From 43416c4717d2430c3e11f042294d12b781fee2e1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:09:30 +0800
Subject: [PATCH] Failed lstm experiment
---
code/train_preangles.py | 118 +++++++++++++++-------------------------------------------
1 files changed, 31 insertions(+), 87 deletions(-)
diff --git a/code/train_preangles.py b/code/train_preangles.py
index 5f23b25..4752aef 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
@@ -124,15 +123,18 @@
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)
+ if 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 == 'AFLW2000':
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'BIWI':
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFLW':
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
+ elif args.dataset == 'AFLW_aug':
+ pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
elif args.dataset == 'AFW':
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
else:
@@ -144,9 +146,9 @@
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
@@ -155,24 +157,30 @@
optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
{'params': get_non_ignored_params(model), 'lr': args.lr},
- {'params': get_fc_params(model), 'lr': args.lr * 2}],
+ {'params': get_fc_params(model), 'lr': args.lr * 5}],
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))
+ # start = time.time()
+ for i, (images, labels, cont_labels, name) in enumerate(train_loader):
+ # 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)
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)
@@ -183,27 +191,25 @@
pitch_predicted = softmax(pre_pitch)
roll_predicted = softmax(pre_roll)
- yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1)
- pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1)
- roll_predicted = torch.sum(roll_predicted * idx_tensor, 1)
+ yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) * 3 - 99
+ pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) * 3 - 99
+ roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) * 3 - 99
- 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())
+ loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)
+ 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
loss_roll += alpha * loss_reg_roll
- loss_yaw *= 0.35
-
loss_seq = [loss_yaw, loss_pitch, loss_roll]
- # loss_seq = [loss_reg_yaw, loss_reg_pitch, loss_reg_roll]
grad_seq = [torch.Tensor(1).cuda(gpu) for _ in range(len(loss_seq))]
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'
@@ -217,65 +223,3 @@
print 'Taking snapshot...'
torch.save(model.state_dict(),
'output/snapshots/' + args.output_string + '_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 = softmax(pre_yaw)
- pitch_predicted = softmax(pre_pitch)
- roll_predicted = 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/' + args.output_string + '_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/' + args.output_string + '_epoch_'+ str(num_epochs+epoch+1) + '.pkl')
-
-
- # Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/' + args.output_string + '_epoch_' + str(num_epochs+epoch+1) + '.pkl')
--
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