77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
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#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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import paddle
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import paddle.nn as nn
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from .rec_ctc_loss import CTCLoss
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from .basic_loss import DMLLoss
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class DistillationDMLLoss(DMLLoss):
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"""
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"""
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def __init__(self,
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model_name_list1=[],
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model_name_list2=[],
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key=None,
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name="loss_dml"):
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super().__init__(name=name)
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if not isinstance(model_name_list1, (list, )):
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model_name_list1 = [model_name_list1]
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if not isinstance(model_name_list2, (list, )):
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model_name_list2 = [model_name_list2]
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assert len(model_name_list1) == len(model_name_list2)
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self.model_name_list1 = model_name_list1
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self.model_name_list2 = model_name_list2
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self.key = key
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def forward(self, predicts, batch):
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loss_dict = dict()
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for idx in range(len(self.model_name_list1)):
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out1 = predicts[self.model_name_list1[idx]]
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out2 = predicts[self.model_name_list2[idx]]
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if self.key is not None:
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out1 = out1[self.key]
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out2 = out2[self.key]
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loss = super().forward(out1, out2)
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if isinstance(loss, dict):
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assert len(loss) == 1
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loss = list(loss.values())[0]
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loss_dict["{}_{}".format(self.name, idx)] = loss
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return loss_dict
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class DistillationCTCLoss(CTCLoss):
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def __init__(self, model_name_list=[], key=None, name="loss_ctc"):
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super().__init__()
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self.model_name_list = model_name_list
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self.key = key
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self.name = name
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def forward(self, predicts, batch):
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loss_dict = dict()
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for model_name in self.model_name_list:
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out = predicts[model_name]
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if self.key is not None:
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out = out[self.key]
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loss = super().forward(out, batch)
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if isinstance(loss, dict):
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assert len(loss) == 1
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loss = list(loss.values())[0]
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loss_dict["{}_{}".format(self.name, model_name)] = loss
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return loss_dict
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