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!6248 change the API interface of op ConfusionMulGrad

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Merge pull request !6248 from lihongkang/v2_master
pull/6248/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 02c2a61065 74e3adebb8
commit 111efc7538
4 changed files with 62 additions and 78 deletions
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3
mindspore/ops/operations/__init__.py
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@ -67,7 +67,7 @@ from .nn_ops import (LSTM, SGD, Adam, FusedSparseAdam, FusedSparseLazyAdam, Appl
GetNext, L2Normalize, LayerNorm, L2Loss, CTCLoss, CTCLossV2, CTCGreedyDecoder,
LogSoftmax,
MaxPool, DataFormatDimMap,
AvgPool, Conv2DBackpropInput, ConfusionMulGrad,
AvgPool, Conv2DBackpropInput,
MaxPoolWithArgmax, OneHot, Pad, MirrorPad, PReLU, ReLU, ReLU6, ReLUV2, HSwish, HSigmoid,
ResizeBilinear, Sigmoid,
SigmoidCrossEntropyWithLogits,
@ -136,7 +136,6 @@ __all__ = [
'LogSoftmax',
'SoftmaxCrossEntropyWithLogits',
'ROIAlign',
'ConfusionMulGrad',
'SparseSoftmaxCrossEntropyWithLogits',
'SGD',
'ApplyMomentum',

61
mindspore/ops/operations/_inner_ops.py
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@ -19,6 +19,7 @@ from ..._checkparam import Rel
from ..._checkparam import Validator as validator
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register
from ..operations.math_ops import _infer_shape_reduce
class StridedSliceAICPU(PrimitiveWithInfer):
@ -681,3 +682,63 @@ class DynamicRNN(PrimitiveWithInfer):
validator.check_tensor_type_same({"h dtype": h_dtype}, (mstype.float32, mstype.float16), self.name)
validator.check_tensor_type_same({"c dtype": c_dtype}, (mstype.float32, mstype.float16), self.name)
return b_dtype, x_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype
class ConfusionMulGrad(PrimitiveWithInfer):
"""
`output0` is the dot product result of input0 and input1.
`output1` is the dot product result of input0 and input1, then apply the reducesum operation on it.
Args:
axis (Union[int, tuple[int], list[int]]): The dimensions to reduce.
Default:(), reduce all dimensions. Only constant value is allowed.
keep_dims (bool):
- If true, keep these reduced dimensions and the length as 1.
- If false, don't keep these dimensions. Default:False.
Inputs:
- **input_0** (Tensor) - The input Tensor.
- **input_1** (Tensor) - The input Tensor.
- **input_2** (Tensor) - The input Tensor.
Outputs:
- **output_0** (Tensor) - The same shape as `input0`.
- **output_1** (Tensor)
- If axis is (), and keep_dims is false, the output is a 0-D array representing
the sum of all elements in the input array.
- If axis is int, set as 2, and keep_dims is false,
the shape of output is :math:`(x_1,x_3,...,x_R)`.
- If axis is tuple(int), set as (2,3), and keep_dims is false,
the shape of output is :math:`(x_1,x_4,...x_R)`.
Examples:
>>> confusion_mul_grad = P.ConfusionMulGrad()
>>> input_0 = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
>>> input_1 = Tensor(np.random.randint(0, 4, (2, 3)), mindspore.float32)
>>> input_2 = Tensor(np.random.randint(-4, 0, (2, 3)), mindspore.float32)
>>> output_0, output_1 = confusion_mul_grad(input_0, input_1, input_2)
output_0:
[[ 3. 1. 0.]
[-6. 2. -2.]]
output_1:
-3.0
"""
@prim_attr_register
def __init__(self, axis=(), keep_dims=False):
self.init_prim_io_names(inputs=["input0", "input1", "input2"], outputs=["output0", "output1"])
self.axis_ = validator.check_value_type("axis", axis, [int, tuple, list], self.name)
self.keep_dims_ = validator.check_value_type("keep_dims", keep_dims, [bool], self.name)
def infer_shape(self, input0_shape, input1_shape, input2_shape):
outshape0 = input0_shape
outshape1 = _infer_shape_reduce(input1_shape, self.axis_, self.keep_dims_, self.name)
return outshape0, outshape1
def infer_dtype(self, input0_dtype, input1_dtype, input2_dtype):
validator.check_subclass("input0_dtype", input0_dtype, mstype.tensor, self.name)
validator.check_subclass("input1_dtype", input1_dtype, mstype.tensor, self.name)
validator.check_subclass("input2_dtype", input2_dtype, mstype.tensor, self.name)
return input0_dtype, input1_dtype

