From 54818d253649ff588ed0054d10dabb2a3a170309 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 10 八月 2017 04:08:12 +0800
Subject: [PATCH] Doing pretty well now with resnet50 and adam with low learning rate. Also fixed test script to use large batches.
---
code/test_on_video.py | 4
code/train_resnet_bins_comb.py | 198 +++++++++++++++++++
practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb | 42 ++-
code/test_resnet_bins.py | 34 ++-
code/train_resnet_bins.py | 47 ++++
code/train_resnet_bins_comb_dup.py | 198 +++++++++++++++++++
practice/smoothing_ypr.ipynb | 42 ++-
7 files changed, 512 insertions(+), 53 deletions(-)
diff --git a/code/test_on_video.py b/code/test_on_video.py
index 247c2db..20dfaac 100644
--- a/code/test_on_video.py
+++ b/code/test_on_video.py
@@ -48,9 +48,9 @@
# 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)
+ # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
print 'Loading snapshot.'
# Load snapshot
diff --git a/code/test_resnet_bins.py b/code/test_resnet_bins.py
index 00d2109..699c9c9 100644
--- a/code/test_resnet_bins.py
+++ b/code/test_resnet_bins.py
@@ -42,16 +42,15 @@
args = parse_args()
cudnn.enabled = True
- batch_size = 1
gpu = args.gpu_id
snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl')
# 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)
+ # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
print 'Loading snapshot.'
# Load snapshot
@@ -66,7 +65,7 @@
pose_dataset = datasets.AFLW2000_binned(args.data_dir, args.filename_list,
transformations)
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
- batch_size=batch_size,
+ batch_size=args.batch_size,
num_workers=2)
model.cuda(gpu)
@@ -88,12 +87,14 @@
pitch_error = .0
roll_error = .0
+ l1loss = torch.nn.L1Loss(size_average=False)
+
for i, (images, labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
total += labels.size(0)
- label_yaw = labels[:,0]
- label_pitch = labels[:,1]
- label_roll = labels[:,2]
+ label_yaw = labels[:,0].float()
+ label_pitch = labels[:,1].float()
+ label_roll = labels[:,2].float()
yaw, pitch, roll = model(images)
@@ -107,14 +108,18 @@
roll_predicted = F.softmax(roll)
# Continuous predictions
- yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor)
- pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor)
- roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor)
+ 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)
+
+ yaw_predicted = yaw_predicted.cpu()
+ pitch_predicted = pitch_predicted.cpu()
+ roll_predicted = roll_predicted.cpu()
# Mean absolute error
- yaw_error += abs(yaw_predicted - label_yaw[0]) * 3
- pitch_error += abs(pitch_predicted - label_pitch[0]) * 3
- roll_error += abs(roll_predicted - label_roll[0]) * 3
+ yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3)
+ pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3)
+ roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3)
