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hidden range

This commit is contained in:
jiangjinsheng 2020-06-15 17:22:14 +08:00
parent 14b997448a
commit 032e921298
7 changed files with 74 additions and 82 deletions
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5
mindspore/nn/layer/embedding.py
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@ -45,8 +45,9 @@ class Embedding(Cell):
Inputs:
- **input** (Tensor) - Tensor of shape :math:`(\text{batch_size}, \text{input_length})`. The element of
the Tensor should be integer and not larger than vocab_size. else the corresponding embedding vector is zero
if larger than vocab_size.
the Tensor should be integer and not larger than vocab_size. else the corresponding embedding vector is zero
if larger than vocab_size.
Outputs:
Tensor of shape :math:`(\text{batch_size}, \text{input_length}, \text{embedding_size})`.

3
mindspore/ops/_grad/grad_array_ops.py
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@ -17,6 +17,7 @@
from .. import operations as P
from ..operations import _grad_ops as G
from ..operations import _inner_ops as inner
from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ..functional import broadcast_gradient_args
from .. import functional as F
@ -341,7 +342,7 @@ def get_bprop_sparse_gather_v2(self):
return bprop
@bprop_getters.register(P.Range)
@bprop_getters.register(inner.Range)
def get_bprop_range(self):
"""Generate bprop for Range"""

7
mindspore/ops/operations/__init__.py
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@ -23,7 +23,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
Diag, DiagPart, DType, ExpandDims, Eye,
Fill, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue, Range,
Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
SameTypeShape, ScatterAdd, ScatterMax, ScatterUpdate,
ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
Shape, Size, Slice, Split, EmbeddingLookup,
@ -75,7 +75,7 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
ApplyAdaMax, ApplyAdadelta, ApplyAdagrad, ApplyAdagradV2,
ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
CheckValid, MakeRefKey, Partial, Depend, CheckBprop, ConfusionMatrix)
CheckValid, MakeRefKey, Partial, Depend, CheckBprop)
from . import _quant_ops
from ._quant_ops import *
from .thor_ops import *
@ -303,13 +303,12 @@ __all__ = [
"Atan",
"Atanh",
"BasicLSTMCell",
"ConfusionMatrix",
"BroadcastTo",
"Range",
"DataFormatDimMap",
"ApproximateEqual",
"InplaceUpdate",
"InTopK",
"DataFormatDimMap"
]
__all__.extend(_quant_ops.__all__)

61
mindspore/ops/operations/_inner_ops.py
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@ -15,9 +15,10 @@
"""Inner operators."""
from ..._checkparam import Rel
from ..._checkparam import Validator as validator
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register
from ..primitive import PrimitiveWithInfer, prim_attr_register
class ExtractImagePatches(PrimitiveWithInfer):
@ -96,3 +97,61 @@ class ExtractImagePatches(PrimitiveWithInfer):
"""infer dtype"""
validator.check_tensor_type_same({"input_x": input_x}, mstype.number_type, self.name)
return input_x
class Range(PrimitiveWithInfer):
r"""
Creates a sequence of numbers.
Set `input_x` as :math:`x_i` for each element, `output` as follows:
.. math::
\text{output}(x_i) = x_i * \text{delta} + \text{start}
Args:
start (float): If `limit` is `None`, the value acts as limit in the range and first entry
defaults to `0`. Otherwise, it acts as first entry in the range.
limit (float): Acts as upper limit of sequence. If `None`, defaults to the value of `start`
while set the first entry of the range to `0`. It can not be equal to `start`.
delta (float): Increment of the range. It can not be equal to zero. Default: 1.0.
Inputs:
- **input_x** (Tensor) - The assistant data. A `1-D` tensor of type float32 or int32.
Outputs:
Tensor, has the same shape and dtype as `input_x`.
Examples:
>>> range = P.Range(1.0, 8.0, 2.0)
>>> x = Tensor(np.array([1, 2, 3, 2]), mindspore.int32)
>>> range(x)
[3, 5, 7, 5]
"""
@prim_attr_register
def __init__(self, start, limit=None, delta=1.0):
self.init_prim_io_names(inputs=['x'], outputs=['y'])
self.delta = validator.check_value_type("delta", delta, [float], self.name)
validator.check_value_type("start", start, [float], self.name)
if limit is None:
self.start = 0.0
self.limit = start
self.add_prim_attr("start", self.start)
self.add_prim_attr("limit", self.limit)
else:
validator.check_value_type("limit", limit, [float], self.name)
validator.check('start', self.start, 'limit', self.limit, Rel.NE, self.name)
if self.delta == 0.0:
raise ValueError("The input of `delta` can not be equal to zero.")
if self.delta > 0.0 and self.start > self.limit:
raise ValueError(f"Limit should be greater than start when delta:{self.delta} is more than zero, "
f"but got start:{self.start}, limit:{self.limit}")
if self.delta < 0.0 and self.start < self.limit:
raise ValueError(f"Start should be greater than limit when delta:{self.delta} is less than zero, "
f"but got start:{self.start}, limit:{self.limit}")
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_type_same({'x_dtype': x_dtype}, [mstype.float32, mstype.int32], self.name)
return x_dtype

