From 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 08 九月 2017 11:15:10 +0800
Subject: [PATCH] Finetune layer working
---
code/train.py | 137 +++++++++++++++++++++++++++++++--------------
1 files changed, 95 insertions(+), 42 deletions(-)
diff --git a/code/train.py b/code/train.py
index f98bbc3..826793d 100644
--- a/code/train.py
+++ b/code/train.py
@@ -33,6 +33,8 @@
default=0, type=int)
parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.',
default=5, type=int)
+ parser.add_argument('--num_epochs_ft', dest='num_epochs_ft', help='Maximum number of finetuning epochs.',
+ default=5, type=int)
parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
default=16, type=int)
parser.add_argument('--lr', dest='lr', help='Base learning rate.',
@@ -41,9 +43,7 @@
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)
-
args = parser.parse_args()
-
return args
def get_ignored_params(model):
@@ -66,6 +66,7 @@
b.append(model.fc_yaw)
b.append(model.fc_pitch)
b.append(model.fc_roll)
+ b.append(model.fc_finetune)
for i in range(len(b)):
for j in b[i].modules():
for k in j.parameters():
@@ -86,6 +87,7 @@
cudnn.enabled = True
num_epochs = args.num_epochs
+ num_epochs_ft = args.num_epochs_ft
batch_size = args.batch_size
gpu = args.gpu_id
@@ -95,17 +97,22 @@
# 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 +120,10 @@
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)
@@ -125,38 +131,31 @@
optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': args.lr},
{'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)
print 'Ready to train network.'
+ print 'First phase of training.'
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()
- yaw, pitch, roll = model(images)
+ model.zero_grad()
- loss_yaw = criterion(yaw, label_yaw)
- loss_pitch = criterion(pitch, label_pitch)
- loss_roll = criterion(roll, label_roll)
+ pre_yaw, pre_pitch, pre_roll, angles = model(images)
- # 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()
+ # Cross entropy loss
+ loss_yaw = criterion(pre_yaw, label_yaw)
+ loss_pitch = criterion(pre_pitch, label_pitch)
+ loss_roll = criterion(pre_roll, label_roll)
# MSE loss
- yaw_predicted = F.softmax(yaw)
- pitch_predicted = F.softmax(pitch)
- roll_predicted = F.softmax(roll)
+ yaw_predicted = F.softmax(pre_yaw)
+ pitch_predicted = F.softmax(pre_pitch)
+ roll_predicted = F.softmax(pre_roll)
yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1)
pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1)
@@ -166,33 +165,87 @@
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()
-
- # print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
- # %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0]))
if (i+1) % 100 == 0:
print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
%(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/hopenet50_epoch_'+ str(i+1) + '.pkl')
# Save models at numbered epochs.
- if epoch % 1 == 0 and epoch < num_epochs - 1:
+ if epoch % 1 == 0 and epoch < num_epochs:
print 'Taking snapshot...'
torch.save(model.state_dict(),
- 'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl')
+ 'output/snapshots/hopenet50_epoch_'+ str(epoch+1) + '.pkl')
+
+ print 'Second phase of training (finetuning layer).'
+ for epoch in range(num_epochs_ft):
+ 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))
+ label_angles = Variable(labels[:,:3].cuda(gpu))
+
+ optimizer.zero_grad()
+ model.zero_grad()
+
+ pre_yaw, pre_pitch, pre_roll, angles = model(images)
+
+ # Cross entropy loss
+ loss_yaw = criterion(pre_yaw, label_yaw)
+ loss_pitch = criterion(pre_pitch, label_pitch)
+ loss_roll = criterion(pre_roll, label_roll)
+
+ # MSE loss
+ yaw_predicted = F.softmax(pre_yaw)
+ pitch_predicted = F.softmax(pre_pitch)
+ roll_predicted = F.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)
+
+ loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw.float())
+ loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch.float())
+ loss_reg_roll = reg_criterion(roll_predicted, label_roll.float())
+
+ # Total loss
+ loss_yaw += alpha * loss_reg_yaw
+ loss_pitch += alpha * loss_reg_pitch
+ 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, loss_angles]
+ grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
+ torch.autograd.backward(loss_seq, grad_seq)
+ optimizer.step()
+
+ if (i+1) % 100 == 0:
+ print ('Epoch [%d/%d], Iter [%d/%d] Losses: pre-yaw %.4f, pre-pitch %.4f, pre-roll %.4f, finetuning %.4f'
+ %(epoch+1, num_epochs_ft, i+1, len(pose_dataset)//batch_size, loss_yaw.data[0], loss_pitch.data[0], loss_roll.data[0], loss_angles.data[0]))
+ # if epoch == 0:
+ # torch.save(model.state_dict(),
+ # 'output/snapshots/hopenet50_iter_'+ str(i+1) + '.pkl')
+
+ # Save models at numbered epochs.
+ if epoch % 1 == 0 and epoch < num_epochs_ft - 1:
+ print 'Taking snapshot...'
+ torch.save(model.state_dict(),
+ 'output/snapshots/hopenet50_epoch_'+ str(num_epochs+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/hopenet50_epoch_' + str(num_epochs+epoch+1) + '.pkl')
--
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