Summary: new resnet building with brew
Reviewed By: akyrola
Differential Revision: D4945418
fbshipit-source-id: d90463834cbba2c35d625053ba8812e192df0adf
Summary: add an option to use a resnet network instead of alexnet. Modified the resnet.create_resnet50 function slightly to allow specifying different kernel/stride parameters so we can adapt resnet to our image size.
Differential Revision: D4472535
fbshipit-source-id: ed06acf52f6425a1e04d047548eb3c70388d74aa
Summary:
When testing the code, a couple of issues arised:
- we need to have different name for last layer than the preprocessed model, otherwise a shape assertion is created
- preprocess_noaugmentation still needs to do a crop for images larger than 227x227, otherwise things fail.
Reviewed By: viswanathgs
Differential Revision: D4442700
fbshipit-source-id: 05f54e7f17c266280f5ba5bb57af1721fe30df12
Summary:
Some tweaks, hopefully getting us to 0.98 MAP
- no cropping for test dataset (as per patrick)
- spatialBN momentum 0.1 (default is 0.9)
Also added some additional logging and reduced frequency of running of test net and logging.
Reviewed By: viswanathgs
Differential Revision: D4439790
fbshipit-source-id: 700705b811a5fc8c7139a265de96db646605ca5a
Summary:
(Ignore the convolution-op related changes, they will be later patched separately)
This diff ignores work from latest few weeks:
- some refactoring of the flow ops
- no_bias setting
- MAP computation (instead of accuracy) for OC
- adaptive learning rate for Xray concepts
- various small bug fixes
Reviewed By: viswanathgs
Differential Revision: D4329500
fbshipit-source-id: 000d4fd22ec408af5290480c788eb86546bff52e
Summary: Reading Torch docs about Resnets, and soumith's comment, they mention significant memory-saving with in-place ReLu. prigoyal already had this in her code, but I did not. This saves memory a lot: 9851 MiB -> 7497 MiB.
Reviewed By: prigoyal
Differential Revision: D4346100
fbshipit-source-id: e9c5d5e93787f47487fade668b65b9619bfc9741
Summary:
prigoyal sharply noticed a bug in the Resnet models: we have not been checkpointing, nor synchronizing between gpus, the moving average and variance computed by the SpatialBN ops. Particularly the first problen is serious, since models starting from checkpoint would have started from a null-state for SpatialBN. Not synchronizing with the data parallel model is less tragic since each GPU should see very similar data.
Thus I propose keeping track of "computed params", i.e params that are computed from data but not optimized. I don't know if there are other examples, but SpatialBN's moving avg and var definitely are one.
- I modified the checkpointign for xray model to store those blobs + also ensure the synchronization of those blobs
- I modified data parallel model to broadcast those params from gpu0. I first tried averaging, but hit some NCCL deadlocks ... :(
Differential Revision: D4281265
fbshipit-source-id: 933311afeec4b7e9344a13cf2d38aa939c50ac31
