mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
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129 lines
5.2 KiB
Python
129 lines
5.2 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, trunc_normal_, zeros_
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from .rec_ctc_head import CTCHead
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from .rec_sar_head import SARHead
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from .rec_nrtr_head import Transformer
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class FCTranspose(nn.Layer):
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def __init__(self, in_channels, out_channels, only_transpose=False):
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super().__init__()
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self.only_transpose = only_transpose
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if not self.only_transpose:
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self.fc = nn.Linear(in_channels, out_channels, bias_attr=False)
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def forward(self, x):
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if self.only_transpose:
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return x.transpose([0, 2, 1])
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else:
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return self.fc(x.transpose([0, 2, 1]))
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class AddPos(nn.Layer):
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def __init__(self, dim, w):
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super().__init__()
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self.dec_pos_embed = self.create_parameter(
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shape=[1, w, dim], default_initializer=zeros_)
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self.add_parameter("dec_pos_embed", self.dec_pos_embed)
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trunc_normal_(self.dec_pos_embed)
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def forward(self,x):
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x = x + self.dec_pos_embed[:, :paddle.shape(x)[1], :]
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return x
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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.use_pool = kwargs.get('use_pool', False)
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self.use_pos = kwargs.get('use_pos', False)
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self.in_channels = in_channels
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if self.use_pool:
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self.pool = nn.AvgPool2D(kernel_size=[3, 2], stride=[3, 2], padding=0)
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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 == 'NRTRHead':
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gtc_args = self.head_list[idx][name]
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max_text_length = gtc_args.get('max_text_length', 25)
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nrtr_dim = gtc_args.get('nrtr_dim', 256)
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num_decoder_layers = gtc_args.get('num_decoder_layers', 4)
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if self.use_pos:
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self.before_gtc = nn.Sequential(
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nn.Flatten(2), FCTranspose(in_channels, nrtr_dim), AddPos(nrtr_dim, 80))
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else:
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self.before_gtc = nn.Sequential(
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nn.Flatten(2), FCTranspose(in_channels, nrtr_dim))
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self.gtc_head = Transformer(
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d_model=nrtr_dim,
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nhead=nrtr_dim // 32,
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num_encoder_layers=-1,
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beam_size=-1,
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num_decoder_layers=num_decoder_layers,
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max_len=max_text_length,
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dim_feedforward=nrtr_dim * 4,
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out_channels=out_channels_list['NRTRLabelDecode'])
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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.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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if self.use_pool:
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x = self.pool(x.reshape([0, 3, -1, self.in_channels]).transpose([0, 3, 1, 2]))
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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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else:
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gtc_out = self.gtc_head(self.before_gtc(x), targets[1:])
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head_out['nrtr'] = gtc_out
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return head_out
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