tingying2020 commented on a change in pull request #15519: [Numpy] arctan2, a
new branch for pr.
URL: https://github.com/apache/incubator-mxnet/pull/15519#discussion_r303236597
##########
File path: tests/python/gpu/test_operator_gpu.py
##########
@@ -2300,6 +2300,81 @@ def test_math():
for op in ops:
run_math(op, shape, dtype, check_value=check_value)
+@with_seed()
[email protected]_np_shape
+def test_np_arctan2():
+ @npx.use_np_shape
+ class TestArctan2(HybridBlock):
+ def __init__(self):
+ super(TestArctan2, self).__init__()
+
+ def hybrid_forward(self, F, x1, x2):
+ return F.np.arctan2(x1, x2)
+
+ #Reduce dimension of src to dimention of des.
+ def dimReduce(src, des):
+ srcShape = src.shape
+ desShape = des.shape
+ if len(desShape) == 0:
+ return src.sum()
+ redu = []
+ for i, j in zip(range(len(srcShape)-1, -1, -1), range(len(desShape)-1,
-1, -1)):
+ if srcShape[i] != desShape[j] and desShape[j] == 1:
+ redu.append(i)
+ if j == 0:
+ for k in range(0, i):
+ redu.append(k)
+ break
+ if len(redu) > 0:
+ src = np.reshape(src.sum(axis=tuple(redu)), desShape)
+ return src
+
+ types = ['float64', 'float32', 'float16']
+ for hybridize in [True, False]:
+ for shape1, shape2 in [[(1,), (1,)], # single elements
+ [(4, 5), (4, 5)], # normal case
+ [(3, 2), (3, 2)], # tall matrices
+ [(), ()], # scalar only
+ [(3, 0, 2), (3, 0, 2)], # zero-dim
+ [(3, 4, 5), (4, 1)], # trailing dim
broadcasting
+ [(3, 4, 5), ()], # scalar broadcasting
+ [(), (1, 2, 3)], # scalar broadcasting
+ [(4, 3), (4, 1)], # single
broadcasting
+ [(3, 4, 5), (3, 1, 5)] # single
broadcasting in the middle
+ ]:
+ for oneType in types:
+ if oneType == 'float16':
+ rtol=1e-2
+ atol = 1e-2
+ else:
+ rtol=1e-3
+ atol = 1e-5
+ test_arctan2 = TestArctan2()
+ if hybridize:
+ test_arctan2.hybridize()
+ x1 = rand_ndarray(shape1, dtype=oneType).as_np_ndarray()
+ x2 = rand_ndarray(shape2, dtype=oneType).as_np_ndarray()
+ x11 = x1.asnumpy()
+ x21 = x2.asnumpy()
+ x1.attach_grad()
+ x2.attach_grad()
+ np_out = np.arctan2(x1.asnumpy(), x2.asnumpy())
+ with mx.autograd.record():
+ mx_out = test_arctan2(x1, x2)
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol,
atol=atol)
+ mx_out.backward()
+ np_backward_1 = x21 / (x11 * x11 + x21 * x21)
+ np_backward_2 = -1 * x11 / (x11 * x11 + x21 * x21)
+ np_backward_1 = dimReduce(np_backward_1, x11)
+ np_backward_2 = dimReduce(np_backward_2, x21)
+ assert_almost_equal(x1.grad.asnumpy(), np_backward_1,
rtol=rtol, atol=atol)
+ assert_almost_equal(x2.grad.asnumpy(), np_backward_2,
rtol=rtol, atol=atol)
+
+ mx_out = mx.np.arctan2(x1, x2)
+ np_out = np.arctan2(x1.asnumpy(), x2.asnumpy())
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol,
atol=atol)
+
Review comment:
Fixed, thank you.
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