!6590 fix bugs of op Debug, ReLUV2, EditDistance and Dense

Merge pull request !6590 from lihongkang/v2_master

mindspore/nn/layer/basic.py

@@ -165,7 +165,7 @@ class Dense(Cell):
         \text{outputs} = \text{activation}(\text{inputs} * \text{kernel} + \text{bias}),
 
     where :math:`\text{activation}` is the activation function passed as the activation
-    argument (if passed in), :math:`\text{activation}` is a weight matrix with the same
+    argument (if passed in), :math:`\text{kernel}` is a weight matrix with the same
     data type as the inputs created by the layer, and :math:`\text{bias}` is a bias vector
     with the same data type as the inputs created by the layer (only if has_bias is True).
 

mindspore/ops/_grad/grad_debug_ops.py

@@ -66,12 +66,3 @@ def get_bprop_insert_gradient_of(self):
     def bprop(x, out, dout):
         return (f(dout),)
     return bprop
-
-
-@bprop_getters.register(P.Debug)
-def get_bprop_debug(self):
-    """Generate bprop for Debug"""
-
-    def bprop(x, out, dout):
-        return dout
-    return bprop

mindspore/ops/operations/__init__.py

@@ -39,7 +39,7 @@ from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast
                        _VirtualDiv, _GetTensorSlice,
                        _HostAllGather, _HostReduceScatter)
 from .debug_ops import (ImageSummary, InsertGradientOf, HookBackward, ScalarSummary,
-                        TensorSummary, HistogramSummary, Debug, Print, Assert)
+                        TensorSummary, HistogramSummary, Print, Assert)
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 
@@ -200,7 +200,6 @@ __all__ = [
     'ImageSummary',
     'TensorSummary',
     'HistogramSummary',
-    "Debug",
     "Print",
     "Assert",
     'InsertGradientOf',
@@ -375,6 +374,7 @@ __all__ = [
     "ParallelConcat",
     "Push",
     "Pull",
+    "ReLUV2",
     'SparseToDense',
 ]
 

mindspore/ops/operations/array_ops.py

@@ -3619,6 +3619,12 @@ class EditDistance(PrimitiveWithInfer):
         Tensor, a dense tensor with rank `R-1` and float32 data type.
 
     Examples:
+        >>> import numpy as np
+        >>> from mindspore import context
+        >>> from mindspore import Tensor
+        >>> import mindspore.nn as nn
+        >>> import mindspore.ops.operations as P
+        >>> context.set_context(mode=context.GRAPH_MODE)
         >>> class EditDistance(nn.Cell):
         >>>     def __init__(self, hypothesis_shape, truth_shape, normalize=True):
         >>>         super(EditDistance, self).__init__()
@@ -3645,6 +3651,7 @@ class EditDistance(PrimitiveWithInfer):
     def __init__(self, normalize=True):
         """Initialize EditDistance"""
         self.normalize = validator.check_value_type("normalize", normalize, [bool], self.name)
+        self.set_const_input_indexes([2, 5])
 
     def __infer__(self, h_indices, h_values, h_shape, truth_indices, truth_values, truth_shape):
         validator.check_const_input('hypothesis_shape', h_shape['value'], self.name)

mindspore/ops/operations/debug_ops.py

@@ -18,7 +18,7 @@ from types import FunctionType, MethodType
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
-from ..primitive import prim_attr_register, PrimitiveWithInfer, Primitive
+from ..primitive import prim_attr_register, PrimitiveWithInfer
 
 
 def _check_summary_param(name, value, class_name):
@@ -342,32 +342,6 @@ class Print(PrimitiveWithInfer):
         return mstype.int32
 
 
-class Debug(Primitive):
-    """
-    Prints tensor value.
-
-    Inputs:
-        - **value** (Tensor) - The value of tensor.
-
-    Examples:
-        >>> class DebugNN(nn.Cell):
-        >>>     def __init__(self,):
-        >>>         self.debug = nn.Debug()
-        >>>
-        >>>     def construct(self, x, y):
-        >>>         x = self.add(x, y)
-        >>>         self.debug(x)
-        >>>         return x
-    """
-
-    @prim_attr_register
-    def __init__(self):
-        """init"""
-
-    def __call__(self, *args, **kwargs):
-        pass
-
-
 class Assert(PrimitiveWithInfer):
     """
     Asserts that the given condition is true.