mirror of
https://github.com/zebrajr/pytorch.git
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83 lines
3.4 KiB
Python
83 lines
3.4 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 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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self.assertFalse(np.any(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=np.str))
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workspace.FeedBlob('default', np.array('-1', dtype=np.str))
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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.str)
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self.assertEqual(output.shape, expected.shape)
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self.assertTrue(np.all(np.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', 'lengths'],
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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.FeedBlob('lengths', np.array([], 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], dtype=np.float)
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self.assertEqual(output.shape, expected.shape)
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self.assertFalse(np.any(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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self.assertFalse(np.any(output - expected))
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