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64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/loss/stroke_focus_loss.py
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"""
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import cv2
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import sys
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import time
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import string
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import random
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import numpy as np
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import paddle.nn as nn
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import paddle
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class StrokeFocusLoss(nn.Layer):
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def __init__(self, character_dict_path=None, **kwargs):
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super(StrokeFocusLoss, self).__init__(character_dict_path)
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self.mse_loss = nn.MSELoss()
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self.ce_loss = nn.CrossEntropyLoss()
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self.l1_loss = nn.L1Loss()
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self.english_stroke_alphabet = "0123456789"
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self.english_stroke_dict = {}
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for index in range(len(self.english_stroke_alphabet)):
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self.english_stroke_dict[self.english_stroke_alphabet[index]] = index
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stroke_decompose_lines = open(character_dict_path, "r").readlines()
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self.dic = {}
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for line in stroke_decompose_lines:
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line = line.strip()
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character, sequence = line.split()
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self.dic[character] = sequence
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def forward(self, pred, data):
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sr_img = pred["sr_img"]
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hr_img = pred["hr_img"]
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mse_loss = self.mse_loss(sr_img, hr_img)
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word_attention_map_gt = pred["word_attention_map_gt"]
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word_attention_map_pred = pred["word_attention_map_pred"]
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hr_pred = pred["hr_pred"]
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sr_pred = pred["sr_pred"]
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attention_loss = paddle.nn.functional.l1_loss(
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word_attention_map_gt, word_attention_map_pred
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)
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loss = (mse_loss + attention_loss * 50) * 100
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return {"mse_loss": mse_loss, "attention_loss": attention_loss, "loss": loss}
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