Trance-0/NoteNextra-origin
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
Zheyuan Wu
2025-09-02 23:45:36 -05:00
parent 872a341b9b
commit 16da530768
4 changed files with 247 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
content/CSE332S/CSE332S_L13.md
View File

@@ -41,7 +41,7 @@ void Foo::setValue(int v) {
}
```
![Image of memory layout](https://notenextra.trance-0.com/images/CSE332S/CPP_Function_Memory.png)
![Image of memory layout](https://notenextra.trance-0.com/CSE332S/CPP_Function_Memory.png)
### Memory, Lifetimes, and Scopes

246
content/CSE5519/CSE5519_E1.md
View File

@@ -4,12 +4,14 @@
>
> This topic is presented by Me. and will be the most detailed one for this course, perhaps.
This topic is mainly about Depth Estimation from Monocular Images. (Boring, not even RANSAC)
## PoseNet
A Convolutional Network for Real-Time 6-DOF Camera Relocalization (ICCV 2015)
Problem solving:
Camera Pose: Camera position and orientation.
[link to the paper](https://arxiv.org/pdf/1505.07427)
Convolutional neural network (convnet) we train to estimate camera pose directly
@@ -23,6 +25,242 @@ $$
$q$ is a quaternion, $x$ is a 3D camera position.
Arbitrary 4D values are easily mapped to legitimate rotations by normalizing them to unit length.
### Regression function
Use Stochastic Gradient Descent (SGD) to optimize the network parameters.
$$
loss(I)=\|\hat{x}-x\|_2+\beta\left\|\hat{q}-\frac{q}{\|q\|}\right\|_2
$$
$\hat{x}$ is the estimated camera position, $x$ is the ground truth camera position, $\hat{q}$ is the estimated camera orientation, $q$ is the ground truth camera orientation.
$\beta$ is a hyperparameter that scale the loss of the camera orientation so that the network can balance on estimating the camera position and orientation approximately the same weight.
### Network architecture
Based on GoogleNet (SOTA in 2014), but with a few changes:
- Replace all three softmax classifiers with affine regressors.
- Insert another fully connected layer before final regressor of feature size 2048
- At test time, normalize the quaternion to unit length.
```python
from network import Network
class GoogLeNet(Network):
def setup(self):
(self.feed('data')
.conv(7, 7, 64, 2, 2, name='conv1')
.max_pool(3, 3, 2, 2, name='pool1')
.lrn(2, 2e-05, 0.75, name='norm1')
.conv(1, 1, 64, 1, 1, name='reduction2')
.conv(3, 3, 192, 1, 1, name='conv2')
.lrn(2, 2e-05, 0.75, name='norm2')
.max_pool(3, 3, 2, 2, name='pool2')
.conv(1, 1, 96, 1, 1, name='icp1_reduction1')
.conv(3, 3, 128, 1, 1, name='icp1_out1'))
(self.feed('pool2')
.conv(1, 1, 16, 1, 1, name='icp1_reduction2')
.conv(5, 5, 32, 1, 1, name='icp1_out2'))
(self.feed('pool2')
.max_pool(3, 3, 1, 1, name='icp1_pool')
.conv(1, 1, 32, 1, 1, name='icp1_out3'))
(self.feed('pool2')
.conv(1, 1, 64, 1, 1, name='icp1_out0'))
(self.feed('icp1_out0',
'icp1_out1',
'icp1_out2',
'icp1_out3')
.concat(3, name='icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_reduction1')
