From 3a4e390c77a8e11e2b30eabd2a1113d501ff08fc Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Tue, 13 Nov 2018 16:07:41 +0000 Subject: [PATCH] update '==>' to '=>' --- train_imgreid_xent.py | 6 +++--- train_imgreid_xent_htri.py | 6 +++--- train_vidreid_xent.py | 6 +++--- train_vidreid_xent_htri.py | 6 +++--- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/train_imgreid_xent.py b/train_imgreid_xent.py index 75b5567..d25f086 100755 --- a/train_imgreid_xent.py +++ b/train_imgreid_xent.py @@ -101,7 +101,7 @@ def main(): start_time = time.time() ranklogger = RankLogger(args.source_names, args.target_names) train_time = 0 - print("==> Start training") + print("=> Start training") if args.fixbase_epoch > 0: print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch)) @@ -123,7 +123,7 @@ def main(): scheduler.step() if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch: - print("==> Test") + print("=> Test") for name in args.target_names: print("Evaluating {} ...".format(name)) @@ -236,7 +236,7 @@ def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20], retur print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1))) - print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size)) + print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size)) m, n = qf.size(0), gf.size(0) distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \ diff --git a/train_imgreid_xent_htri.py b/train_imgreid_xent_htri.py index bd4a9ae..96ef9bd 100755 --- a/train_imgreid_xent_htri.py +++ b/train_imgreid_xent_htri.py @@ -104,7 +104,7 @@ def main(): start_time = time.time() ranklogger = RankLogger(args.source_names, args.target_names) train_time = 0 - print("==> Start training") + print("=> Start training") if args.fixbase_epoch > 0: print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch)) @@ -126,7 +126,7 @@ def main(): scheduler.step() if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch: - print("==> Test") + print("=> Test") for name in args.target_names: print("Evaluating {} ...".format(name)) @@ -253,7 +253,7 @@ def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20], retur print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1))) - print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size)) + print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size)) m, n = qf.size(0), gf.size(0) distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \ diff --git a/train_vidreid_xent.py b/train_vidreid_xent.py index 4385d64..6eca7d1 100755 --- a/train_vidreid_xent.py +++ b/train_vidreid_xent.py @@ -102,7 +102,7 @@ def main(): start_time = time.time() ranklogger = RankLogger(args.source_names, args.target_names) train_time = 0 - print("==> Start training") + print("=> Start training") if args.fixbase_epoch > 0: print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch)) @@ -124,7 +124,7 @@ def main(): scheduler.step() if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch: - print("==> Test") + print("=> Test") for name in args.target_names: print("Evaluating {} ...".format(name)) @@ -250,7 +250,7 @@ def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1))) - print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size * args.seq_len)) + print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size * args.seq_len)) m, n = qf.size(0), gf.size(0) distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \ diff --git a/train_vidreid_xent_htri.py b/train_vidreid_xent_htri.py index 8a3bd11..d8299f2 100755 --- a/train_vidreid_xent_htri.py +++ b/train_vidreid_xent_htri.py @@ -106,7 +106,7 @@ def main(): start_time = time.time() ranklogger = RankLogger(args.source_names, args.target_names) train_time = 0 - print("==> Start training") + print("=> Start training") if args.fixbase_epoch > 0: print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch)) @@ -128,7 +128,7 @@ def main(): scheduler.step() if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch: - print("==> Test") + print("=> Test") for name in args.target_names: print("Evaluating {} ...".format(name)) @@ -267,7 +267,7 @@ def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1))) - print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size * args.seq_len)) + print("=> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch_size * args.seq_len)) m, n = qf.size(0), gf.size(0) distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \