129 lines
5.2 KiB
Python
129 lines
5.2 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import math
|
|
import paddle
|
|
from paddle import ParamAttr
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
from ppocr.modeling.necks.rnn import Im2Seq, EncoderWithRNN, EncoderWithFC, SequenceEncoder, EncoderWithSVTR, trunc_normal_, zeros_
|
|
from .rec_ctc_head import CTCHead
|
|
from .rec_sar_head import SARHead
|
|
from .rec_nrtr_head import Transformer
|
|
|
|
|
|
class FCTranspose(nn.Layer):
|
|
def __init__(self, in_channels, out_channels, only_transpose=False):
|
|
super().__init__()
|
|
self.only_transpose = only_transpose
|
|
if not self.only_transpose:
|
|
self.fc = nn.Linear(in_channels, out_channels, bias_attr=False)
|
|
|
|
def forward(self, x):
|
|
if self.only_transpose:
|
|
return x.transpose([0, 2, 1])
|
|
else:
|
|
return self.fc(x.transpose([0, 2, 1]))
|
|
|
|
class AddPos(nn.Layer):
|
|
def __init__(self, dim, w):
|
|
super().__init__()
|
|
self.dec_pos_embed = self.create_parameter(
|
|
shape=[1, w, dim], default_initializer=zeros_)
|
|
self.add_parameter("dec_pos_embed", self.dec_pos_embed)
|
|
trunc_normal_(self.dec_pos_embed)
|
|
|
|
def forward(self,x):
|
|
x = x + self.dec_pos_embed[:, :paddle.shape(x)[1], :]
|
|
return x
|
|
|
|
|
|
class MultiHead(nn.Layer):
|
|
def __init__(self, in_channels, out_channels_list, **kwargs):
|
|
super().__init__()
|
|
self.head_list = kwargs.pop('head_list')
|
|
self.use_pool = kwargs.get('use_pool', False)
|
|
self.use_pos = kwargs.get('use_pos', False)
|
|
self.in_channels = in_channels
|
|
if self.use_pool:
|
|
self.pool = nn.AvgPool2D(kernel_size=[3, 2], stride=[3, 2], padding=0)
|
|
self.gtc_head = 'sar'
|
|
assert len(self.head_list) >= 2
|
|
for idx, head_name in enumerate(self.head_list):
|
|
name = list(head_name)[0]
|
|
if name == 'SARHead':
|
|
# sar head
|
|
sar_args = self.head_list[idx][name]
|
|
self.sar_head = eval(name)(in_channels=in_channels, \
|
|
out_channels=out_channels_list['SARLabelDecode'], **sar_args)
|
|
elif name == 'NRTRHead':
|
|
gtc_args = self.head_list[idx][name]
|
|
max_text_length = gtc_args.get('max_text_length', 25)
|
|
nrtr_dim = gtc_args.get('nrtr_dim', 256)
|
|
num_decoder_layers = gtc_args.get('num_decoder_layers', 4)
|
|
if self.use_pos:
|
|
self.before_gtc = nn.Sequential(
|
|
nn.Flatten(2), FCTranspose(in_channels, nrtr_dim), AddPos(nrtr_dim, 80))
|
|
else:
|
|
self.before_gtc = nn.Sequential(
|
|
nn.Flatten(2), FCTranspose(in_channels, nrtr_dim))
|
|
|
|
self.gtc_head = Transformer(
|
|
d_model=nrtr_dim,
|
|
nhead=nrtr_dim // 32,
|
|
num_encoder_layers=-1,
|
|
beam_size=-1,
|
|
num_decoder_layers=num_decoder_layers,
|
|
max_len=max_text_length,
|
|
dim_feedforward=nrtr_dim * 4,
|
|
out_channels=out_channels_list['NRTRLabelDecode'])
|
|
elif name == 'CTCHead':
|
|
# ctc neck
|
|
self.encoder_reshape = Im2Seq(in_channels)
|
|
neck_args = self.head_list[idx][name]['Neck']
|
|
encoder_type = neck_args.pop('name')
|
|
self.ctc_encoder = SequenceEncoder(in_channels=in_channels, \
|
|
encoder_type=encoder_type, **neck_args)
|
|
# ctc head
|
|
head_args = self.head_list[idx][name]['Head']
|
|
self.ctc_head = eval(name)(in_channels=self.ctc_encoder.out_channels, \
|
|
out_channels=out_channels_list['CTCLabelDecode'], **head_args)
|
|
else:
|
|
raise NotImplementedError(
|
|
'{} is not supported in MultiHead yet'.format(name))
|
|
|
|
def forward(self, x, targets=None):
|
|
if self.use_pool:
|
|
x = self.pool(x.reshape([0, 3, -1, self.in_channels]).transpose([0, 3, 1, 2]))
|
|
ctc_encoder = self.ctc_encoder(x)
|
|
ctc_out = self.ctc_head(ctc_encoder, targets)
|
|
head_out = dict()
|
|
head_out['ctc'] = ctc_out
|
|
head_out['ctc_neck'] = ctc_encoder
|
|
# eval mode
|
|
if not self.training:
|
|
return ctc_out
|
|
if self.gtc_head == 'sar':
|
|
sar_out = self.sar_head(x, targets[1:])
|
|
head_out['sar'] = sar_out
|
|
else:
|
|
gtc_out = self.gtc_head(self.before_gtc(x), targets[1:])
|
|
head_out['nrtr'] = gtc_out
|
|
return head_out
|