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_preangles.py | 19 +++++++++++--------
1 files changed, 11 insertions(+), 8 deletions(-)
diff --git a/code/test_preangles.py b/code/test_preangles.py
index 1203578..9899572 100644
--- a/code/test_preangles.py
+++ b/code/test_preangles.py
@@ -97,12 +97,12 @@
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, angles = model(images)
@@ -121,15 +121,18 @@
roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu()
# 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)
+ yaw_error += torch.sum(torch.abs(yaw_predicted * 3 - 99 - label_yaw))
+ pitch_error += torch.sum(torch.abs(pitch_predicted * 3 - 99 - label_pitch))
+ roll_error += torch.sum(torch.abs(roll_predicted * 3 - 99 - 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.batch_size == 1:
+ error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw) * 3), torch.sum(torch.abs(pitch_predicted - label_pitch) * 3), torch.sum(torch.abs(roll_predicted - label_roll) * 3))
+ cv2_img = cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1)
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)
--
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