haojin2 commented on a change in pull request #15861: Numpy det and slogdet
operator
URL: https://github.com/apache/incubator-mxnet/pull/15861#discussion_r313719958
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -213,6 +213,94 @@ def test_np_dot():
assert False
+def test_np_linalg_det():
+ class TestDet(HybridBlock):
+ def __init__(self):
+ super(TestDet, self).__init__()
+
+ def hybrid_forward(self, F, a):
+ return F.np.linalg.det(a)
+
+ # test non zero size input
+ tensor_shapes = [
+ (5, 5),
+ (3, 3, 3),
+ (2, 2, 2, 2, 2),
+ (1, 1)
+ ]
+
+ for hybridize in [True, False]:
+ for shape in tensor_shapes:
+ for dtype in [_np.float32, _np.float64]:
+ a_shape = (1,) + shape
+ test_det = TestDet()
+ if hybridize:
+ test_det.hybridize()
+ a = rand_ndarray(shape = a_shape, dtype =
dtype).as_np_ndarray()
+ a.attach_grad()
+
+ np_out = _np.linalg.det(a.asnumpy())
+ with mx.autograd.record():
+ mx_out = test_det(a)
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol = 1e-1,
atol = 1e-1)
+ mx_out.backward()
+
+ # Test imperative once again
+ mx_out = np.linalg.det(a)
+ np_out = _np.linalg.det(a.asnumpy())
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-1,
atol=1e-1)
+
+ # test numeric gradient
+ a_sym = mx.sym.Variable("a").as_np_ndarray()
+ mx_sym = mx.sym.np.linalg.det(a_sym).as_nd_ndarray()
+ check_numeric_gradient(mx_sym, [a.as_nd_ndarray()],
+ rtol=1e-1, atol=1e-1, dtype = dtype)
Review comment:
fix all spaces around `=` operators.
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