mikemwx commented on a change in pull request #15302: [Numpy] Numpy hstack
URL: https://github.com/apache/incubator-mxnet/pull/15302#discussion_r302873300
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File path: tests/python/unittest/test_numpy_op.py
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@@ -860,6 +860,72 @@ def get_new_shape(shape, axis):
assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
+@with_seed()
[email protected]_np_shape
+def test_np_hstack():
+ class TestHStack(HybridBlock):
+ def __init__(self):
+ super(TestHStack, self).__init__()
+
+ def hybrid_forward(self, F, a, *args):
+ return F.np.hstack([a] + list(args))
+
+ def get_new_shape(shape):
+ if len(shape) == 0:
+ l = random.randint(0,3)
+ if l == 0:
+ return shape
+ else:
+ return (l,)
+ shape_lst = list(shape)
+ axis = 1 if len(shape) > 1 else 0
+ shape_lst[axis] = random.randint(0, 5)
+ return tuple(shape_lst)
+
+ shapes = [
+ (),
+ (1,),
+ (2,1),
+ (2,2,4),
+ (2,0,0),
+ (0,1,3),
+ (2,0,3),
+ (2,3,4,5)
+ ]
+ for hybridize in [True, False]:
+ for shape in shapes:
+ test_hstack = TestHStack()
+ if hybridize:
+ test_hstack.hybridize()
+ # test symbolic forward
+ a = np.random.uniform(size=get_new_shape(shape))
+ a.attach_grad()
+ b = np.random.uniform(size=get_new_shape(shape))
+ b.attach_grad()
+ c = np.random.uniform(size=get_new_shape(shape))
+ c.attach_grad()
+ d = np.random.uniform(size=get_new_shape(shape))
+ d.attach_grad()
+ with mx.autograd.record():
+ mx_out = test_hstack(a, b, c, d)
+ np_out = _np.hstack((a.asnumpy(), b.asnumpy(), c.asnumpy(),
d.asnumpy()))
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+ # test symbolic backward
+ mx_out.backward()
+ assert_almost_equal(a.grad.asnumpy(), _np.ones(a.shape),
rtol=1e-3, atol=1e-5)
+ assert_almost_equal(b.grad.asnumpy(), _np.ones(b.shape),
rtol=1e-3, atol=1e-5)
+ assert_almost_equal(c.grad.asnumpy(), _np.ones(c.shape),
rtol=1e-3, atol=1e-5)
+ assert_almost_equal(d.grad.asnumpy(), _np.ones(d.shape),
rtol=1e-3, atol=1e-5)
+
+ # test imperative
+ mx_out = np.hstack((a, b, c, d))
+ np_out = _np.hstack((a.asnumpy(),b.asnumpy(), c.asnumpy(),
d.asnumpy()))
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+
Review comment:
Done
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