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https://github.com/saymrwulf/pytorch.git
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cb5af39c69
Summary: Noticed this wasn't vectorized, could be handy. Reviewed By: kennyhorror Differential Revision: D5308593 fbshipit-source-id: c2b35ece34831f0546f010a1ebe0b89f1a7d9446
45 lines
1.3 KiB
Python
45 lines
1.3 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 hypothesis import given
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import hypothesis.strategies as st
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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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class TestClip(hu.HypothesisTestCase):
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@given(X=hu.tensor(),
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min_=st.floats(min_value=-1, max_value=0),
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max_=st.floats(min_value=0, max_value=1),
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inplace=st.booleans(),
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**hu.gcs)
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def test_clip(self, X, min_, max_, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X[np.abs(X - min_) < 0.05] += 0.1
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X[np.abs(X - max_) < 0.05] += 0.1
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def clip_ref(X):
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X = X.clip(min_, max_)
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return (X,)
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op = core.CreateOperator(
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"Clip",
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["X"], ["Y" if not inplace else "X"],
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min=min_,
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max=max_)
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self.assertReferenceChecks(gc, op, [X], clip_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X], 0, [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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