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77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
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 TestSparseToDense(TestCase):
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def test_sparse_to_dense(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6, 7], dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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print(output)
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expected = np.zeros(1000, dtype=np.int32)
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expected[2] = 1 + 7
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expected[4] = 2
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expected[999] = 6
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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_invalid_inputs(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6], 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_with_data_to_infer_dim(self):
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op = core.CreateOperator(
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'SparseToDense',
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['indices', 'values', 'data_to_infer_dim'],
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['output'])
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workspace.FeedBlob(
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'indices',
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np.array([2, 4, 999, 2], dtype=np.int32))
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workspace.FeedBlob(
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'values',
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np.array([1, 2, 6, 7], dtype=np.int32))
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workspace.FeedBlob(
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'data_to_infer_dim',
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np.array(np.zeros(1500, ), dtype=np.int32))
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workspace.RunOperatorOnce(op)
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output = workspace.FetchBlob('output')
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print(output)
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expected = np.zeros(1500, dtype=np.int32)
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expected[2] = 1 + 7
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expected[4] = 2
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expected[999] = 6
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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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