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https://github.com/saymrwulf/pytorch.git
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e42c14e819
Summary: ScaleGradient is a helper operator that does no actual numerical computation, and in the gradient computation phase scales the gradient from being computed through it. Differential Revision: D4920719 fbshipit-source-id: 0e1e0888f79594be874fdbdda5ccef7389064c50
43 lines
1.1 KiB
Python
43 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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from caffe2.python import core
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from hypothesis import given
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import hypothesis.strategies as st
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import caffe2.python.hypothesis_test_util as hu
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import numpy as np
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import unittest
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class TestScaleGradient(hu.HypothesisTestCase):
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@given(X=hu.tensor(min_dim=0, max_dim=3),
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scale=st.floats(min_value=-100, max_value=100),
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**hu.gcs_cpu_only)
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def test_scale_gradient(self, X, scale, gc, dc):
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if isinstance(X, float):
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X = np.array(X, dtype=np.float32)
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op = core.CreateOperator(
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"ScaleGradient", ["X"], ["X"],
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scale=float(scale),
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)
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def pass_through(x):
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return [x]
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def grad_ref(grad, o, x):
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return [x[0] * float(scale)]
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self.assertReferenceChecks(
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gc, op, [X], pass_through,
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output_to_grad='X',
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grad_reference=grad_ref,
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)
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self.assertDeviceChecks(dc, op, [X], [0])
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if __name__ == "__main__":
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unittest.main()
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