mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-03 19:29:18 +00:00
88 lines
2.9 KiB
Python
Executable File
88 lines
2.9 KiB
Python
Executable File
# 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
|
|
from paddle import ParamAttr, nn
|
|
from paddle.nn import functional as F
|
|
|
|
|
|
def get_para_bias_attr(l2_decay, k):
|
|
regularizer = paddle.regularizer.L2Decay(l2_decay)
|
|
stdv = 1.0 / math.sqrt(k * 1.0)
|
|
initializer = nn.initializer.Uniform(-stdv, stdv)
|
|
weight_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
|
|
bias_attr = ParamAttr(regularizer=regularizer, initializer=initializer)
|
|
return [weight_attr, bias_attr]
|
|
|
|
|
|
class CTCHead(nn.Layer):
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
fc_decay=0.0004,
|
|
mid_channels=None,
|
|
return_feats=False,
|
|
**kwargs):
|
|
super(CTCHead, self).__init__()
|
|
if mid_channels is None:
|
|
weight_attr, bias_attr = get_para_bias_attr(
|
|
l2_decay=fc_decay, k=in_channels)
|
|
self.fc = nn.Linear(
|
|
in_channels,
|
|
out_channels,
|
|
weight_attr=weight_attr,
|
|
bias_attr=bias_attr)
|
|
else:
|
|
weight_attr1, bias_attr1 = get_para_bias_attr(
|
|
l2_decay=fc_decay, k=in_channels)
|
|
self.fc1 = nn.Linear(
|
|
in_channels,
|
|
mid_channels,
|
|
weight_attr=weight_attr1,
|
|
bias_attr=bias_attr1)
|
|
|
|
weight_attr2, bias_attr2 = get_para_bias_attr(
|
|
l2_decay=fc_decay, k=mid_channels)
|
|
self.fc2 = nn.Linear(
|
|
mid_channels,
|
|
out_channels,
|
|
weight_attr=weight_attr2,
|
|
bias_attr=bias_attr2)
|
|
self.out_channels = out_channels
|
|
self.mid_channels = mid_channels
|
|
self.return_feats = return_feats
|
|
|
|
def forward(self, x, targets=None):
|
|
if self.mid_channels is None:
|
|
predicts = self.fc(x)
|
|
else:
|
|
x = self.fc1(x)
|
|
predicts = self.fc2(x)
|
|
|
|
if self.return_feats:
|
|
result = (x, predicts)
|
|
else:
|
|
result = predicts
|
|
if not self.training:
|
|
predicts = F.softmax(predicts, axis=2)
|
|
result = predicts
|
|
|
|
return result
|