From ec99c6649af6bdbd3c836f20cdc81170e7045cc8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 14 九月 2017 10:06:48 +0800
Subject: [PATCH] Training hopenet and normal for different alpha values on AFLW
---
code/test_preangles.py | 41 +++++++++++++++--------------------------
1 files changed, 15 insertions(+), 26 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index 67e4744..1203578 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -33,6 +33,7 @@
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('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)
args = parser.parse_args()
@@ -43,12 +44,12 @@
cudnn.enabled = True
gpu = args.gpu_id
- snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl')
+ snapshot_path = args.snapshot
# 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, 0)
# ResNet18
# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
@@ -59,15 +60,22 @@
print 'Loading data.'
- # transformations = transforms.Compose([transforms.Scale(224),
- # transforms.RandomCrop(224), transforms.ToTensor()])
-
transformations = transforms.Compose([transforms.Scale(224),
- transforms.RandomCrop(224), transforms.ToTensor(),
+ transforms.CenterCrop(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)
@@ -79,10 +87,6 @@
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
total = 0
- n_margins = 20
- yaw_correct = np.zeros(n_margins)
- pitch_correct = np.zeros(n_margins)
- roll_correct = np.zeros(n_margins)
idx_tensor = [idx for idx in xrange(66)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
@@ -121,29 +125,14 @@
pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3)
roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3)
- # Binned Accuracy
- # for er in xrange(n_margins):
- # yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1))
- # pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1))
- # roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1))
-
- # print label_yaw[0], yaw_bpred[0,0]
-
# Save images with pose cube.
# TODO: fix for larger batch size
if args.save_viz:
name = name[0]
cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
- #print os.path.join('output/images', name + '.jpg')
- #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99
- #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99
utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99)
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
print('Test error in degrees of the model on the ' + str(total) +
' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
pitch_error / total, roll_error / total))
-
- # Binned accuracy
- # for idx in xrange(len(yaw_correct)):
- # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total
--
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