58
mindspore/ops/operations/array_ops.py
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@ -556,64 +556,6 @@ class SparseGatherV2(GatherV2):
"""
class Range(PrimitiveWithInfer):
r"""
Creates a sequence of numbers.
Set `input_x` as :math:`x_i` for each element, `output` as follows:
.. math::
\text{output}(x_i) = x_i * \text{delta} + \text{start}
Args:
start (float): If `limit` is `None`, the value acts as limit in the range and first entry
defaults to `0`. Otherwise, it acts as first entry in the range.
limit (float): Acts as upper limit of sequence. If `None`, defaults to the value of `start`
while set the first entry of the range to `0`. It can not be equal to `start`.
delta (float): Increment of the range. It can not be equal to zero. Default: 1.0.
Inputs:
- **input_x** (Tensor) - The assistant data. A `1-D` tensor of type float32 or int32.
Outputs:
Tensor, has the same shape and dtype as `input_x`.
Examples:
>>> range = P.Range(1.0, 8.0, 2.0)
>>> x = Tensor(np.array([1, 2, 3, 2]), mindspore.int32)
>>> range(x)
[3, 5, 7, 5]
"""
@prim_attr_register
def __init__(self, start, limit=None, delta=1.0):
self.init_prim_io_names(inputs=['x'], outputs=['y'])
self.delta = validator.check_value_type("delta", delta, [float], self.name)
validator.check_value_type("start", start, [float], self.name)
if limit is None:
self.start = 0.0
self.limit = start
self.add_prim_attr("start", self.start)
self.add_prim_attr("limit", self.limit)
else:
validator.check_value_type("limit", limit, [float], self.name)
validator.check('start', self.start, 'limit', self.limit, Rel.NE, self.name)
if self.delta == 0.0:
raise ValueError("The input of `delta` can not be equal to zero.")
if self.delta > 0.0 and self.start > self.limit:
raise ValueError(f"Limit should be greater than start when delta:{self.delta} is more than zero, "
f"but got start:{self.start}, limit:{self.limit}")
if self.delta < 0.0 and self.start < self.limit:
raise ValueError(f"Start should be greater than limit when delta:{self.delta} is less than zero, "
f"but got start:{self.start}, limit:{self.limit}")
def infer_shape(self, x_shape):
return x_shape
def infer_dtype(self, x_dtype):
validator.check_tensor_type_same({'x_dtype': x_dtype}, [mstype.float32, mstype.int32], self.name)
return x_dtype
class EmbeddingLookup(PrimitiveWithInfer):
"""
Returns a slice of input tensor based on the specified indices.

15
tests/ut/python/ops/test_array_ops.py
View File

@ -25,6 +25,7 @@ from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.nn import Cell
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops import prim_attr_register
from mindspore.ops.primitive import PrimitiveWithInfer
import mindspore.context as context
@ -286,19 +287,10 @@ class SpaceToBatchNDNet(Cell):
return self.space_to_batch_nd(x)
class ConfusionMatrixNet(Cell):
def __init__(self):
super(ConfusionMatrixNet, self).__init__()
self.confusion_matrix = P.ConfusionMatrix(4, "int32")
def construct(self, x, y):
return self.confusion_matrix(x, y)
class RangeNet(Cell):
def __init__(self):
super(RangeNet, self).__init__()
self.range_ops = P.Range(1.0, 8.0, 2.0)
self.range_ops = inner.Range(1.0, 8.0, 2.0)
def construct(self, x):
return self.range_ops(x)
@ -344,9 +336,6 @@ test_case_array_ops = [
('BatchToSpaceNDNet', {
'block': BatchToSpaceNDNet(),
'desc_inputs': [Tensor(np.random.rand(4, 1, 1, 1).astype(np.float16))]}),
('ConfusionMatrixNet', {
'block': ConfusionMatrixNet(),
'desc_inputs': [Tensor([0, 1, 1, 3], ms.int32), Tensor([0, 1, 1, 3], ms.int32)]}),
('RangeNet', {
'block': RangeNet(),
'desc_inputs': [Tensor(np.array([1, 2, 3, 2]), ms.int32)]}),

7
tests/ut/python/ops/test_ops.py
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@ -25,6 +25,7 @@ from mindspore.common import dtype as mstype
from mindspore.ops import functional as F
from mindspore.ops import operations as P
from mindspore.ops.operations import _grad_ops as G
from mindspore.ops.operations import _inner_ops as inner
from ..ut_filter import non_graph_engine
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
@ -1051,7 +1052,7 @@ test_case_nn_ops = [
'desc_inputs': [[3, 1, 2], Tensor(np.array([0, 1]).astype(np.int32))],
'desc_bprop': [[2, 1, 2]]}),
('Range', {
'block': P.Range(1.0, 5.0),
'block': inner.Range(1.0, 5.0),
'desc_inputs': [Tensor(np.ones([10]).astype(np.float32))],
'desc_bprop': [[10]]}),
('UnsortedSegmentSum', {
@ -1454,7 +1455,7 @@ test_case_array_ops = [
'desc_inputs': [(Tensor(np.array([1], np.float32)),
Tensor(np.array([1], np.float32)),
Tensor(np.array([1], np.float32)))],
'desc_bprop': [[3,]]}),
'desc_bprop': [[3, ]]}),
('Pack_0', {
'block': NetForPackInput(P.Pack()),
'desc_inputs': [[2, 2], [2, 2], [2, 2]],
@ -1527,7 +1528,7 @@ test_case_array_ops = [
Tensor(np.array([0, 1, 1]).astype(np.int32))],
'desc_bprop': [Tensor(np.array([[1, 2, 3], [4, 2, 1]]).astype(np.float32))]}),
('BroadcastTo', {
'block': P.BroadcastTo((2,3)),
'block': P.BroadcastTo((2, 3)),
'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.float32))],
'desc_bprop': [Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.float32))]}),
('InTopK', {