add moment and nonzero.

mindspore/nn/layer/math.py

@@ -19,12 +19,13 @@ from mindspore.ops import operations as P
 from mindspore.ops.operations import _inner_ops as inner
 from mindspore.common.tensor import Tensor
 from mindspore.ops.primitive import constexpr
+from mindspore.ops import functional as F
 from ..cell import Cell
 from ...common import dtype as mstype
 from ..._checkparam import Validator as validator
 
 
-__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace', 'LGamma', 'MatMul']
+__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace', 'LGamma', 'MatMul', 'Moments']
 
 
 class ReduceLogSumExp(Cell):
@@ -451,3 +452,65 @@ class MatMul(Cell):
             matmul_broadcast = self.squeeze_right_op(matmul_broadcast)
 
         return matmul_broadcast
+
+
+@constexpr
+def _check_input_dtype(param_name, input_dtype, allow_dtypes, cls_name):
+    validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
+
+
+class Moments(Cell):
+    """
+    Calculate the mean and variance of `x`.
+
+    Args:
+        axis (Union[int, tuple(int)]): Calculates the mean and variance along the specified axis. Default: ().
+        keep_dims (bool): If true, The dimension of mean and variance are identical with input's.
+                          If false, don't keep these dimensions. Default: False.
+
+    Inputs:
+        - **input_x** (Tensor) - The tensor to be calculated. Only float16 and float32 are supported.
+
+    Outputs:
+        - **mean** (Tensor) - The mean of input x, with the same date type as input x.
+        - **variance** (Tensor) - The variance of input x, with the same date type as input x.
+
+    Examples:
+        >>> net = nn.Moments(axis=3, keep_dims=True)
+        >>> input_x = Tensor(np.array([[[[1, 2, 3, 4], [3, 4, 5, 6]]]]), mindspore.float32)
+        >>> mean, var = net(input_x)
+        mean: [[[[2.5], [4.5]]]]
+        var:  [[[[1.25], [1.25]]]]
+    """
+
+    def __init__(self, axis=None, keep_dims=None):
+        super(Moments, self).__init__()
+        if axis is None:
+            axis = ()
+        if isinstance(axis, tuple):
+            for idx, item in enumerate(axis):
+                validator.check_value_type("axis[%d]" % idx, item, [int], self.cls_name)
+        self.axis = validator.check_value_type('axis', axis, [int, tuple], self.cls_name)
+        if keep_dims is None:
+            keep_dims = False
+        self.keep_dims = validator.check_value_type('keep_dims', keep_dims, [bool], self.cls_name)
+        self.cast = P.Cast()
+        self.reduce_mean = P.ReduceMean(keep_dims=True)
+        self.square_diff = P.SquaredDifference()
+        self.squeeze = P.Squeeze(self.axis)
+
+    def construct(self, x):
+        tensor_dtype = x.dtype
+        _check_input_dtype("input x", tensor_dtype, [mstype.float16, mstype.float32], self.cls_name)
+        if tensor_dtype == mstype.float16:
+            x = self.cast(x, mstype.float32)
+        mean = self.reduce_mean(x, self.axis)
+        variance = self.reduce_mean(self.square_diff(x, F.stop_gradient(mean)), self.axis)
+        if not self.keep_dims:
+            mean = self.squeeze(mean)
+            variance = self.squeeze(variance)
+        if tensor_dtype == mstype.float16:
+            mean = self.cast(mean, mstype.float16)
+            variance = self.cast(variance, mstype.float16)
+            return mean, variance
+        return mean, variance

mindspore/ops/composite/__init__.py

@@ -27,6 +27,7 @@ from .multitype_ops.add_impl import hyper_add
 from .multitype_ops.ones_like_impl import ones_like
 from .multitype_ops.zeros_like_impl import zeros_like
 from .random_ops import normal, laplace, uniform, gamma, poisson, multinomial
+from .math_ops import count_nonzero
 
 
 __all__ = [
@@ -47,4 +48,5 @@ __all__ = [
     'gamma',
     'poisson',
     'multinomial',
-    'clip_by_value',]
+    'clip_by_value',
+    'count_nonzero']

mindspore/ops/composite/math_ops.py

@@ -0,0 +1,75 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""math Operations."""
+from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
+from mindspore.common import dtype as mstype
+from mindspore._checkparam import Validator as validator
+from mindspore.ops.primitive import constexpr
+from mindspore.ops import functional as F
+from .. import operations as P
+
+
+@constexpr
+def _check_validate_axis(axis, name):
+    if isinstance(axis, (tuple, list)):
+        for idx, item in enumerate(axis):
+            validator.check_value_type("axis[%d]" % idx, item, [int], name)
+    axis = validator.check_value_type('axis', axis, [int, tuple, list], name)
+    return axis
+
+
+@constexpr
+def _check_validate_keepdims(keep_dims, name):
+    keep_dims = validator.check_value_type('keep_dims', keep_dims, [bool], name)
+    return keep_dims
+
+
+def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
+    """
+    Count number of nonzero elements across axis of input tensor
+
+    Args:
+        - **x** (Tensor[Number]) - Input data is used to count non-zero numbers.
+        - **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Only constant value is allowed.
+                                                         Default: (), reduce all dimensions.
+
+        - **keep_dims** (bool) - If true, keep these reduced dimensions and the length is 1.
+                                 If false, don't keep these dimensions. Default: False.
+        - **dtype** (Union[Number, mstype.bool_]) - The data type of the output tensor. Only constant value is allowed.
+                                                    Default: mstype.int32
+
+    Returns:
+          Tensor, number of nonzero element. The data type is dtype.
+
+    Examples:
+        >>> input_tensor = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
+        >>> nonzero_num = count_nonzero(x=input_x, axis=[0, 1], keep_dims=True, dtype=mstype.int32)
+        nonzero_num: [[3]]
+    """
+
+    const_utils.check_valid_type(F.dtype(x), mstype.number_type, 'input x')
+    axis = _check_validate_axis(axis, "count_nonzero")
+    keep_dims = _check_validate_keepdims(keep_dims, "count_nonzero")
+    const_utils.check_valid_type(dtype, mstype.number_type + (mstype.bool_,), 'dtype')
+
+    not_equal = P.NotEqual()
+    cast = P.Cast()
+    reduce_sum = P.ReduceSum(keep_dims)
+    nonzero_bool = not_equal(x, 0)
+    # ReduceSum only support float16 or float32 tensor.
+    nonzero_val = cast(nonzero_bool, mstype.float16)
+    nonzero_num = cast(reduce_sum(nonzero_val, axis), dtype)
+
+    return nonzero_num

