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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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import numpy.testing as npt
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from caffe2.python import core, workspace
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from hypothesis import given
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class TestEnsureClipped(hu.HypothesisTestCase):
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@given(
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X=hu.arrays(dims=[5, 10], elements=hu.floats(min_value=-1.0, max_value=1.0)),
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in_place=st.booleans(),
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sparse=st.booleans(),
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indices=hu.arrays(dims=[5], elements=st.booleans()),
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**hu.gcs_cpu_only
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)
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def test_ensure_clipped(self, X, in_place, sparse, indices, gc, dc):
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if (not in_place) and sparse:
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return
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param = X.astype(np.float32)
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m, n = param.shape
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indices = np.array(np.nonzero(indices)[0], dtype=np.int64)
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grad = np.random.rand(len(indices), n)
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workspace.FeedBlob("indices", indices)
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workspace.FeedBlob("grad", grad)
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workspace.FeedBlob("param", param)
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input = ["param", "indices", "grad"] if sparse else ["param"]
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output = "param" if in_place else "output"
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op = core.CreateOperator("EnsureClipped", input, output, min=0.0)
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workspace.RunOperatorOnce(op)
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def ref():
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return (
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np.array(
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[np.clip(X[i], 0, None) if i in indices else X[i] for i in range(m)]
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)
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if sparse
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else np.clip(X, 0, None)
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)
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npt.assert_allclose(workspace.blobs[output], ref(), rtol=1e-3)
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