From beb9f36419d0df03c3248757f54af032a633e05c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期六, 12 八月 2017 11:49:10 +0800
Subject: [PATCH] AFLW training ready.
---
code/train_AFLW.py | 17 ++++++++++-------
1 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/code/train_AFLW.py b/code/train_AFLW.py
index 13bfc29..65ea83c 100644
--- a/code/train_AFLW.py
+++ b/code/train_AFLW.py
@@ -41,6 +41,10 @@
default='', type=str)
parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
default='', type=str)
+ parser.add_argument('--finetune', dest='finetune', help='Boolean: finetune or from Imagenet pretrain.',
+ default=False, type=bool)
+ parser.add_argument('--snapshot', dest='snapshot', help='Path to finetune snapshot.',
+ default='', type=str)
args = parser.parse_args()
@@ -105,7 +109,7 @@
transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
transforms.ToTensor()])
- pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
+ pose_dataset = datasets.AFLW(args.data_dir, args.filename_list,
transformations)
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=batch_size,
@@ -113,10 +117,10 @@
num_workers=2)
model.cuda(gpu)
- criterion = nn.CrossEntropyLoss().cuda()
- reg_criterion = nn.MSELoss().cuda()
+ criterion = nn.CrossEntropyLoss().cuda(gpu)
+ reg_criterion = nn.MSELoss().cuda(gpu)
# Regression loss coefficient
- alpha = 0.01
+ alpha = 0.1
idx_tensor = [idx for idx in xrange(66)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
@@ -135,7 +139,6 @@
label_roll = Variable(labels[:,2].cuda(gpu))
optimizer.zero_grad()
- model.zero_grad()
yaw, pitch, roll = model(images)
@@ -175,13 +178,13 @@
%(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
if epoch == 0:
torch.save(model.state_dict(),
- 'output/snapshots/resnet50_AFW_iter_'+ str(i+1) + '.pkl')
+ 'output/snapshots/resnet50_AFLW_iter_'+ str(i+1) + '.pkl')
# Save models at numbered epochs.
if epoch % 1 == 0 and epoch < num_epochs - 1:
print 'Taking snapshot...'
torch.save(model.state_dict(),
- 'output/snapshots/resnet50_AFW_epoch_'+ str(epoch+1) + '.pkl')
+ 'output/snapshots/resnet50_AFLW_epoch_'+ str(epoch+1) + '.pkl')
# Save the final Trained Model
torch.save(model.state_dict(), 'output/snapshots/resnet50_AFLW_epoch' + str(epoch+1) + '.pkl')
--
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