storife / ranet Goto Github PK
View Code? Open in Web Editor NEWRANet: Ranking Attention Network for Fast Video Object Segmentation (VOS), ICCV2019
License: Apache License 2.0
RANet: Ranking Attention Network for Fast Video Object Segmentation (VOS), ICCV2019
License: Apache License 2.0
My GPU is RTX2080ti with 12G, but it is not still satisfied with your code. So what kind of your GPU is?
According to the Figure 2 in the paper, the decoder takes two things in, one is merged similarity maps and mask from 'Merge' module, the other is the features of the current frame extracted directly by Siamese Encoder.
But I can't find anywhere how the decoder uses those two things. Are they concatenated together before going into the Decoder, or there is some sort of operation? Such as convolution, element-wise multiplication or just simple addition?
Hi!
I run this code on 1 GPU(Nvidia 1080ti). And I think your code every time process 1 to 4 frames, ringt?But when I compute the forward pass(only time between "output=model(*input)"), it shows about 230ms when batch =4. It equals about 60ms a frame. It's different from your paper... Am I wrong?
Here is my code(I add time code in RANet_lib.py):
` inputs = [torch.cat(Img)[index_select], torch.cat(KFea)[index_select], torch.cat(KMsk)[index_select], torch.cat(PMsk)[index_select]]
torch.cuda.synchronize()
stime = time.time()
outputs, _ = model(*inputs)
torch.cuda.synchronize()
etime = time.time()
model_time = (etime-stime)*1000
print('time:',model_time)`
Any other location(not Baidu) form which i can get the model?
Thanks
Hi, I followed the instructions and am trying to generate the predictions for DAVIS17testdev using the RANet_video_multi.pth. However, I am facing the following error :
Environment :
Python - 3.6.9
Torchvision version - 0.2.0
Can you please tell me how to resolve this ?
===> Setting ...... ../models/exists using device ID: [0] ===> Building model Single-object mode save root = ../predictions/RANet_Video_17test_dev Change to multi-object mode Traceback (most recent call last): File "RANet.py", line 104, in <module> predict_SVOS(params='RANet_video_multi.pth', dataset='17test_dev', save_root='../predictions/RANet_Video_17test_dev') File "RANet.py", line 83, in predict_SVOS checkpoint_load(opt.workfolder + params, model) File "/home/swain/Documents/VOS/Methods/RANet/codes/RANet_lib/RANet_lib.py", line 38, in checkpoint_load checkpoint = torch.load(fpath) File "/home/swain/anaconda3/envs/vos/lib/python3.6/site-packages/torch/serialization.py", line 426, in load return _load(f, map_location, pickle_module, **pickle_load_args) File "/home/swain/anaconda3/envs/vos/lib/python3.6/site-packages/torch/serialization.py", line 603, in _load magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: invalid load key, '<'.
hello, i'm really interest in your work, is it convenient for you to provide the train code?
In your ablation study, you performed the w/o ranking experiment to show the importance of the RAM, but what's the difference between two of them, thanks!
Thank you for your work, but when can the source code be released?
Thanks for your awesome work! BTW, would you plan to release the training code or not?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.