From abe876183052e9da9c3d633e41386c5c1f4fc1e6 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 08 九月 2017 05:51:42 +0800
Subject: [PATCH] Before adding refinement layer
---
code/train.py | 56 +++++++++++++++++++++++++++-----------------------------
1 files changed, 27 insertions(+), 29 deletions(-)
diff --git a/code/train.py b/code/train.py
index f98bbc3..5d7fc7d 100644
--- a/code/train.py
+++ b/code/train.py
@@ -95,17 +95,22 @@
# 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)
# ResNet18
- model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
- load_filtered_state_dict(model, model_zoo.load_url(model_urls['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']))
print 'Loading data.'
- transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
- transforms.ToTensor()])
+ # transformations = transforms.Compose([transforms.Scale(224),
+ # transforms.RandomCrop(224),
+ # transforms.ToTensor()])
- pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
+ transformations = transforms.Compose([transforms.Scale(250),
+ transforms.RandomCrop(224), transforms.ToTensor(),
+ transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+
+ pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list,
transformations)
train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
batch_size=batch_size,
@@ -113,11 +118,10 @@
num_workers=2)
model.cuda(gpu)
- criterion = nn.CrossEntropyLoss()
- reg_criterion = nn.MSELoss()
+ criterion = nn.CrossEntropyLoss().cuda()
+ reg_criterion = nn.MSELoss().cuda()
# Regression loss coefficient
alpha = 0.01
- lsm = nn.Softmax()
idx_tensor = [idx for idx in xrange(66)]
idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
@@ -126,32 +130,28 @@
{'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
lr = args.lr)
# optimizer = torch.optim.SGD([{'params': get_ignored_params(model), 'lr': args.lr},
- # {'params': get_non_ignored_params(model), 'lr': args.lr}],
- # lr = args.lr, momentum=0.9)
- # optimizer = torch.optim.RMSprop([{'params': get_ignored_params(model), 'lr': args.lr},
- # {'params': get_non_ignored_params(model), 'lr': args.lr * 10}],
- # lr = args.lr)
+ # {'params': get_non_ignored_params(model), 'lr': args.lr}],
+ # lr = args.lr,
+ # momentum = 0.9, weight_decay=0.01)
print 'Ready to train network.'
for epoch in range(num_epochs):
for i, (images, 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)
+ 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))
optimizer.zero_grad()
+ model.zero_grad()
+
yaw, pitch, roll = model(images)
+ # Cross entropy loss
loss_yaw = criterion(yaw, label_yaw)
loss_pitch = criterion(pitch, label_pitch)
loss_roll = criterion(roll, label_roll)
-
- # loss_seq = [loss_yaw, loss_pitch, loss_roll]
- # grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
- # torch.autograd.backward(loss_seq, grad_seq)
- # optimizer.step()
# MSE loss
yaw_predicted = F.softmax(yaw)
@@ -166,15 +166,13 @@
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())
- # print yaw_predicted[0], label_yaw.data[0]
-
+ # 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]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
- model.zero_grad()
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
@@ -186,13 +184,13 @@
%(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/resnet18_sgd_iter_'+ str(i+1) + '.pkl')
+ # 'output/snapshots/resnet50_lbatch_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/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl')
+ 'output/snapshots/resnet50_norm_30rot_epoch_'+ str(epoch+1) + '.pkl')
# Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl')
+ torch.save(model.state_dict(), 'output/snapshots/resnet50_norm_30rot_epoch_' + str(epoch+1) + '.pkl')
--
Gitblit v1.8.0