pytorch/caffe2/python/operator_test/tile_op_test.py
Jerry Pan 8a0ebed4c9 Caffe2: Tile operator
Summary: Caffe2: Tile operator

Differential Revision: D4630698

fbshipit-source-id: 1aa5c3c9d7fcfc17f78c80fd4b752595280266a0
2017-02-28 23:17:26 -08:00

48 lines
1.4 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
import caffe2.python.hypothesis_test_util as hu
class TestTile(hu.HypothesisTestCase):
@given(M=st.integers(min_value=1, max_value=10),
K=st.integers(min_value=1, max_value=10),
N=st.integers(min_value=1, max_value=10),
tiles=st.integers(min_value=1, max_value=3),
axis=st.integers(min_value=0, max_value=2),
**hu.gcs)
def test_tile(self, M, K, N, tiles, axis, gc, dc):
X = np.random.rand(M, K, N).astype(np.float32)
op = core.CreateOperator(
'Tile', ['X'], 'out',
tiles=tiles,
axis=axis,
)
def tile_ref(X, tiles, axis):
dims = [1, 1, 1]
dims[axis] = tiles
tiled_data = np.tile(X, tuple(dims))
return (tiled_data,)
# Check against numpy reference
self.assertReferenceChecks(gc, op, [X, tiles, axis],
tile_ref)
# Check over multiple devices
self.assertDeviceChecks(dc, op, [X], [0])
# Gradient check wrt X
self.assertGradientChecks(gc, op, [X], 0, [0])
if __name__ == "__main__":
import unittest
unittest.main()