# Binned Accuracy
# for er in xrange(n_margins):
@@ -125,13 +130,14 @@
# print label_yaw[0], yaw_bpred[0,0]
# Save images with pose cube.
+ # TODO: fix for larger batch size
if args.save_viz:
name = name[0]
cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
#print os.path.join('output/images', name + '.jpg')
#print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99
#print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99
- utils.plot_pose_cube(cv2_img, yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99)
+ utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99)
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
print('Test error in degrees of the model on the ' + str(total) +
diff --git a/code/train_resnet_bins.py b/code/train_resnet_bins.py
index dab3800..f98bbc3 100644
--- a/code/train_resnet_bins.py
+++ b/code/train_resnet_bins.py
@@ -6,6 +6,7 @@
from torchvision import transforms
import torchvision
import torch.backends.cudnn as cudnn
+import torch.nn.functional as F
import cv2
import matplotlib.pyplot as plt
@@ -113,11 +114,19 @@
model.cuda(gpu)
criterion = nn.CrossEntropyLoss()
+ reg_criterion = nn.MSELoss()
+ # 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)
+
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}],
+ # {'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}],
@@ -134,24 +143,56 @@
optimizer.zero_grad()
yaw, pitch, roll = model(images)
+
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)
+ pitch_predicted = F.softmax(pitch)
+ roll_predicted = F.softmax(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())
+
+ # print yaw_predicted[0], label_yaw.data[0]
+
+ 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')
# 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_cr_epoch_'+ str(epoch+1) + '.pkl')
+ 'output/snapshots/resnet18_sgd_epoch_'+ str(epoch+1) + '.pkl')
# Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/resnet18_cr_epoch_' + str(epoch+1) + '.pkl')
+ torch.save(model.state_dict(), 'output/snapshots/resnet18_sgd_epoch_' + str(epoch+1) + '.pkl')
diff --git a/code/train_resnet_bins_comb.py b/code/train_resnet_bins_comb.py
new file mode 100644
index 0000000..eb23590
--- /dev/null
+++ b/code/train_resnet_bins_comb.py
@@ -0,0 +1,198 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.nn.functional as F
+
+import cv2
+import matplotlib.pyplot as plt
+import sys
+import os
+import argparse
+
+import datasets
+import hopenet
+import torch.utils.model_zoo as model_zoo
+
+model_urls = {
+ 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+ 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+ 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+ 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+ 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+def parse_args():
+ """Parse input arguments."""
+ parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
+ 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('--batch_size', dest='batch_size', help='Batch size.',
+ default=16, type=int)
+ parser.add_argument('--lr', dest='lr', help='Base learning rate.',
+ default=0.001, type=float)
+ parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
+ 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):
+ # Generator function that yields ignored params.
+ b = []
+ b.append(model.conv1)
+ b.append(model.bn1)
+ b.append(model.layer1)
+ b.append(model.layer2)
+ b.append(model.layer3)
+ b.append(model.layer4)
+ for i in range(len(b)):
+ for j in b[i].modules():
+ for k in j.parameters():
+ yield k
+
+def get_non_ignored_params(model):
+ # Generator function that yields params that will be optimized.
+ b = []
+ b.append(model.fc_yaw)
+ b.append(model.fc_pitch)
+ b.append(model.fc_roll)
+ for i in range(len(b)):
+ for j in b[i].modules():
+ for k in j.parameters():
+ yield k
+
+def load_filtered_state_dict(model, snapshot):
+ # By user apaszke from discuss.pytorch.org
+ model_dict = model.state_dict()
+ # 1. filter out unnecessary keys
+ snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+ # 2. overwrite entries in the existing state dict
+ model_dict.update(snapshot)
+ # 3. load the new state dict
+ model.load_state_dict(model_dict)
+
+if __name__ == '__main__':
+ args = parse_args()
+
+ cudnn.enabled = True
+ num_epochs = args.num_epochs
+ batch_size = args.batch_size
+ gpu = args.gpu_id
+
+ if not os.path.exists('output/snapshots'):
+ os.makedirs('output/snapshots')
+
+ # 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)
+ # 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()])
+
+ pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
+ transformations)
+ train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
+ batch_size=batch_size,
+ shuffle=True,
+ num_workers=2)
+
+ model.cuda(gpu)
+ criterion = nn.CrossEntropyLoss()
+ reg_criterion = nn.MSELoss()
+ # Regression loss coefficient
+ alpha = 0.1
+ lsm = nn.Softmax()
+
+ idx_tensor = [idx for idx in xrange(66)]
+ idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
+
+ 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, weight_decay=5e-4)
+ # 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.'
+
+ 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)
+
+ optimizer.zero_grad()
+ yaw, pitch, roll = model(images)
+
+ 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)
+ pitch_predicted = F.softmax(pitch)
+ roll_predicted = F.softmax(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())
+
+ # print yaw_predicted[0], label_yaw.data[0]
+
+ 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/resnet50_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_epoch_'+ str(epoch+1) + '.pkl')
+
+ # Save the final Trained Model
+ torch.save(model.state_dict(), 'output/snapshots/resnet50_epoch_' + str(epoch+1) + '.pkl')
diff --git a/code/train_resnet_bins_comb_dup.py b/code/train_resnet_bins_comb_dup.py
new file mode 100644
index 0000000..b435b89
--- /dev/null
+++ b/code/train_resnet_bins_comb_dup.py
@@ -0,0 +1,198 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from torchvision import transforms
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.nn.functional as F
+
+import cv2
+import matplotlib.pyplot as plt
+import sys
+import os
+import argparse
+
+import datasets
+import hopenet
+import torch.utils.model_zoo as model_zoo
+
+model_urls = {
+ 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+ 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+ 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+ 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+ 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+def parse_args():
+ """Parse input arguments."""
+ parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
+ 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('--batch_size', dest='batch_size', help='Batch size.',
+ default=16, type=int)
+ parser.add_argument('--lr', dest='lr', help='Base learning rate.',
+ default=0.001, type=float)
+ parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
+ 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):
+ # Generator function that yields ignored params.
+ b = []
+ b.append(model.conv1)
+ b.append(model.bn1)
+ b.append(model.layer1)
+ b.append(model.layer2)
+ b.append(model.layer3)
+ b.append(model.layer4)
+ for i in range(len(b)):
+ for j in b[i].modules():
+ for k in j.parameters():
+ yield k
+
+def get_non_ignored_params(model):
+ # Generator function that yields params that will be optimized.
+ b = []
+ b.append(model.fc_yaw)
+ b.append(model.fc_pitch)
+ b.append(model.fc_roll)
+ for i in range(len(b)):
+ for j in b[i].modules():
+ for k in j.parameters():
+ yield k
+
+def load_filtered_state_dict(model, snapshot):
+ # By user apaszke from discuss.pytorch.org
+ model_dict = model.state_dict()
+ # 1. filter out unnecessary keys
+ snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
+ # 2. overwrite entries in the existing state dict
+ model_dict.update(snapshot)
+ # 3. load the new state dict
+ model.load_state_dict(model_dict)
+
+if __name__ == '__main__':
+ args = parse_args()
+
+ cudnn.enabled = True
+ num_epochs = args.num_epochs
+ batch_size = args.batch_size
+ gpu = args.gpu_id
+
+ if not os.path.exists('output/snapshots'):
+ os.makedirs('output/snapshots')
+
+ # 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)
+ # 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()])
+
+ pose_dataset = datasets.Pose_300W_LP_binned(args.data_dir, args.filename_list,
+ transformations)
+ train_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
+ batch_size=batch_size,
+ shuffle=True,
+ num_workers=2)
+
+ model.cuda(gpu)
+ criterion = nn.CrossEntropyLoss()
+ reg_criterion = nn.MSELoss()
+ # Regression loss coefficient
+ alpha = 0.1
+ lsm = nn.Softmax()
+
+ idx_tensor = [idx for idx in xrange(66)]
+ idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)
+
+ 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, weight_decay=5e-4)
+ # 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.'
