From ec44ac453f794a5368e702315addfedcea3a4299 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 19 九月 2017 06:01:47 +0800
Subject: [PATCH] Added continuous labels
---
code/test.py | 44 ++++++++++++++++++++++++--------------------
1 files changed, 24 insertions(+), 20 deletions(-)
diff --git a/code/test.py b/code/test.py
index 9ff35e6..b69c3a9 100644
--- a/code/test.py
+++ b/code/test.py
@@ -88,42 +88,46 @@
# 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()
pre_yaw, pre_pitch, pre_roll, angles = model(images)
- yaw = angles[-1][:,0].cpu().data
- pitch = angles[-1][:,1].cpu().data
- roll = angles[-1][:,2].cpu().data
+ yaw = angles[0][:,0].cpu().data * 3 - 99
+ pitch = angles[0][:,1].cpu().data * 3 - 99
+ roll = angles[0][:,2].cpu().data * 3 - 99
+
+ for idx in xrange(1,args.iter_ref+1):
+ yaw += angles[idx][:,0].cpu().data
+ pitch += angles[idx][:,1].cpu().data
+ roll += angles[idx][:,2].cpu().data
# Mean absolute error
- yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3)
- pitch_error += torch.sum(torch.abs(pitch - label_pitch) * 3)
- roll_error += torch.sum(torch.abs(roll - label_roll) * 3)
+ 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'))
+ 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)
--
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