forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
key_split_ops_test.py
45 lines (40 loc) · 1.4 KB
/
key_split_ops_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import hypothesis.strategies as st
from caffe2.python import core, workspace
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import numpy as np
class TestKeySplitOps(hu.HypothesisTestCase):
@given(
X=hu.arrays(
dims=[1000],
dtype=np.int64,
elements=st.integers(min_value=0, max_value=100)
),
**hu.gcs_cpu_only
)
def test_key_split_op(self, X, gc, dc):
categorical_limit = max(X) + 1
workspace.ResetWorkspace()
workspace.FeedBlob('X', X)
output_blobs = ['Y_%d' % i for i in range(categorical_limit)]
op = core.CreateOperator(
'KeySplit', ['X'],
output_blobs,
categorical_limit=categorical_limit
)
workspace.RunOperatorOnce(op)
output_vecs = [
workspace.blobs[output_blobs[i]] for i in range(categorical_limit)
]
expected_output_vecs = [[] for _ in range(categorical_limit)]
for i, x in enumerate(X):
expected_output_vecs[x].append(i)
for i in range(categorical_limit):
np.testing.assert_array_equal(
output_vecs[i],
np.array(expected_output_vecs[i], dtype=np.int32)
)