mirror of
https://github.com/zebrajr/pytorch.git
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77ae903650
Summary: A single negative index can crash the job today. We want to skip a few of them but not a lot. If we skip too many then we will force the job to crash. Reviewed By: kennyhorror Differential Revision: D6003461 fbshipit-source-id: 7881ed6c2cfa78c7bda90c7aa01e81ca00fd08a6
170 lines
7.1 KiB
Python
170 lines
7.1 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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from caffe2.python import core, workspace
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from caffe2.python.test_util import TestCase
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import numpy as np
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class TestSparseToDenseMask(TestCase):
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def test_sparse_to_dense_mask_float(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default', 'lengths'],
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['output'],
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mask=[999999999, 2, 6])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 6, 1, 2, 999999999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.float))
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workspace.FeedBlob('default', np.array(-1, dtype=np.float))
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workspace.FeedBlob('lengths', np.array([3, 4], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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expected = np.array([[-1, 1, 3], [6, 7, -1]], dtype=np.float)
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_mask_invalid_inputs(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default', 'lengths'],
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['output'],
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mask=[999999999, 2])
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workspace.FeedBlob(
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'indices',
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np.array([2000000000000, 999999999, 2, 3, 4, 5], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 3, 4, 5, 6], dtype=np.float))
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workspace.FeedBlob('default', np.array(-1, dtype=np.float))
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workspace.FeedBlob('lengths', np.array([6], dtype=np.int32))
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try:
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workspace.RunOperatorOnce(op)
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except RuntimeError:
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self.fail("Exception raised with only one negative index")
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workspace.FeedBlob(
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'indices',
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np.array([2000000000000, 999999999, -2, -3, -4, -5], dtype=np.int32))
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with self.assertRaises(RuntimeError):
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workspace.RunOperatorOnce(op)
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def test_sparse_to_dense_mask_subtensor(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default', 'lengths'],
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['output'],
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mask=[999999999, 2, 888, 6])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 6, 999999999, 2], dtype=np.int64))
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workspace.FeedBlob(
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'values',
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np.array([[[1, -1]], [[2, -2]], [[3, -3]], [[4, -4]], [[5, -5]]],
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dtype=np.float))
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workspace.FeedBlob('default', np.array([[-1, 0]], dtype=np.float))
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workspace.FeedBlob('lengths', np.array([2, 3], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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expected = np.array([
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[[[-1, 0]], [[1, -1]], [[-1, 0]], [[-1, 0]]],
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[[[4, -4]], [[5, -5]], [[-1, 0]], [[3, -3]]]], dtype=np.float)
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_mask_string(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default', 'lengths'],
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['output'],
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mask=[999999999, 2, 6])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 6, 1, 2, 999999999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array(['1', '2', '3', '4', '5', '6', '7'], dtype='S'))
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workspace.FeedBlob('default', np.array('-1', dtype='S'))
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workspace.FeedBlob('lengths', np.array([3, 4], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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expected =\
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np.array([['-1', '1', '3'], ['6', '7', '-1']], dtype='S')
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_mask_empty_lengths(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default'],
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['output'],
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mask=[1, 2, 6])
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workspace.FeedBlob('indices', np.array([2, 4, 6], dtype=np.int32))
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workspace.FeedBlob('values', np.array([1, 2, 3], dtype=np.float))
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workspace.FeedBlob('default', np.array(-1, dtype=np.float))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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expected = np.array([-1, 1, 3], dtype=np.float)
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_mask_no_lengths(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default'],
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['output'],
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mask=[1, 2, 6])
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workspace.FeedBlob('indices', np.array([2, 4, 6], dtype=np.int32))
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workspace.FeedBlob('values', np.array([1, 2, 3], dtype=np.float))
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workspace.FeedBlob('default', np.array(-1, dtype=np.float))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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expected = np.array([-1, 1, 3], dtype=np.float)
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self.assertEqual(output.shape, expected.shape)
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np.testing.assert_array_equal(output, expected)
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def test_sparse_to_dense_mask_presence_mask(self):
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op = core.CreateOperator(
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'SparseToDenseMask',
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['indices', 'values', 'default', 'lengths'],
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['output', 'presence_mask'],
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mask=[11, 12],
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return_presence_mask=True)
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workspace.FeedBlob('indices', np.array([11, 12, 13], dtype=np.int32))
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workspace.FeedBlob('values', np.array([11, 12, 13], dtype=np.float))
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workspace.FeedBlob('default', np.array(-1, dtype=np.float))
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workspace.FeedBlob('lengths', np.array([1, 2], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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presence_mask = workspace.FetchBlob('presence_mask')
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expected_output = np.array([[11, -1], [-1, 12]], dtype=np.float)
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expected_presence_mask = np.array(
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[[True, False], [False, True]],
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dtype=np.bool)
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self.assertEqual(output.shape, expected_output.shape)
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np.testing.assert_array_equal(output, expected_output)
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self.assertEqual(presence_mask.shape, expected_presence_mask.shape)
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np.testing.assert_array_equal(presence_mask, expected_presence_mask)
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