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!10022 fix example and Validator for LogUniformCandidateSampler 

From: @yanzhenxiang2020
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2020-12-16 20:38:12 +08:00 committed by Gitee
parent aee1ff43ef f5be64f97e
commit eda6ce12ed
2 changed files with 58 additions and 55 deletions
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93
mindspore/ops/operations/array_ops.py
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@ -4134,7 +4134,7 @@ class Meshgrid(PrimitiveWithInfer):
"""
Generates coordinate matrices from given coordinate tensors.
Given N one-dimensional coordinate tensors, returns a list outputs of N N-D
Given N one-dimensional coordinate tensors, returns a tuple outputs of N N-D
coordinate tensors for evaluating expressions on an N-D grid.
@ -4144,12 +4144,15 @@ class Meshgrid(PrimitiveWithInfer):
instructions for the first two dimensions are swapped.
Inputs:
- **input_x** (Union[tuple, list]) - A Tuple or list of N 1-D Tensor objects.
The length of input_x should be greater than 1
- **input** (Union[tuple]) - A Tuple of N 1-D Tensor objects.
The length of input should be greater than 1
Outputs:
Tensors, A Tuple of N N-D Tensor objects.
Supported Platforms:
``Ascend``
Examples:
>>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
>>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
@ -4158,45 +4161,45 @@ class Meshgrid(PrimitiveWithInfer):
>>> meshgrid = ops.Meshgrid(indexing="xy")
>>> output = meshgrid(inputs)
>>> print(output)
(Tensor(shape=[3, 4, 6], dtype=Int32, value=
(Tensor(shape=[3, 4, 5], dtype=Int32, value=
[[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]]]),
Tensor(shape=[3, 4, 6], dtype=Int32, value=
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]]]),
Tensor(shape=[3, 4, 5], dtype=Int32, value=
[[[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5]],
[[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6]],
[[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7]]]),
Tensor(shape=[3, 4, 6], dtype=Int32, value=
[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5],
[5, 5, 5, 5, 5]],
[[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6],
[6, 6, 6, 6, 6]],
[[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7],
[7, 7, 7, 7, 7]]]),
Tensor(shape=[3, 4, 5], dtype=Int32, value=
[[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]],
[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]],
[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]]]))
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]],
[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]],
[[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2],
[8, 9, 0, 1, 2]]]))
"""
@prim_attr_register
@ -4208,12 +4211,12 @@ class Meshgrid(PrimitiveWithInfer):
self.indexing = indexing
def infer_shape(self, x_shape):
validator.check_value_type("shape", x_shape, [tuple, list], self.name)
validator.check_int(len(x_shape), 2, Rel.GE, "len of input_x", self.name)
validator.check_value_type("shape", x_shape, [tuple], self.name)
validator.check_int(len(x_shape), 2, Rel.GE, "len of input", self.name)
n = len(x_shape)
shape_0 = []
for s in x_shape:
validator.check_int(len(s), 1, Rel.EQ, 'each_input_rank', self.name)
validator.check_int(len(s), 1, Rel.EQ, 'each input rank', self.name)
shape_0.append(s[0])
if self.indexing == "xy":
shape_0[0], shape_0[1] = shape_0[1], shape_0[0]
@ -4221,7 +4224,7 @@ class Meshgrid(PrimitiveWithInfer):
return out_shape
def infer_dtype(self, x_type):
validator.check_subclass("input_x[0]", x_type[0], mstype.tensor, self.name)
validator.check_subclass("input[0]", x_type[0], mstype.tensor, self.name)
n = len(x_type)
for i in range(1, n):
validator.check('x_type[%d]' % i, x_type[i], 'base', x_type[0], Rel.EQ, self.name, TypeError)
@ -4393,8 +4396,8 @@ class EditDistance(PrimitiveWithInfer):
>>> edit_distance = EditDistance(hypothesis_shape, truth_shape)
>>> output = edit_distance(hypothesis_indices, hypothesis_values, truth_indices, truth_values)
>>> print(output)
[[1. 1.]
[1. 1.]]
[[1. 1.]
[1. 1.]]
"""
@prim_attr_register

20
mindspore/ops/operations/random_ops.py
View File

@ -621,14 +621,14 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
``Ascend``
Examples:
>>> sampler = ops.LogUniformCandidateSampler(1, 5, True, 5)
>>> sampler = ops.LogUniformCandidateSampler(2, 5, True, 5)
>>> output1, output2, output3 = sampler(Tensor(np.array([[1, 7], [0, 4], [3, 3]])))
>>> print(output1, output2, output3)
[3, 2, 0, 4, 1],
[[9.23129916e-01, 4.93363708e-01],
[9.92489874e-01, 6.58063710e-01],
[7.35534430e-01, 7.35534430e-01]],
[7.35534430e-01, 8.26258004e-01, 9.92489874e-01, 6.58063710e-01, 9.23129916e-01]
[3 2 0 4 1]
[[0.92312991 0.49336370]
[0.99248987 0.65806371]
[0.73553443 0.73553443]]
[0.73553443 0.82625800 0.99248987 0.65806371 0.92312991]
"""
@ -645,14 +645,14 @@ class LogUniformCandidateSampler(PrimitiveWithInfer):
self.num_true = Validator.check_number("num_true", num_true, 1, Rel.GE, self.name)
self.num_sampled = Validator.check_number("num_sampled", num_sampled, 1, Rel.GE, self.name)
if unique:
Validator.check_number("range_max", range_max, num_sampled, Rel.GE, self.name)
Validator.check("range_max", range_max, "num_sampled", num_sampled, Rel.GE, self.name)
self.range_max = range_max
self.unique = unique
self.seed = seed
self.seed = Validator.check_number("seed", seed, 0, Rel.GE, self.name)
def infer_shape(self, true_classes_shape):
Validator.check("true_classes shape rank", len(true_classes_shape), "expect", 2, Rel.EQ, self.name)
Validator.check_int(true_classes_shape[1], self.num_true, Rel.EQ, 'true_classes_shape', self.name)
Validator.check_int(len(true_classes_shape), 2, Rel.EQ, "dim of true_classes", self.name)
Validator.check("true_classes_shape[1]", true_classes_shape[1], "num_true", self.num_true, Rel.EQ, self.name)
return (self.num_sampled,), true_classes_shape, (self.num_sampled,)
def infer_dtype(self, true_classes_type):