reminisce commented on a change in pull request #16720: [Numpy] Implement numpy
operator 'average'
URL: https://github.com/apache/incubator-mxnet/pull/16720#discussion_r346594183
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -598,6 +598,100 @@ def _test_np_exception(func, shape, dim):
_test_np_exception(func, shape, dim)
+@with_seed()
+@use_np
+def test_np_average():
+ class TestAverage(HybridBlock):
+ def __init__(self, axis=None, returned=False):
+ super(TestAverage, self).__init__()
+ # necessary initializations
+ self._axis = axis
+ self._returned = returned
+
+ def hybrid_forward(self, F, a, weights):
+ return F.np.average(a, weights=weights, axis=self._axis,
returned=self._returned)
+
+ def avg_backward(a, w, avg, axes):
+ # avg = sum(a * w) / sum(w)
+ if axes is not None and not isinstance(axes, tuple) and axes < 0:
+ axes += a.ndim
+ if w is None:
+ return [_np.ones(shape=a.shape, dtype=a.dtype)/(a.size/avg.size),
None]
+ onedim = a.ndim != w.ndim
+ if onedim:
+ new_shape = [a.shape[i] if i == axes else 1 for i in range(a.ndim)]
+ w = w.reshape(new_shape)
+ w = _np.broadcast_to(w, a.shape)
+
+ # partial a = w / sum(w)
+ # partial w = (a*sum(w) - sum(a*w)) / (sum(w) * sum(w))
+ scl = _np.sum(w, axis=axes, keepdims=True)
+ a_grad = _np.divide(w, scl)
+ w_grad = _np.divide(a*scl-_np.sum(a*w, axis=axes, keepdims=True),
scl*scl)
+
+ if onedim:
+ axis = []
+ for i in range(a.ndim):
+ if i != axes:
+ axis.append(i)
+ w_grad = _np.sum(w_grad, axis=tuple(axis))
+ return [a_grad, w_grad]
+
+ tensor_shapes = [
+ ((3, 5), (3, 5), None), # (a_shape, w_shape, axes)
+ ((4, 5, 6), (4, 5, 6), (0, 2)),
+ ((3,), (3,), 0),
+ ((2, 3), (3,), 1),
+ ((2, 3, 4), (2,), 0),
+ ((2, 3, 4), (3,), 1),
+ ((2, 3, 4), (4,), -1),
+ ((2, 3, 4, 5), (5,), 3)
+ ]
+
+ for hybridize in [True, False]:
+ for returned in [True, False]:
+ for a_shape, w_shape, axes in tensor_shapes:
+ for dtype in ['float32', 'float64']:
+ for is_weighted in [True, False]:
+ test_average = TestAverage(axes, returned)
+ if hybridize:
+ test_average.hybridize()
+ a = np.random.uniform(-1.0, 1.0, size=a_shape,
dtype=dtype)
+ a.attach_grad()
Review comment:
Please test `a.attach_grad('null')`, `a.attach_grad('write')`,
`a.attach_grad('add')`.
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