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new 4fb5241 [MXNET-155] Add support for np.int32 and np.int64 as basic
index types (#10434)
4fb5241 is described below
commit 4fb5241b47c8147690fd6408b55cb694d544656e
Author: reminisce <wujun....@gmail.com>
AuthorDate: Sat Apr 7 19:08:15 2018 -0700
[MXNET-155] Add support for np.int32 and np.int64 as basic index types
(#10434)
* Add support for np.int32 and np.int64 as basic index types
* Address cr
* Remove unnecessary import
---
python/mxnet/base.py | 4 +-
tests/python/unittest/test_ndarray.py | 80 +++++++++++++++++++++++++---
tests/python/unittest/test_sparse_ndarray.py | 11 ++++
3 files changed, 87 insertions(+), 8 deletions(-)
diff --git a/python/mxnet/base.py b/python/mxnet/base.py
index 156ef35..9790e09 100644
--- a/python/mxnet/base.py
+++ b/python/mxnet/base.py
@@ -37,14 +37,14 @@ __all__ = ['MXNetError']
if sys.version_info[0] == 3:
string_types = str,
numeric_types = (float, int, np.generic)
- integer_types = int
+ integer_types = (int, np.int32, np.int64)
# this function is needed for python3
# to convert ctypes.char_p .value back to python str
py_str = lambda x: x.decode('utf-8')
else:
string_types = basestring,
numeric_types = (float, int, long, np.generic)
- integer_types = (int, long)
+ integer_types = (int, long, np.int32, np.int64)
py_str = lambda x: x
class _NullType(object):
diff --git a/tests/python/unittest/test_ndarray.py
b/tests/python/unittest/test_ndarray.py
index 4265bf8..6f2bd45 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -1061,35 +1061,103 @@ def test_ndarray_indexing():
x_grad[index] = value
assert same(x_grad.asnumpy(), x.grad.asnumpy())
+ def np_int(index, int_type=np.int32):
+ def convert(num):
+ if num is None:
+ return num
+ else:
+ return int_type(num)
+
+ if isinstance(index, slice):
+ return slice(convert(index.start), convert(index.stop),
convert(index.step))
+ elif isinstance(index, tuple): # tuple of slices and integers
+ ret = []
+ for elem in index:
+ if isinstance(elem, slice):
+ ret.append(slice(convert(elem.start), convert(elem.stop),
convert(elem.step)))
+ else:
+ ret.append(convert(elem))
+ return tuple(ret)
+ else:
+ assert False
+
shape = (8, 16, 9, 9)
np_array = np.arange(np.prod(shape), dtype='int32').reshape(shape)
# index_list is a list of tuples. The tuple's first element is the index,
the second one is a boolean value
# indicating whether we should expect the result as a scalar compared to
numpy.
- index_list = [(0, False), (5, False), (-1, False),
- (slice(5), False), (slice(1, 5), False), (slice(1, 5, 2),
False),
- (slice(7, 0, -1), False), (slice(None, 6), False),
(slice(None, 6, 3), False),
- (slice(1, None), False), (slice(1, None, 3), False),
(slice(None, None, 2), False),
- (slice(None, None, -1), False), (slice(None, None, -2),
False),
+ index_list = [(0, False), (np.int32(0), False), (np.int64(0), False),
+ (5, False), (np.int32(5), False), (np.int64(5), False),
+ (-1, False), (np.int32(-1), False), (np.int64(-1), False),
+ (slice(5), False), (np_int(slice(5), np.int32), False),
(np_int(slice(5), np.int64), False),
+ (slice(1, 5), False), (np_int(slice(1, 5), np.int32),
False), (np_int(slice(1, 5), np.int64), False),
+ (slice(1, 5, 2), False), (np_int(slice(1, 5, 2), np.int32),
False),
+ (np_int(slice(1, 5, 2), np.int64), False),
+ (slice(7, 0, -1), False), (np_int(slice(7, 0, -1)), False),
+ (np_int(slice(7, 0, -1), np.int64), False),
+ (slice(None, 6), False), (np_int(slice(None, 6)), False),
+ (np_int(slice(None, 6), np.int64), False),
+ (slice(None, 6, 3), False), (np_int(slice(None, 6, 3)),
False),
+ (np_int(slice(None, 6, 3), np.int64), False),
+ (slice(1, None), False), (np_int(slice(1, None)), False),
+ (np_int(slice(1, None), np.int64), False),
+ (slice(1, None, 3), False), (np_int(slice(1, None, 3)),
False),
+ (np_int(slice(1, None, 3), np.int64), False),
+ (slice(None, None, 2), False), (np_int(slice(None, None,
2)), False),
+ (np_int(slice(None, None, 2), np.int64), False),
+ (slice(None, None, -1), False),
+ (np_int(slice(None, None, -1)), False), (np_int(slice(None,
None, -1), np.int64), False),
+ (slice(None, None, -2), False),
