.output_dir, "natural_science_scores.jsonl")
)
avg_score.update(scores)
avg_all = sum(avg_score.values()) / len(avg_score)
avg_score['Overall'] = avg_all
keys = ""
values = ""
for k, v in avg_score.items():
keys += f"{k} "
values += f"{v:.2f} "
print(keys)
print(values)
writer = open(os.path.join(args.output_dir, "results.txt"), 'w')
print(f"write results to file {os.path.join(args.output_dir, 'results.txt')}")
writer.write(keys + "\n")
writer.write(values + "\n")
writer.close() |