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from caffe2.python import core
from hypothesis import given, settings
import hypothesis.strategies as st
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import numpy as np
import unittest
class TestThresholdedRelu(serial.SerializedTestCase):
# test case 1 - default alpha - we do reference and dc checks.
# test case 2 does dc and reference checks over range of alphas.
# test case 3 does gc over range of alphas.
@serial.given(input=hu.tensor(),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
def test_thresholded_relu_1(self, input, gc, dc, engine):
X = input
op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
engine=engine)
def defaultRef(X):
Y = np.copy(X)
Y[Y <= 1.0] = 0.0
return (Y,)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertReferenceChecks(gc, op, [X], defaultRef)
@given(input=hu.tensor(),
alpha=st.floats(min_value=1.0, max_value=5.0),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
def test_thresholded_relu_2(self, input, alpha, gc, dc, engine):
X = input
op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
alpha=alpha, engine=engine)
def ref(X):
Y = np.copy(X)
Y[Y <= alpha] = 0.0
return (Y,)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertReferenceChecks(gc, op, [X], ref)
@given(input=hu.tensor(),
alpha=st.floats(min_value=1.1, max_value=5.0),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=10000)
def test_thresholded_relu_3(self, input, alpha, gc, dc, engine):
X = TestThresholdedRelu.fix_input(input)
op = core.CreateOperator("ThresholdedRelu", ["X"], ["Y"],
alpha=float(alpha), engine=engine)
self.assertGradientChecks(gc, op, [X], 0, [0])
@staticmethod
def fix_input(input):
# go away from alpha to avoid derivative discontinuities
input += 0.02 * np.sign(input)
return input
if __name__ == "__main__":
unittest.main()
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