This PR adds _train_mode_ and _inference_mode_ as special args to SequentialDict. These special args enable one to control, at instantiation time or run time, whether the underlying model should be run in train mode (or not) and whether it should be run in inference mode (or not).
This is a more direct version of #141.
Depends upon the following:
[ ] #175
Type of change
Please check all relevant options.
[x] Improvement (non-breaking)
[ ] Bug fix (non-breaking)
[ ] New feature (non-breaking)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
What does this PR do?
This PR adds
_train_mode_
and_inference_mode_
as special args toSequentialDict
. These special args enable one to control, at instantiation time or run time, whether the underlying model should be run in train mode (or not) and whether it should be run in inference mode (or not).This is a more direct version of #141.
Depends upon the following:
Type of change
Please check all relevant options.
Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
pytest
CUDA_VISIBLE_DEVICES=0 python -m mart experiment=CIFAR10_CNN_Adv trainer=gpu trainer.precision=16
reports 70% (21 sec/epoch).CUDA_VISIBLE_DEVICES=0,1 python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp trainer.precision=16 trainer.devices=2 model.optimizer.lr=0.2 trainer.max_steps=2925 datamodule.ims_per_batch=256 datamodule.world_size=2
reports 70% (14 sec/epoch).Before submitting
pre-commit run -a
command without errorsDid you have fun?
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