| 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 = {}
|
|
|
| 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):
|
|
|
| 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('+------------------|------------------+')
|
|
|
