blob: 13547d6d327dc79c1f2adc6398f1788efdc811d9 [file] [log] [blame]
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)