Summary: In case of distributed task, load_from_db() loads to wrong workspace (when used inside a Python op). Passing which workspace to use explicitly so that it loads to the one Python op is being run.
Reviewed By: kennyhorror
Differential Revision: D4653692
fbshipit-source-id: 94585c012b05ee38b9ce5e8ef0efdd50aa41dd2b
Summary:
Shape inference allows Caffe2 to compute shapes of blobs without running a model. Update InferShapesAndTypes() to accept an optional blob:dimensions map so that external input blobs do not need to be part of the workspace.
InferShapesAndTypes() in workspace.py conditionally calls the ...from_workspace or ...from_map bindings. Note I favored a small amount of code duplication here for the sake of readability. InferShapesAndTypes() in operator.cc has been refactored into mirrored entry points, invoking a common helper.
Other minor changes to address linter warnings.
Reviewed By: dzhulgakov
Differential Revision: D4524873
fbshipit-source-id: 56f863b759c016d7f23523f06fda3aa5bba22357
Summary:
Running RunNet() in python in a loop can be a performance issue if the python code is doing a lot of other processing, such as data input, because python's Global Interpreter lock (GIL) will prevent the RunNet() to be called. This can easily be fixed by making RunNet() run multiple iterations inside the C++ land. (Another way to accomplish the same thing is to use Caffe2's "execution plans", but that requires more setup).
+ fixed timing reporting in my OC workflow
+ improved one error log in data_workers.py
Sorry for piggypagging those small changes, but landing diffs currently is slow...
Reviewed By: rpenggithub
Differential Revision: D4523575
fbshipit-source-id: 039a647576efad5dd9afda74df478ac22b43c103
Summary:
This is a bit large diff, sorry about it. It includes basic shape and type inference functionality, based on YQ's Schema scaffolding. I added some helper functions to make it easier to write simple translations.
Bigger refactoring was needed for ConvPoolBase so that we could use the shape inference already there in the schema.
I annotated enough operators to be able to infer forward-pass of shapes for basic convnet, and added test for that. I intend to bootcamp some annotations and annotate enough to handle Resnets fully. Need to think about gradients, if they could be annotated in an easier way.
Only shapes are now exposed to Python, types will follow later. Also the inference is not called yet anywhere but unit test.
Also I am not sure if everything is in the best location in the code, but shouldn't be hard to move stuff around.
Reviewed By: dzhulgakov
Differential Revision: D4436818
fbshipit-source-id: eebee5937ccc9ac09c245465302388a1fae6933c
Summary:
Turns out that building on raspbian is easy as a cake for caffe2 - cmake is awesome.
Closes https://github.com/caffe2/caffe2/pull/112
Differential Revision: D4480985
Pulled By: Yangqing
fbshipit-source-id: 5dbe5e1e71d8680dea7a5ec8a9ce7fbe6aa5270a
Summary: Replace ParseFromString with ParseProtobufFromLargeString to get around the limitation of the 64MB limit.
Reviewed By: Yangqing
Differential Revision: D4466226
fbshipit-source-id: b68a6efc76955db294ddb0d23bbaf03b69e4952a
Summary: Makes it much nicer to spot errors, especially in iPython notebook.
Reviewed By: kennyhorror
Differential Revision: D4465726
fbshipit-source-id: c0adaf5168248a70987ff9d5dfce54a622ff2219
Summary:
DPER has very strange python ops that play with Workspace - they are somewhat similar to LoadOp/SaveOp, so I guess the semantics is fine.
Thus it makes sense to allow python operators to receive workspace pointer similarly to regular Operators.
I didn't figure out a better way to implement optional argument than just checking the number of args function receives on python side.
Reviewed By: ajtulloch
Differential Revision: D4242943
fbshipit-source-id: d97d4227815b741c8f884cfe254b06d2b56b5a41
Summary:
This is #2 of a series of changes. It did the following:
(1) a few refactor of the MKL memory interface
(2) an initial MKLContext to deal with MKL specific computations
(3) Provide MKLMemory access in Python with the blob feeder/fetcher registration.
Reviewed By: dzhulgakov
Differential Revision: D4210123
fbshipit-source-id: adea1f1ffbd0b9ffdd55092676468c16bec08992
