From 9a02f63f4d5692399a95cb889e8f7629a165c28e Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 21 九月 2017 05:56:20 +0800
Subject: [PATCH] next
---
code/train_preangles.py | 36 ++++++++++++++++++------------------
1 files changed, 18 insertions(+), 18 deletions(-)
diff --git a/code/train_preangles.py b/code/train_preangles.py
index afb98c8..6622d3f 100644
--- a/code/train_preangles.py
+++ b/code/train_preangles.py
@@ -18,6 +18,8 @@
import hopenet
import torch.utils.model_zoo as model_zoo
+import time
+
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
@@ -32,8 +34,6 @@
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
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)
@@ -103,7 +103,6 @@
cudnn.enabled = True
num_epochs = args.num_epochs
- num_epochs_ft = args.num_epochs_ft
batch_size = args.batch_size
gpu = args.gpu_id
@@ -123,7 +122,6 @@
transformations = transforms.Compose([transforms.Scale(240),
transforms.RandomCrop(224), transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
-
if args.dataset == 'Pose_300W_LP':
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
@@ -146,9 +144,9 @@
num_workers=2)
model.cuda(gpu)
- softmax = nn.Softmax()
- criterion = nn.CrossEntropyLoss().cuda()
- reg_criterion = nn.MSELoss().cuda()
+ softmax = nn.Softmax().cuda(gpu)
+ criterion = nn.CrossEntropyLoss().cuda(gpu)
+ reg_criterion = nn.MSELoss().cuda(gpu)
# Regression loss coefficient
alpha = args.alpha
@@ -161,25 +159,26 @@
lr = args.lr)
print 'Ready to train network.'
-
print 'First phase of training.'
for epoch in range(num_epochs):
+ start = time.time()
for i, (images, labels, cont_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))
+ print i
+ print 'start: ', time.time() - start
+ 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(cont_labels[:,:3].cuda(gpu))
- label_yaw_cont = Variable(cont_labels[:,0].cuda(gpu))
- label_pitch_cont = Variable(cont_labels[:,1].cuda(gpu))
- label_roll_cont = Variable(cont_labels[:,2].cuda(gpu))
+ label_angles = Variable(cont_labels[:,:3]).cuda(gpu)
+ label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)
+ label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
+ label_roll_cont = Variable(cont_labels[:,2]).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)
@@ -198,7 +197,6 @@
loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)
- # print yaw_predicted, label_yaw.float(), loss_reg_yaw
# Total loss
loss_yaw += alpha * loss_reg_yaw
loss_pitch += alpha * loss_reg_pitch
@@ -209,6 +207,8 @@
torch.autograd.backward(loss_seq, grad_seq)
optimizer.step()
+ print 'end: ', time.time() - start
+
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]))
--
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