!12586 support invert bool tensor

From: @zhangbuxue
Reviewed-by: @zh_qh,@ginfung,@zh_qh
Signed-off-by: @zh_qh,@zh_qh

mindspore/_extends/parse/resources.py

@@ -93,7 +93,7 @@ convert_object_map = {
     T.xor:          NO_IMPLEMENT,
     T.pos:          multitype_ops.uadd,
     T.neg:          multitype_ops.negative,
-    T.invert:       NO_IMPLEMENT,
+    T.invert:       F.logical_not,
     T.not_:         multitype_ops.logical_not,
     T.eq:           multitype_ops.equal,
     T.ne:           multitype_ops.not_equal,

mindspore/common/tensor.py

@@ -153,6 +153,10 @@ class Tensor(Tensor_):
         out = tensor_operator_registry.get('__neg__')(self)
         return out
 
+    def __invert__(self):
+        out = tensor_operator_registry.get('__logical_not__')(self)
+        return out
+
     def __bool__(self):
         data = self.asnumpy()
         if data.shape == ():

mindspore/ops/functional.py

@@ -223,6 +223,7 @@ tensor_operator_registry.register('__lt__', tensor_lt)
 tensor_operator_registry.register('__le__', tensor_le)
 tensor_operator_registry.register('__gt__', tensor_gt)
 tensor_operator_registry.register('__ge__', tensor_ge)
+tensor_operator_registry.register('__logical_not__', logical_not)
 tensor_operator_registry.register('shape', shape)
 tensor_operator_registry.register('squeeze', squeeze)
 # support GE backend for no compare operators

mindspore/ops/operations/math_ops.py

@@ -3104,9 +3104,15 @@ class LogicalNot(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_dtype):
-        validator.check_tensor_dtype_valid("x", x_dtype, [mstype.bool_], self.name)
+        validator.check_tensor_dtype_valid("x", x_dtype, [mstype.bool_], self.name + " or '~' operator")
         return mstype.tensor_type(mstype.bool_)
 
+    def infer_value(self, x):
+        if x is not None:
+            x = x.asnumpy()
+            return Tensor(np.logical_not(x))
+        return None
+
 
 class LogicalAnd(_LogicBinaryOp):
     """
@@ -3145,6 +3151,14 @@ class LogicalAnd(_LogicBinaryOp):
     def infer_dtype(self, x_dtype, y_dtype):
         return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.name)
 
+    def infer_value(self, x, y):
+        if x is not None and y is not None:
+            x = x.asnumpy()
+            y = y.asnumpy()
+            out = np.array(np.logical_and(x, y))
+            return Tensor(out)
+        return None
+
 
 class LogicalOr(_LogicBinaryOp):
     """
@@ -3183,6 +3197,14 @@ class LogicalOr(_LogicBinaryOp):
     def infer_dtype(self, x_dtype, y_dtype):
         return _LogicBinaryOp.do_infer_dtype(x_dtype, y_dtype, (mstype.bool_,), self.name)
 
+    def infer_value(self, x, y):
+        if x is not None and y is not None:
+            x = x.asnumpy()
+            y = y.asnumpy()
+            out = np.array(np.logical_or(x, y))
+            return Tensor(out)
+        return None
+
 
 class IsNan(PrimitiveWithInfer):
     """

tests/ut/python/pipeline/parse/test_invert.py

@@ -0,0 +1,63 @@
+# Copyright 2021 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.
+# ============================================================================
+""" test '~' """
+import numpy as np
+import pytest
+
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+
+class InvertNet(nn.Cell):
+    def __init__(self):
+        super(InvertNet, self).__init__()
+        self.t = Tensor(np.array([True, False, True]))
+
+    def construct(self, x):
+        invert_t = ~self.t
+        invert_x = ~x
+        ret = (invert_t, invert_x)
+        return ret
+
+
+def test_invert_bool_tensor():
+    net = InvertNet()
+    input_x = Tensor(np.array([False, True, False]))
+
+    context.set_context(mode=context.PYNATIVE_MODE)
+    ret = net(input_x)
+    assert (ret[0].asnumpy() == np.array([False, True, False])).all()
+    assert (ret[1].asnumpy() == np.array([True, False, True])).all()
+
+    context.set_context(mode=context.GRAPH_MODE)
+    net(input_x)
+
+
+def test_invert_int_tensor():
+    net = InvertNet()
+    input_x = Tensor(np.array([1, 2, 3], np.int32))
+
+    context.set_context(mode=context.PYNATIVE_MODE)
+    with pytest.raises(TypeError) as err:
+        net(input_x)
+    assert "For 'LogicalNot or '~' operator', the type of `x` should be subclass of Tensor[Bool], " \
+           "but got Tensor[Int32]" in str(err.value)
+
+    context.set_context(mode=context.GRAPH_MODE)
+    with pytest.raises(TypeError) as err:
+        net(input_x)
+    assert "For 'LogicalNot or '~' operator', the type of `x` should be subclass of Tensor[Bool], " \
+           "but got Tensor[Int32]" in str(err.value)