206 lines
7.2 KiB
Python
206 lines
7.2 KiB
Python
# 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.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
import paddle.nn.functional as F
|
|
|
|
|
|
class BalanceLoss(nn.Layer):
|
|
def __init__(self,
|
|
balance_loss=True,
|
|
main_loss_type='DiceLoss',
|
|
negative_ratio=3,
|
|
return_origin=False,
|
|
eps=1e-6,
|
|
**kwargs):
|
|
"""
|
|
The BalanceLoss for Differentiable Binarization text detection
|
|
args:
|
|
balance_loss (bool): whether balance loss or not, default is True
|
|
main_loss_type (str): can only be one of ['CrossEntropy','DiceLoss',
|
|
'Euclidean','BCELoss', 'MaskL1Loss'], default is 'DiceLoss'.
|
|
negative_ratio (int|float): float, default is 3.
|
|
return_origin (bool): whether return unbalanced loss or not, default is False.
|
|
eps (float): default is 1e-6.
|
|
"""
|
|
super(BalanceLoss, self).__init__()
|
|
self.balance_loss = balance_loss
|
|
self.main_loss_type = main_loss_type
|
|
self.negative_ratio = negative_ratio
|
|
self.return_origin = return_origin
|
|
self.eps = eps
|
|
|
|
if self.main_loss_type == "CrossEntropy":
|
|
self.loss = nn.CrossEntropyLoss()
|
|
elif self.main_loss_type == "Euclidean":
|
|
self.loss = nn.MSELoss()
|
|
elif self.main_loss_type == "DiceLoss":
|
|
self.loss = DiceLoss(self.eps)
|
|
elif self.main_loss_type == "BCELoss":
|
|
self.loss = BCELoss(reduction='none')
|
|
elif self.main_loss_type == "MaskL1Loss":
|
|
self.loss = MaskL1Loss(self.eps)
|
|
else:
|
|
loss_type = [
|
|
'CrossEntropy', 'DiceLoss', 'Euclidean', 'BCELoss', 'MaskL1Loss'
|
|
]
|
|
raise Exception(
|
|
"main_loss_type in BalanceLoss() can only be one of {}".format(
|
|
loss_type))
|
|
|
|
def forward(self, pred, gt, mask=None):
|
|
"""
|
|
The BalanceLoss for Differentiable Binarization text detection
|
|
args:
|
|
pred (variable): predicted feature maps.
|
|
gt (variable): ground truth feature maps.
|
|
mask (variable): masked maps.
|
|
return: (variable) balanced loss
|
|
"""
|
|
# if self.main_loss_type in ['DiceLoss']:
|
|
# # For the loss that returns to scalar value, perform ohem on the mask
|
|
# mask = ohem_batch(pred, gt, mask, self.negative_ratio)
|
|
# loss = self.loss(pred, gt, mask)
|
|
# return loss
|
|
|
|
positive = gt * mask
|
|
negative = (1 - gt) * mask
|
|
|
|
positive_count = int(positive.sum())
|
|
negative_count = int(
|
|
min(negative.sum(), positive_count * self.negative_ratio))
|
|
loss = self.loss(pred, gt, mask=mask)
|
|
|
|
if not self.balance_loss:
|
|
return loss
|
|
|
|
positive_loss = positive * loss
|
|
negative_loss = negative * loss
|
|
negative_loss = paddle.reshape(negative_loss, shape=[-1])
|
|
if negative_count > 0:
|
|
sort_loss = negative_loss.sort(descending=True)
|
|
negative_loss = sort_loss[:negative_count]
|
|
# negative_loss, _ = paddle.topk(negative_loss, k=negative_count_int)
|
|
balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
|
|
positive_count + negative_count + self.eps)
|
|
else:
|
|
balance_loss = positive_loss.sum() / (positive_count + self.eps)
|
|
if self.return_origin:
|
|
return balance_loss, loss
|
|
|
|
return balance_loss
|
|
|
|
|
|
class DiceLoss(nn.Layer):
|
|
def __init__(self, eps=1e-6):
|
|
super(DiceLoss, self).__init__()
|
|
self.eps = eps
|
|
|
|
def forward(self, pred, gt, mask, weights=None):
|
|
"""
|
|
DiceLoss function.
