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ppocr/losses/rec_satrn_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.
+"""
+This code is refer from:
+https://github.com/open-mmlab/mmocr/blob/1.x/mmocr/models/textrecog/module_losses/ce_module_loss.py
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+
+
+class SATRNLoss(nn.Layer):
+    def __init__(self, **kwargs):
+        super(SATRNLoss, self).__init__()
+        ignore_index = kwargs.get("ignore_index", 92)  # 6626
+        self.loss_func = paddle.nn.loss.CrossEntropyLoss(
+            reduction="none", ignore_index=ignore_index
+        )
+
+    def forward(self, predicts, batch):
+        predict = predicts[
+            :, :-1, :
+        ]  # ignore last index of outputs to be in same seq_len with targets
+        label = batch[1].astype("int64")[
+            :, 1:
+        ]  # ignore first index of target in loss calculation
+        batch_size, num_steps, num_classes = (
+            predict.shape[0],
+            predict.shape[1],
+            predict.shape[2],
+        )
+        assert (
+            len(label.shape) == len(list(predict.shape)) - 1
+        ), "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
+
+        inputs = paddle.reshape(predict, [-1, num_classes])
+        targets = paddle.reshape(label, [-1])
+        loss = self.loss_func(inputs, targets)
+        return {"loss": loss.mean()}