mirror of
https://github.com/zebrajr/pytorch.git
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301ab97e41
Summary: If we go to prod some of the sparse features might be empty or for some reason batch might be empty. It's a good idea to be sure that we can run empty batches. Reviewed By: dzhulgakov Differential Revision: D4197297 fbshipit-source-id: 1a154ebf625d1a39fd15354a154cf100f525ae9a
105 lines
4.4 KiB
Python
105 lines
4.4 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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np.testing.assert_array_equal(output, expected)
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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=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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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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