| |
| |
| |
| |
| 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_shape_inference(self): |
| indices = np.array([2, 4, 999, 2], dtype=np.int32) |
| values = np.array([[1, 2], [2, 4], [6, 7], [7, 8]], dtype=np.int32) |
| data_to_infer_dim = np.array(np.zeros(1500, ), dtype=np.int32) |
| op = core.CreateOperator( |
| 'SparseToDense', |
| ['indices', 'values', 'data_to_infer_dim'], |
| ['output']) |
| workspace.FeedBlob('indices', indices) |
| workspace.FeedBlob('values', values) |
| workspace.FeedBlob('data_to_infer_dim', data_to_infer_dim) |
| |
| net = core.Net("sparse_to_dense") |
| net.Proto().op.extend([op]) |
| shapes, types = workspace.InferShapesAndTypes( |
| [net], |
| blob_dimensions={ |
| "indices": indices.shape, |
| "values": values.shape, |
| "data_to_infer_dim": data_to_infer_dim.shape, |
| }, |
| blob_types={ |
| "indices": core.DataType.INT32, |
| "values": core.DataType.INT32, |
| "data_to_infer_dim": core.DataType.INT32, |
| }, |
| ) |
| assert ( |
| "output" in shapes and "output" in types |
| ), "Failed to infer the shape or type of output" |
| self.assertEqual(shapes["output"], [1500, 2]) |
| self.assertEqual(types["output"], core.DataType.INT32) |
| |
| |
| 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) |
