From f111cb002b9c6065fdf6bb274ce5857a9e875e8c Mon Sep 17 00:00:00 2001
From: chenshijun <csj_sky@126.com>
Date: 星期三, 05 六月 2019 15:38:49 +0800
Subject: [PATCH] face rectangle
---
code/train_hopenet.py | 10 +++++-----
1 files changed, 5 insertions(+), 5 deletions(-)
diff --git a/code/train_hopenet.py b/code/train_hopenet.py
index 6278a87..56b9ca3 100644
--- a/code/train_hopenet.py
+++ b/code/train_hopenet.py
@@ -96,7 +96,7 @@
saved_state_dict = torch.load(args.snapshot)
model.load_state_dict(saved_state_dict)
- print 'Loading data.'
+ print('Loading data.')
transformations = transforms.Compose([transforms.Scale(240),
transforms.RandomCrop(224), transforms.ToTensor(),
@@ -119,7 +119,7 @@
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
- print 'Error: not a valid dataset name'
+ print('Error: not a valid dataset name')
sys.exit()
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
@@ -142,7 +142,7 @@
{'params': get_fc_params(model), 'lr': args.lr * 5}],
lr = args.lr)
- print 'Ready to train network.'
+ print('Ready to train network.')
for epoch in range(num_epochs):
for i, (images, labels, cont_labels, name) in enumerate(train_loader):
images = Variable(images).cuda(gpu)
@@ -184,7 +184,7 @@
loss_roll += alpha * loss_reg_roll
loss_seq = [loss_yaw, loss_pitch, loss_roll]
- grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
+ grad_seq = [torch.ones(1).cuda(gpu) for _ in range(len(loss_seq))]
optimizer.zero_grad()
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
@@ -195,6 +195,6 @@
# Save models at numbered epochs.
if epoch % 1 == 0 and epoch < num_epochs:
- print 'Taking snapshot...'
+ print('Taking snapshot...')
torch.save(model.state_dict(),
'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')
--
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