mindspore/ops/operations/_quant_ops.py

@@ -241,9 +241,9 @@ class FakeQuantWithMinMaxVarsGradient(PrimitiveWithInfer):
         - **max** (Tensor) - Value of the max range of the input data x.
 
     Outputs:
-        - **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape date type as input x.
-        - **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape date type as input min.
-        - **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape date type as input max.
+        - **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape and date type as input x.
+        - **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape and date type as input min.
+        - **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape and date type as input max.
 
     Examples:
         >>> gradients = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
@@ -356,9 +356,9 @@ class FakeQuantWithMinMaxVarsPerChannelGradient(PrimitiveWithInfer):
         - **max** (Tensor) - Value of the max range of the input data x.
 
     Outputs:
-        - **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape date type as input x.
-        - **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape date type as input min.
-        - **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape date type as input max.
+        - **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape and date type as input x.
+        - **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape and date type as input min.
+        - **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape and date type as input max.
 
     Examples:
         >>> gradients = Tensor(np.random.rand(3, 16, 3, 4), mstype.float32)

mindspore/ops/operations/array_ops.py

@@ -489,6 +489,7 @@ class DynamicShape(Primitive):
         self.add_prim_attr('is_dynamic_shape', True)
         self.add_prim_attr("dynamic_shape_depends", [0])
 
+
 class Squeeze(PrimitiveWithInfer):
     """
     Returns a tensor with the same type but dimensions of 1 are removed based on `axis`.

tests/ut/python/ops/test_ops.py

@@ -26,7 +26,7 @@ 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 mindspore.ops.operations._quant_ops import FakeQuantWithMinMaxVars, FakeQuantWithMinMaxVarsPerChannel
+from mindspore.ops.operations import _quant_ops as Q
 from ..ut_filter import non_graph_engine
 from ....mindspore_test_framework.mindspore_test import mindspore_test
 from ....mindspore_test_framework.pipeline.forward.compile_forward \
@@ -216,6 +216,31 @@ class HistogramSummaryNet(nn.Cell):
         return out
 
 
+class Moments(nn.Cell):
+    """Moments net definition"""
+
+    def __init__(self, axis=None, keep_dims=None):
+        super(Moments, self).__init__()
+        self.moments = nn.Moments(axis=axis, keep_dims=keep_dims)
+
+    def construct(self, input_x):
+        mean, variance = self.moments(input_x)
+        return mean, variance
+
+
+class CountNonZero(nn.Cell):
+    """CountNonZero net definition"""
+
+    def __init__(self, axis, keep_dims, dtype):
+        super(CountNonZero, self).__init__()
+        self.axis = axis
+        self.keep_dims = keep_dims
+        self.dtype = dtype
+    def construct(self, input_x):
+        nonzero_num = C.count_nonzero(input_x, self.axis, self.keep_dims, self.dtype)
+        return nonzero_num
+
+
 class ScatterUpdate(nn.Cell):
     """ScatterUpdate net definition"""
 
@@ -1057,14 +1082,22 @@ test_case_math_ops = [
         'desc_inputs': [Tensor(np.array([[True, False, False], [False, True, True]])),
                         [2, 3], [2, 3]],
         'desc_bprop': [[2, 3]]}),
+    ('Moments', {
+        'block': Moments(axis=(), keep_dims=False),
+        'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4).astype(np.float32))],
+        'skip': ['backward']}),
+    ('CountNonZero', {
+        'block': CountNonZero(axis=(), keep_dims=False, dtype=mstype.int32),
+        'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4).astype(np.float32))],
+        'skip': ['backward']}),
     ('FakeQuantWithMinMaxVars', {
-        'block': FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False),
+        'block': Q.FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False),
         'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 5), mstype.float32),
                         Tensor(np.array([-6]), mstype.float32),
                         Tensor(np.array([6]), mstype.float32)],
         'desc_bprop': [Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)]}),
     ('FakeQuantWithMinMaxVarsPerChannel', {
-        'block': FakeQuantWithMinMaxVarsPerChannel(num_bits=8, narrow_range=False),
+        'block': Q.FakeQuantWithMinMaxVarsPerChannel(num_bits=8, narrow_range=False),
         'desc_inputs': [Tensor(np.random.rand(3, 16, 5, 4), mstype.float32),
                         Tensor(np.array([-6, -1, -2, -3]), mstype.float32),
                         Tensor(np.array([6, 1, 2, 3]), mstype.float32)],