pytorch

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import numpy as np
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from hypothesis import given, settings
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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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import caffe2.python.serialized_test.serialized_test_util as serial
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class TestClip(serial.SerializedTestCase):
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    @given(X=hu.tensor(min_dim=0),
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           min_=st.floats(min_value=-2, max_value=0),
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           max_=st.floats(min_value=0, max_value=2),
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           inplace=st.booleans(),
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           **hu.gcs)
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    @settings(deadline=10000)
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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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        if np.isscalar(X):
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            X = np.array([], dtype=np.float32)
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        else:
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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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    @given(X=hu.tensor(min_dim=0),
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           inplace=st.booleans(),
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           **hu.gcs)
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    def test_clip_default(self, X, inplace, gc, dc):
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        # go away from the origin point to avoid kink problems
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        if np.isscalar(X):
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            X = np.array([], dtype=np.float32)
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        else:
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            X += 0.04 * np.sign(X)
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        def clip_ref(X):
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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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        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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if __name__ == "__main__":
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    import unittest
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    unittest.main()
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