from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from caffe2.python import core, workspace from caffe2.python.test_util import TestCase import numpy as np class TestSparseToDense(TestCase): def test_sparse_to_dense(self): op = core.CreateOperator( 'SparseToDense', ['indices', 'values'], ['output']) workspace.FeedBlob( 'indices', np.array([2, 4, 999, 2], dtype=np.int32)) workspace.FeedBlob( 'values', np.array([1, 2, 6, 7], dtype=np.int32)) workspace.RunOperatorOnce(op) output = workspace.FetchBlob('output') print(output) expected = np.zeros(1000, dtype=np.int32) expected[2] = 1 + 7 expected[4] = 2 expected[999] = 6 self.assertEqual(output.shape, expected.shape) np.testing.assert_array_equal(output, expected) def test_sparse_to_dense_invalid_inputs(self): op = core.CreateOperator( 'SparseToDense', ['indices', 'values'], ['output']) workspace.FeedBlob( 'indices', np.array([2, 4, 999, 2], dtype=np.int32)) workspace.FeedBlob( 'values', np.array([1, 2, 6], dtype=np.int32)) with self.assertRaises(RuntimeError): workspace.RunOperatorOnce(op) def test_sparse_to_dense_with_data_to_infer_dim(self): op = core.CreateOperator( 'SparseToDense', ['indices', 'values', 'data_to_infer_dim'], ['output']) workspace.FeedBlob( 'indices', np.array([2, 4, 999, 2], dtype=np.int32)) workspace.FeedBlob( 'values', np.array([1, 2, 6, 7], dtype=np.int32)) workspace.FeedBlob( 'data_to_infer_dim', np.array(np.zeros(1500, ), dtype=np.int32)) workspace.RunOperatorOnce(op) output = workspace.FetchBlob('output') print(output) expected = np.zeros(1500, dtype=np.int32) expected[2] = 1 + 7 expected[4] = 2 expected[999] = 6 self.assertEqual(output.shape, expected.shape) np.testing.assert_array_equal(output, expected)