Open wildbrother opened 3 years ago
zylo117
commented
3 years ago It works fine for me. I'm now using torch 1.8.1+cu111. At very least, I can infer with D7. I'm guessing it's a bug of pytorch or cuda.
0 N/A N/A 428708 C /usr/bin/python3.8 4153MiB
wildbrother
commented
3 years ago ohh... I catch it. your opinion is right!..
that situation never occur..!!
I think you have to fix your README.
thank you for your fast reply. I have never seen polite writer like you in GitHub. Thanks!
wildbrother
commented
3 years ago I have two more question.
Q1.
when i run d1 model train . batch_size=4 , with 4 GPU //
the gpu memory is 3200mb
gpu 4 , batchsize 4 -> one batch for 1 gpu. and, I can't train D6 model in batch:4 with RTX TITAN X 4
Is it normal memory usage in training??
Q2. when i upgrade my torch to 1.8.1 I got a message in Val.Epoche // I'd never get an message in this phase, when my torch ver. is 1.4.1
is it critical bug..?
wildbrother
commented
3 years ago It works fine for me. I'm now using torch 1.8.1+cu111. At very least, I can infer with D7. I'm guessing it's a bug of pytorch or cuda.
0 N/A N/A 428708 C /usr/bin/python3.8 4153MiB
bloom1123
commented
2 years ago It works fine for me. I'm now using torch 1.8.1+cu111. At very least, I can infer with D7. I'm guessing it's a bug of pytorch or cuda.
0 N/A N/A 428708 C /usr/bin/python3.8 4153MiB
I have the same env, but I can't get the 32 FPS when I use the efficient_test.py, I just get 16pfs, but I don't know the reason
Hi, I have a problem with gpu memory increase
I ran your test code(inference) and gpu memory increased 8GB. (when D3) I can't use your bigger model because of this situation.
I found which statement occur this situation
on your model.py -> EfficientNet(nn.Module) the variable x hold the gpu memory. and this variable size goes up in the 'for loop'
// the statement : x = block(x, drop_connect_rate = drop_connect_rate) (maybe "stacking x" makes holded gpu memory size bigger)
"torch.cuda.empty_cache()" is can't clear gpu memory because the variable hold the memory.
this is the cmd print results .
I am suffered very long time because of this situation.
MY env
torch == 1.4.0 torch_vision == 0.5.0 python == 3.6 CUDA 10.2 with cudnn
maybe I can't change my python/ CUDA version because of my co-work. but the other envs are matched with the env which you wrote in this GitHub.
Plz. I need your help