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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)
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