mirror of
https://github.com/saymrwulf/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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from hypothesis import given
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import hypothesis.strategies as st
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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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class TestUnmaskOp(hu.HypothesisTestCase):
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@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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