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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 | |