72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
#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, model_name_pairs=[], act=None, key=None,
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name="loss_dml"):
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super().__init__(act=act, name=name)
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assert isinstance(model_name_pairs, list)
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self.key = key
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self.model_name_pairs = model_name_pairs
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def forward(self, predicts, batch):
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loss_dict = dict()
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for idx, pair in enumerate(self.model_name_pairs):
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out1 = predicts[pair[0]]
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out2 = predicts[pair[1]]
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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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for key in loss:
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loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[
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key]
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else:
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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 idx, model_name in enumerate(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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for key in loss:
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loss_dict["{}_{}_{}".format(self.name, model_name,
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idx)] = loss[key]
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else:
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loss_dict["{}_{}".format(self.name, model_name)] = loss
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return loss_dict
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