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fix bugs

This commit is contained in:
lihongkang 2020-11-04 20:28:47 +08:00
parent e62c116277
commit 3ade82e8f2
8 changed files with 32 additions and 20 deletions
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2
mindspore/nn/layer/pooling.py
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@ -185,7 +185,7 @@ class MaxPool1d(_PoolNd):
Examples:
>>> max_pool = nn.MaxPool1d(kernel_size=3, strides=1)
>>> x = Tensor(np.random.randint(0, 10, [1, 2, 4]), mindspore.float32)
>>> output = pool(x)
>>> output = max_pool(x)
>>> output.shape
(1, 2, 2)
"""

3
mindspore/nn/learning_rate_schedule.py
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@ -20,7 +20,6 @@ from ..common import dtype as mstype
from ..ops import operations as P
from .cell import Cell
from .._checkparam import Validator as validator
from .._checkparam import Rel
class LearningRateSchedule(Cell):
@ -246,7 +245,7 @@ class CosineDecayLR(LearningRateSchedule):
>>> min_lr = 0.01
>>> max_lr = 0.1
>>> decay_steps = 4
>>> global_step = Tensor(2, mstype.int32)
>>> global_steps = Tensor(2, mstype.int32)
>>> cosine_decay_lr = CosineDecayLR(min_lr, max_lr, decay_steps)
>>> cosine_decay_lr(global_steps)
"""

2
mindspore/nn/loss/loss.py
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@ -543,7 +543,7 @@ class CosineEmbeddingLoss(_Loss):
>>> x1 = Tensor(np.array([[0.3, 0.8], [0.4, 0.3]]), mindspore.float32)
>>> x2 = Tensor(np.array([[0.4, 1.2], [-0.4, -0.9]]), mindspore.float32)
>>> y = Tensor(np.array([1,-1]), mindspore.int32)
>>> cosine_embedding_loss = P.CosineEmbeddingLoss()
>>> cosine_embedding_loss = nn.CosineEmbeddingLoss()
>>> cosine_embedding_loss(x1, x2, y)
[0.0003426671]
"""

6
mindspore/ops/operations/array_ops.py
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@ -1125,6 +1125,7 @@ class ScalarToArray(PrimitiveWithInfer):
>>> op = P.ScalarToArray()
>>> data = 1.0
>>> output = op(data)
1.0
"""
@prim_attr_register
@ -1156,6 +1157,7 @@ class ScalarToTensor(PrimitiveWithInfer):
>>> op = P.ScalarToTensor()
>>> data = 1
>>> output = op(data, mindspore.float32)
1.0
"""
@prim_attr_register
@ -2987,7 +2989,7 @@ class ScatterMul(_ScatterOp):
Examples:
>>> input_x = Parameter(Tensor(np.array([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]]), mindspore.float32), name="x")
>>> indices = Tensor(np.array([0, 1]), mindspore.int32)
>>> updates = Tensor(np.ones([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32)
>>> updates = Tensor(np.array([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]), mindspore.float32)
>>> scatter_mul = P.ScatterMul()
>>> output = scatter_mul(input_x, indices, updates)
[[2.0, 2.0, 2.0], [4.0, 4.0, 4.0]]
@ -3496,7 +3498,7 @@ class BatchToSpaceND(PrimitiveWithInfer):
This operation will divide batch dimension N into blocks with block_shape, the output tensor's N dimension
is the corresponding number of blocks after division. The output tensor's H, W dimension is product of original H, W
dimension and block_shape with given amount to crop from dimension, respectively.B
dimension and block_shape with given amount to crop from dimension, respectively.
Args:
block_shape (Union[list(int), tuple(int)]): The block shape of dividing block with all value >= 1.

