141 lines
6.4 KiB
Python
141 lines
6.4 KiB
Python
# copyright (c) 2021 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import nn
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import paddle
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from .det_basic_loss import DiceLoss
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from ppocr.utils.e2e_utils.extract_batchsize import pre_process
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class PGLoss(nn.Layer):
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def __init__(self,
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tcl_bs,
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max_text_length,
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max_text_nums,
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pad_num,
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eps=1e-6,
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**kwargs):
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super(PGLoss, self).__init__()
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self.tcl_bs = tcl_bs
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self.max_text_nums = max_text_nums
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self.max_text_length = max_text_length
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self.pad_num = pad_num
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self.dice_loss = DiceLoss(eps=eps)
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def border_loss(self, f_border, l_border, l_score, l_mask):
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l_border_split, l_border_norm = paddle.tensor.split(
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l_border, num_or_sections=[4, 1], axis=1)
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f_border_split = f_border
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b, c, h, w = l_border_norm.shape
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l_border_norm_split = paddle.expand(
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x=l_border_norm, shape=[b, 4 * c, h, w])
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b, c, h, w = l_score.shape
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l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
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b, c, h, w = l_mask.shape
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l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
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border_diff = l_border_split - f_border_split
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abs_border_diff = paddle.abs(border_diff)
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border_sign = abs_border_diff < 1.0
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border_sign = paddle.cast(border_sign, dtype='float32')
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border_sign.stop_gradient = True
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border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
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(abs_border_diff - 0.5) * (1.0 - border_sign)
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border_out_loss = l_border_norm_split * border_in_loss
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border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
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(paddle.sum(l_border_score * l_border_mask) + 1e-5)
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return border_loss
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def direction_loss(self, f_direction, l_direction, l_score, l_mask):
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l_direction_split, l_direction_norm = paddle.tensor.split(
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l_direction, num_or_sections=[2, 1], axis=1)
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f_direction_split = f_direction
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b, c, h, w = l_direction_norm.shape
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l_direction_norm_split = paddle.expand(
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x=l_direction_norm, shape=[b, 2 * c, h, w])
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b, c, h, w = l_score.shape
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l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
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b, c, h, w = l_mask.shape
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l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
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direction_diff = l_direction_split - f_direction_split
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abs_direction_diff = paddle.abs(direction_diff)
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direction_sign = abs_direction_diff < 1.0
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direction_sign = paddle.cast(direction_sign, dtype='float32')
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direction_sign.stop_gradient = True
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direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
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(abs_direction_diff - 0.5) * (1.0 - direction_sign)
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direction_out_loss = l_direction_norm_split * direction_in_loss
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direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
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(paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
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return direction_loss
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def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
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f_char = paddle.transpose(f_char, [0, 2, 3, 1])
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tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
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tcl_pos = paddle.cast(tcl_pos, dtype=int)
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f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
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f_tcl_char = paddle.reshape(f_tcl_char,
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[-1, 64, 37]) # len(Lexicon_Table)+1
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f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
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f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
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b, c, l = tcl_mask.shape
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tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
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tcl_mask_fg.stop_gradient = True
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f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
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-20.0)
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f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
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f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
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N, B, _ = f_tcl_char_ld.shape
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input_lengths = paddle.to_tensor([N] * B, dtype='int64')
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cost = paddle.nn.functional.ctc_loss(
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log_probs=f_tcl_char_ld,
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labels=tcl_label,
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input_lengths=input_lengths,
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label_lengths=label_t,
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blank=self.pad_num,
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reduction='none')
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cost = cost.mean()
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return cost
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def forward(self, predicts, labels):
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images, tcl_maps, tcl_label_maps, border_maps \
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, direction_maps, training_masks, label_list, pos_list, pos_mask = labels
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# for all the batch_size
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pos_list, pos_mask, label_list, label_t = pre_process(
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label_list, pos_list, pos_mask, self.max_text_length,
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self.max_text_nums, self.pad_num, self.tcl_bs)
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f_score, f_border, f_direction, f_char = predicts['f_score'], predicts['f_border'], predicts['f_direction'], \
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predicts['f_char']
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score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
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border_loss = self.border_loss(f_border, border_maps, tcl_maps,
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training_masks)
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direction_loss = self.direction_loss(f_direction, direction_maps,
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tcl_maps, training_masks)
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ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
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loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
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losses = {
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'loss': loss_all,
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"score_loss": score_loss,
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"border_loss": border_loss,
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"direction_loss": direction_loss,
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"ctc_loss": ctc_loss
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}
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return losses
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