Comments (8)
for your reference the expected training outputs would be something like:
Python version: 3.6.10 |Anaconda, Inc.| (default, May 8 2020, 02:54:21) [GCC 7.3.0]
OS version: Linux (4.15.0-50-generic)
Numpy version: 1.19.1
Pytorch version: 1.7.0a0+8deb4fe
MONAI flags: HAS_EXT = False, USE_COMPILED = False
Optional dependencies:
Pytorch Ignite version: 0.4.2
Nibabel version: 3.2.0
scikit-image version: 0.15.0
Pillow version: 8.0.1
Tensorboard version: 1.15.0+nv
gdown version: 3.12.2
TorchVision version: 0.8.0a0
ITK version: 5.1.1
tqdm version: 4.51.0
For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
INFO:root:training: image/label (199) folder: COVID-19-20_v2/Train
INFO:root:training: train 160 val 39, folder: COVID-19-20_v2/Train
INFO:root:batch size 2
BasicUNet features: (32, 32, 64, 128, 256, 32).
INFO:root:epochs 500, lr 0.0001, momentum 0.95
INFO:ignite.engine.engine.SupervisedTrainer:Engine run resuming from iteration 0, epoch 0 until 500 epochs
INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/500, Iter: 1/80 -- train_loss: 1.5370
INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/500, Iter: 2/80 -- train_loss: 1.5101
INFO:ignite.engine.engine.SupervisedTrainer:Epoch: 1/500, Iter: 3/80 -- train_loss: 1.4932
...
from tutorials.
Thank you! Right now, I trying to figure out why I am getting ^C
right after BasicUNet features: (32, 32, 64, 128, 256, 32).
. That is the part that I'm trying to figure out.
from tutorials.
Thank you! Right now, I trying to figure out why I am getting
^C
right afterBasicUNet features: (32, 32, 64, 128, 256, 32).
. That is the part that I'm trying to figure out.
I haven't tried it with a colab instance, but perhaps you need to put
!pip install "git+https://github.com/Project-MONAI/MONAI#egg=monai[nibabel,ignite,tqdm]"
as the very first cell
from tutorials.
I'm testing it out now to see if that works.
from tutorials.
Using it on the first cell in Cola doesn't work
from tutorials.
Tentatively I think it may because I'm running into out of memory in Colab.
google colab setting a '^C' in the proccess
from tutorials.
Tentatively I think it may because I'm running into out of memory in Colab.
ok if that's the case, perhaps you could reduce the number of features for the network features=(32, 32, 64, 128, 256, 32),
btw the full training will take more than 24 hours, I'm not sure whether the colab instance supports this type of long session
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@wyli I was able to get it working with colab. You have to increase the RAM.
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