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ppocr/losses/ace_loss.py

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+# copyright (c) 2021 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/viig99/LS-ACELoss
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+
+
+class ACELoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.loss_func = nn.CrossEntropyLoss(
+            weight=None, ignore_index=0, reduction="none", soft_label=True, axis=-1
+        )
+
+    def __call__(self, predicts, batch):
+        if isinstance(predicts, (list, tuple)):
+            predicts = predicts[-1]
+
+        B, N = predicts.shape[:2]
+        div = paddle.to_tensor([N]).astype("float32")
+
+        predicts = nn.functional.softmax(predicts, axis=-1)
+        aggregation_preds = paddle.sum(predicts, axis=1)
+        aggregation_preds = paddle.divide(aggregation_preds, div)
+
+        length = batch[2].astype("float32")
+        batch = batch[3].astype("float32")
+        batch[:, 0] = paddle.subtract(div, length)
+        batch = paddle.divide(batch, div)
+
+        loss = self.loss_func(aggregation_preds, batch)
+        return {"loss_ace": loss}