utils.py

import sys
import numpy as np
from wxeval import wx_calc_map_label


def show_progressbar(rate, *args, **kwargs):
    '''
    :param rate: [current, total]
    :param args: other show
    '''
    inx = rate[0] + 1
    count = rate[1]
    bar_length = 30
    rate[0] = int(np.around(rate[0] * float(bar_length) / rate[1])) if rate[1] > bar_length else rate[0]
    rate[1] = bar_length if rate[1] > bar_length else rate[1]
    num = len(str(count))
    str_show = ('\r%' + str(num) + 'd / ' + '%' + str(num) + 'd  (%' + '3.2f%%) [') % (
    inx, count, float(inx) / count * 100)
    for i in range(rate[0]):
        str_show += '='

    if rate[0] < rate[1] - 1:
        str_show += '>'

    for i in range(rate[0], rate[1] - 1, 1):
        str_show += '.'
    str_show += '] '
    for l in args:
        str_show += ' ' + str(l)

    for key in kwargs:
        try:
            str_show += ' ' + key + ': %.4f' % kwargs[key]
        except Exception:
            str_show += ' ' + key + ': ' + str(kwargs[key])
    if inx == count:
        str_show += '\n'

    sys.stdout.write(str_show)
    sys.stdout.flush()


def cal_map_bi(relation_score, labels):
    print('======================== Bi-modal Retrieval ==========================')
    print('======================================================================')
    result1_all = wx_calc_map_label(
        relation_score, labels, k=0, dist_method='COS')
    result2_all = wx_calc_map_label(
        np.transpose(relation_score), labels, k=0, dist_method='COS')
    result1_50 = wx_calc_map_label(
        relation_score, labels, k=50, dist_method='COS')
    result2_50 = wx_calc_map_label(
        np.transpose(relation_score), labels, k=50, dist_method='COS')
    print("MAP@50: Image query:{:.3f}, Text query:{:.3f}".format(result1_50, result2_50))
    print("MAP@All: Image query:{:.3f}, Text query:{:.3f}".format(result1_all, result2_all))

    return
# def cal_map_bi(relation_score, labels, best_map_bi_50=0., best_map_bi_all=0., current_epoch=0, dataset_name=None):
#     print('======================== Bi-modal Retrieval ==========================')
#     print('======================================================================')
#     result1_all = wx_calc_map_label(
#         relation_score, labels, k=0, dist_method='COS')
#     result2_all = wx_calc_map_label(
#         np.transpose(relation_score), labels, k=0, dist_method='COS')
#     result1_50 = wx_calc_map_label(
#         relation_score, labels, k=50, dist_method='COS')
#     result2_50 = wx_calc_map_label(
#         np.transpose(relation_score), labels, k=50, dist_method='COS')
#     print("MAP@50: Image query:{:.3f}, Text query:{:.3f}".format(result1_50, result2_50))
#     print("MAP@All: Image query:{:.3f}, Text query:{:.3f}".format(result1_all, result2_all))
#
#     mean_map_50 = (result1_50 + result2_50) / 2.0
#     if mean_map_50 > best_map_bi_50:
#         best_map_bi_50 = mean_map_50
#         print('best map@50 improves to: {:.3f}, detail: i2t: {:.3f}, t2i: {:.3f}'.format(best_map_bi_50, result1_50, result2_50))
#     else:
#         print('best map@50 still is: {:.3f}'.format(best_map_bi_50))
#
#     mean_map_all = (result1_all + result2_all) / 2.0
#     if mean_map_all > best_map_bi_all:
#         best_map_bi_all = mean_map_all
#         if dataset_name is not None:
#             import scipy.io as sio
#             # sio.savemat(dataset_name+'_latent_xentropy.mat',{'XTe1proj':img_feas, 'XTe2proj':text_feas, 'testLabel':labels})
#         print('best map@All improves to: {:.3f}, detail: i2t: {:.3f}, t2i: {:.3f}'.format(best_map_bi_all, result1_all, result2_all))
#         best_epoch = current_epoch
#         print('best epoch: {:.3f}'.format(best_epoch))
#     else:
#         print('best map@All still is: {:.3f}'.format(best_map_bi_all))
#     return best_map_bi_50, best_map_bi_all, best_epoch