From 1702e018e4d09eb2b46edf9aa5c39ef99c11808c Mon Sep 17 00:00:00 2001 From: KaiyangZhou Date: Fri, 27 Apr 2018 10:03:24 +0100 Subject: [PATCH] update print --- train_img_model_cent.py | 11 ++++++----- train_img_model_xent.py | 11 ++++++----- train_img_model_xent_htri.py | 11 ++++++----- train_vid_model_xent.py | 11 ++++++----- train_vid_model_xent_htri.py | 13 +++++++------ 5 files changed, 31 insertions(+), 26 deletions(-) diff --git a/train_img_model_cent.py b/train_img_model_cent.py index f4d17cc..4dfa4d5 100644 --- a/train_img_model_cent.py +++ b/train_img_model_cent.py @@ -162,11 +162,10 @@ def main(): start_time = time.time() best_rank1 = -np.inf best_epoch = 0 + print("==> Start training") for epoch in range(start_epoch, args.max_epoch): - print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) - - train(model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu) + train(epoch, model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() @@ -194,7 +193,7 @@ def main(): elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) -def train(model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu): +def train(epoch, model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent, trainloader, use_gpu): model.train() losses = AverageMeter() @@ -216,7 +215,9 @@ def train(model, criterion_xent, criterion_cent, optimizer_model, optimizer_cent losses.update(loss.item(), pids.size(0)) if (batch_idx+1) % args.print_freq == 0: - print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) + print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format( + epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg + )) def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]): model.eval() diff --git a/train_img_model_xent.py b/train_img_model_xent.py index 952dd45..6e0e641 100755 --- a/train_img_model_xent.py +++ b/train_img_model_xent.py @@ -157,11 +157,10 @@ def main(): start_time = time.time() best_rank1 = -np.inf best_epoch = 0 + print("==> Start training") for epoch in range(start_epoch, args.max_epoch): - print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) - - train(model, criterion, optimizer, trainloader, use_gpu) + train(epoch, model, criterion, optimizer, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() @@ -189,7 +188,7 @@ def main(): elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) -def train(model, criterion, optimizer, trainloader, use_gpu): +def train(epoch, model, criterion, optimizer, trainloader, use_gpu): model.train() losses = AverageMeter() @@ -207,7 +206,9 @@ def train(model, criterion, optimizer, trainloader, use_gpu): losses.update(loss.item(), pids.size(0)) if (batch_idx+1) % args.print_freq == 0: - print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) + print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format( + epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg + )) def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]): model.eval() diff --git a/train_img_model_xent_htri.py b/train_img_model_xent_htri.py index 1c36974..7a460d2 100755 --- a/train_img_model_xent_htri.py +++ b/train_img_model_xent_htri.py @@ -166,11 +166,10 @@ def main(): start_time = time.time() best_rank1 = -np.inf best_epoch = 0 + print("==> Start training") for epoch in range(start_epoch, args.max_epoch): - print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) - - train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu) + train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() @@ -198,7 +197,7 @@ def main(): elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) -def train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu): +def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu): model.train() losses = AverageMeter() @@ -229,7 +228,9 @@ def train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu losses.update(loss.item(), pids.size(0)) if (batch_idx+1) % args.print_freq == 0: - print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) + print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format( + epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg + )) def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]): model.eval() diff --git a/train_vid_model_xent.py b/train_vid_model_xent.py index ec07c4e..fc60ca6 100755 --- a/train_vid_model_xent.py +++ b/train_vid_model_xent.py @@ -154,11 +154,10 @@ def main(): start_time = time.time() best_rank1 = -np.inf best_epoch = 0 + print("==> Start training") for epoch in range(start_epoch, args.max_epoch): - print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) - - train(model, criterion, optimizer, trainloader, use_gpu) + train(epoch, model, criterion, optimizer, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() @@ -186,7 +185,7 @@ def main(): elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) -def train(model, criterion, optimizer, trainloader, use_gpu): +def train(epoch, model, criterion, optimizer, trainloader, use_gpu): model.train() losses = AverageMeter() @@ -201,7 +200,9 @@ def train(model, criterion, optimizer, trainloader, use_gpu): losses.update(loss.item(), pids.size(0)) if (batch_idx+1) % args.print_freq == 0: - print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) + print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format( + epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg + )) def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20]): model.eval() diff --git a/train_vid_model_xent_htri.py b/train_vid_model_xent_htri.py index b36cb79..444d85d 100755 --- a/train_vid_model_xent_htri.py +++ b/train_vid_model_xent_htri.py @@ -140,7 +140,7 @@ def main(): criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu) criterion_htri = TripletLoss(margin=args.margin) - + optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay) if args.stepsize > 0: scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma) @@ -163,11 +163,10 @@ def main(): start_time = time.time() best_rank1 = -np.inf best_epoch = 0 + print("==> Start training") for epoch in range(start_epoch, args.max_epoch): - print("==> Epoch {}/{}".format(epoch+1, args.max_epoch)) - - train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu) + train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu) if args.stepsize > 0: scheduler.step() @@ -195,7 +194,7 @@ def main(): elapsed = str(datetime.timedelta(seconds=elapsed)) print("Finished. Total elapsed time (h:m:s): {}".format(elapsed)) -def train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu): +def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu): model.train() losses = AverageMeter() @@ -217,7 +216,9 @@ def train(model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu losses.update(loss.item(), pids.size(0)) if (batch_idx+1) % args.print_freq == 0: - print("Batch {}/{}\t Loss {:.6f} ({:.6f})".format(batch_idx+1, len(trainloader), losses.val, losses.avg)) + print("Epoch {}/{}\t Batch {}/{}\t Loss {:.6f} ({:.6f})".format( + epoch+1, args.max_epoch, batch_idx+1, len(trainloader), losses.val, losses.avg + )) def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20]): model.eval()