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ppocr/losses/rec_ppformulanet_loss.py

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+# copyright (c) 2024 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.
+import paddle
+import paddle.nn as nn
+
+
+class PPFormulaNet_S_Loss(nn.Layer):
+    """
+    PP=FormulaNet-S adopt CrossEntropyLoss for network training.
+    """
+
+    def __init__(self, vocab_size=50000, parallel_step=1):
+        super(PPFormulaNet_S_Loss, self).__init__()
+        self.ignore_index = -100
+        self.vocab_size = vocab_size
+        self.parallel_step = int(parallel_step)
+        self.pad_token_id = 1
+        # ignore padding characters during training
+        self.cross = nn.CrossEntropyLoss(
+            reduction="mean", ignore_index=self.ignore_index
+        )
+
+    def forward(self, preds, batch):
+        logits, masked_label = preds
+
+        word_loss = self.cross(
+            paddle.reshape(logits, [-1, logits.shape[-1]]),
+            paddle.reshape(masked_label[:, self.parallel_step :], [-1]),
+        )
+        loss = word_loss
+        return {
+            "loss": loss,
+            "word_loss": word_loss,
+        }
+
+
+class PPFormulaNet_L_Loss(nn.Layer):
+    """
+    PPFormulaNet_L adopt CrossEntropyLoss for network training.
+    """
+
+    def __init__(self, vocab_size=50000):
+        super(PPFormulaNet_L_Loss, self).__init__()
+        self.ignore_index = -100
+        self.vocab_size = vocab_size
+        self.pad_token_id = 1
+        # ignore padding characters during training
+        self.cross = nn.CrossEntropyLoss(
+            reduction="mean", ignore_index=self.ignore_index
+        )
+
+    def forward(self, preds, batch):
+        logits, masked_label = preds
+
+        word_loss = self.cross(
+            paddle.reshape(logits, [-1, logits.shape[-1]]),
+            paddle.reshape(masked_label[:, 1:], [-1]),
+        )
+        loss = word_loss
+        return {
+            "loss": loss,
+            "word_loss": word_loss,
+        }