I observed that in the experiment, you used more experts, such as 3 or 4 experts, and in this case, the number of parameters and computation of the base network increased, but I think you should add a more comparative experiment to show whether the overall performance gain is more attributed to your proposed training method or to the increase of the network parameters. For example, when comparing with other methods, you should keep the network computation and number of parameters consistent while also keeping the network structure consistent, because I noticed that your ResNet network structure is different from the original network structure, although you claim that your computation is consistent. But I suspect that the change in network structure may have a significant impact on the overall performance as well. Anyway, my idea is that when comparing with the baseline method, the network structure used in the baseline method should be the same as your network structure, so that we can exclude the effect of the change of the network structure, and finally we can conclude that the various loss functions proposed in your paper are meaningful and valid.

Describe the error A clear and concise description of what the problem is. Hi, thanks for your great work.

After training Stage 1 and obtain the checkpoint, I start running the code for stage 2. However, there is an error "RuntimeError: grad can be implicitly created only for scalar outputs" in self._train_epoch(epoch). Google said this is because the loss is not scalar. I have no idea what is wrong with this code since it is packaged very well. Expect your reply.

Hi, authors! Thank you for doing such an inspiring job and opening the source code! There is a problem when I using your code, the diversity loss seems not having a great effect.

I run your code "RIDE Without Distill (Stage 1)" of 3 experts on CIFAR100-LT using your config, and got validation accuracy 47.8%. And I tried to do some ablation, I make "additional_diversity_factor"=0.45 (the original setting is -0.45), got validation accuracy 48.0%, which is even 0.2% higher than 47.8%. I didn't change any thing else of your codes. Could you help me figure out the problem?

Thanks a lot!

Hi, there Thanks for your great job! During my train (ResNext50 on Imagenet-LT), I used 8 A100 (total-batch size: 1024). I found at the start of every epoch, the dataloader will stuck for around 30 seconds. Even I change the code into DDP mode, there still get stuck 30 seconds. I wonder whether there's some problem related to the ImageNetLTDataLoader?

Best,

Describe the error A clear and concise description of what your question is. Thank you for your work in long-tailed recognition problem. Your work is excellent, and i want to use RIDE in my research file. However, i was confusion in understanding formula 13 and 14. For example, what do γ means? and How to compute it? as well as what do α means? and How to compute it?

Thanks for your great work! But I am confused about the reason why diversity loss works . The paper says that it's a regularization term to encourage complementary decisions. Why we need complementary decisions from experts? Don't we want the same correct answer? If the experts' decision is diverse , how can we assure the final output is correct?

Describe the error A clear and concise description of what the problem is. This question is caused by the top_choices_num which is from stage 2 of training, when I use my data of 37 category for training. This error will be solved when the top_choices_num is set 30. I try to understand this parameter, but I am failure by the code. I would be grateful if you would explain this parameter. Your work is very meaningful to solve the long tail problem, thank you for sharing.

When I use n_gpu=1, everything is OK. When I use n_gpu=4 in trainning, the procedure makes error as bellow:

Traceback (most recent call last): File "train.py", line 110, in main(config) File "train.py", line 75, in main trainer.train() File "/home/xxx/workspace/Oracle/RIDE_IR/base/base_trainer.py", line 76, in train result = self._train_epoch(epoch) File "/home/xxx/workspace/Oracle/RIDE_IR/trainer/trainer.py", line 133, in _train_epoch "logits": self.real_model.backbone.logits File "/home/xxx/.conda/envs/pytorch_jhon/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1131, in getattr type(self).name, name)) AttributeError: 'ResNet_s' object has no attribute 'logits'

When I try to fix this, I add "self.logits = []" at Resnet_s intit stage of ride_resenet_cifa.py , another mistake is occured as below:

Traceback (most recent call last): File "train.py", line 110, in main(config) File "train.py", line 75, in main trainer.train() File "/home/xxx/workspace/Oracle/RIDE_IR/base/base_trainer.py", line 76, in train result = self._train_epoch(epoch) File "/home/xxx/workspace/Oracle/RIDE_IR/trainer/trainer.py", line 148, in _train_epoch loss.backward() AttributeError: 'int' object has no attribute 'backward'

Have you ever encounted errors like this and could you offer any help? Thanks so much. My conda torch related packages version are as below: ffmpeg 4.3 hf484d3e_0 pytorch pytorch 1.9.0 py3.7_cuda10.2_cudnn7.6.5_0 pytorch torchaudio 0.9.0 py37 pytorch torchvision 0.10.0 py37_cu102 pytorch python 3.7.11 h12debd9_0 defaults

Hi @TonyLianLong,

I noticed that the hyper-parameters reported in your paper is same with LDAM:

1. weight decay is 2e-4
2. lr decay steps are 120 and 160

but in your config files, they are changed into:

1. weight decay is 5e-4
2. lr decay steps are 160, 180

I am quite confused, could you please explain it?

Thanks for your great work, here is an error when I try to use the model as given in the model zoo on iNaturalist data set. The model is based on ResNet50 backbone with 4 experts and distillation. When I try to use the model as the pretrained model to initialize the model during stage 2. It produces the following errors. It seems that the parameters of some layers are not well loaded. My command lines are: python train.py -c "configs/config_iNaturalist_resnet50_ride_ea.json" -r afs/RIDE/RIDE_model/imagenet_4experts_distill/checkpoint-epoch5.pth --reduce_dimension 1 --num_experts 4

The reported errors are as follows:

Loading checkpoint: ./RIDE/RIDE_model/imagenet_4experts_distill/checkpoint-epoch5.pth ...
Traceback (most recent call last):
File "/root/workspace/env_run/utils/util.py", line 59, in load_state_dict
own_state["module."+name].copy_(param)
RuntimeError: The size of tensor a (64) must match the size of tensor b (128) at non-singleton dimension 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 110, in
main(config)
File "train.py", line 73, in main
lr_scheduler=lr_scheduler)
File "/root/workspace/env_run/trainer/trainer.py", line 14, in init
super().init(model, criterion, metric_ftns, optimizer, config)
File "/root/workspace/env_run/base/base_trainer.py", line 59, in init
self._resume_checkpoint(config.resume, state_dict_only=state_dict_only)
File "/root/workspace/env_run/base/base_trainer.py", line 211, in _resume_checkpoint
load_state_dict(self.model, state_dict)
File "/root/workspace/env_run/utils/util.py", line 63, in load_state_dict
print("Error in copying parameter {}, source shape: {}, destination shape: {}".format(name, param.shape, own_sta te[name].shape))
KeyError: 'backbone.layer1.0.conv1.weight'

Hi, @TonyLianLong :

Thanks for your contribution, is there any instruction for the implementations of cRT and τ-norm? or could you please kindly provide the hyperparameters of cRT and τ-norm used in in your paper?