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 | 25 +++++++++++++++++--------
1 files changed, 17 insertions(+), 8 deletions(-)
diff --git a/code/train_hopenet.py b/code/train_hopenet.py
index 600a9ae..56b9ca3 100644
--- a/code/train_hopenet.py
+++ b/code/train_hopenet.py
@@ -27,6 +27,7 @@
default=16, type=int)
parser.add_argument('--lr', dest='lr', help='Base learning rate.',
default=0.001, type=float)
+ parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
default='', type=str)
parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
@@ -34,7 +35,8 @@
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('--dataset', dest='dataset', help='Dataset type.', default='Pose_300W_LP', type=str)
+ parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.',
+ default='', type=str)
args = parser.parse_args()
return args
@@ -87,9 +89,14 @@
# ResNet50 structure
model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
- load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
- print 'Loading data.'
+ if args.snapshot == '':
+ load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
+ else:
+ saved_state_dict = torch.load(args.snapshot)
+ model.load_state_dict(saved_state_dict)
+
+ print('Loading data.')
transformations = transforms.Compose([transforms.Scale(240),
transforms.RandomCrop(224), transforms.ToTensor(),
@@ -99,6 +106,8 @@
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'Pose_300W_LP_random_ds':
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'Synhead':
+ pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'BIWI':
@@ -110,7 +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,
@@ -133,7 +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)
@@ -149,7 +158,7 @@
label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)
# Forward pass
- yaw, pitch, roll, angles = model(images)
+ yaw, pitch, roll = model(images)
# Cross entropy loss
loss_yaw = criterion(yaw, label_yaw)
@@ -175,7 +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()
@@ -186,6 +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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