61
mindspore/ops/operations/nn_ops.py
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@ -27,7 +27,6 @@ from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common import dtype as mstype
from ..primitive import Primitive, PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register
from ..operations.math_ops import _infer_shape_reduce
def _check_positive_int_or_tuple(arg_name, arg_value, prim_name, allow_four=False, ret_four=False):
@ -5151,66 +5150,6 @@ class SparseApplyFtrlV2(PrimitiveWithInfer):
return var_dtype, accum_dtype, linear_dtype
class ConfusionMulGrad(PrimitiveWithInfer):
"""
`output0` is the dot product result of input0 and input1.
`output1` is the dot product result of input0 and input1, then apply the reducesum operation on it.
Args:
axis (Union[int, tuple[int], list[int]]): The dimensions to reduce.
Default:(), reduce all dimensions. Only constant value is allowed.
keep_dims (bool):
- If true, keep these reduced dimensions and the length as 1.
- If false, don't keep these dimensions. Default: False.
Inputs:
- **input_0** (Tensor) - The input Tensor.
- **input_1** (Tensor) - The input Tensor.
- **input_2** (Tensor) - The input Tensor.
Outputs:
- **output_0** (Tensor) - The same shape as `input0`.
- **output_1** (Tensor)
- If axis is (), and keep_dims is False, the output is a 0-D array representing
the sum of all elements in the input array.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(x_1,x_3,...,x_R)`.
- If axis is tuple(int), set as (2,3), and keep_dims is False,
the shape of output is :math:`(x_1,x_4,...x_R)`.
Examples:
>>> confusion_mul_grad = P.ConfusionMulGrad()
>>> input_0 = Tensor(np.random.randint(-2, 2, (2, 3)), mindspore.float32)
>>> input_1 = Tensor(np.random.randint(0, 4, (2, 3)), mindspore.float32)
>>> input_2 = Tensor(np.random.randint(-4, 0, (2, 3)), mindspore.float32)
>>> output_0, output_1 = confusion_mul_grad(input_0, input_1, input_2)
output_0:
[[ 3. 1. 0.]
[-6. 2. -2.]]
output_1:
-3.0
"""
@prim_attr_register
def __init__(self, axis=(), keep_dims=False):
self.init_prim_io_names(inputs=["input0", "input1", "input2"], outputs=["output0", "output1"])
self.axis_ = validator.check_value_type("axis", axis, [int, tuple, list], self.name)
self.keep_dims_ = validator.check_value_type("keep_dims", keep_dims, [bool], self.name)
def infer_shape(self, input0_shape, input1_shape, input2_shape):
outshape0 = input0_shape
outshape1 = _infer_shape_reduce(input1_shape, self.axis_, self.keep_dims_, self.name)
return outshape0, outshape1
def infer_dtype(self, input0_dtype, input1_dtype, input2_dtype):
validator.check_subclass("input0_dtype", input0_dtype, mstype.tensor, self.name)
validator.check_subclass("input1_dtype", input1_dtype, mstype.tensor, self.name)
validator.check_subclass("input2_dtype", input2_dtype, mstype.tensor, self.name)
return input0_dtype, input1_dtype
class Dropout(PrimitiveWithInfer):
"""
During training, randomly zeroes some of the elements of the input tensor with probability.

15
tests/ut/python/ops/test_ops.py
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@ -2329,21 +2329,6 @@ test_case_other_ops = [
'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
Tensor(np.array([1.2]).astype(np.float32))],
'skip': ['backward']}),
('ConfusionMulGrad_1', {
'block': P.ConfusionMulGrad(axis=[0], keep_dims=False),
'desc_inputs': [[3, 2], [3, 2], [3, 2]],
'desc_bprop': [[3, 2], [2]],
'skip': ['backward']}),
('ConfusionMulGrad_2', {
'block': P.ConfusionMulGrad(axis=[0], keep_dims=True),
'desc_inputs': [[3, 2], [3, 2], [3, 2]],
'desc_bprop': [[3, 2], [1, 2]],
'skip': ['backward']}),
('ConfusionMulGrad_3', {
'block': P.ConfusionMulGrad(axis=(), keep_dims=True),
'desc_inputs': [[2, 3, 4], [2, 3, 4], [2, 3, 4]],
'desc_bprop': [[2, 3, 4], [1, 1, 1]],
'skip': ['backward']}),
('HistogramSummary', {
'block': HistogramSummaryNet(),
'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),

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