From 6664c6d52fad58e396861946a3bed7d5afc4d44d Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期五, 07 七月 2017 10:53:52 +0800
Subject: [PATCH] Training for hopenet works.
---
code/datasets.py | 49 +++++++++++-
code/hopenet.py | 64 ++++++++++++++++
code/test_resnet_bins.py | 26 +++--
code/train_resnet_bins.py | 94 +++++++++++++++++-----
4 files changed, 195 insertions(+), 38 deletions(-)
diff --git a/code/datasets.py b/code/datasets.py
index 030059f..3750e71 100644
--- a/code/datasets.py
+++ b/code/datasets.py
@@ -84,10 +84,51 @@
# We get the pose in radians
pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
- # And convert to positive degrees.
- pose = pose * 180 / np.pi + 90
+ # And convert to degrees.
+ pitch = pose[0] * 180 / np.pi
+ yaw = pose[1] * 180 / np.pi
+ roll = pose[2] * 180 / np.pi
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ labels = torch.LongTensor(np.digitize([yaw, pitch, roll], bins) - 1)
- label = torch.FloatTensor(pose)
+ if self.transform is not None:
+ img = self.transform(img)
+
+ return img, labels, self.X_train[index]
+
+ def __len__(self):
+ # 122,450
+ return self.length
+
+class AFLW2000_binned(Dataset):
+ def __init__(self, data_dir, filename_path, transform, img_ext='.jpg', annot_ext='.mat'):
+ self.data_dir = data_dir
+ self.transform = transform
+ self.img_ext = img_ext
+ self.annot_ext = annot_ext
+
+ filename_list = get_list_from_filenames(filename_path)
+
+ self.X_train = filename_list
+ self.y_train = filename_list
+ self.length = len(filename_list)
+
+ def __getitem__(self, index):
+ img = Image.open(os.path.join(self.data_dir, self.X_train[index] + self.img_ext))
+ img = img.convert('RGB')
+
+ # We get the pose in radians
+ pose = utils.get_ypr_from_mat(os.path.join(self.data_dir, self.y_train[index] + self.annot_ext))
+ # And convert to degrees.
+ pitch, yaw, roll = pose * 180 / np.pi
+ # Bin values
+ bins = np.array(range(-99, 102, 3))
+ binned_pitch = torch.DoubleTensor(np.digitize(pitch, bins) - 1)
+ binned_yaw = torch.DoubleTensor(np.digitize(yaw, bins) - 1)
+ binned_roll = torch.DoubleTensor(np.digitize(roll, bins) - 1)
+
+ label = binned_yaw, binned_pitch, binned_roll
if self.transform is not None:
img = self.transform(img)
@@ -95,7 +136,7 @@
return img, label, self.X_train[index]
def __len__(self):
- # 122,450
+ # 2,000
return self.length
def get_list_from_filenames(file_path):
diff --git a/code/hopenet.py b/code/hopenet.py
index e6f8f50..e5c1ed2 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -2,6 +2,7 @@
import torch.nn as nn
import torchvision.datasets as dsets
from torch.autograd import Variable
+import math
# CNN Model (2 conv layer)
class Simple_CNN(nn.Module):
@@ -37,3 +38,66 @@
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
+
+class Hopenet(nn.Module):
+ # This is just Hopenet with 3 output layers for yaw, pitch and roll.
+ def __init__(self, block, layers, num_bins):
+ self.inplanes = 64
+ super(Hopenet, self).__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+ self.avgpool = nn.AvgPool2d(7)
+ self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
+ self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
+ self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+ m.weight.data.normal_(0, math.sqrt(2. / n))
+ elif isinstance(m, nn.BatchNorm2d):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2d(self.inplanes, planes * block.expansion,
+ kernel_size=1, stride=stride, bias=False),
+ nn.BatchNorm2d(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+ x = self.layer4(x)
+
+ x = self.avgpool(x)
+ x = x.view(x.size(0), -1)
+ yaw = self.fc_yaw(x)
+ pitch = self.fc_pitch(x)
+ roll = self.fc_roll(x)
+
+ return yaw, pitch, roll
diff --git a/code/test_resnet_bins.py b/code/test_resnet_bins.py
index 34fc8f5..0a093ee 100644
--- a/code/test_resnet_bins.py
+++ b/code/test_resnet_bins.py
@@ -13,7 +13,7 @@
import os
import argparse
-from datasets import AFLW2000
+import datasets
import hopenet
import utils
@@ -55,9 +55,10 @@
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 = AFLW2000(args.data_dir, args.filename_list,
+ 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,
@@ -69,7 +70,9 @@
# Test the Model
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
- error = .0
+ yaw_correct = 0
+ pitch_correct = 0
+ roll_correct = 0
total = 0
for i, (images, labels, name) in enumerate(test_loader):
images = Variable(images).cuda(gpu)
