mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
# copyright (c) 2020 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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from paddle import nn
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from ppocr.modeling.heads.rec_ctc_head import get_para_bias_attr
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class Im2Seq(nn.Layer):
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def __init__(self, in_channels, **kwargs):
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super().__init__()
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self.out_channels = in_channels
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == 1
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x = x.squeeze(axis=2)
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x = x.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
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return x
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class EncoderWithRNN(nn.Layer):
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def __init__(self, in_channels, hidden_size):
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super(EncoderWithRNN, self).__init__()
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self.out_channels = hidden_size * 2
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self.lstm = nn.LSTM(
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in_channels, hidden_size, direction='bidirectional', num_layers=2)
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def forward(self, x):
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x, _ = self.lstm(x)
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return x
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class EncoderWithFC(nn.Layer):
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def __init__(self, in_channels, hidden_size):
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super(EncoderWithFC, self).__init__()
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self.out_channels = hidden_size
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weight_attr, bias_attr = get_para_bias_attr(
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l2_decay=0.00001, k=in_channels)
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self.fc = nn.Linear(
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in_channels,
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hidden_size,
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weight_attr=weight_attr,
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bias_attr=bias_attr,
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name='reduce_encoder_fea')
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def forward(self, x):
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x = self.fc(x)
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return x
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class SequenceEncoder(nn.Layer):
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def __init__(self, in_channels, encoder_type, hidden_size=48, **kwargs):
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super(SequenceEncoder, self).__init__()
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self.encoder_reshape = Im2Seq(in_channels)
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self.out_channels = self.encoder_reshape.out_channels
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if encoder_type == 'reshape':
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self.only_reshape = True
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else:
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support_encoder_dict = {
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'reshape': Im2Seq,
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'fc': EncoderWithFC,
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'rnn': EncoderWithRNN
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}
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assert encoder_type in support_encoder_dict, '{} must in {}'.format(
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encoder_type, support_encoder_dict.keys())
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self.encoder = support_encoder_dict[encoder_type](
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self.encoder_reshape.out_channels, hidden_size)
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self.out_channels = self.encoder.out_channels
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self.only_reshape = False
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def forward(self, x):
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x = self.encoder_reshape(x)
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if not self.only_reshape:
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x = self.encoder(x)
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return x
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