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results_assemble.py
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results_assemble.py
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"""
-------------------------------File info-------------------------
% - File name: results_assemble.py
% - Description:
% -
% -
% - Input:
% - Output: None
% - Calls: None
% - usage:
% - Version： V1.0
% - Last update: 2022-05-19
% Copyright (C) PRMI, South China university of technology; 2022
% ------For Educational and Academic Purposes Only ------
% - Author : Chester.Wei.Xie, PRMI, SCUT/ GXU
% - Contact: [email protected]
------------------------------------------------------------------
"""
import torch
import numpy as np
def show_results_summary_v2(_num_sessions, results_dic, num_trial):
#
base_avg_over_sessions = []
all_avg_novel_over_sessions = []
pre_avg_novel_over_sessoins = []
curr_avg_novel_over_sessions = []
both_avg_over_sessions = []
print(f'\n =====> Results summary (assemble over {num_trial} trials), (Support set: 5 way 5 shot)')
print(f'-------------------- Average of class-wise acc (%)--------------------------------')
print(f'\n Session ', end=" ")
for _, n in enumerate(results_dic.keys()):
print(f'{n}', end="\t")
print(f'Average', end="\t")
print(f'\n Base ', end="\t")
for _, n in enumerate(results_dic.keys()):
temp = results_dic[n]['Ave_class_wise_acc_base']
print(f'{temp * 100:.2f}', end="\t")
base_avg_over_sessions.append(temp)
print(f'{np.mean(base_avg_over_sessions) * 100:.2f}', end="\t")
print(f'\n All Novel ', end=" ")
for _, n in enumerate(results_dic.keys()):
if n == 0:
print(f'-', end="\t")
else:
temp = results_dic[n]['Ave_class_wise_acc_all_novel']
print(f'{temp * 100:.2f}', end="\t")
all_avg_novel_over_sessions.append(temp)
print(f'{np.mean(all_avg_novel_over_sessions) * 100:.2f}', end="\t")
print(f'\n Previous Novel ', end=" ")
for _, n in enumerate(results_dic.keys()):
if n == 0:
print(f'-', end="\t")
else:
temp = results_dic[n]['Ave_class_wise_acc_previous_novel']
print(f'{temp * 100:.2f}', end="\t")
pre_avg_novel_over_sessoins.append(temp)
print(f'{np.mean(pre_avg_novel_over_sessoins) * 100:.2f}', end="\t")
print(f'\n Current Novel ', end=" ")
for _, n in enumerate(results_dic.keys()):
if n == 0:
print(f'-', end="\t")
else:
temp = results_dic[n]['Ave_class_wise_acc_current_novel']
print(f'{temp * 100:.2f}', end="\t")
curr_avg_novel_over_sessions.append(temp)
print(f'{np.mean(curr_avg_novel_over_sessions) * 100:.2f}', end="\t")
print(f'\n Both ', end="\t")
for _, n in enumerate(results_dic.keys()):
temp = results_dic[n]['Ave_acc_of_both']
print(f'{temp * 100:.2f}', end="\t")
both_avg_over_sessions.append(temp)
print(f'{np.mean(both_avg_over_sessions) * 100:.2f}', end="\t")
print(f'\n --------------------------------------------------------------------------------\n ')
PD = results_dic[0]['Ave_acc_of_both'] - results_dic[_num_sessions - 1]['Ave_acc_of_both']
FR_both = PD / results_dic[0]['Ave_acc_of_both']
tem = results_dic[0]['Ave_class_wise_acc_base'] - results_dic[_num_sessions - 1]['Ave_class_wise_acc_base']
FR_base = tem / results_dic[0]['Ave_class_wise_acc_base']
MR_both = 1-FR_both
MR_base = 1-FR_base
CPS = 0.5 * MR_base + 0.5 * np.mean(all_avg_novel_over_sessions)
print(' ==> PD: {:.2f} (define by CEC); \n'.format(PD * 100))
print(' =====> FR_both: {:.2f},'
' MR_both: {:.2f}; \n'.format(FR_both * 100, MR_both * 100))
print(' =====> FR_base: {:.2f},'
' MR_base: {:.2f}; \n'.format(FR_base * 100, MR_base * 100))
print(' =====> Average of all novel acc over {} incremental sessions: {:.2f};'.format(
_num_sessions - 1, np.mean(all_avg_novel_over_sessions) * 100))
print(' =====> CPS: {:.2f} \n'.format(CPS * 100))
def get_results_assemble(result_path):
result_dic = torch.load(result_path)
new_result_dic = {}
num_trial = len(result_dic)
num_sessions = None
for key in result_dic:
num_sessions = len(result_dic[key])
Ave_class_wise_acc_base_list = []
Ave_class_wise_acc_all_novel_list = []
Ave_class_wise_acc_previous_novel_list = []
Ave_class_wise_acc_current_novel_list = []
Ave_acc_of_both_list = []
for session in range(num_sessions):
for trial_key in result_dic.keys():
result_one_trial = result_dic[trial_key]
result_one_session = result_one_trial[session]
if session == 0:
Ave_class_wise_acc_base_list.append(result_one_session['Ave_class_wise_acc_base'])
Ave_acc_of_both_list.append(result_one_session['Ave_acc_of_both'])
else:
Ave_class_wise_acc_base_list.append(result_one_session['Ave_class_wise_acc_base'])
Ave_class_wise_acc_all_novel_list.append(result_one_session['Ave_class_wise_acc_all_novel'])
Ave_class_wise_acc_previous_novel_list.append(result_one_session['Ave_class_wise_acc_previous_novel'])
Ave_class_wise_acc_current_novel_list.append(result_one_session['Ave_class_wise_acc_current_novel'])
Ave_acc_of_both_list.append(result_one_session['Ave_acc_of_both'])
if session == 0:
result_one_session_new = {'Ave_class_wise_acc_base': np.mean(Ave_class_wise_acc_base_list),
'Ave_class_wise_acc_all_novel': None,
'Ave_class_wise_acc_previous_novel': None,
'Ave_class_wise_acc_current_novel': None,
'Ave_acc_of_both': np.mean(Ave_acc_of_both_list)
}
else:
result_one_session_new = {'Ave_class_wise_acc_base': np.mean(Ave_class_wise_acc_base_list),
'Ave_class_wise_acc_all_novel': np.mean(Ave_class_wise_acc_all_novel_list),
'Ave_class_wise_acc_previous_novel': np.mean(
Ave_class_wise_acc_previous_novel_list),
'Ave_class_wise_acc_current_novel': np.mean(
Ave_class_wise_acc_current_novel_list),
'Ave_acc_of_both': np.mean(Ave_acc_of_both_list)
}
new_result_dic[session] = result_one_session_new
Ave_class_wise_acc_base_list = []
Ave_class_wise_acc_all_novel_list = []
Ave_class_wise_acc_previous_novel_list = []
Ave_class_wise_acc_current_novel_list = []
Ave_acc_of_both_list = []
print(f'===========> \n\n\n\n')
show_results_summary_v2(num_sessions, new_result_dic, num_trial)
if __name__ == "__main__":
result_dir = 'exp/Nsynth-100-FS_Finetune_5way_5shot_2022-06-26-23:02:06/test_results_100_trial.pth'
get_results_assemble(result_dir)