@@ -78,13 +81,14 @@
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
# TODO: There are more efficient ways.
+ yaw_correct += (outputs[:][0] == labels[:][0])
+ pitch_correct += (outputs[:][])
for idx in xrange(len(outputs)):
- # if abs(outputs[idx].data[1] - labels[idx].data[1]) * 180 / np.pi > 30:
- print name
- print abs(outputs[idx].data - labels[idx].data) * 180 / np.pi, 180 * outputs[idx].data / np.pi, labels[idx].data * 180 / np.pi
- # error += utils.mse_loss(outputs[idx], labels[idx])
- error += abs(outputs[idx].data - labels[idx].data) * 180 / np.pi
+ yaw_correct += (outputs[idx].data[0] == labels[idx].data[0])
+ pitch_correct += (outputs[idx].data[1] == labels[idx].data[1])
+ roll_correct += (outputs[idx].data[2] == labels[idx].data[2])
- print('Test MSE error of the model on the ' + str(total) +
- ' test images: %.4f' % (error / total))
+ print('Test accuracies of the model on the ' + str(total) +
+ ' test images. Yaw: %.4f %%, Pitch: %.4f %%, Roll: %.4f %%' % (yaw_correct / total,
+ pitch_correct / total, roll_correct / total))
diff --git a/code/train_resnet_bins.py b/code/train_resnet_bins.py
index f2ec5f2..1bbf5be 100644
--- a/code/train_resnet_bins.py
+++ b/code/train_resnet_bins.py
@@ -13,8 +13,17 @@
import os
import argparse
-from datasets import Pose_300W_LP
+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."""
@@ -36,6 +45,41 @@
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()
@@ -47,21 +91,16 @@
if not os.path.exists('output/snapshots'):
os.makedirs('output/snapshots')
- model = torchvision.models.resnet18(pretrained=True)
- for param in model.parameters():
- param.requires_grad = False
- # Parameters of newly constructed modules have requires_grad=True by default
- num_ftrs = model.fc.in_features
- model.fc_pitch = nn.Linear(num_ftrs, 3)
- model.fc_yaw = nn.Linear(num_ftrs, 3)
- model.fc_roll = nn.Linear(num_ftrs, )
-
+ # ResNet18 with 3 outputs.
+ model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66)
+ load_filtered_state_dict(model, model_zoo.load_url(model_urls['resnet18']))
+
print 'Loading data.'
- transformations = transforms.Compose([transforms.Scale(230),transforms.RandomCrop(224),
+ transformations = transforms.Compose([transforms.Scale(224),transforms.RandomCrop(224),
transforms.ToTensor()])
- pose_dataset = Pose_300W_LP(args.data_dir, args.filename_list,
+ 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,
@@ -69,31 +108,40 @@
num_workers=2)
model.cuda(gpu)
- criterion = nn.MSELoss(size_average = True)
- optimizer = torch.optim.Adam(model.fc.parameters(), lr = args.lr)
+ criterion = nn.CrossEntropyLoss()
+ optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': .0},
+ {'params': get_non_ignored_params(model), 'lr': args.lr}],
+ lr = args.lr)
print 'Ready to train network.'
for epoch in range(num_epochs):
- for i, (images, labels) in enumerate(train_loader):
+ for i, (images, labels, name) in enumerate(train_loader):
images = Variable(images).cuda(gpu)
- labels = Variable(labels).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()
- outputs = model(images)
- loss = criterion(outputs, labels)
- loss.backward()
+ 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()
if (i+1) % 100 == 0:
- print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
- %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, loss.data[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]))
# Save models at even numbered epochs.
if epoch % 1 == 0 and epoch < num_epochs - 1:
print 'Taking snapshot...'
torch.save(model.state_dict(),
- 'output/snapshots/resnet18_epoch_' + str(epoch+1) + '.pkl')
+ 'output/snapshots/resnet18_binned_epoch_' + str(epoch+1) + '.pkl')
# Save the final Trained Model
- torch.save(model.state_dict(), 'output/snapshots/resnet18_epoch_' + str(epoch+1) + '.pkl')
+ torch.save(model.state_dict(), 'output/snapshots/resnet18_binned_epoch_' + str(epoch+1) + '.pkl')
--
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