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e2e_pg_loss.py
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# copyright (c) 2021 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
from paddle import nn
import paddle
from .det_basic_loss import DiceLoss
from ppocr.utils.e2e_utils.extract_batchsize import pre_process
class PGLoss(nn.Layer):
def __init__(self,
tcl_bs,
max_text_length,
max_text_nums,
pad_num,
eps=1e-6,
**kwargs):
super(PGLoss, self).__init__()
self.tcl_bs = tcl_bs
self.max_text_nums = max_text_nums
self.max_text_length = max_text_length
self.pad_num = pad_num
self.dice_loss = DiceLoss(eps=eps)
def border_loss(self, f_border, l_border, l_score, l_mask):
l_border_split, l_border_norm = paddle.tensor.split(
l_border, num_or_sections=[4, 1], axis=1)
f_border_split = f_border
b, c, h, w = l_border_norm.shape
l_border_norm_split = paddle.expand(
x=l_border_norm, shape=[b, 4 * c, h, w])
b, c, h, w = l_score.shape
l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
b, c, h, w = l_mask.shape
l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
border_diff = l_border_split - f_border_split
abs_border_diff = paddle.abs(border_diff)
border_sign = abs_border_diff < 1.0
border_sign = paddle.cast(border_sign, dtype='float32')
border_sign.stop_gradient = True
border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
(abs_border_diff - 0.5) * (1.0 - border_sign)
border_out_loss = l_border_norm_split * border_in_loss
border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
(paddle.sum(l_border_score * l_border_mask) + 1e-5)
return border_loss
def direction_loss(self, f_direction, l_direction, l_score, l_mask):
l_direction_split, l_direction_norm = paddle.tensor.split(
l_direction, num_or_sections=[2, 1], axis=1)
f_direction_split = f_direction
b, c, h, w = l_direction_norm.shape
l_direction_norm_split = paddle.expand(
x=l_direction_norm, shape=[b, 2 * c, h, w])
b, c, h, w = l_score.shape
l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
b, c, h, w = l_mask.shape
l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
direction_diff = l_direction_split - f_direction_split
abs_direction_diff = paddle.abs(direction_diff)
direction_sign = abs_direction_diff < 1.0
direction_sign = paddle.cast(direction_sign, dtype='float32')
direction_sign.stop_gradient = True
direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
(abs_direction_diff - 0.5) * (1.0 - direction_sign)
direction_out_loss = l_direction_norm_split * direction_in_loss
direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
(paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
return direction_loss
def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
f_char = paddle.transpose(f_char, [0, 2, 3, 1])
tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
tcl_pos = paddle.cast(tcl_pos, dtype=int)
f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
f_tcl_char = paddle.reshape(
f_tcl_char, [-1, 64, self.pad_num + 1]) # len(Lexicon_Table)+1
f_tcl_char_fg, f_tcl_char_bg = paddle.split(
f_tcl_char, [self.pad_num, 1], axis=2)
f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
b, c, l = tcl_mask.shape
tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, self.pad_num * l])
tcl_mask_fg.stop_gradient = True
f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
-20.0)
f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
N, B, _ = f_tcl_char_ld.shape
input_lengths = paddle.to_tensor([N] * B, dtype='int64')
cost = paddle.nn.functional.ctc_loss(
log_probs=f_tcl_char_ld,
labels=tcl_label,
input_lengths=input_lengths,
label_lengths=label_t,
blank=self.pad_num,
reduction='none')
cost = cost.mean()
return cost
def forward(self, predicts, labels):
images, tcl_maps, tcl_label_maps, border_maps \
, direction_maps, training_masks, label_list, pos_list, pos_mask = labels
# for all the batch_size
pos_list, pos_mask, label_list, label_t = pre_process(
label_list, pos_list, pos_mask, self.max_text_length,
self.max_text_nums, self.pad_num, self.tcl_bs)
f_score, f_border, f_direction, f_char = predicts['f_score'], predicts['f_border'], predicts['f_direction'], \
predicts['f_char']
score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
border_loss = self.border_loss(f_border, border_maps, tcl_maps,
training_masks)
direction_loss = self.direction_loss(f_direction, direction_maps,
tcl_maps, training_masks)
ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
losses = {
'loss': loss_all,
"score_loss": score_loss,
"border_loss": border_loss,
"direction_loss": direction_loss,
"ctc_loss": ctc_loss
}
return losses