From c495a0f6b13b794bab9f6e3423d5038ce645d816 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 13 九月 2017 21:12:59 +0800
Subject: [PATCH] Batch testing and hopenet training complete
---
code/train.py | 24 +++++++++++-------------
1 files changed, 11 insertions(+), 13 deletions(-)
diff --git a/code/train.py b/code/train.py
index ff060af..50eeb82 100644
--- a/code/train.py
+++ b/code/train.py
@@ -46,6 +46,8 @@
parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.',
default=0.001, type=float)
+ parser.add_argument('--iter_ref', dest='iter_ref', help='Number of iterative refinement passes.',
+ default=1, type=int)
args = parser.parse_args()
return args
@@ -111,7 +113,7 @@
# 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)
load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet50']))
@@ -177,14 +179,12 @@
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())
- # print yaw_predicted, label_yaw.float(), loss_reg_yaw
# Total loss
loss_yaw += alpha * loss_reg_yaw
loss_pitch += alpha * loss_reg_pitch
loss_roll += alpha * loss_reg_roll
loss_seq = [loss_yaw, loss_pitch, loss_roll]
- # loss_seq = [loss_reg_yaw, loss_reg_pitch, loss_reg_roll]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
@@ -226,9 +226,9 @@
pitch_predicted = softmax(pre_pitch)
roll_predicted = softmax(pre_roll)
- yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1)
- pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1)
- roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1)
+ yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1)
+ pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1)
+ roll_predicted = torch.sum(roll_predicted * idx_tensor, 1)
loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
@@ -240,9 +240,11 @@
loss_roll += alpha * loss_reg_roll
# Finetuning loss
- loss_angles = reg_criterion(angles[0], label_angles.float())
+ loss_seq = [loss_yaw, loss_pitch, loss_roll]
+ for idx in xrange(args.iter_ref):
+ loss_angles = reg_criterion(angles[idx], label_angles.float())
+ loss_seq.append(loss_angles)
- loss_seq = [loss_yaw, loss_pitch, loss_roll, loss_angles]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
@@ -255,11 +257,7 @@
# 'output/snapshots/' + args.output_string + '_iter_'+ str(i+1) + '.pkl')
# Save models at numbered epochs.
- if epoch % 1 == 0 and epoch < num_epochs_ft - 1:
+ if epoch % 1 == 0 and epoch < num_epochs_ft:
print 'Taking snapshot...'
torch.save(model.state_dict(),
'output/snapshots/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')
-
-
- # Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/' + args.output_string + '_epoch_' + str(num_epochs+epoch+1) + '.pkl')
--
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