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!9124 fix grad of the Identity 

From: @yanzhenxiang2020
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2020-12-03 20:28:59 +08:00 committed by Gitee
parent 6b9e402790 cb391ba234
commit 756594d000
2 changed files with 18 additions and 6 deletions
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10
mindspore/ops/_grad/grad_array_ops.py
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@ -456,6 +456,16 @@ def get_bprop_sparse_gather_v2(self):
return bprop
@bprop_getters.register(P.Identity)
def get_bprop_identity(self):
"""Generate bprop for Identity"""
def bprop(x, out, dout):
return (dout,)
return bprop
@bprop_getters.register(inner.Range)
def get_bprop_range(self):
"""Generate bprop for Range"""

14
mindspore/ops/operations/array_ops.py
View File

@ -4105,14 +4105,14 @@ class Meshgrid(PrimitiveWithInfer):
Tensors, A Tuple of N N-D Tensor objects.
Examples:
>>> x = np.array([1, 2, 3, 4]).astype(np.int32)
>>> y = np.array([5, 6, 7]).astype(np.int32)
>>> z = np.array([8, 9, 0, 1, 2]).astype(np.int32)
>>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
>>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
>>> z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
>>> inputs = (x, y, z)
>>> meshgrid = ops.Meshgrid(indexing="xy")
>>> output = meshgrid(inputs)
>>> print(output)
(Tensor(shape=[3, 4, 6], dtype=UInt32, value=
(Tensor(shape=[3, 4, 6], dtype=Int32, value=
[[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
@ -4125,7 +4125,7 @@ class Meshgrid(PrimitiveWithInfer):
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]]]),
Tensor(shape=[3, 4, 6], dtype=UInt32, value=
Tensor(shape=[3, 4, 6], dtype=Int32, value=
[[[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5],
@ -4138,7 +4138,7 @@ class Meshgrid(PrimitiveWithInfer):
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7]]]),
Tensor(shape=[3, 4, 6], dtype=UInt32, value=
Tensor(shape=[3, 4, 6], dtype=Int32, value=
[[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
@ -4612,6 +4612,8 @@ class Identity(PrimitiveWithInfer):
"""Initialize identity"""
def __infer__(self, x):
validator.check_subclass("x", x['dtype'], mstype.tensor, self.name)
validator.check_tensor_dtype_valid('x', x['dtype'], mstype.number_type + (mstype.bool_,), self.name)
out = {'shape': x['shape'],
'dtype': x['dtype'],
'value': None}