246 lines
8.3 KiB
Python
246 lines
8.3 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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import paddle
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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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from ppocr.modeling.backbones.rec_svtrnet import Block, ConvBNLayer, trunc_normal_, zeros_, ones_
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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 BidirectionalLSTM(nn.Layer):
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def __init__(self, input_size,
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hidden_size,
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output_size=None,
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num_layers=1,
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dropout=0,
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direction=False,
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time_major=False,
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with_linear=False):
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super(BidirectionalLSTM, self).__init__()
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self.with_linear = with_linear
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self.rnn = nn.LSTM(input_size,
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hidden_size,
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num_layers=num_layers,
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dropout=dropout,
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direction=direction,
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time_major=time_major)
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# text recognition the specified structure LSTM with linear
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if self.with_linear:
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self.linear = nn.Linear(hidden_size * 2, output_size)
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def forward(self, input_feature):
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recurrent, _ = self.rnn(input_feature) # batch_size x T x input_size -> batch_size x T x (2*hidden_size)
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if self.with_linear:
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output = self.linear(recurrent) # batch_size x T x output_size
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return output
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return recurrent
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class EncoderWithCascadeRNN(nn.Layer):
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def __init__(self, in_channels, hidden_size, out_channels, num_layers=2, with_linear=False):
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super(EncoderWithCascadeRNN, self).__init__()
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self.out_channels = out_channels[-1]
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self.encoder = nn.LayerList(
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[BidirectionalLSTM(
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in_channels if i == 0 else out_channels[i - 1],
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hidden_size,
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output_size=out_channels[i],
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num_layers=1,
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direction='bidirectional',
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with_linear=with_linear)
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for i in range(num_layers)]
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)
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def forward(self, x):
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for i, l in enumerate(self.encoder):
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x = l(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 EncoderWithSVTR(nn.Layer):
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def __init__(
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self,
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in_channels,
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dims=64, # XS
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depth=2,
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hidden_dims=120,
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use_guide=False,
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num_heads=8,
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qkv_bias=True,
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mlp_ratio=2.0,
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drop_rate=0.1,
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attn_drop_rate=0.1,
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drop_path=0.,
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qk_scale=None):
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super(EncoderWithSVTR, self).__init__()
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self.depth = depth
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self.use_guide = use_guide
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self.conv1 = ConvBNLayer(
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in_channels, in_channels // 8, padding=1, act=nn.Swish)
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self.conv2 = ConvBNLayer(
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in_channels // 8, hidden_dims, kernel_size=1, act=nn.Swish)
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self.svtr_block = nn.LayerList([
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Block(
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dim=hidden_dims,
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num_heads=num_heads,
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mixer='Global',
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HW=None,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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qk_scale=qk_scale,
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drop=drop_rate,
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act_layer=nn.Swish,
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attn_drop=attn_drop_rate,
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drop_path=drop_path,
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norm_layer='nn.LayerNorm',
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epsilon=1e-05,
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prenorm=False) for i in range(depth)
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])
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self.norm = nn.LayerNorm(hidden_dims, epsilon=1e-6)
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self.conv3 = ConvBNLayer(
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hidden_dims, in_channels, kernel_size=1, act=nn.Swish)
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# last conv-nxn, the input is concat of input tensor and conv3 output tensor
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self.conv4 = ConvBNLayer(
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2 * in_channels, in_channels // 8, padding=1, act=nn.Swish)
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self.conv1x1 = ConvBNLayer(
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in_channels // 8, dims, kernel_size=1, act=nn.Swish)
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self.out_channels = dims
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Linear):
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trunc_normal_(m.weight)
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if isinstance(m, nn.Linear) and m.bias is not None:
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zeros_(m.bias)
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elif isinstance(m, nn.LayerNorm):
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zeros_(m.bias)
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ones_(m.weight)
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def forward(self, x):
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# for use guide
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if self.use_guide:
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z = x.clone()
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z.stop_gradient = True
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else:
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z = x
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# for short cut
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h = z
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# reduce dim
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z = self.conv1(z)
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z = self.conv2(z)
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# SVTR global block
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B, C, H, W = z.shape
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z = z.flatten(2).transpose([0, 2, 1])
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for blk in self.svtr_block:
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z = blk(z)
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z = self.norm(z)
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# last stage
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z = z.reshape([0, H, W, C]).transpose([0, 3, 1, 2])
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z = self.conv3(z)
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z = paddle.concat((h, z), axis=1)
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z = self.conv1x1(self.conv4(z))
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return z
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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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self.encoder_type = encoder_type
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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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'svtr': EncoderWithSVTR,
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'cascadernn': EncoderWithCascadeRNN
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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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if encoder_type == "svtr":
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self.encoder = support_encoder_dict[encoder_type](
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self.encoder_reshape.out_channels, **kwargs)
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elif encoder_type == 'cascadernn':
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self.encoder = support_encoder_dict[encoder_type](
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self.encoder_reshape.out_channels, hidden_size, **kwargs)
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else:
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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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if self.encoder_type != 'svtr':
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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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else:
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x = self.encoder(x)
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x = self.encoder_reshape(x)
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return x
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