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Fix some bugs about API.

This commit is contained in:
liuxiao93 2020-11-06 17:04:21 +08:00
parent a8478839c9
commit 0a1155f938
5 changed files with 141 additions and 91 deletions
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3
mindspore/ops/operations/__init__.py
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@ -53,7 +53,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
NPUAllocFloatStatus, NPUClearFloatStatus,
NPUGetFloatStatus, Pow, RealDiv, IsNan, IsInf, IsFinite, FloatStatus,
Reciprocal, CumSum, HistogramFixedWidth, SquaredDifference, Xdivy, Xlogy,
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod, IFMR,
Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e, TruncateDiv, TruncateMod,
Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps, Tan, TensorDot)
from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, Poisson, UniformInt, UniformReal,
@ -98,7 +98,6 @@ __all__ = [
'EditDistance',
'CropAndResize',
'TensorAdd',
'IFMR',
'Argmax',
'Argmin',
'ArgMaxWithValue',

66
mindspore/ops/operations/_quant_ops.py
View File

@ -43,7 +43,8 @@ __all__ = ["MinMaxUpdatePerLayer",
"BatchNormFoldGradD",
"BatchNormFold2_D",
"BatchNormFold2GradD",
"BatchNormFold2GradReduce"
"BatchNormFold2GradReduce",
"IFMR"
]
@ -1384,3 +1385,66 @@ class WtsARQ(PrimitiveWithInfer):
validator.check_tensor_type_same({"w_min": w_min_dtype}, valid_types, self.name)
validator.check_tensor_type_same({"w_max": w_max_dtype}, valid_types, self.name)
return w_dtype
class IFMR(PrimitiveWithInfer):
"""
The TFMR(Input Feature Map Reconstruction).
Args:
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.
- **data_min** (Tensor) - A Tensor of min value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **data_max** (Tensor) - A Tensor of max value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **cumsum** (Tensor) - A `1-D` Tensor of cumsum bin of data. With int32 data type.
Outputs:
- **scale** (Tensor) - A tensor of optimal scale, the shape is :math:`(1)`. Data dtype is float32.
- **offset** (Tensor) - A tensor of optimal offset, the shape is :math:`(1)`. Data dtype is float32.
Examples:
>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
>>> data_min = Tensor([0.1], mstype.float32)
>>> data_max = Tensor([0.5], mstype.float32)
>>> cumsum = Tensor(np.random.rand(4).astype(np.int32))
>>> ifmr = Q.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=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)
for item in search_range:
validator.check_positive_float(item, "item of search_range", self.name)
validator.check('search_range[1]', search_range[1], 'search_range[0]', search_range[0], Rel.GE, self.name)
validator.check_value_type("search_step", search_step, [float], self.name)
validator.check_value_type("offset_flag", with_offset, [bool], self.name)
def infer_shape(self, data_shape, data_min_shape, data_max_shape, cumsum_shape):
validator.check_equal_int(len(data_min_shape), 1, "dims of data_min", self.name)
validator.check_equal_int(data_min_shape[0], 1, "data_min[0]", self.name)
validator.check_equal_int(len(data_max_shape), 1, "dims of data_max", self.name)
validator.check_equal_int(data_max_shape[0], 1, "data_max[0]", self.name)
validator.check_equal_int(len(cumsum_shape), 1, "dims of cumsum", self.name)
return (1,), (1,)
def infer_dtype(self, data_dtype, data_min_dtype, data_max_dtype, cumsum_dtype):
tuple(map(partial(validator.check_tensor_dtype_valid,
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
("input_value", "input_min", "input_max"),
(data_dtype, data_min_dtype, data_max_dtype)))
validator.check_tensor_dtype_valid("input_bins", cumsum_dtype, [mstype.int32], self.name)
return mstype.tensor_type(mstype.float32), mstype.tensor_type(mstype.float32)

