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- # 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.
- # ============================================================================
- """OhemLoss."""
- import mindspore.nn as nn
- import mindspore.common.dtype as mstype
- from mindspore.ops import operations as P
- from mindspore.ops import functional as F
-
-
- class OhemLoss(nn.Cell):
- """Ohem loss cell."""
- def __init__(self, num, ignore_label):
- super(OhemLoss, self).__init__()
- self.mul = P.Mul()
- self.shape = P.Shape()
- self.one_hot = nn.OneHot(-1, num, 1.0, 0.0)
- self.squeeze = P.Squeeze()
- self.num = num
- self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
- self.mean = P.ReduceMean()
- self.select = P.Select()
- self.reshape = P.Reshape()
- self.cast = P.Cast()
- self.not_equal = P.NotEqual()
- self.equal = P.Equal()
- self.reduce_sum = P.ReduceSum(keep_dims=False)
- self.fill = P.Fill()
- self.transpose = P.Transpose()
- self.ignore_label = ignore_label
- self.loss_weight = 1.0
-
- def construct(self, logits, labels):
- logits = self.transpose(logits, (0, 2, 3, 1))
- logits = self.reshape(logits, (-1, self.num))
- labels = F.cast(labels, mstype.int32)
- labels = self.reshape(labels, (-1,))
- one_hot_labels = self.one_hot(labels)
- losses = self.cross_entropy(logits, one_hot_labels)[0]
- weights = self.cast(self.not_equal(labels, self.ignore_label), mstype.float32) * self.loss_weight
- weighted_losses = self.mul(losses, weights)
- loss = self.reduce_sum(weighted_losses, (0,))
- zeros = self.fill(mstype.float32, self.shape(weights), 0.0)
- ones = self.fill(mstype.float32, self.shape(weights), 1.0)
- present = self.select(self.equal(weights, zeros), zeros, ones)
- present = self.reduce_sum(present, (0,))
-
- zeros = self.fill(mstype.float32, self.shape(present), 0.0)
- min_control = self.fill(mstype.float32, self.shape(present), 1.0)
- present = self.select(self.equal(present, zeros), min_control, present)
- loss = loss / present
- return loss
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