# 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, 37]) # len(Lexicon_Table)+1 f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 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, 36 * 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') loss_out = paddle.fluid.layers.warpctc(f_tcl_char_ld, tcl_label, self.pad_num, True, input_lengths, label_t) cost = paddle.fluid.layers.squeeze(loss_out, [-1]) 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