+
+ 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)
+
+ optimizer.zero_grad()
+ yaw, pitch, roll = model(images)
+
+ 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)
+ pitch_predicted = F.softmax(pitch)
+ roll_predicted = F.softmax(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())
+
+ # print yaw_predicted[0], label_yaw.data[0]
+
+ 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/resnet50_lowlr_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_lowlr_epoch_'+ str(epoch+1) + '.pkl')
+
+ # Save the final Trained Model
+ torch.save(model.state_dict(), 'output/snapshots/resnet50_lowlr_epoch_' + str(epoch+1) + '.pkl')
diff --git a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
index a411c30..8102abf 100644
--- a/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
+++ b/practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 156,
+ "execution_count": 187,
"metadata": {
"collapsed": false
},
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 157,
+ "execution_count": 188,
"metadata": {
"collapsed": false
},
@@ -26,13 +26,13 @@
"video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
"bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
"\n",
- "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
- "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+ "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
+ "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
]
},
{
"cell_type": "code",
- "execution_count": 158,
+ "execution_count": 189,
"metadata": {
"collapsed": false
},
@@ -41,7 +41,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[-6.069214 -0.831665 0.53318 ..., -3.836042 -3.868275 -2.377155]\n",
+ "[ 4.170376 0.790443 -0.178368 ..., -3.437805 0.396835 -1.276176]\n",
"(8508,)\n",
"(53464,)\n"
]
@@ -93,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 159,
+ "execution_count": 190,
"metadata": {
"collapsed": false
},
@@ -107,31 +107,39 @@
}
],
"source": [
- "window_len = 6\n",
+ "window_len = 5\n",
"pad = window_len / 2\n",
"window = 'flat'\n",
+ "window_2 = 'flat'\n",
+ "window_len_2 = 7\n",
+ "pad_2 = window_len_2 / 2\n",
"\n",
"s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
"t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
"u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
"\n",
- "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
- "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
- "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
- "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+ "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+ "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+ "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+ "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
"\n",
"if window == 'flat':\n",
" w=np.ones(window_len, 'd')\n",
"else:\n",
" w=eval('np.' + window + '(window_len)')\n",
+ " \n",
+ "if window_2 == 'flat':\n",
+ " w_2=np.ones(window_len_2, 'd')\n",
+ "else:\n",
+ " w_2=eval('np.' + window_2 + '(window_len_2)') \n",
"\n",
"y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
"p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
"r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
- "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
- "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
- "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
- "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+ "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+ "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+ "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+ "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
"\n",
"pose_dict = {}\n",
"bbox_dict = {}\n",
@@ -151,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 160,
+ "execution_count": 191,
"metadata": {
"collapsed": false
},
diff --git a/practice/smoothing_ypr.ipynb b/practice/smoothing_ypr.ipynb
index a411c30..96dc33f 100644
--- a/practice/smoothing_ypr.ipynb
+++ b/practice/smoothing_ypr.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 156,
+ "execution_count": 197,
"metadata": {
"collapsed": false
},
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 157,
+ "execution_count": 198,
"metadata": {
"collapsed": false
},
@@ -26,13 +26,13 @@
"video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
"bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
"\n",
- "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
- "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
+ "annot_path = '../output/video/output-SGT036_resnet50_lowlr_epoch_20.txt'\n",
+ "output_string = 'SGT036_resnet50_lowlr_epoch_20_smoothed'"
]
},
{
"cell_type": "code",
- "execution_count": 158,
+ "execution_count": 199,
"metadata": {
"collapsed": false
},
@@ -41,7 +41,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[-6.069214 -0.831665 0.53318 ..., -3.836042 -3.868275 -2.377155]\n",
+ "[ 4.170376 0.790443 -0.178368 ..., -3.437805 0.396835 -1.276176]\n",
"(8508,)\n",
"(53464,)\n"
]
@@ -93,7 +93,7 @@
},
{
"cell_type": "code",
- "execution_count": 159,
+ "execution_count": 200,
"metadata": {
"collapsed": false
},
@@ -107,31 +107,39 @@
}
],
"source": [
- "window_len = 6\n",
+ "window_len = 7\n",
"pad = window_len / 2\n",
"window = 'flat'\n",
+ "window_2 = 'flat'\n",
+ "window_len_2 = 7\n",
+ "pad_2 = window_len_2 / 2\n",
"\n",
"s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
"t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
"u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
"\n",
- "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
- "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
- "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
- "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
+ "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
+ "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
+ "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
+ "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
"\n",
"if window == 'flat':\n",
" w=np.ones(window_len, 'd')\n",
"else:\n",
" w=eval('np.' + window + '(window_len)')\n",
+ " \n",
+ "if window_2 == 'flat':\n",
+ " w_2=np.ones(window_len_2, 'd')\n",
+ "else:\n",
+ " w_2=eval('np.' + window_2 + '(window_len_2)') \n",
"\n",
"y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
"p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
"r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
- "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
- "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
- "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
- "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
+ "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
+ "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
+ "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
+ "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
"\n",
"pose_dict = {}\n",
"bbox_dict = {}\n",
@@ -151,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 160,
+ "execution_count": 201,
"metadata": {
"collapsed": false
},
--
Gitblit v1.8.0