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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
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
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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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import hypothesis.strategies as st
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import numpy as np
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class TestUnmaskOp(serial.SerializedTestCase):
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@serial.given(N=st.integers(min_value=2, max_value=20),
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dtype=st.sampled_from([
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np.bool_,
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np.int8,
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np.int16,
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np.int32,
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np.int64,
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np.uint8,
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np.uint16,
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np.float16,
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np.float32,
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np.float64]),
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**hu.gcs)
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def test(self, N, dtype, gc, dc):
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if dtype is np.bool_:
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all_value = np.random.choice(a=[True, False], size=N)
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else:
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all_value = (np.random.rand(N) * N).astype(dtype)
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M = np.random.randint(1, N)
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split = sorted(np.random.randint(1, N, size=M))
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indices = np.random.permutation(N)
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pieces = np.split(indices, split)
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def ref(*args, **kwargs):
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return (all_value,)
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inputs = []
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inputs_names = []
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for i, piece in enumerate(pieces):
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piece.sort()
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mask = np.zeros(N, dtype=np.bool_)
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mask[piece] = True
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values = all_value[piece]
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inputs.extend([mask, values])
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inputs_names.extend(["mask%d" % i, "value%d" % i])
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op = core.CreateOperator(
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'BooleanUnmask',
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inputs_names,
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'output')
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self.assertReferenceChecks(gc, op, inputs, ref)
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self.assertDeviceChecks(dc, op, inputs, [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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