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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/scene-text-telescope/loss/text_focus_loss.py
"""
import paddle.nn as nn
import paddle
import numpy as np
import pickle as pkl
standard_alphebet = '-0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
standard_dict = {}
for index in range(len(standard_alphebet)):
standard_dict[standard_alphebet[index]] = index
def load_confuse_matrix(confuse_dict_path):
f = open(confuse_dict_path, 'rb')
data = pkl.load(f)
f.close()
number = data[:10]
upper = data[10:36]
lower = data[36:]
end = np.ones((1, 62))
pad = np.ones((63, 1))
rearrange_data = np.concatenate((end, number, lower, upper), axis=0)
rearrange_data = np.concatenate((pad, rearrange_data), axis=1)
rearrange_data = 1 / rearrange_data
rearrange_data[rearrange_data == np.inf] = 1
rearrange_data = paddle.to_tensor(rearrange_data)
lower_alpha = 'abcdefghijklmnopqrstuvwxyz'
# upper_alpha = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
for i in range(63):
for j in range(63):
if i != j and standard_alphebet[j] in lower_alpha:
rearrange_data[i][j] = max(rearrange_data[i][j], rearrange_data[i][j + 26])
rearrange_data = rearrange_data[:37, :37]
return rearrange_data
def weight_cross_entropy(pred, gt, weight_table):
batch = gt.shape[0]
weight = weight_table[gt]
pred_exp = paddle.exp(pred)
pred_exp_weight = weight * pred_exp
loss = 0
for i in range(len(gt)):
loss -= paddle.log(pred_exp_weight[i][gt[i]] / paddle.sum(pred_exp_weight, 1)[i])
return loss / batch
class TelescopeLoss(nn.Layer):
def __init__(self, confuse_dict_path):
super(TelescopeLoss, self).__init__()
self.weight_table = load_confuse_matrix(confuse_dict_path)
self.mse_loss = nn.MSELoss()
self.ce_loss = nn.CrossEntropyLoss()
self.l1_loss = nn.L1Loss()
def forward(self, pred, data):
sr_img = pred["sr_img"]
hr_img = pred["hr_img"]
sr_pred = pred["sr_pred"]
text_gt = pred["text_gt"]
word_attention_map_gt = pred["word_attention_map_gt"]
word_attention_map_pred = pred["word_attention_map_pred"]
mse_loss = self.mse_loss(sr_img, hr_img)
attention_loss = self.l1_loss(word_attention_map_gt, word_attention_map_pred)
recognition_loss = weight_cross_entropy(sr_pred, text_gt, self.weight_table)
loss = mse_loss + attention_loss * 10 + recognition_loss * 0.0005
return {
"mse_loss": mse_loss,
"attention_loss": attention_loss,
"loss": loss
}
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