prec_meter.update(prec)
if (step+1) % cfg.steps_per_log == 0 or (step+1)%len(train_loader) == 0:
log = '\tStep {}/{} in Ep {}, {:.2f}s, loss{:.4f}, prec{:.4f}'.format(
step+1, dataset_L, epoch+1, time.time()-step_st, loss_meter.val, prec_meter.val)
print(log)
##############
# epoch log #
##############
log = 'Ep{}, {:.2f}s, loss {:.4f}, prec {:.4f}'.format(
epoch+1, time.time() - ep_st, loss_meter.avg, prec_meter.avg)
print(log)
# model ckpt
if (epoch + 1) % cfg.epochs_per_save == 0:
ckpt_file = os.path.join(cfg.exp_dir, 'model', 'ckpt_epoch%d.pth'%(epoch+1))
save_ckpt(modules_optims, epoch+1, 0, ckpt_file)
##########################
# test on validation set #
##########################
if (epoch + 1) % cfg.epochs_per_val == 0:
result = reid_evaluate(feat_func, test_set, **cfg.test_kwargs)
print('-' * 60)
print('Evaluation on %s set:' % (cfg.test_split))
for evaluation in result.keys():
print('%s:' % (evaluation))
print("mAP: %.4f, Rank1: %.4f, Rank5: %.4f, Rank10: %.4f" % (result[evaluation]['mAP'], result[evaluation]['CMC'][0, 0],\
result[evaluation]['CMC'][0, 4], result[evaluation]['CMC'][0, 9]))
print('-' * 60)
if (step+1) % cfg.steps_per_log == 0 or (step+1)%len(train_loader) == 0:
log = '\tStep {}/{} in Ep {}, {:.2f}s, loss{:.4f}'.format(
step+1, dataset_L, epoch+1, time.time()-step_st, loss_meter.val)
print(log)
##############
# epoch log #
##############
log = 'Ep{}, {:.2f}s, loss {:.4f}'.format(
epoch+1, time.time() - ep_st, loss_meter.avg)
print(log)
# model ckpt
if (epoch + 1) % cfg.epochs_per_save == 0:
ckpt_file = os.path.join(cfg.exp_dir, 'model', 'ckpt_epoch%d.pth'%(epoch+1))
save_ckpt(modules_optims, epoch+1, 0, ckpt_file)
##########################
# test on validation set #
##########################
if (epoch + 1) % cfg.epochs_per_val == 0:
result = reid_evaluate(feat_func, test_set, **cfg.test_kwargs)
print('-' * 60)
print('Evaluation on %s set:' % (cfg.test_split))
for evaluation in result.keys():
print('%s:' % (evaluation))
print("mAP: %.4f, Rank1: %.4f, Rank5: %.4f, Rank10: %.4f" % (result[evaluation]['mAP'], result[evaluation]['CMC'][0, 0],\
result[evaluation]['CMC'][0, 4], result[evaluation]['CMC'][0, 9]))
print('-' * 60)
# log to TensorBoard