!25675 Fix the random failure of ut tests in ci

Merge pull request !25675 from YuJianfeng/clean

tests/ut/python/optimizer/test_bprop_mindir_dfx.py

@@ -1,65 +0,0 @@
-# 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.
-# ============================================================================
-"""DFX test for bprop mindir"""
-
-import os
-import numpy as np
-
-import mindspore.nn as nn
-from mindspore import Tensor
-from mindspore.ops import operations as P
-import mindspore.ops as ops
-import mindspore.ops._grad as g
-
-
-class Net(nn.Cell):
-    def __init__(self, op):
-        super(Net, self).__init__()
-        self.op = op
-
-    def construct(self, *inputs):
-        return self.op(*inputs)
-
-
-class GradNet(nn.Cell):
-    def __init__(self, network):
-        super(GradNet, self).__init__()
-        self.grad = ops.GradOperation(get_all=True)
-        self.network = network
-
-    def construct(self, *inputs):
-        gout = self.grad(self.network)(*inputs)
-        return gout
-
-
-def test_remove_bprop_fle():
-    """
-    Feature: Bprop pre-compilation.
-    Description: Remove a bprop file, compile a grad net with a bprop not defined in this file.
-    Expectation: The grad net can be complied successfully.
-    """
-    bprop_path = g.__file__
-    bprop_installed_dir = bprop_path[: bprop_path.rindex('/')]
-    nn_bprop_path = bprop_installed_dir + '/grad_nn_ops.py'
-    new_path = bprop_installed_dir + '/new'
-    os.mkdir(new_path)
-    new_nn_bprop_path = bprop_installed_dir + '/new/grad_nn_ops.py'
-    os.rename(nn_bprop_path, new_nn_bprop_path)
-    x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
-    ones_like = Net(P.OnesLike())
-    grad = GradNet(ones_like)
-    grad.compile(x)
-    os.rename(new_nn_bprop_path, nn_bprop_path)
-    os.rmdir(new_path)