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dcml's Introduction

Hi there 👋

I'm Wenzhao Zheng, a postdoctoral fellow at BAIR, UC Berkeley, working with by Prof. Kurt Keutzer. I received my Ph.D. and B.S. from Tsinghua University, supervised by Jie Zhou and Jiwen Lu.

Previous Efforts

We build the first academic surround-camera 3D occupancy prediction model TPVFormer🎉.

Current Interests

🦙Large Models + 🚙Autonomous Driving -> 🤖AGI

  • 🦙 Large Models: Efficient/Small LLMs, Multimodal Models, Video Generation Models, Large Action Models...
  • 🚙 Autonomous Driving: 3D Occupancy Prediction, End-to-End Driving, World Models, 3D Scene Reconstruction...

Collaborations

If you want to work with me (in person or remotely) at 🐻UC Berkeley (Co-supervised by Prof. Kurt Keutzer), 💜Tsinghua University (Co-supervised by Prof. Jiwen Lu), and/or 🔴Peking University (Co-supervised by Prof. Shanghang Zhang), feel free to drop me an email at [email protected]. I could support GPUs if we are a good fit.

dcml's People

Contributors

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Stargazers

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Watchers

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Forkers

repo-collection aarashfeizi chenyanlinzhugoushou

dcml's Issues

Implementation of S_m^k?

DCML/architectures/multifeature_resnet50.py

Lines 61 to 70 in 01a7220

for i in range(self.pars.compos_num):
prob.append(torch.nn.functional.softmax(self.compos_layer[i](detach_embedding), dim = 1))
prob0 = prob[i]
log_prob = -torch.log(prob0)
index = prob0.max(dim = 1, keepdim = True)[1]
supervise = torch.zeros_like(prob0).scatter_(1, index, 1.0)
supervise_loss = supervise_loss + torch.sum(log_prob*supervise)/x.size(0)
for i in range(self.pars.compos_num):
mod_x.append(soft_select(x_embedding, prob[i], self.pars.ensemble_num, self.pars.embed_dim))

There is a difference between your paper and your code.
According to this code block, it seems like you didn't implement S_m^k. This causes C_m^k directly equals to T_m^k.

Please point me out if I were wrong.

AttributeError: module 'batchminer' has no attribute 'BATCHMINING_METHODS'

This error occurs when I run the following code：
python main.py --dataset cub200 --tau 55 --gamma 0.2 --gpu 0 --seed 0 --compos_num 4 --ensemble_num 4 --embed_dim 128 --bs 100 --n_epochs 300 --samples_per_class 2 --loss margin --batch_mining distance --arch resent50_frozen_normalize

Traceback (most recent call last):
File "main.py", line 108, in
import criteria as criteria
File "/home/disk/github/DCML-main/criteria/init.py", line 1, in
from criteria import triplet, margin, proxynca, npair
File "/home/disk/github/DCML-main/criteria/triplet.py", line 6, in
ALLOWED_MINING_OPS = list(batchminer.BATCHMINING_METHODS.keys())
AttributeError: module 'batchminer' has no attribute 'BATCHMINING_METHODS'

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