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https://github.com/zebrajr/pytorch.git
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2c01afd2a6
Summary: Adding ability to reuse workspace in Do op and unit tests Reviewed By: akyrola Differential Revision: D6037992 fbshipit-source-id: 73d6a14001f667f7ca5e1e02ff39911dc65e4cd1
78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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import numpy as np
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import unittest
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class DoOpTest(TestCase):
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def test_operator(self):
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def make_net():
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subnet = core.Net('subnet')
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subnet.Add(["X", "Y"], "Z")
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net = core.Net("net")
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net.CreateScope([], "W")
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net.Do(
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["outer_X", "outer_Y", "W"],
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["outer_Z", "W"],
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net=subnet.Proto(),
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inner_blobs=["X", "Y", "Z"],
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outer_blobs_idx=[0, 1, 2],
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)
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return net
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net = make_net()
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workspace.ResetWorkspace()
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workspace.FeedBlob("outer_X", np.asarray([1, 2]))
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workspace.FeedBlob("outer_Y", np.asarray([3, 4]))
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workspace.RunNetOnce(net)
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outer_Z_val = workspace.FetchBlob("outer_Z")
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self.assertTrue(np.all(outer_Z_val == np.asarray([4, 6])))
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def test_reuse_workspace(self):
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def make_net():
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param_init_subnet = core.Net('param_init_subnet')
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param_init_subnet.ConstantFill([], "X", shape=[1], value=1)
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param_init_subnet.ConstantFill([], "Y", shape=[1], value=2)
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subnet = core.Net("subnet")
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subnet.Add(["X", "Y"], "Z")
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net = core.Net("net")
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net.CreateScope([], "W")
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net.Do(
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"W", "W",
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net=param_init_subnet.Proto(),
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inner_blobs=[],
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outer_blobs_idx=[],
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)
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net.Do(
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"W", ["outer_Z", "W"],
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net=subnet.Proto(),
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inner_blobs=["Z"],
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outer_blobs_idx=[0],
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reuse_workspace=True,
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)
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return net
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net = make_net()
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workspace.ResetWorkspace()
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workspace.RunNetOnce(net)
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outer_Z_val = workspace.FetchBlob("outer_Z")
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self.assertTrue(np.all(outer_Z_val == np.asarray([3])))
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if __name__ == '__main__':
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unittest.main()
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