64
mindspore/ops/operations/math_ops.py
View File

@ -16,7 +16,6 @@
"""Operators for math."""
import copy
from functools import partial
import numpy as np
from ... import context
@ -3679,66 +3678,3 @@ class Eps(PrimitiveWithInfer):
'dtype': input_x['dtype'],
}
return out
class IFMR(PrimitiveWithInfer):
"""
The TFMR(Input Feature Map Reconstruction).
Args:
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.
- **data_min** (Tensor) - A Tensor of min value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **data_max** (Tensor) - A Tensor of max value of feature map, the shape is :math:`(1)`.
With float16 or float32 data type.
- **cumsum** (Tensor) - A `1-D` Tensor of cumsum bin of data. With int32 data type.
Outputs:
- **scale** (Tensor) - A tensor of optimal scale, the shape is :math:`(1)`. Data dtype is float32.
- **offset** (Tensor) - A tensor of optimal offset, the shape is :math:`(1)`. Data dtype is float32.
Examples:
>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
>>> data_min = Tensor([0.1], mstype.float32)
>>> data_max = Tensor([0.5], mstype.float32)
>>> cumsum = Tensor(np.random.rand(4).astype(np.int32))
>>> 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=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)
for item in search_range:
validator.check_positive_float(item, "item of search_range", self.name)
validator.check('search_range[1]', search_range[1], 'search_range[0]', search_range[0], Rel.GE, self.name)
validator.check_value_type("search_step", search_step, [float], self.name)
validator.check_value_type("offset_flag", with_offset, [bool], self.name)
def infer_shape(self, data_shape, data_min_shape, data_max_shape, cumsum_shape):
validator.check_equal_int(len(data_min_shape), 1, "dims of data_min", self.name)
validator.check_equal_int(data_min_shape[0], 1, "data_min[0]", self.name)
validator.check_equal_int(len(data_max_shape), 1, "dims of data_max", self.name)
validator.check_equal_int(data_max_shape[0], 1, "data_max[0]", self.name)
validator.check_equal_int(len(cumsum_shape), 1, "dims of cumsum", self.name)
return (1,), (1,)
def infer_dtype(self, data_dtype, data_min_dtype, data_max_dtype, cumsum_dtype):
tuple(map(partial(validator.check_tensor_dtype_valid,
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
("input_value", "input_min", "input_max"),
(data_dtype, data_min_dtype, data_max_dtype)))
validator.check_tensor_dtype_valid("input_bins", cumsum_dtype, [mstype.int32], self.name)
return mstype.tensor_type(mstype.float32), mstype.tensor_type(mstype.float32)

