!8318 Fix some bugs about API.

From: @liu_xiao_93
Reviewed-by: @liangchenghui
Signed-off-by: @liangchenghui

mindspore/ops/operations/__init__.py

@@ -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',

mindspore/ops/operations/_quant_ops.py

@@ -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)

mindspore/ops/operations/math_ops.py

@@ -16,7 +16,6 @@
 """Operators for math."""
 
 import copy
-from functools import partial
 
 import numpy as np
 from ... import context
@@ -3680,66 +3679,3 @@ 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)

mindspore/ops/operations/nn_ops.py

@@ -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)
     """
 
@@ -5662,13 +5696,30 @@ 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)
     """

tests/ut/python/ops/test_ops.py

@@ -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))],