From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:11:23 +0800
Subject: [PATCH] next

---
 code/train_finetune.py |   17 ++++++++++-------
 1 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/code/train_finetune.py b/code/train_finetune.py
index 0081d2a..10eb6ad 100644
--- a/code/train_finetune.py
+++ b/code/train_finetune.py
@@ -150,6 +150,7 @@
     softmax = nn.Softmax()
     criterion = nn.CrossEntropyLoss().cuda()
     reg_criterion = nn.MSELoss().cuda()
+    smooth_l1_loss = nn.SmoothL1Loss().cuda()
     # Regression loss coefficient
     alpha = args.alpha
 
@@ -165,12 +166,10 @@
 
     print 'Second phase of training (finetuning layer).'
     for epoch in range(num_epochs_ft):
-        for i, (images, labels, name) in enumerate(train_loader):
+        for i, (images, labels, cont_labels, name) in enumerate(train_loader):
             images = Variable(images.cuda(gpu))
-            label_yaw = Variable(labels[:,0].cuda(gpu))
-            label_pitch = Variable(labels[:,1].cuda(gpu))
-            label_roll = Variable(labels[:,2].cuda(gpu))
-            label_angles = Variable(labels[:,:3].cuda(gpu))
+
+            label_angles = Variable(cont_labels[:,:3].cuda(gpu))
 
             optimizer.zero_grad()
             model.zero_grad()
@@ -180,9 +179,13 @@
             # Finetuning loss
             loss_seq = []
             for idx in xrange(1,len(angles)):
-                label_angles_residuals = label_angles.float() - angles[0]
+                label_angles_residuals = label_angles - (angles[0] * 3 - 99)
+                # for idy in xrange(1,idx):
+                #     label_angles_residuals += angles[idy] * 3 - 99
                 label_angles_residuals = label_angles_residuals.detach()
-                loss_angles = reg_criterion(angles[idx], label_angles_residuals)
+                # Reconvert to other unit
+                label_angles_residuals = label_angles_residuals / 3.0 + 33
+                loss_angles = smooth_l1_loss(angles[idx], label_angles_residuals)
                 loss_seq.append(loss_angles)
 
             grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]

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