Raghuraman Krishnamoorthi 84ee8ace12 Quantization aware training: Freeze batch norm support (#26624) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/26624 For QAT we need to be able to control batch norm for all modules from the top. Adding helper functions to enable/disable batch norm freezing during training ghstack-source-id: 91008297 Test Plan: buck test caffe2/test:quantization -- --print-passing-details Differential Revision: D17512199 fbshipit-source-id: f7b981e2b1966ab01c4dbb161030177274a998b6		2019-09-30 00:37:03 -07:00
..
_intrinsic	Quantization aware training: Freeze batch norm support (#26624)	2019-09-30 00:37:03 -07:00
backends	Remove Module._backend as it's not used anymore.	2019-08-29 15:43:49 -07:00
modules	Renames tensor.renamed -> rename, tensor.names_ -> rename_ (#26548)	2019-09-22 15:38:26 -07:00
parallel	Revert D16428208: [pytorch][PR] only scatter in forward if multi-device per process	2019-07-27 22:41:20 -07:00
qat	Add intrinsic module mappings (#23753)	2019-08-15 09:37:24 -07:00
quantized	Improve repr for quantized modules	2019-09-28 15:15:14 -07:00
utils	Add device check before accessing data_ptr in PackLayer (#26056)	2019-09-12 19:25:42 -07:00
__init__.py
__init__.pyi	Fixes #25454	2019-08-30 07:59:26 -07:00
_reduction.py
_VF.py
common_types.pyi	Fix Typing Error for Padding with asymmetric signatures (#24895)	2019-08-20 14:14:12 -07:00
cpp.py
functional.py	Update ONNX Export for Interpolate in Opset 11 (#26778)	2019-09-25 05:43:20 -07:00
functional.pyi.in
grad.py
init.py
parameter.py
parameter.pyi	Fix typing on nn.Parameter (#25586)	2019-09-09 07:54:27 -07:00