From aa250c942f87a277b971cac7e39721802d84e641 Mon Sep 17 00:00:00 2001 From: Sam Gross Date: Thu, 2 Mar 2017 16:49:46 -0500 Subject: [PATCH] Revert "add flush to print" --- imagenet/main.py | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/imagenet/main.py b/imagenet/main.py index 9e7b30238a..a494c2a6ca 100644 --- a/imagenet/main.py +++ b/imagenet/main.py @@ -1,4 +1,3 @@ -from __future__ import print_function import argparse import os import shutil @@ -60,10 +59,10 @@ def main(): # create model if args.pretrained: - print("=> using pre-trained model '{}'".format(args.arch), flush=True) + print("=> using pre-trained model '{}'".format(args.arch)) model = models.__dict__[args.arch](pretrained=True) else: - print("=> creating model '{}'".format(args.arch), flush=True) + print("=> creating model '{}'".format(args.arch)) model = models.__dict__[args.arch]() if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): @@ -75,16 +74,15 @@ def main(): # optionally resume from a checkpoint if args.resume: if os.path.isfile(args.resume): - print("=> loading checkpoint '{}'".format(args.resume), flush=True) + print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_prec1 = checkpoint['best_prec1'] model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {})" - .format(args.evaluate, checkpoint['epoch']), flush=True) + .format(args.evaluate, checkpoint['epoch'])) else: - print("=> no checkpoint found at '{}'".format(args.resume), - flush=True) + print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True @@ -191,8 +189,7 @@ def train(train_loader, model, criterion, optimizer, epoch): 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( epoch, i, len(train_loader), batch_time=batch_time, - data_time=data_time, loss=losses, top1=top1, top5=top5), - flush=True) + data_time=data_time, loss=losses, top1=top1, top5=top5)) def validate(val_loader, model, criterion): @@ -231,10 +228,10 @@ def validate(val_loader, model, criterion): 'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t' 'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format( i, len(val_loader), batch_time=batch_time, loss=losses, - top1=top1, top5=top5), flush=True) + top1=top1, top5=top5)) print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}' - .format(top1=top1, top5=top5), flush=True) + .format(top1=top1, top5=top5)) return top1.avg