From 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 08 九月 2017 11:15:10 +0800
Subject: [PATCH] Finetune layer working
---
code/test.py | 38 ++++++++------------------------------
1 files changed, 8 insertions(+), 30 deletions(-)
diff --git a/code/test.py b/code/test.py
index b9be11e..8e8fe50 100644
--- a/code/test.py
+++ b/code/test.py
@@ -100,44 +100,22 @@
label_pitch = labels[:,1].float()
label_roll = labels[:,2].float()
- yaw, pitch, roll = model(images)
-
- # 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()
+ pre_yaw, pre_pitch, pre_roll, angles = model(images)
+ yaw = angles[:,0].cpu().data
+ pitch = angles[:,1].cpu().data
+ roll = angles[:,2].cpu().data
# 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) * 3)
+ pitch_error += torch.sum(torch.abs(pitch - label_pitch) * 3)
+ roll_error += torch.sum(torch.abs(roll - label_roll) * 3)
# 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)
+ 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) +
--
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