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ppocr/losses/det_db_loss.py

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+# copyright (c) 2019 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/WenmuZhou/DBNet.pytorch/blob/master/models/losses/DB_loss.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+
+from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+
+
+class DBLoss(nn.Layer):
+    """
+    Differentiable Binarization (DB) Loss Function
+    args:
+        param (dict): the super parameter for DB Loss
+    """
+
+    def __init__(
+        self,
+        balance_loss=True,
+        main_loss_type="DiceLoss",
+        alpha=5,
+        beta=10,
+        ohem_ratio=3,
+        eps=1e-6,
+        **kwargs,
+    ):
+        super(DBLoss, self).__init__()
+        self.alpha = alpha
+        self.beta = beta
+        self.dice_loss = DiceLoss(eps=eps)
+        self.l1_loss = MaskL1Loss(eps=eps)
+        self.bce_loss = BalanceLoss(
+            balance_loss=balance_loss,
+            main_loss_type=main_loss_type,
+            negative_ratio=ohem_ratio,
+        )
+
+    def forward(self, predicts, labels):
+        predict_maps = predicts["maps"]
+        (
+            label_threshold_map,
+            label_threshold_mask,
+            label_shrink_map,
+            label_shrink_mask,
+        ) = labels[1:]
+        shrink_maps = predict_maps[:, 0, :, :]
+        threshold_maps = predict_maps[:, 1, :, :]
+        binary_maps = predict_maps[:, 2, :, :]
+
+        loss_shrink_maps = self.bce_loss(
+            shrink_maps, label_shrink_map, label_shrink_mask
+        )
+        loss_threshold_maps = self.l1_loss(
+            threshold_maps, label_threshold_map, label_threshold_mask
+        )
+        loss_binary_maps = self.dice_loss(
+            binary_maps, label_shrink_map, label_shrink_mask
+        )
+        loss_shrink_maps = self.alpha * loss_shrink_maps
+        loss_threshold_maps = self.beta * loss_threshold_maps
+        # CBN loss
+        if "distance_maps" in predicts.keys():
+            distance_maps = predicts["distance_maps"]
+            cbn_maps = predicts["cbn_maps"]
+            cbn_loss = self.bce_loss(
+                cbn_maps[:, 0, :, :], label_shrink_map, label_shrink_mask
+            )
+        else:
+            dis_loss = paddle.to_tensor([0.0])
+            cbn_loss = paddle.to_tensor([0.0])
+
+        loss_all = loss_shrink_maps + loss_threshold_maps + loss_binary_maps
+        losses = {
+            "loss": loss_all + cbn_loss,
+            "loss_shrink_maps": loss_shrink_maps,
+            "loss_threshold_maps": loss_threshold_maps,
+            "loss_binary_maps": loss_binary_maps,
+            "loss_cbn": cbn_loss,
+        }
+        return losses