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import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import numpy.testing as npt
from caffe2.python import core, workspace
from hypothesis import given
class TestEnsureClipped(hu.HypothesisTestCase):
@given(
X=hu.arrays(dims=[5, 10], elements=hu.floats(min_value=-1.0, max_value=1.0)),
in_place=st.booleans(),
sparse=st.booleans(),
indices=hu.arrays(dims=[5], elements=st.booleans()),
**hu.gcs_cpu_only
)
def test_ensure_clipped(self, X, in_place, sparse, indices, gc, dc):
if (not in_place) and sparse:
return
param = X.astype(np.float32)
m, n = param.shape
indices = np.array(np.nonzero(indices)[0], dtype=np.int64)
grad = np.random.rand(len(indices), n)
workspace.FeedBlob("indices", indices)
workspace.FeedBlob("grad", grad)
workspace.FeedBlob("param", param)
input = ["param", "indices", "grad"] if sparse else ["param"]
output = "param" if in_place else "output"
op = core.CreateOperator("EnsureClipped", input, output, min=0.0)
workspace.RunOperatorOnce(op)
def ref():
return (
np.array(
[np.clip(X[i], 0, None) if i in indices else X[i] for i in range(m)]
)
if sparse
else np.clip(X, 0, None)
)
npt.assert_allclose(workspace.blobs[output], ref(), rtol=1e-3)
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