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import numpy as np
from hypothesis import given, settings
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestClip(serial.SerializedTestCase):
@given(X=hu.tensor(min_dim=0),
min_=st.floats(min_value=-2, max_value=0),
max_=st.floats(min_value=0, max_value=2),
inplace=st.booleans(),
**hu.gcs)
@settings(deadline=10000)
def test_clip(self, X, min_, max_, inplace, gc, dc):
# go away from the origin point to avoid kink problems
if np.isscalar(X):
X = np.array([], dtype=np.float32)
else:
X[np.abs(X - min_) < 0.05] += 0.1
X[np.abs(X - max_) < 0.05] += 0.1
def clip_ref(X):
X = X.clip(min_, max_)
return (X,)
op = core.CreateOperator(
"Clip",
["X"], ["Y" if not inplace else "X"],
min=min_,
max=max_)
self.assertReferenceChecks(gc, op, [X], clip_ref)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [X], [0])
# Gradient check wrt X
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(X=hu.tensor(min_dim=0),
inplace=st.booleans(),
**hu.gcs)
def test_clip_default(self, X, inplace, gc, dc):
# go away from the origin point to avoid kink problems
if np.isscalar(X):
X = np.array([], dtype=np.float32)
else:
X += 0.04 * np.sign(X)
def clip_ref(X):
return (X,)
op = core.CreateOperator(
"Clip",
["X"], ["Y" if not inplace else "X"])
self.assertReferenceChecks(gc, op, [X], clip_ref)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [X], [0])
if __name__ == "__main__":
import unittest
unittest.main()
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