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- # 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.
- # ============================================================================
- """define loss for glore_resnet50"""
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.common import dtype as mstype
- from mindspore.nn.loss.loss import _Loss
- from mindspore.ops import functional as F
- from mindspore.ops import operations as P
- import mindspore.ops as ops
-
-
- class SoftmaxCrossEntropyExpand(nn.Cell): # pylint: disable=missing-docstring
- def __init__(self, sparse=False):
- super(SoftmaxCrossEntropyExpand, self).__init__()
- self.exp = ops.Exp()
- self.sum = ops.ReduceSum(keep_dims=True)
- self.onehot = ops.OneHot()
- self.on_value = Tensor(1.0, mstype.float32)
- self.off_value = Tensor(0.0, mstype.float32)
- self.div = ops.RealDiv()
- self.log = ops.Log()
- self.sum_cross_entropy = ops.ReduceSum(keep_dims=False)
- self.mul = ops.Mul()
- self.mul2 = ops.Mul()
- self.mean = ops.ReduceMean(keep_dims=False)
- self.sparse = sparse
- self.max = ops.ReduceMax(keep_dims=True)
- self.sub = ops.Sub()
- self.eps = Tensor(1e-24, mstype.float32)
-
- def construct(self, logit, label): # pylint: disable=missing-docstring
- logit_max = self.max(logit, -1)
- exp = self.exp(self.sub(logit, logit_max))
- exp_sum = self.sum(exp, -1)
- softmax_result = self.div(exp, exp_sum)
- if self.sparse:
- label = self.onehot(label, ops.shape(logit)[1], self.on_value, self.off_value)
-
- softmax_result_log = self.log(softmax_result + self.eps)
- loss = self.sum_cross_entropy((self.mul(softmax_result_log, label)), -1)
- loss = self.mul2(ops.scalar_to_array(-1.0), loss)
- loss = self.mean(loss, -1)
-
- return loss
-
-
- class CrossEntropySmooth(_Loss):
- """CrossEntropy"""
-
- def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
- super(CrossEntropySmooth, self).__init__()
- self.onehot = P.OneHot()
- self.sparse = sparse
- self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
- self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
- self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)
-
- def construct(self, logit, label):
- if self.sparse:
- label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
- loss = self.ce(logit, label)
- return loss
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