1
mindspore/ops/operations/inner_ops.py
View File

@ -35,6 +35,7 @@ class ScalarCast(PrimitiveWithInfer):
Examples:
>>> scalar_cast = P.ScalarCast()
>>> output = scalar_cast(255.0, mindspore.int32)
255
"""
@prim_attr_register

24
mindspore/ops/operations/math_ops.py
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@ -495,6 +495,8 @@ class ReduceMax(_Reduce):
>>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceMax(keep_dims=True)
>>> output = op(input_x, 1)
>>> output.shape
(3, 1, 5, 6)
"""
@prim_attr_register
@ -572,6 +574,8 @@ class ReduceProd(_Reduce):
>>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceProd(keep_dims=True)
>>> output = op(input_x, 1)
>>> output.shape
(3, 1, 5, 6)
"""
@ -730,8 +734,7 @@ class BatchMatMul(MatMul):
[[[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]]
[[3. 3. 3. 3.]]],
[[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
@ -744,8 +747,7 @@ class BatchMatMul(MatMul):
[[[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]]
[[3. 3. 3. 3.]]],
[[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
[[3. 3. 3. 3.]]
@ -3683,11 +3685,11 @@ class IFMR(PrimitiveWithInfer):
The TFMR(Input Feature Map Reconstruction).
Args:
min_percentile (float): Min init percentile.
max_percentile (float): Max init percentile.
search_range Union[list(float), tuple(float)]: Range of searching.
search_step (float): Step size of searching.
with_offset (bool): Whether using offset.
min_percentile (float): Min init percentile. Default: 0.999999.
max_percentile (float): Max init percentile. Default: 0.999999.
search_range Union[list(float), tuple(float)]: Range of searching. Default: [0.7, 1.3].
search_step (float): Step size of searching. Default: 0.01.
with_offset (bool): Whether using offset. Default: True.
Inputs:
- **data** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
@ -3709,10 +3711,12 @@ class IFMR(PrimitiveWithInfer):
>>> ifmr = P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
search_step=1.0, with_offset=False)
>>> output = ifmr(data, data_min, data_max, cumsum)
([7.87401572e-03], [0.00000000e+00])
"""
@prim_attr_register
def __init__(self, min_percentile, max_percentile, search_range, search_step, with_offset):
def __init__(self, min_percentile=0.999999, max_percentile=0.999999, search_range=(0.7, 1.3), search_step=0.01,
with_offset=True):
validator.check_value_type("min_percentile", min_percentile, [float], self.name)
validator.check_value_type("max_percentile", max_percentile, [float], self.name)
validator.check_value_type("search_range", search_range, [list, tuple], self.name)

12
mindspore/ops/operations/nn_ops.py
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@ -319,6 +319,8 @@ class ReLU6(PrimitiveWithInfer):
>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> relu6 = P.ReLU6()
>>> result = relu6(input_x)
[[0. 4. 0.]
[2. 0. 6.]]
"""
@prim_attr_register
@ -352,7 +354,7 @@ class ReLUV2(PrimitiveWithInfer):
>>> relu_v2 = P.ReLUV2()
>>> output = relu_v2(input_x)
([[[[1., 0.], [0., 4.]], [[0., 6.], [7., 0.]]]],
[[[[1, 0], [2, 0]], [[2, 0], [1, 0]]]])
[[[[[1, 0], [2, 0]], [[2, 0], [1, 0]]]]])
"""
@prim_attr_register
@ -892,7 +894,7 @@ class BatchNorm(PrimitiveWithInfer):
- **reserve_space_2** (Tensor) - Tensor of shape :math:`(C,)`.
Examples:
>>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
>>> input_x = Tensor(np.ones([32, 64]), mindspore.float32)
>>> scale = Tensor(np.ones([64]), mindspore.float32)
>>> bias = Tensor(np.ones([64]), mindspore.float32)
>>> mean = Tensor(np.ones([64]), mindspore.float32)
@ -2558,7 +2560,11 @@ class ResizeBilinear(PrimitiveWithInfer):
>>> tensor = Tensor([[[[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]]]], mindspore.float32)
>>> resize_bilinear = P.ResizeBilinear((5, 5))
>>> result = resize_bilinear(tensor)
>>> assert result.shape == (1, 1, 5, 5)
[[[[1. 2. 3. 4. 5.]
[1. 2. 3. 4. 5.]
[1. 2. 3. 4. 5.]
[1. 2. 3. 4. 5.]
[1. 2. 3. 4. 5.]]]]
"""
@prim_attr_register

2
mindspore/ops/operations/other_ops.py
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@ -45,7 +45,7 @@ class Assign(PrimitiveWithCheck):
>>>
>>> def construct(self, x):
>>> P.Assign()(self.y, x)
>>> return x
>>> return self.y
>>> x = Tensor([2.0], mindspore.float32)
>>> net = Net()
>>> net(x)