|
|
"""
|
|
|
|
assert pred.shape == gt.shape
|
|
assert pred.shape == mask.shape
|
|
if weights is not None:
|
|
assert weights.shape == mask.shape
|
|
mask = weights * mask
|
|
intersection = paddle.sum(pred * gt * mask)
|
|
|
|
union = paddle.sum(pred * mask) + paddle.sum(gt * mask) + self.eps
|
|
loss = 1 - 2.0 * intersection / union
|
|
assert loss <= 1
|
|
return loss
|
|
|
|
|
|
class MaskL1Loss(nn.Layer):
|
|
def __init__(self, eps=1e-6):
|
|
super(MaskL1Loss, self).__init__()
|
|
self.eps = eps
|
|
|
|
def forward(self, pred, gt, mask):
|
|
"""
|
|
Mask L1 Loss
|
|
"""
|
|
loss = (paddle.abs(pred - gt) * mask).sum() / (mask.sum() + self.eps)
|
|
loss = paddle.mean(loss)
|
|
return loss
|
|
|
|
|
|
class BCELoss(nn.Layer):
|
|
def __init__(self, reduction='mean'):
|
|
super(BCELoss, self).__init__()
|
|
self.reduction = reduction
|
|
|
|
def forward(self, input, label, mask=None, weight=None, name=None):
|
|
loss = F.binary_cross_entropy(input, label, reduction=self.reduction)
|
|
return loss
|
|
|
|
|
|
def ohem_single(score, gt_text, training_mask, ohem_ratio):
|
|
pos_num = (int)(np.sum(gt_text > 0.5)) - (
|
|
int)(np.sum((gt_text > 0.5) & (training_mask <= 0.5)))
|
|
|
|
if pos_num == 0:
|
|
# selected_mask = gt_text.copy() * 0 # may be not good
|
|
selected_mask = training_mask
|
|
selected_mask = selected_mask.reshape(
|
|
1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
|
|
return selected_mask
|
|
|
|
neg_num = (int)(np.sum(gt_text <= 0.5))
|
|
neg_num = (int)(min(pos_num * ohem_ratio, neg_num))
|
|
|
|
if neg_num == 0:
|
|
selected_mask = training_mask
|
|
selected_mask = selected_mask.reshape(
|
|
1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
|
|
return selected_mask
|
|
|
|
neg_score = score[gt_text <= 0.5]
|
|
# 将负样本得分从高到低排序
|
|
neg_score_sorted = np.sort(-neg_score)
|
|
threshold = -neg_score_sorted[neg_num - 1]
|
|
# 选出 得分高的 负样本 和正样本 的 mask
|
|
selected_mask = ((score >= threshold) |
|
|
(gt_text > 0.5)) & (training_mask > 0.5)
|
|
selected_mask = selected_mask.reshape(
|
|
1, selected_mask.shape[0], selected_mask.shape[1]).astype('float32')
|
|
return selected_mask
|
|
|
|
|
|
def ohem_batch(scores, gt_texts, training_masks, ohem_ratio):
|
|
scores = scores.numpy()
|
|
gt_texts = gt_texts.numpy()
|
|
training_masks = training_masks.numpy()
|
|
|
|
selected_masks = []
|
|
for i in range(scores.shape[0]):
|
|
selected_masks.append(
|
|
ohem_single(scores[i, :, :], gt_texts[i, :, :], training_masks[
|
|
i, :, :], ohem_ratio))
|
|
|
|
selected_masks = np.concatenate(selected_masks, 0)
|
|
selected_masks = paddle.to_variable(selected_masks)
|
|
|
|
return selected_masks
|