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ppocr/losses/rec_cppd_loss.py

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+# copyright (c) 2023 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
+from paddle import nn
+import paddle.nn.functional as F
+
+
+class CPPDLoss(nn.Layer):
+    def __init__(
+        self, smoothing=False, ignore_index=100, sideloss_weight=1.0, **kwargs
+    ):
+        super(CPPDLoss, self).__init__()
+        self.edge_ce = nn.CrossEntropyLoss(reduction="mean", ignore_index=ignore_index)
+        self.char_node_ce = nn.CrossEntropyLoss(reduction="mean")
+        self.pos_node_ce = nn.BCEWithLogitsLoss(reduction="mean")
+        self.smoothing = smoothing
+        self.ignore_index = ignore_index
+        self.sideloss_weight = sideloss_weight
+
+    def label_smoothing_ce(self, preds, targets):
+        non_pad_mask = paddle.not_equal(
+            targets,
+            paddle.zeros(targets.shape, dtype=targets.dtype) + self.ignore_index,
+        )
+        tgts = paddle.where(
+            targets
+            == (paddle.zeros(targets.shape, dtype=targets.dtype) + self.ignore_index),
+            paddle.zeros(targets.shape, dtype=targets.dtype),
+            targets,
+        )
+        eps = 0.1
+        n_class = preds.shape[1]
+        one_hot = F.one_hot(tgts, preds.shape[1])
+        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
+        log_prb = F.log_softmax(preds, axis=1)
+        loss = -(one_hot * log_prb).sum(axis=1)
+        loss = loss.masked_select(non_pad_mask).mean()
+        return loss
+
+    def forward(self, pred, batch):
+        node_feats, edge_feats = pred
+        node_tgt = batch[2]
+        char_tgt = batch[1]
+
+        loss_char_node = self.char_node_ce(
+            node_feats[0].flatten(0, 1), node_tgt[:, :-26].flatten(0, 1)
+        )
+        loss_pos_node = self.pos_node_ce(
+            node_feats[1].flatten(0, 1), node_tgt[:, -26:].flatten(0, 1).cast("float32")
+        )
+        loss_node = loss_char_node + loss_pos_node
+
+        edge_feats = edge_feats.flatten(0, 1)
+        char_tgt = char_tgt.flatten(0, 1)
+        if self.smoothing:
+            loss_edge = self.label_smoothing_ce(edge_feats, char_tgt)
+        else:
+            loss_edge = self.edge_ce(edge_feats, char_tgt)
+
+        return {
+            "loss": self.sideloss_weight * loss_node + loss_edge,
+            "loss_node": self.sideloss_weight * loss_node,
+            "loss_edge": loss_edge,
+        }