52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
|
#copyright (c) 2019 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
|
||
|
|
import paddle.fluid as fluid
|
||
|
|
from paddle.fluid.param_attr import ParamAttr
|
||
|
|
from .rec_seq_encoder import SequenceEncoder
|
||
|
|
from ..common_functions import get_para_bias_attr
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
|
||
|
|
class CTCPredict(object):
|
||
|
|
def __init__(self, params):
|
||
|
|
super(CTCPredict, self).__init__()
|
||
|
|
self.char_num = params['char_num']
|
||
|
|
self.encoder = SequenceEncoder(params)
|
||
|
|
self.encoder_type = params['encoder_type']
|
||
|
|
|
||
|
|
def __call__(self, inputs, labels=None, mode=None):
|
||
|
|
encoder_features = self.encoder(inputs)
|
||
|
|
if self.encoder_type != "reshape":
|
||
|
|
encoder_features = fluid.layers.concat(encoder_features, axis=1)
|
||
|
|
name = "ctc_fc"
|
||
|
|
para_attr, bias_attr = get_para_bias_attr(
|
||
|
|
l2_decay=0.0004, k=encoder_features.shape[1], name=name)
|
||
|
|
predict = fluid.layers.fc(input=encoder_features,
|
||
|
|
size=self.char_num + 1,
|
||
|
|
param_attr=para_attr,
|
||
|
|
bias_attr=bias_attr,
|
||
|
|
name=name)
|
||
|
|
decoded_out = fluid.layers.ctc_greedy_decoder(
|
||
|
|
input=predict, blank=self.char_num)
|
||
|
|
predicts = {'predict': predict, 'decoded_out': decoded_out}
|
||
|
|
return predicts
|