text: 
  
  
  
  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)
  