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ppocr/losses/rec_multi_loss.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+
+from .rec_ctc_loss import CTCLoss
+from .rec_sar_loss import SARLoss
+from .rec_nrtr_loss import NRTRLoss
+
+
+class MultiLoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.loss_funcs = {}
+        self.loss_list = kwargs.pop("loss_config_list")
+        self.weight_1 = kwargs.get("weight_1", 1.0)
+        self.weight_2 = kwargs.get("weight_2", 1.0)
+        for loss_info in self.loss_list:
+            for name, param in loss_info.items():
+                if param is not None:
+                    kwargs.update(param)
+                loss = eval(name)(**kwargs)
+                self.loss_funcs[name] = loss
+
+    def forward(self, predicts, batch):
+        self.total_loss = {}
+        total_loss = 0.0
+        # batch [image, label_ctc, label_sar, length, valid_ratio]
+        for name, loss_func in self.loss_funcs.items():
+            if name == "CTCLoss":
+                loss = (
+                    loss_func(predicts["ctc"], batch[:2] + batch[3:])["loss"]
+                    * self.weight_1
+                )
+            elif name == "SARLoss":
+                loss = (
+                    loss_func(predicts["sar"], batch[:1] + batch[2:])["loss"]
+                    * self.weight_2
+                )
+            elif name == "NRTRLoss":
+                loss = (
+                    loss_func(predicts["gtc"], batch[:1] + batch[2:])["loss"]
+                    * self.weight_2
+                )
+            else:
+                raise NotImplementedError(
+                    "{} is not supported in MultiLoss yet".format(name)
+                )
+            self.total_loss[name] = loss
+            total_loss += loss
+        self.total_loss["loss"] = total_loss
+        return self.total_loss