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# 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.
"""
This code is refer from:
https://github.com/hikopensource/DAVAR-Lab-OCR/blob/main/davarocr/davar_rcg/models/sequence_heads/counting_head.py
"""
import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
from .rec_att_head import AttentionLSTM
kaiming_init_ = KaimingNormal()
zeros_ = Constant(value=0.)
ones_ = Constant(value=1.)
class CNTHead(nn.Layer):
def __init__(self,
embed_size=512,
encode_length=26,
out_channels=38,
**kwargs):
super(CNTHead, self).__init__()
self.out_channels = out_channels
self.Wv_fusion = nn.Linear(embed_size, embed_size, bias_attr=False)
self.Prediction_visual = nn.Linear(encode_length * embed_size,
self.out_channels)
def forward(self, visual_feature):
b, c, h, w = visual_feature.shape
visual_feature = visual_feature.reshape([b, c, h * w]).transpose(
[0, 2, 1])
visual_feature_num = self.Wv_fusion(visual_feature) # batch * 26 * 512
b, n, c = visual_feature_num.shape
# using visual feature directly calculate the text length
visual_feature_num = visual_feature_num.reshape([b, n * c])
prediction_visual = self.Prediction_visual(visual_feature_num)
return prediction_visual
class RFLHead(nn.Layer):
def __init__(self,
in_channels=512,
hidden_size=256,
batch_max_legnth=25,
out_channels=38,
use_cnt=True,
use_seq=True,
**kwargs):
super(RFLHead, self).__init__()
assert use_cnt or use_seq
self.use_cnt = use_cnt
self.use_seq = use_seq
if self.use_cnt:
self.cnt_head = CNTHead(
embed_size=in_channels,
encode_length=batch_max_legnth + 1,
out_channels=out_channels,
**kwargs)
if self.use_seq:
self.seq_head = AttentionLSTM(
in_channels=in_channels,
out_channels=out_channels,
hidden_size=hidden_size,
**kwargs)
self.batch_max_legnth = batch_max_legnth
self.num_class = out_channels
self.apply(self.init_weights)
def init_weights(self, m):
if isinstance(m, nn.Linear):
kaiming_init_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
zeros_(m.bias)
def forward(self, x, targets=None):
cnt_inputs, seq_inputs = x
if self.use_cnt:
cnt_outputs = self.cnt_head(cnt_inputs)
else:
cnt_outputs = None
if self.use_seq:
if self.training:
seq_outputs = self.seq_head(seq_inputs, targets[0],
self.batch_max_legnth)
else:
seq_outputs = self.seq_head(seq_inputs, None,
self.batch_max_legnth)
return cnt_outputs, seq_outputs
else:
return cnt_outputs
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