From 43416c4717d2430c3e11f042294d12b781fee2e1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:09:30 +0800
Subject: [PATCH] Failed lstm experiment
---
code/test.py | 93 ++++++++++++++++++++++------------------------
1 files changed, 45 insertions(+), 48 deletions(-)
diff --git a/code/test.py b/code/test.py
index 4b1a655..4983105 100644
--- a/code/test.py
+++ b/code/test.py
@@ -27,12 +27,14 @@
default='', type=str)
parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
default='', type=str)
- parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.',
+ parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.',
default='', type=str)
parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
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()
@@ -43,12 +45,12 @@
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, args.iter_ref)
# ResNet18
# model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
@@ -60,10 +62,23 @@
print 'Loading data.'
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_binned(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 == 'Pose_300W_LP':
+ pose_dataset = datasets.Pose_300W_LP(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)
@@ -75,65 +90,47 @@
# 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)
-
yaw_error = .0
pitch_error = .0
roll_error = .0
l1loss = torch.nn.L1Loss(size_average=False)
- for i, (images, labels, name) in enumerate(test_loader):
+ for i, (images, labels, cont_labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
- total += labels.size(0)
- label_yaw = labels[:,0].float()
- label_pitch = labels[:,1].float()
- label_roll = labels[:,2].float()
+ total += cont_labels.size(0)
+ label_yaw = cont_labels[:,0].float()
+ label_pitch = cont_labels[:,1].float()
+ label_roll = cont_labels[:,2].float()
- yaw, pitch, roll = model(images)
+ pre_yaw, pre_pitch, pre_roll, angles = model(images)
+ yaw = angles[0][:,0].cpu().data * 3 - 99
+ pitch = angles[0][:,1].cpu().data * 3 - 99
+ roll = angles[0][:,2].cpu().data * 3 - 99
- # Binned predictions
- _, yaw_bpred = torch.max(yaw.data, 1)
- _, pitch_bpred = torch.max(pitch.data, 1)
- _, roll_bpred = torch.max(roll.data, 1)
-
- # Continuous predictions
- yaw_predicted = utils.softmax_temperature(yaw.data, 1)
- pitch_predicted = utils.softmax_temperature(pitch.data, 1)
- roll_predicted = utils.softmax_temperature(roll.data, 1)
-
- yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu()
- pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu()
- roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu()
+ for idx in xrange(1,args.iter_ref+1):
+ yaw += angles[idx][:,0].cpu().data * 3 - 99
+ pitch += angles[idx][:,1].cpu().data * 3 - 99
+ roll += angles[idx][:,2].cpu().data * 3 - 99
# Mean absolute error
- yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3)
- 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]
+ yaw_error += torch.sum(torch.abs(yaw - label_yaw))
+ pitch_error += torch.sum(torch.abs(pitch - label_pitch))
+ roll_error += torch.sum(torch.abs(roll - label_roll))
# 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)
+ if args.dataset == 'BIWI':
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
+ else:
+ cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
+
+ if args.batch_size == 1:
+ error_string = 'y %.4f, p %.4f, r %.4f' % (torch.sum(torch.abs(yaw - label_yaw) * 3), torch.sum(torch.abs(pitch - label_pitch) * 3), torch.sum(torch.abs(roll - label_roll) * 3))
+ cv2_img = cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=2, color=(0,255,0), thickness=2)
+ utils.plot_pose_cube(cv2_img, yaw[0] * 3 - 99, pitch[0] * 3 - 99, roll[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) +
--
Gitblit v1.8.0