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gt_feature_folder = ''
output_feature_folder = ''
import os
import json
import random
import re
import numpy as np
from config import *
def load_npy_files(folder_path_list):
"""
Load all .npy files from a specified folder and return a list of numpy arrays.
"""
npy_list = []
for file_path in folder_path_list:
if file_path.endswith('.npy'):
# file_path = os.path.join(folder_path, file_name)
np_array = np.load(file_path)[0]
npy_list.append(np_array)
return npy_list
def average_npy(npy_list):
"""
Compute the average of a list of numpy arrays.
"""
return np.mean(npy_list, axis=0)
def cosine_similarity(vec1, vec2):
"""
Compute cosine similarity between two numpy arrays.
"""
dot_product = np.dot(vec1, vec2)
norm_vec1 = np.linalg.norm(vec1)
norm_vec2 = np.linalg.norm(vec2)
cosine_sim = dot_product / (norm_vec1 * norm_vec2)
return cosine_sim
def test_generated_results_similarity():
gt_feature_paths = []
for gt_feature_file in os.listdir(gt_feature_folder):
gt_feature_paths.append(os.path.join(gt_feature_folder, gt_feature_file))
gt_features = load_npy_files(gt_feature_paths)
gt_avg_feature = average_npy(gt_features)
clamp2score_list = []
for output_feature_file in os.listdir(output_feature_folder):
output_feature_path = os.path.join(output_feature_folder, output_feature_file)
output_feature = np.load(output_feature_path)[0]
clamp2score = cosine_similarity(gt_avg_feature, output_feature)
clamp2score_list.append(clamp2score)
avg_clampscore = sum(clamp2score_list) / len(clamp2score_list)
print('average clamp 2 score:', avg_clampscore)
if __name__ == '__main__':
test_generated_results_similarity()
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