74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
# copyright (c) 2022 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppocr.modeling.necks.rnn import Im2Seq, EncoderWithRNN, EncoderWithFC, SequenceEncoder, EncoderWithSVTR
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from .rec_ctc_head import CTCHead
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from .rec_sar_head import SARHead
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class MultiHead(nn.Layer):
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def __init__(self, in_channels, out_channels_list, **kwargs):
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super().__init__()
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self.head_list = kwargs.pop('head_list')
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self.gtc_head = 'sar'
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assert len(self.head_list) >= 2
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for idx, head_name in enumerate(self.head_list):
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name = list(head_name)[0]
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if name == 'SARHead':
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# sar head
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sar_args = self.head_list[idx][name]
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self.sar_head = eval(name)(in_channels=in_channels, \
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out_channels=out_channels_list['SARLabelDecode'], **sar_args)
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elif name == 'CTCHead':
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# ctc neck
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self.encoder_reshape = Im2Seq(in_channels)
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neck_args = self.head_list[idx][name]['Neck']
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encoder_type = neck_args.pop('name')
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self.encoder = encoder_type
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self.ctc_encoder = SequenceEncoder(in_channels=in_channels, \
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encoder_type=encoder_type, **neck_args)
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# ctc head
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head_args = self.head_list[idx][name]['Head']
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self.ctc_head = eval(name)(in_channels=self.ctc_encoder.out_channels, \
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out_channels=out_channels_list['CTCLabelDecode'], **head_args)
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else:
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raise NotImplementedError(
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'{} is not supported in MultiHead yet'.format(name))
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def forward(self, x, targets=None):
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ctc_encoder = self.ctc_encoder(x)
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ctc_out = self.ctc_head(ctc_encoder, targets)
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head_out = dict()
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head_out['ctc'] = ctc_out
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head_out['ctc_neck'] = ctc_encoder
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# eval mode
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if not self.training:
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return ctc_out
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if self.gtc_head == 'sar':
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sar_out = self.sar_head(x, targets[1:])
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head_out['sar'] = sar_out
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return head_out
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else:
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return head_out
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