+ (np_int(slice(None, None, -2), np.int32), False),
(np_int(slice(None, None, -2), np.int64), False),
+ ((slice(None), slice(None), 1, 8), False),
+ (np_int((slice(None), slice(None), 1, 8)), False),
+ (np_int((slice(None), slice(None), 1, 8), np.int64), False),
((slice(None), slice(None), 1, 8), False),
+ (np_int((slice(None), slice(None), 1, 8)), False),
+ (np_int((slice(None), slice(None), 1, 8), np.int64), False),
((slice(None), 2, slice(1, 5), 1), False),
- ((1, 2, 3), False), ((1, 2, 3, 4), True),
+ (np_int((slice(None), 2, slice(1, 5), 1)), False),
+ (np_int((slice(None), 2, slice(1, 5), 1), np.int64), False),
+ ((1, 2, 3), False),
+ (np_int((1, 2, 3)), False),
+ (np_int((1, 2, 3), np.int64), False),
+ ((1, 2, 3, 4), True),
+ (np_int((1, 2, 3, 4)), True),
+ (np_int((1, 2, 3, 4), np.int64), True),
((slice(None, None, -1), 2, slice(1, 5), 1), False),
+ (np_int((slice(None, None, -1), 2, slice(1, 5), 1)), False),
+ (np_int((slice(None, None, -1), 2, slice(1, 5), 1),
np.int64), False),
((slice(None, None, -1), 2, slice(1, 7, 2), 1), False),
+ (np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)),
False),
+ (np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1),
np.int64), False),
((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7,
3)), False),
+ (np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8),
slice(0, 7, 3))), False),
+ (np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8),
slice(0, 7, 3)), np.int64), False),
((slice(1, 8, 2), 1, slice(3, 8), 2), False),
+ (np_int((slice(1, 8, 2), 1, slice(3, 8), 2)), False),
+ (np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64),
False),
([1], False), ([1, 2], False), ([2, 1, 3], False), ([7, 5,
0, 3, 6, 2, 1], False),
(np.array([6, 3], dtype=np.int32), False),
(np.array([[3, 4], [0, 6]], dtype=np.int32), False),
(np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32),
False),
+ (np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64),
False),
(np.array([[2], [0], [1]], dtype=np.int32), False),
+ (np.array([[2], [0], [1]], dtype=np.int64), False),
(mx.nd.array([4, 7], dtype=np.int32), False),
+ (mx.nd.array([4, 7], dtype=np.int64), False),
(mx.nd.array([[3, 6], [2, 1]], dtype=np.int32), False),
+ (mx.nd.array([[3, 6], [2, 1]], dtype=np.int64), False),
(mx.nd.array([[7, 3], [2, 6], [0, 5], [4, 1]],
dtype=np.int32), False),
+ (mx.nd.array([[7, 3], [2, 6], [0, 5], [4, 1]],
dtype=np.int64), False),
((1, [2, 3]), False), ((1, [2, 3], np.array([[3], [0]],
dtype=np.int32)), False),
+ ((1, [2, 3]), False), ((1, [2, 3], np.array([[3], [0]],
dtype=np.int64)), False),
((1, [2], np.array([[5], [3]], dtype=np.int32),
slice(None)), False),
+ ((1, [2], np.array([[5], [3]], dtype=np.int64),
slice(None)), False),
((1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2,
5)), False),
+ ((1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2,
5)), False),
((1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2,
5, 2)), False),
+ ((1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2,
5, 2)), False),
((1, [2], np.array([[3]], dtype=np.int32), slice(None, None,
-1)), False),
+ ((1, [2], np.array([[3]], dtype=np.int64), slice(None, None,
-1)), False),
((1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7],
[2, 4]], dtype=np.int64)), False),
((1, [2], mx.nd.array([[4]], dtype=np.int32),
mx.nd.array([[1, 3], [5, 7]], dtype='int64')),
False),
diff --git a/tests/python/unittest/test_sparse_ndarray.py
b/tests/python/unittest/test_sparse_ndarray.py
index 25eaf42..ae3260a 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -638,6 +638,17 @@ def test_create_row_sparse():
rsp_copy = mx.nd.array(rsp_created)
assert(same(rsp_copy.asnumpy(), rsp_created.asnumpy()))
+ # add this test since we added np.int32 and np.int64 to integer_types
+ if len(shape) == 2:
+ for np_int_type in (np.int32, np.int64):
+ shape = list(shape)
+ shape = [np_int_type(x) for x in shape]
+ arg1 = tuple(shape)
+ mx.nd.sparse.row_sparse_array(arg1, tuple(shape))
+ shape[0] += 1
+ assert_exception(mx.nd.sparse.row_sparse_array, ValueError,
arg1, tuple(shape))
+
+
@with_seed()
def test_create_sparse_nd_infer_shape():
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