Summary: These return views in Python 3 which would not do anything in a lot of usages currently present in Caffe2. This diff simply removes (almost) all usages of these two in Caffe2 and sub projects in favor of comprehensions which are also easier to read/understand
Reviewed By: akyrola
Differential Revision: D5142049
fbshipit-source-id: e800631d2df7d0823fed698cae46c486038007dc
Summary: new resnet building with brew
Reviewed By: akyrola
Differential Revision: D4945418
fbshipit-source-id: d90463834cbba2c35d625053ba8812e192df0adf
Summary:
Incorporating definition of cell's output and illustraing it's usage by adding dropout to all types of cell.
I think that we should try to get rid of aliases in RecurrentNetwork, so output of applied_over_sequence is also always (state_1_all, state_2_all, ...). This way we can merge get_output_from_single_step, get_output_from_sequence and get_outputs_with_grads into a single method
Let me know what do you think!
Reviewed By: jhcross
Differential Revision: D4992913
fbshipit-source-id: 737939be336ad145f84e8733cd255d4f7188ef70
Summary:
downloaded_size need to be added with the length of returned data_chunk.
When the last block's size less than chunk, the percentage should exceed 100%
Closes https://github.com/caffe2/caffe2/pull/329
Differential Revision: D4922227
Pulled By: Yangqing
fbshipit-source-id: 7d05d9bbf2dad0a9d330be96b60e658908185a46
Summary:
This fixes some bugs in the downloader. TODO: fix the URL
Closes https://github.com/caffe2/caffe2/pull/255
Reviewed By: Yangqing
Differential Revision: D4851555
Pulled By: bwasti
fbshipit-source-id: 56d01617ccaddcd40b0fb8e4be137cb4c7a52e91
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
