Add unittest

tests/python/unittest/test_dynamic_shape.py

@@ -0,0 +1,54 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import numpy as np
+import mxnet as mx
+from mxnet import gluon
+from numpy.testing import assert_allclose, assert_array_equal
+from mxnet.test_utils import *
+from mxnet.base import _as_list
+from mxnet.attribute import AttrScope
+from common import with_seed
+
+
+def test_dynamic_shape():
+
+    class _TestBlock(gluon.HybridBlock):
+
+        def __init__(self):
+            super(_TestBlock, self).__init__()
+
+        def hybrid_forward(self, F, data, index):
+            return F.contrib.boolean_mask(data, index)
+
+    block = _TestBlock()
+    block.hybridize()
+    data = mx.nd.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]])
+    index = mx.nd.array([0, 1, 1])
+    data.attach_grad()
+    with mx.autograd.record():
+        result = block(data, index)
+    result.backward()
+    result_nd = np.array([[4, 5, 6], [7, 8, 9]])
+    data_grad_nd = np.array([[0., 0., 0.], [1., 1., 1.], [1., 1., 1.]])
+    assert_almost_equal(result.asnumpy(), result_nd)
+    assert_almost_equal(data.grad.asnumpy(), data_grad_nd)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()