97
mindspore/ops/operations/nn_ops.py
View File

@ -601,10 +601,10 @@ class FusedBatchNorm(Primitive):
Inputs:
- **input_x** (Tensor) - Tensor of shape :math:`(N, C)`.
- **scale** (Tensor) - Tensor of shape :math:`(C,)`.
- **bias** (Tensor) - Tensor of shape :math:`(C,)`.
- **mean** (Tensor) - Tensor of shape :math:`(C,)`.
- **variance** (Tensor) - Tensor of shape :math:`(C,)`.
- **scale** (Parameter) - Tensor of shape :math:`(C,)`.
- **bias** (Parameter) - Tensor of shape :math:`(C,)`.
- **mean** (Parameter) - Tensor of shape :math:`(C,)`.
- **variance** (Parameter) - Tensor of shape :math:`(C,)`.
Outputs:
Tuple of 5 Tensor, the normalized input and the updated parameters.
@ -616,13 +616,30 @@ class FusedBatchNorm(Primitive):
- **updated_moving_variance** (Tensor) - Tensor of shape :math:`(C,)`.
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as P
>>> class FusedBatchNormNet(nn.Cell):
>>> def __init__(self):
>>> super(FusedBatchNormNet, self).__init__()
>>> self.fused_batch_norm = P.FusedBatchNorm()
>>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale")
>>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias")
>>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean")
>>> self.variance = Parameter(Tensor(np.ones([64]), mindspore.float32), name="variance")
>>>
>>> def construct(self, input_x):
>>> out = self.fused_batch_norm(input_x, self.scale, self.bias, self.mean, self.variance)
>>> return out
>>>
>>> input_x = Tensor(np.ones([128, 64, 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)
>>> variance = Tensor(np.ones([64]), mindspore.float32)
>>> op = P.FusedBatchNorm()
>>> output = op(input_x, scale, bias, mean, variance)
>>> net = FusedBatchNormNet()
>>> output = net(input_x)
>>> output[0].shape
(128, 64, 32, 64)
"""
__mindspore_signature__ = (
sig.make_sig('input_x', dtype=sig.sig_dtype.T2),
@ -673,12 +690,12 @@ class FusedBatchNormEx(PrimitiveWithInfer):
Inputs:
- **input_x** (Tensor) - The input of FusedBatchNormEx, Tensor of shape :math:`(N, C)`,
data type: float16 or float32.
- **scale** (Tensor) - Parameter scale, same with gamma above-mentioned, Tensor of shape :math:`(C,)`,
- **scale** (Parameter) - Parameter scale, same with gamma above-mentioned, Tensor of shape :math:`(C,)`,
data type: float32.
- **bias** (Tensor) - Parameter bias, same with beta above-mentioned, Tensor of shape :math:`(C,)`,
- **bias** (Parameter) - Parameter bias, same with beta above-mentioned, Tensor of shape :math:`(C,)`,
data type: float32.
- **mean** (Tensor) - mean value, Tensor of shape :math:`(C,)`, data type: float32.
- **variance** (Tensor) - variance value, Tensor of shape :math:`(C,)`, data type: float32.
- **mean** (Parameter) - mean value, Tensor of shape :math:`(C,)`, data type: float32.
- **variance** (Parameter) - variance value, Tensor of shape :math:`(C,)`, data type: float32.
Outputs:
Tuple of 6 Tensors, the normalized input, the updated parameters and reserve.
@ -692,13 +709,30 @@ class FusedBatchNormEx(PrimitiveWithInfer):
- **reserve** (Tensor) - reserve space, Tensor of shape :math:`(C,)`, data type: float32.
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as P
>>> class FusedBatchNormExNet(nn.Cell):
>>> def __init__(self):
>>> super(FusedBatchNormExNet, self).__init__()
>>> self.fused_batch_norm_ex = P.FusedBatchNormEx()
>>> self.scale = Parameter(Tensor(np.ones([64]), mindspore.float32), name="scale")
>>> self.bias = Parameter(Tensor(np.ones([64]), mindspore.float32), name="bias")
>>> self.mean = Parameter(Tensor(np.ones([64]), mindspore.float32), name="mean")
>>> self.variance = Parameter(Tensor(np.ones([64]), mindspore.float32), name="variance")
>>>
>>> def construct(self, input_x):
>>> out = self.fused_batch_norm_ex(input_x, self.scale, self.bias, self.mean, self.variance)
>>> return out
>>>
>>> input_x = Tensor(np.ones([128, 64, 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)
>>> variance = Tensor(np.ones([64]), mindspore.float32)
>>> op = P.FusedBatchNormEx()
>>> output = op(input_x, scale, bias, mean, variance)
>>> net = FusedBatchNormExNet()
>>> output = net(input_x)
>>> output[0].shape
(128, 64, 32, 64)
"""
__mindspore_signature__ = (
sig.make_sig('input_x', dtype=sig.sig_dtype.T2),
@ -756,7 +790,7 @@ class BNTrainingReduce(PrimitiveWithInfer):
Examples:
>>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32)
>>> bn_training_reduce = P.BNTrainingReduce(input_x)
>>> bn_training_reduce = P.BNTrainingReduce()
>>> output = bn_training_reduce(input_x)
"""
@ -5657,13 +5691,30 @@ class DynamicRNN(PrimitiveWithInfer):
Has the same type with input `b`.
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import numpy as np
>>> from mindspore import Parameter
>>> from mindspore import Tensor
>>> from mindspore.ops import operations as P
>>> import mindspore.context as context
>>> context.set_context(mode=context.GRAPH_MODE)
>>> class DynamicRNNNet(nn.Cell):
>>> def __init__(self):
>>> super(DynamicRNNNet, self).__init__()
>>> self.dynamic_rnn = P.DynamicRNN()
>>>
>>> def construct(self, x, w, b, init_h, init_c):
>>> out = self.dynamic_rnn(x, w, b, None, init_h, init_c)
>>> return out
>>>
>>> x = Tensor(np.random.rand(2, 16, 64).astype(np.float16))
>>> w = Tensor(np.random.rand(96, 128).astype(np.float16))
>>> b = Tensor(np.random.rand(128).astype(np.float16))
>>> init_h = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
>>> init_c = Tensor(np.random.rand(1, 16, 32).astype(np.float16))
>>> dynamic_rnn = P.DynamicRNN()
>>> output = dynamic_rnn(x, w, b, None, init_h, init_c)
>>> net = DynamicRNNNet()
>>> output = net(x, w, b, init_h, init_c)
>>> output[0].shape
(2, 16, 32)
"""

2
tests/ut/python/ops/test_ops.py
View File

@ -1446,7 +1446,7 @@ test_case_math_ops = [
'desc_inputs': [[3, 4, 5], [2, 3, 4, 5]],
'desc_bprop': [[2, 3, 4, 5]]}),
('IFMR', {
'block': P.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
'block': Q.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
search_step=1.0, with_offset=False),
'desc_inputs': [[3, 4, 5], Tensor([0.1], mstype.float32), Tensor([0.9], mstype.float32),
Tensor(np.random.rand(4).astype(np.int32))],