# 랜덤하게 이미지를 한장 뽑아내는 함수 np.random.seed = 100 def random_test_image(): """Pick a random test image from the test directory""" c = np.random.choice(cat_df['category']) root = testdir + c + '/' img_path = root + np.random.choice(os.listdir(root)) return img_path # 랜덤하게 이미지를 선택한 후 Top5 예측치를 확인하는 함수 train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) train_util.display_prediction(random_test_image(), model, topk=5,
# 랜덤하게 이미지를 한장 뽑아내는 함수 np.random.seed = 100 def random_test_image(): """Pick a random test image from the test directory""" c = np.random.choice(cat_df['category']) root = testdir + c + '/' img_path = root + np.random.choice(os.listdir(root)) return img_path avg_inference_time = 0 # 랜덤하게 이미지를 선택한 후 Top5 예측치를 확인하는 함수 avg_inference_time += train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) avg_inference_time += train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) avg_inference_time += train_util.display_prediction(random_test_image(), model, topk=5, model_name=model_choice) time.sleep(1.5) avg_inference_time += train_util.display_prediction(random_test_image(), model, topk=5,
# 랜덤하게 이미지를 한장 뽑아내는 함수 np.random.seed = 100 def random_test_image(): """Pick a random test image from the test directory""" c = np.random.choice(cat_df['category']) root = testdir + c + '/' img_path = root + np.random.choice(os.listdir(root)) return img_path avg_inference_time = 0 # 랜덤하게 이미지를 선택한 후 Top5 예측치를 확인하는 함수 for a in range(10): avg_inference_time += train_util.display_prediction( random_test_image(), model, topk=5, model_name=model_choice, etc='[K_' + str(K_fold) + '_' + str(k) + '_fold] ') time.sleep(1.1) total_avg_inference_time += avg_inference_time print(f'총 10회 추론시간 평균 : {avg_inference_time*1000.0/10:.2f}ms') results.append( train_util.evaluate(model, dataloaders['test'], criterion, n_classes)) results[k] = results[k].merge( cat_df, left_on='class', right_on='category').drop(columns=['category']) results[k].sort_values('top1', ascending=False, inplace=True)