.conv(3, 3, 192, 1, 1, name='icp2_out1'))
(self.feed('icp2_in')
.conv(1, 1, 32, 1, 1, name='icp2_reduction2')
.conv(5, 5, 96, 1, 1, name='icp2_out2'))
(self.feed('icp2_in')
.max_pool(3, 3, 1, 1, name='icp2_pool')
.conv(1, 1, 64, 1, 1, name='icp2_out3'))
(self.feed('icp2_in')
.conv(1, 1, 128, 1, 1, name='icp2_out0'))
(self.feed('icp2_out0',
'icp2_out1',
'icp2_out2',
'icp2_out3')
.concat(3, name='icp2_out')
.max_pool(3, 3, 2, 2, name='icp3_in')
.conv(1, 1, 96, 1, 1, name='icp3_reduction1')
.conv(3, 3, 208, 1, 1, name='icp3_out1'))
(self.feed('icp3_in')
.conv(1, 1, 16, 1, 1, name='icp3_reduction2')
.conv(5, 5, 48, 1, 1, name='icp3_out2'))
(self.feed('icp3_in')
.max_pool(3, 3, 1, 1, name='icp3_pool')
.conv(1, 1, 64, 1, 1, name='icp3_out3'))
(self.feed('icp3_in')
.conv(1, 1, 192, 1, 1, name='icp3_out0'))
(self.feed('icp3_out0',
'icp3_out1',
'icp3_out2',
'icp3_out3')
.concat(3, name='icp3_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls1_pool')
.conv(1, 1, 128, 1, 1, name='cls1_reduction_pose')
.fc(1024, name='cls1_fc1_pose')
.fc(3, relu=False, name='cls1_fc_pose_xyz'))
(self.feed('cls1_fc1_pose')
.fc(4, relu=False, name='cls1_fc_pose_wpqr'))
(self.feed('icp3_out')
.conv(1, 1, 112, 1, 1, name='icp4_reduction1')
.conv(3, 3, 224, 1, 1, name='icp4_out1'))
(self.feed('icp3_out')
.conv(1, 1, 24, 1, 1, name='icp4_reduction2')
.conv(5, 5, 64, 1, 1, name='icp4_out2'))
(self.feed('icp3_out')
.max_pool(3, 3, 1, 1, name='icp4_pool')
.conv(1, 1, 64, 1, 1, name='icp4_out3'))
(self.feed('icp3_out')
.conv(1, 1, 160, 1, 1, name='icp4_out0'))
(self.feed('icp4_out0',
'icp4_out1',
'icp4_out2',
'icp4_out3')
.concat(3, name='icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_reduction1')
.conv(3, 3, 256, 1, 1, name='icp5_out1'))
(self.feed('icp4_out')
.conv(1, 1, 24, 1, 1, name='icp5_reduction2')
.conv(5, 5, 64, 1, 1, name='icp5_out2'))
(self.feed('icp4_out')
.max_pool(3, 3, 1, 1, name='icp5_pool')
.conv(1, 1, 64, 1, 1, name='icp5_out3'))
(self.feed('icp4_out')
.conv(1, 1, 128, 1, 1, name='icp5_out0'))
(self.feed('icp5_out0',
'icp5_out1',
'icp5_out2',
'icp5_out3')
.concat(3, name='icp5_out')
.conv(1, 1, 144, 1, 1, name='icp6_reduction1')
.conv(3, 3, 288, 1, 1, name='icp6_out1'))
(self.feed('icp5_out')
.conv(1, 1, 32, 1, 1, name='icp6_reduction2')
.conv(5, 5, 64, 1, 1, name='icp6_out2'))
(self.feed('icp5_out')
.max_pool(3, 3, 1, 1, name='icp6_pool')
.conv(1, 1, 64, 1, 1, name='icp6_out3'))
(self.feed('icp5_out')
.conv(1, 1, 112, 1, 1, name='icp6_out0'))
(self.feed('icp6_out0',
'icp6_out1',
'icp6_out2',
'icp6_out3')
.concat(3, name='icp6_out')
.avg_pool(5, 5, 3, 3, padding='VALID', name='cls2_pool')
.conv(1, 1, 128, 1, 1, name='cls2_reduction_pose')
.fc(1024, name='cls2_fc1')
.fc(3, relu=False, name='cls2_fc_pose_xyz'))
(self.feed('cls2_fc1')
.fc(4, relu=False, name='cls2_fc_pose_wpqr'))
(self.feed('icp6_out')
