I was trying to train some models available in the Metalhead (0.7) + Flux (0.13) using an NVIDIA GTX 1080 Ti (11 GB ) using some toy data (512 images size:224x224x3). I was running the code that I show at the end of the post using Metalhead.MobileNetv3(nclasses=2), Metalhead.ResNet34(pretrain=false,nclasses = 2) . However I was not able to run (memory problems) with Metalhead.ResNet50(pretrain=false,nclasses = 2) or Metalhead.ConvNeXt(:tiny, nclasses=2).
**With other training platforms, I was able to train models like ResNet50. **
Any problem with my training approach? Any advice?
Thank you in advance!
device=gpu
#model=Metalhead.MobileNetv3(nclasses=2)|>device # OK!!!!!!!!!!!!!!!!!! 58% de memoria
#model =ResNet34(pretrain=false,nclasses = 2)|> device #OK!!!!!!!!!!!!!! 99% de memoria
model =ResNet50(pretrain=false,nclasses = 2)|> device #notOK
#model =Metalhead.ConvNeXt(:tiny, nclasses=2)|> device #notOK
loss(ŷ, y) = logitcrossentropy(ŷ, y)
ps = Flux.params(model);
η = 3e-4 # learning rate
λ = 1e-6
#opt = ADAM(args.η)
opt = AdaBelief(η)
if λ > 0 # add weight decay, equivalent to L2 regularization
opt = Optimiser(WeightDecay(λ), opt)
end
#GC.gc(true); CUDA.reclaim();
for ind=1:10
@info ind
for (x, y) in traindl
y=onehotbatch(y, ["normal","abnormal"])
x, y = x |> device, y |> device
gs = Flux.gradient(ps) do
ŷ = model(x)
loss(ŷ, y)
end
Flux.Optimise.update!(opt, ps, gs)
end
end