Fixed in master.
Thanks Sylvain. I changed in my local lib the fastai/callback.py file but now I got another error:
AttributeError: 'list' object has no attribute 'parameters'
Facing the same issue in colab:
name ‘split_bn_bias’ is not defined
I have no idea where this one comes from, I’ll need a reproducible example to fix it.
(I’m using fastai 1.0.45 on Windows 10) The error appears when I run the lesson7-wgan.ipynb notebook.
I am using Google Colab and installed the library using https://course.fast.ai/setup/colab
In the notebook lesson7-superres-gan.ipynb, following error is thrown while calling fit() on GANLearner:
learn.get_preds() isn’t CLI friendly. it sends \r which impacts user’s console outputs. For example, this test we have:
def test_get_preds(): learn = fake_learner() a = learn.get_preds() assert learn.data.batch_size == len(a)
was resulting in the test name disappearing (see the first PASSED is lacking its name?)
collected 3 items PASSED tests/test_basic_train.py::test_save_load PASSED tests/test_basic_train.py::test_save_load_mem_leak PASSED
I had to capture its stdout to fix that:
def test_get_preds(): learn = fake_learner() with CaptureStdout() as cs: a = learn.get_preds() assert learn.data.batch_size == len(a)
now we get:
collected 3 items tests/test_basic_train.py::test_get_preds PASSED tests/test_basic_train.py::test_save_load PASSED tests/test_basic_train.py::test_save_load_mem_leak PASSED
but perhaps it’s ok and we just need to document this side effect and how to overcome it?
Ok, I was stupid with my first fix, now it’s really fixed on master.
@nandakumar212 fixed on master means you won’t have the fix in colab until the next release, unless you do a dev install.
Lesson 7 further discussion ✅
I was running into issue while experimenting with object detection models. None of the methods
Learner.get_preds works. The one method that works is
Learner.show_results. I saw a TODO by Jeremy back in December about refactoring the code there to work with
pred_batch(reconstruct=True). The use of attaching
RecordOnCPU callback to capture the input and target is also rather unintuitive to follow. Is there any new thinking/progress on this? (I have to admit, the library’s intricate design is powerful but also quite formidable for a newcomer to grok. Awesome work and kudos nevertheless!)
Yes, making object detection work end to end is on our TODO and will be done before the second part of the course begins, but it probably won’t fully work right now. I’d stick with calling the models explicitly for now, until we have sorted this out.
I have written a MultiTask API for one of my projects. It extends the DataBlock Api. I don’t know if it worths to add it to the library.
That looks very nice! It may be a bit specific to be included in fastai, but we can definitely link to it with other examples of custom
ItemList or custom model definitions.
Finally, text in mixed precision is debugged and I can properly train an AWD-LSTM in mixed precision. QRNNs don’t support mixed-precision training however.
Haven’t tried Transformer and TransformerWL but there is no reason it shouldn’t work.
@sgugger will you record that, share slides? https://twitter.com/GuggerSylvain/status/1097893976072425472
Actually, it was an area that looked interesting and complex, so would be cool. Maybe even a summary in the docs at some point based on what you share
Please have a look at:
and give us feedback. This just came out.
I’ll share what I can, yes.
would be really usefull for tranformerxl - it really used a lot of gpu mem