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deep-person-reid
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update '==>' to '=>'

pull/119/head
KaiyangZhou 2018-11-13 16:07:41 +00:00
parent fea71947c7
commit 3a4e390c77
4 changed files with 12 additions and 12 deletions
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6
train_imgreid_xent.py
View File

@ -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) + \

6
train_imgreid_xent_htri.py
View File

@ -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) + \

6
train_vidreid_xent.py
View File

@ -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) + \

6
train_vidreid_xent_htri.py
View File

@ -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) + \