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https://github.com/saymrwulf/pytorch.git
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6e1333fe92
Summary: Only CPU impl is available at the moment. Wrote simple cuda kernels. Reviewed By: akyrola Differential Revision: D4577736 fbshipit-source-id: c2540aa9d332fcdeac46cc7f89aab164d107d7a8
36 lines
1.1 KiB
Python
36 lines
1.1 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 __future__ import unicode_literals
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import numpy as np
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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from hypothesis import given
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class DistanceTest(hu.HypothesisTestCase):
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@given(inputs=hu.tensors(n=2,
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min_dim=1,
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max_dim=2,
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dtype=np.float32),
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**hu.gcs)
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def test_dot_product(self, inputs, gc, dc):
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X, Y = inputs
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op = core.CreateOperator(
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'DotProduct',
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['X', 'Y'],
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['DOT'],
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)
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def dot_ref(X, Y):
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return ([np.dot(x, y) for x, y in zip(X, Y)],)
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# Check against numpy dot reference
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self.assertReferenceChecks(gc, op, [X, Y], dot_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X, Y], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, Y], 0, [0])
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# Gradient check wrt Y
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self.assertGradientChecks(gc, op, [X, Y], 1, [0])
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