.conv(1, 1, 160, 1, 1, name='icp7_reduction1')
.conv(3, 3, 320, 1, 1, name='icp7_out1'))
(self.feed('icp6_out')
.conv(1, 1, 32, 1, 1, name='icp7_reduction2')
.conv(5, 5, 128, 1, 1, name='icp7_out2'))
(self.feed('icp6_out')
.max_pool(3, 3, 1, 1, name='icp7_pool')
.conv(1, 1, 128, 1, 1, name='icp7_out3'))
(self.feed('icp6_out')
.conv(1, 1, 256, 1, 1, name='icp7_out0'))
(self.feed('icp7_out0',
'icp7_out1',
'icp7_out2',
'icp7_out3')
.concat(3, name='icp7_out')
.max_pool(3, 3, 2, 2, name='icp8_in')
.conv(1, 1, 160, 1, 1, name='icp8_reduction1')
.conv(3, 3, 320, 1, 1, name='icp8_out1'))
(self.feed('icp8_in')
.conv(1, 1, 32, 1, 1, name='icp8_reduction2')
.conv(5, 5, 128, 1, 1, name='icp8_out2'))
(self.feed('icp8_in')
.max_pool(3, 3, 1, 1, name='icp8_pool')
.conv(1, 1, 128, 1, 1, name='icp8_out3'))
(self.feed('icp8_in')
.conv(1, 1, 256, 1, 1, name='icp8_out0'))
(self.feed('icp8_out0',
'icp8_out1',
'icp8_out2',
'icp8_out3')
.concat(3, name='icp8_out')
.conv(1, 1, 192, 1, 1, name='icp9_reduction1')
.conv(3, 3, 384, 1, 1, name='icp9_out1'))
(self.feed('icp8_out')
.conv(1, 1, 48, 1, 1, name='icp9_reduction2')
.conv(5, 5, 128, 1, 1, name='icp9_out2'))
(self.feed('icp8_out')
.max_pool(3, 3, 1, 1, name='icp9_pool')
.conv(1, 1, 128, 1, 1, name='icp9_out3'))
(self.feed('icp8_out')
.conv(1, 1, 384, 1, 1, name='icp9_out0'))
(self.feed('icp9_out0',
'icp9_out1',
'icp9_out2',
'icp9_out3')
.concat(3, name='icp9_out')
.avg_pool(7, 7, 1, 1, padding='VALID', name='cls3_pool')
.fc(2048, name='cls3_fc1_pose')
.fc(3, relu=False, name='cls3_fc_pose_xyz'))
(self.feed('cls3_fc1_pose')
.fc(4, relu=False, name='cls3_fc_pose_wpqr'))
```
## Unsupervised Learning of Depth and Ego-Motion From Video
(CVPR 2017)
@@ -33,6 +271,10 @@ $q$ is a quaternion, $x$ is a 3D camera position.
Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose (CVPR 2018)
Problem solving:
Depth Estimation from single monocular image.
[link to the paper](https://openaccess.thecvf.com/content_cvpr_2018/papers/Yin_GeoNet_Unsupervised_Learning_CVPR_2018_paper.pdf)
[link to the repository](https://github.com/yzcjtr/GeoNet)

2
content/Math401/Math401_T6.md
View File

@@ -632,7 +632,7 @@ $B$ performs a measurement on the combined state of the one qubit and the entang
$B$ decodes the result and obtains the 2 classical bits sent by $A$.
![Superdense coding](https://notenextra.trance-0.com/Math401/Superdense_coding.png)
Superdense coding](https://notenextra.trance-0.com/Math401/Superdense_coding.png)
## Section 4: Quantum automorphisms and dynamics

2
content/Math4121/Math4121_L29.md
View File

@@ -38,7 +38,7 @@ Proof:
Suppose $S_1, S_2$ is a measurable, and we need to show that $S_1\cup S_2$ is measurable. Given $X$, need to show that
![Finite union cut](https://notenextra.trance-0.com/math4121/Finite_union_cut.png)
![Finite union cut](https://notenextra.trance-0.com/Math4121/Finite_union_cut.png)
$$
m_e(X) = m_e(X_1\cup X_2\cup X_3)+ m_e(X_4)