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add lambda-xent and lambda-htri to balance training

pull/62/head
KaiyangZhou 2018-08-14 17:32:06 +01:00
parent 28ecca9397
commit bd18a9489e
2 changed files with 25 additions and 8 deletions
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6
train_imgreid_xent_htri.py
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@ -76,6 +76,10 @@ parser.add_argument('--num-instances', type=int, default=4,
help="number of instances per identity") help="number of instances per identity")
parser.add_argument('--htri-only', action='store_true', default=False, parser.add_argument('--htri-only', action='store_true', default=False,
help="if this is True, only htri loss is used in training") help="if this is True, only htri loss is used in training")
parser.add_argument('--lambda-xent', type=float, default=1,
help="weight to balance cross entropy loss")
parser.add_argument('--lambda-htri', type=float, default=1,
help="weight to balance hard triplet loss")
# Architecture # Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names()) parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
# Miscs # Miscs
@ -278,7 +282,7 @@ def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader,
else: else:
htri_loss = criterion_htri(features, pids) htri_loss = criterion_htri(features, pids)
loss = xent_loss + htri_loss loss = args.lambda_xent * xent_loss + args.lambda_htri * htri_loss
optimizer.zero_grad() optimizer.zero_grad()
loss.backward() loss.backward()
optimizer.step() optimizer.step()

21
train_vidreid_xent_htri.py
View File

@ -19,7 +19,7 @@ import data_manager
from dataset_loader import ImageDataset, VideoDataset from dataset_loader import ImageDataset, VideoDataset
import transforms as T import transforms as T
import models import models
from losses import CrossEntropyLabelSmooth, TripletLoss from losses import CrossEntropyLabelSmooth, TripletLoss, DeepSupervision
from utils.iotools import save_checkpoint, check_isfile from utils.iotools import save_checkpoint, check_isfile
from utils.avgmeter import AverageMeter from utils.avgmeter import AverageMeter
from utils.logger import Logger from utils.logger import Logger
@ -69,6 +69,10 @@ parser.add_argument('--num-instances', type=int, default=4,
help="number of instances per identity") help="number of instances per identity")
parser.add_argument('--htri-only', action='store_true', default=False, parser.add_argument('--htri-only', action='store_true', default=False,
help="if this is True, only htri loss is used in training") help="if this is True, only htri loss is used in training")
parser.add_argument('--lambda-xent', type=float, default=1,
help="weight to balance cross entropy loss")
parser.add_argument('--lambda-htri', type=float, default=1,
help="weight to balance hard triplet loss")
# Architecture # Architecture
parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names()) parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
parser.add_argument('--pool', type=str, default='avg', choices=['avg', 'max']) parser.add_argument('--pool', type=str, default='avg', choices=['avg', 'max'])
@ -260,13 +264,22 @@ def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader,
outputs, features = model(imgs) outputs, features = model(imgs)
if args.htri_only: if args.htri_only:
# only use hard triplet loss to train the network if isinstance(features, tuple):
loss = DeepSupervision(criterion_htri, features, pids)
else:
loss = criterion_htri(features, pids) loss = criterion_htri(features, pids)
else: else:
# combine hard triplet loss with cross entropy loss if isinstance(outputs, tuple):
xent_loss = DeepSupervision(criterion_xent, outputs, pids)
else:
xent_loss = criterion_xent(outputs, pids) xent_loss = criterion_xent(outputs, pids)
if isinstance(features, tuple):
htri_loss = DeepSupervision(criterion_htri, features, pids)
else:
htri_loss = criterion_htri(features, pids) htri_loss = criterion_htri(features, pids)
loss = xent_loss + htri_loss
loss = args.lambda_xent * xent_loss + args.lambda_htri * htri_loss
optimizer.zero_grad() optimizer.zero_grad()
loss.backward() loss.backward()
optimizer.step() optimizer.step()