update

content/CSE332S/CSE332S_L13.md

@@ -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
 

content/CSE5519/CSE5519_E1.md

@@ -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)

content/Math401/Math401_T6.md

@@ -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
 

content/Math4121/Math4121_L29.md

@@ -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)