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add vis-ranked-res

pull/62/head
KaiyangZhou 2018-08-01 12:04:58 +01:00
parent 56ee811ed0
commit dbbb68b955
4 changed files with 41 additions and 8 deletions
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4
train_imgreid_xent.py
View File

@ -205,7 +205,7 @@ def main():
if args.evaluate:
print("Evaluate only")
_, distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat, dataset,
@ -369,7 +369,7 @@ def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20], retur
print("------------------")
if return_distmat:
return cmc[0], distmat
return distmat
return cmc[0]

15
train_imgreid_xent_htri.py
View File

@ -24,6 +24,7 @@ from utils.iotools import save_checkpoint
from utils.avgmeter import AverageMeter
from utils.logger import Logger
from utils.torchtools import count_num_param
from utils.reidtools import visualize_ranked_results
from eval_metrics import evaluate
from samplers import RandomIdentitySampler
from optimizers import init_optim
@ -98,6 +99,8 @@ parser.add_argument('--use-cpu', action='store_true',
help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str,
help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--vis-ranked-res', action='store_true',
help="visualize ranked results, only available in evaluation mode (default: False)")
args = parser.parse_args()
@ -199,7 +202,13 @@ def main():
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat, dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
@ -294,7 +303,7 @@ def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader,
end = time.time()
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model.eval()
@ -356,6 +365,8 @@ def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]

15
train_vidreid_xent.py
View File

@ -24,6 +24,7 @@ from utils.iotools import save_checkpoint
from utils.avgmeter import AverageMeter
from utils.logger import Logger
from utils.torchtools import set_bn_to_eval, count_num_param
from utils.reidtools import visualize_ranked_results
from eval_metrics import evaluate
from optimizers import init_optim
@ -89,6 +90,8 @@ parser.add_argument('--use-cpu', action='store_true',
help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str,
help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--vis-ranked-res', action='store_true',
help="visualize ranked results, only available in evaluation mode (default: False)")
args = parser.parse_args()
@ -193,7 +196,13 @@ def main():
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, args.pool, use_gpu)
distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat, dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
@ -286,7 +295,7 @@ def train(epoch, model, criterion, optimizer, trainloader, use_gpu, freeze_bn=Fa
end = time.time()
def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20]):
def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model.eval()
@ -360,6 +369,8 @@ def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20])
print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]

15
train_vidreid_xent_htri.py
View File

@ -24,6 +24,7 @@ from utils.iotools import save_checkpoint
from utils.avgmeter import AverageMeter
from utils.logger import Logger
from utils.torchtools import count_num_param
from utils.reidtools import visualize_ranked_results
from eval_metrics import evaluate
from samplers import RandomIdentitySampler
from optimizers import init_optim
@ -90,6 +91,8 @@ parser.add_argument('--use-cpu', action='store_true',
help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str,
help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--vis-ranked-res', action='store_true',
help="visualize ranked results, only available in evaluation mode (default: False)")
args = parser.parse_args()
@ -190,7 +193,13 @@ def main():
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, args.pool, use_gpu)
distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat, dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
@ -276,7 +285,7 @@ def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader,
end = time.time()
def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20]):
def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
batch_time = AverageMeter()
model.eval()
@ -350,6 +359,8 @@ def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20])
print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0]