Comments (14)
@jmercat Thank you for your interest in our work.
Your simplification code is easier to understand and will be helpful to others.
Thanks.
from grip.
@xincoder I realize that I haven't thanked you for your great work and for sharing the code. So thanks a lot, I am grateful. I am working on an idea that might improve your model with minimal changes to it. I'll keep you informed.
from grip.
@jmercat Thank you very much. It makes my day.😄
I really hope to see that our work can inspire others in both academy and industry.
from grip.
hi! @xincoder ,could you tell me what tool you used to draw that algorithm architecture in the paper, thanks in advance!
from grip.
Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.
from grip.
Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.
well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.
from grip.
Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.
The visualized results reported in our paper were generated using Plotly. It is very easy to draw it using any library that you are familiar with, e.g., Matplotlib, etc.
from grip.
Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.
The visualized results reported in our paper were generated using Plotly. It is very easy to draw it using any library that you are familiar with, e.g., Matplotlib, etc.
thanks for instant reply! I'm very new to Plotly-like library, if possible, are you willing to share your visualizing script with me? and then I can use it as introduction to relavent library
from grip.
@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?
from grip.
@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?
@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).
from grip.
@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?
@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).
thanks for the reminder! well, would you or even people accept the interruption from questioners' appreciation? I have wondered so ever.
from grip.
@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?
@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).
thanks for the reminder! well, would you or even people accept the interruption from questioners' appreciation? I have wondered so ever.
@tinmodeHuang ? Sorry, I did not get your point. Would you please provide more details about your question. Thanks.
from grip.
# compute hop steps
@xincoder @jmercat Thanks you,your code is very helpful for me.
But I don't know the code to # compute hop steps is for what? What is max_hop mean? Could you help me, thanks!
# compute hop steps transfer_mat = [np.linalg.matrix_power(A, d) for d in range(self.max_hop + 1)]
from grip.
@moriartyjack0520 Thank you for your question. The hop is a concept of Graph theory. It counts how many paths from one node to others (of a certain length). The code you highlighted above is used to calculate it (by multiplying the "adjacency matrix" several times, this is a simple way to achieve this goal).
from grip.
Related Issues (20)
- Visualize results
- The evaluation results are different HOT 4
- About the test results HOT 8
- Argoverse数据集的训练与测试问题 HOT 2
- Is grip++using heading? HOT 4
- velocity input HOT 2
- In data_process.py line 15 HOT 2
- input preprocessing model HOT 4
- Integrated with HD map (vector map)
- xin_feeder_baidu.py file HOT 2
- How can I install layers.graph_operation_layer in graph_conv_block.py
- model文件中出现了keyerror
- 论文中Fig.5中,有车道线信息,请问这个车道线信息是从apollo数据集中获取的吗? HOT 1
- GRIP code
- GRIP++ on NGSIM HOT 14
- About dataset HOT 1
- GRIP++ on NGSIM
- the question of Urban Traffic Scene(Sample)
- 有关行进方向
- GRIP++ Without NVIDIA HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from grip.