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ppocr/losses/stroke_focus_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/FudanVI/FudanOCR/blob/main/text-gestalt/loss/stroke_focus_loss.py
+"""
+import cv2
+import sys
+import time
+import string
+import random
+import numpy as np
+import paddle.nn as nn
+import paddle
+
+
+class StrokeFocusLoss(nn.Layer):
+    def __init__(self, character_dict_path=None, **kwargs):
+        super(StrokeFocusLoss, self).__init__(character_dict_path)
+        self.mse_loss = nn.MSELoss()
+        self.ce_loss = nn.CrossEntropyLoss()
+        self.l1_loss = nn.L1Loss()
+        self.english_stroke_alphabet = "0123456789"
+        self.english_stroke_dict = {}
+        for index in range(len(self.english_stroke_alphabet)):
+            self.english_stroke_dict[self.english_stroke_alphabet[index]] = index
+
+        stroke_decompose_lines = open(character_dict_path, "r").readlines()
+        self.dic = {}
+        for line in stroke_decompose_lines:
+            line = line.strip()
+            character, sequence = line.split()
+            self.dic[character] = sequence
+
+    def forward(self, pred, data):
+        sr_img = pred["sr_img"]
+        hr_img = pred["hr_img"]
+
+        mse_loss = self.mse_loss(sr_img, hr_img)
+        word_attention_map_gt = pred["word_attention_map_gt"]
+        word_attention_map_pred = pred["word_attention_map_pred"]
+
+        hr_pred = pred["hr_pred"]
+        sr_pred = pred["sr_pred"]
+
+        attention_loss = paddle.nn.functional.l1_loss(
+            word_attention_map_gt, word_attention_map_pred
+        )
+
+        loss = (mse_loss + attention_loss * 50) * 100
+
+        return {"mse_loss": mse_loss, "attention_loss": attention_loss, "loss": loss}