diff --git a/loss/__init__.py b/loss/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/loss/loss.py b/loss/loss.py
deleted file mode 100644
index cff0b0da0e7bbfbe1b5760aee968212bb8ba70c5..0000000000000000000000000000000000000000
--- a/loss/loss.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# 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.
-# ============================================================================
-
-from mindspore import Tensor
-import mindspore.common.dtype as mstype
-import mindspore.nn as nn
-from mindspore.ops import operations as P
-
-
-class SoftmaxCrossEntropyLoss(nn.Cell):
-    def __init__(self, num_cls=21, ignore_label=255):
-        super(SoftmaxCrossEntropyLoss, self).__init__()
-        self.one_hot = P.OneHot(axis=-1)
-        self.on_value = Tensor(1.0, mstype.float32)
-        self.off_value = Tensor(0.0, mstype.float32)
-        self.cast = P.Cast()
-        self.ce = nn.SoftmaxCrossEntropyWithLogits()
-        self.not_equal = P.NotEqual()
-        self.num_cls = num_cls
-        self.ignore_label = ignore_label
-        self.mul = P.Mul()
-        self.sum = P.ReduceSum(False)
-        self.div = P.RealDiv()
-        self.transpose = P.Transpose()
-        self.reshape = P.Reshape()
-
-    def construct(self, logits, labels):
-        labels_int = self.cast(labels, mstype.int32)
-        labels_int = self.reshape(labels_int, (-1,))
-        logits_ = self.transpose(logits, (0, 2, 3, 1))
-        logits_ = self.reshape(logits_, (-1, self.num_cls))
-        weights = self.not_equal(labels_int, self.ignore_label)
-        weights = self.cast(weights, mstype.float32)
-        one_hot_labels = self.one_hot(labels_int, self.num_cls, self.on_value, self.off_value)
-        loss = self.ce(logits_, one_hot_labels)
-        loss = self.mul(weights, loss)
-        loss = self.div(self.sum(loss), self.sum(weights))
-        return loss