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
92 lines
2.9 KiB
Python
92 lines
2.9 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 caffe2.python import core
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from hypothesis import given
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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 hypothesis.extra.numpy as hnp
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class TestGatherOps(hu.HypothesisTestCase):
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@given(rows_num=st.integers(1, 10000),
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index_num=st.integers(0, 5000),
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**hu.gcs)
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def test_gather_ops(self, rows_num, index_num, gc, dc):
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data = np.random.random((rows_num, 10, 20)).astype(np.float32)
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ind = np.random.randint(rows_num, size=(index_num, )).astype('int32')
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op = core.CreateOperator(
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'Gather',
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['data', 'ind'],
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['output'])
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def ref_gather(data, ind):
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if ind.size == 0:
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return [np.zeros((0, 10, 20)).astype(np.float32)]
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output = [r for r in [data[i] for i in ind]]
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return [output]
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self.assertReferenceChecks(gc, op, [data, ind], ref_gather)
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@st.composite
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def _inputs(draw):
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rows_num = draw(st.integers(1, 100))
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index_num = draw(st.integers(1, 10))
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batch_size = draw(st.integers(2, 10))
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return (
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draw(hnp.arrays(
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np.float32,
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(batch_size, rows_num, 2),
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elements=st.floats(-10.0, 10.0),
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)),
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draw(hnp.arrays(
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np.int32,
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(index_num, 1),
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elements=st.integers(0, rows_num - 1),
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)),
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)
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class TestBatchGatherOps(hu.HypothesisTestCase):
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@given(inputs=_inputs(),
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**hu.gcs)
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def test_batch_gather_ops(self, inputs, gc, dc):
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data, ind = inputs
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op = core.CreateOperator(
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'BatchGather',
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['data', 'ind'],
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['output'])
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def ref_batch_gather(data, ind):
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output = []
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for b in range(data.shape[0]):
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output.append([r for r in [data[b][i] for i in ind]])
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return [output]
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self.assertReferenceChecks(gc, op, [data, ind], ref_batch_gather)
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self.assertGradientChecks(gc, op, [data, ind], 0, [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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