I might give that a go too
You haven’t given read access to your notebook so no one can really help you. Also, I wouldn’t want to be @-ing the admins that way if I were you 
, it just makes it less likely to get a reply from them. Grant read access to your notebook.
Cheers,
Tendo
I don’t mind the @ at myself, but yes we would like read/write access 
(I’ll try look this weekend @vijayabhaskar)
2 Likes
Sorry, I thought just clicking the share allows people to view the content, here is the updated link
I don’t usually @ mention admins, I believe this might be a bug in fastai2 (if I’m not doing anything dumb) so I thought Sylvian should look into it.
Thanks!
Asking this question here which was previously posted in this topic
how can I customize the batch sampling method? In metric learning approaches, we need some control over the no. of positive/negative examples in a batch, where can I define this logic ?
Did you have a look here: https://dev.fast.ai/callback.data ?
There are no docs yet but maybe something like WeightedDL is what you’re looking for?
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So we provide wgts for each class and it’ll draw examples for that class with given probability (wgts) ?
I’m not sure if this is a bug, or just unintuitive behavior.
When I try to setup a learner with no weight decay, it doesn’t appear to be so.
learn = cnn_learner(dls, resnet18, wd=0.0)
When I enter learn.opt_func in a notebook and run the cell, it gives me the following output
<function fastai2.optimizer.Adam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-05, wd=0.01, decouple_wd=True)>
which suggests that the opt_func is still setup with wd=0.01.
Do I need to pass in learn.fit(wd=0.0) to truly fit without weight decay?
The learn.wd is not used when you create the optimizer but it is passed each time you call a fit method with no wd. So don’t worry, it will fit without weight decay.
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Good to know, thank you!
What’s the difference between passing in wd when creating cnn_learner(...) vs calling learn.fit(wd=...)? If they refer to the same, then does the wd passed in when calling learn.fit() override the other option?
You might want to call fit with a different wd each time, so that’s why we have that
and as Sylvain pointed out:
So yes, if you pass wd to fit it overrides
3 Likes
That makes it crystal clear. Thank you.
What is the correct way of exporting just the model from unet_learner?
I tried:
import dill
torch.save(learn.model,"unet-best-2.pth",pickle_module=dill)
I also tried withouth using dill and it throws me an error when I make model=torch.load("unet-best-2.pth") in other project.
SOLUTION: using torchscript for saving the model made the trick!
Do you know any better approach?
1 Like
After installing pytorch-nightly I am getting
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-1-1285ae11e650> in <module>
----> 1 from fastai2.basics import *
2 from fastai2.vision import models
3 from fastai2.vision.all import *
4 from fastai2.metrics import *
5 from fastai2.data.all import *
~/anaconda3/envs/seg1/lib/python3.7/site-packages/fastai2/basics.py in <module>
----> 1 from .data.all import *
2 from .optimizer import *
3 from .callback.core import *
4 from .learner import *
5 from .metrics import *
~/anaconda3/envs/seg1/lib/python3.7/site-packages/fastai2/data/all.py in <module>
----> 1 from ..torch_basics import *
2 from .core import *
3 from .load import *
4 from .external import *
5 from .transforms import *
~/anaconda3/envs/seg1/lib/python3.7/site-packages/fastai2/torch_basics.py in <module>
2 from .imports import *
3 from .torch_imports import *
----> 4 from .torch_core import *
5 from .layers import *
~/anaconda3/envs/seg1/lib/python3.7/site-packages/fastai2/torch_core.py in <module>
283 setattr(TensorBase, fn, get_f(fn))
284
--> 285 _patch_tb()
286
287 # Cell
~/anaconda3/envs/seg1/lib/python3.7/site-packages/fastai2/torch_core.py in _patch_tb()
279 for fn in dir(t):
280 if fn in skips: continue
--> 281 f = getattr(t, fn)
282 if isinstance(f, (MethodWrapperType, BuiltinFunctionType, BuiltinMethodType, MethodType, FunctionType)):
283 setattr(TensorBase, fn, get_f(fn))
RuntimeError: imag is not implemented for tensors with non-complex dtypes.
I don’t believe fastai has been made for nightly (I don’t even think it worked with nightly in v1?) @sgugger can comment on that more
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Sad, I have my learners exported. However, I need to load them in pytorch-nightly so I can export the models again to TorchScript
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Hmmm… I may try looking into this myself then for my fastinference library (as if you need it, many people do too). I won’t promise I’ll get it working but I’ll try!