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ppocr/modeling/heads/rec_rfl_head.py

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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=0.0)
+ones_ = Constant(value=1.0)
+
+
+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