Files
pytorch/caffe2/python/ideep_test_util.py
Jinghui 26ddefbda1 [feature request] [Caffe2] Enable MKLDNN support for inference (#6699)
* Add operators based-on IDEEP interfaces

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Enable IDEEP as a caffe2 device

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Add test cases for IDEEP ops

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Add IDEEP as a caffe2 submodule

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Skip test cases if no IDEEP support

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Correct cmake options for IDEEP

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Add dependences on ideep libraries

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Fix issues in IDEEP conv ops and etc.

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Move ideep from caffe2/ideep to caffe2/contrib/ideep

Signed-off-by: Gu Jinghui <jinghui.gu@intel.com>

* Update IDEEP to fix cmake issue

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Fix cmake issue caused by USE_MKL option

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>

* Correct comments in MKL cmake file

Signed-off-by: Gu, Jinghui <jinghui.gu@intel.com>
2018-04-22 21:58:14 -07:00

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Python

## @package ideep_test_util
# Module caffe2.python.ideep_test_util
"""
The IDEEP test utils is a small addition on top of the hypothesis test utils
under caffe2/python, which allows one to more easily test IDEEP related
operators.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import hypothesis.strategies as st
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace
from caffe2.python import hypothesis_test_util as hu
cpu_do = hu.cpu_do
ideep_do = caffe2_pb2.DeviceOption(device_type=caffe2_pb2.IDEEP)
device_options = hu.device_options + ([ideep_do])
def device_checker_device_options():
return st.just(device_options)
def gradient_checker_device_option():
return st.sampled_from(device_options)
gcs = dict(
gc=gradient_checker_device_option(),
dc=device_checker_device_options()
)
gcs_cpu_only = dict(gc=st.sampled_from([cpu_do]), dc=st.just([cpu_do]))
gcs_ideep_only = dict(gc=st.sampled_from([ideep_do]), dc=st.just([ideep_do]))
gcs_cpu_ideep = dict(gc=st.sampled_from([cpu_do, ideep_do]), dc=st.just([cpu_do, ideep_do]))