From d285712b1c0a3e0785a4e5b17debdde650a7c26a Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 13 九月 2017 21:34:38 +0800
Subject: [PATCH] Removed hourglass
---
code/test.py | 22 +++++++++++++++++-----
1 files changed, 17 insertions(+), 5 deletions(-)
diff --git a/code/test.py b/code/test.py
index b01d07e..f2baf63 100644
--- a/code/test.py
+++ b/code/test.py
@@ -33,6 +33,8 @@
default=1, type=int)
parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.',
default=False, type=bool)
+ parser.add_argument('--iter_ref', dest='iter_ref', default=1, type=int)
+ parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)
args = parser.parse_args()
@@ -48,7 +50,7 @@
# ResNet101 with 3 outputs.
# model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66)
# ResNet50
- model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
+ model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66, args.iter_ref)
# ResNet18
# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
@@ -63,8 +65,18 @@
transforms.RandomCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
- pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
+ if args.dataset == 'AFLW2000':
+ pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list,
transformations)
+ elif args.dataset == 'BIWI':
+ pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW':
+ pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFW':
+ pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
+ else:
+ print 'Error: not a valid dataset name'
+ sys.exit()
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=args.batch_size,
num_workers=2)
@@ -98,9 +110,9 @@
label_roll = labels[:,2].float()
pre_yaw, pre_pitch, pre_roll, angles = model(images)
- yaw = angles[0][:,0].cpu().data
- pitch = angles[0][:,1].cpu().data
- roll = angles[0][:,2].cpu().data
+ yaw = angles[args.iter_ref-1][:,0].cpu().data
+ pitch = angles[args.iter_ref-1][:,1].cpu().data
+ roll = angles[args.iter_ref-1][:,2].cpu().data
# Mean absolute error
yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3)
--
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