File size: 3,588 Bytes
5767e72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import io
import os
import json
import zipfile
import argparse
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from constant import *
def submission(model_name, zip_file):
os.makedirs(model_name, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zip_ref:
zip_ref.extractall(model_name)
upload_data = {}
# load your score
for file in os.listdir(model_name):
if file.startswith('.') or file.startswith('__'):
print(f"Skip the file: {file}")
continue
cur_file = os.path.join(model_name, file)
if os.path.isdir(cur_file):
for subfile in os.listdir(cur_file):
if subfile.endswith(".json"):
with open(os.path.join(cur_file, subfile)) as ff:
cur_json = json.load(ff)
if isinstance(cur_json, dict):
for key in cur_json:
upload_data[key.replace('_',' ')] = cur_json[key][0]
elif cur_file.endswith('json'):
with open(cur_file) as ff:
cur_json = json.load(ff)
if isinstance(cur_json, dict):
for key in cur_json:
upload_data[key.replace('_',' ')] = cur_json[key][0]
for key in TASK_INFO:
if key not in upload_data:
upload_data[key] = 0
return upload_data
def get_nomalized_score(upload_data):
# get the normalize score
normalized_score = {}
for key in TASK_INFO:
min_val = NORMALIZE_DIC[key]['Min']
max_val = NORMALIZE_DIC[key]['Max']
normalized_score[key] = (upload_data[key] - min_val) / (max_val - min_val)
normalized_score[key] = normalized_score[key] * DIM_WEIGHT[key]
return normalized_score
def get_quality_score(normalized_score):
quality_score = []
for key in QUALITY_LIST:
quality_score.append(normalized_score[key])
quality_score = sum(quality_score)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
return quality_score
def get_semantic_score(normalized_score):
semantic_score = []
for key in SEMANTIC_LIST:
semantic_score.append(normalized_score[key])
semantic_score = sum(semantic_score)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
return semantic_score
def get_final_score(quality_score,semantic_score):
return (quality_score * QUALITY_WEIGHT + semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
if __name__=="__main__":
parser = argparse.ArgumentParser(description='Load submission file')
parser.add_argument('--zip_file', type=str, required=True, help='Name of the zip file', default='evaluation_results.zip')
parser.add_argument('--model_name', type=str, required=True, help='Name of the model', default='t2v_model')
args = parser.parse_args()
upload_dict = submission(args.model_name, args.zip_file)
print(f"your submission info: \n{upload_dict} \n")
normalized_score = get_nomalized_score(upload_dict)
quality_score = get_quality_score(normalized_score)
semantic_score = get_semantic_score(normalized_score)
final_score = get_final_score(quality_score, semantic_score)
print('+------------------|------------------+')
print(f'| quality score|{quality_score}|')
print(f'| semantic score|{semantic_score}|')
print(f'| total score|{final_score}|')
print('+------------------|------------------+')
|