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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/open-mmlab/mmocr/blob/main/mmocr/models/textdet/dense_heads/fce_head.py | |
| """ | |
| from paddle import nn | |
| from paddle import ParamAttr | |
| import paddle.nn.functional as F | |
| from paddle.nn.initializer import Normal | |
| import paddle | |
| from functools import partial | |
| def multi_apply(func, *args, **kwargs): | |
| pfunc = partial(func, **kwargs) if kwargs else func | |
| map_results = map(pfunc, *args) | |
| return tuple(map(list, zip(*map_results))) | |
| class FCEHead(nn.Layer): | |
| """The class for implementing FCENet head. | |
| FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text | |
| Detection. | |
| [https://arxiv.org/abs/2104.10442] | |
| Args: | |
| in_channels (int): The number of input channels. | |
| scales (list[int]) : The scale of each layer. | |
| fourier_degree (int) : The maximum Fourier transform degree k. | |
| """ | |
| def __init__(self, in_channels, fourier_degree=5): | |
| super().__init__() | |
| assert isinstance(in_channels, int) | |
| self.downsample_ratio = 1.0 | |
| self.in_channels = in_channels | |
| self.fourier_degree = fourier_degree | |
| self.out_channels_cls = 4 | |
| self.out_channels_reg = (2 * self.fourier_degree + 1) * 2 | |
| self.out_conv_cls = nn.Conv2D( | |
| in_channels=self.in_channels, | |
| out_channels=self.out_channels_cls, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| groups=1, | |
| weight_attr=ParamAttr( | |
| name='cls_weights', | |
| initializer=Normal( | |
| mean=0., std=0.01)), | |
| bias_attr=True) | |
| self.out_conv_reg = nn.Conv2D( | |
| in_channels=self.in_channels, | |
| out_channels=self.out_channels_reg, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| groups=1, | |
| weight_attr=ParamAttr( | |
| name='reg_weights', | |
| initializer=Normal( | |
| mean=0., std=0.01)), | |
| bias_attr=True) | |
| def forward(self, feats, targets=None): | |
| cls_res, reg_res = multi_apply(self.forward_single, feats) | |
| level_num = len(cls_res) | |
| outs = {} | |
| if not self.training: | |
| for i in range(level_num): | |
| tr_pred = F.softmax(cls_res[i][:, 0:2, :, :], axis=1) | |
| tcl_pred = F.softmax(cls_res[i][:, 2:, :, :], axis=1) | |
| outs['level_{}'.format(i)] = paddle.concat( | |
| [tr_pred, tcl_pred, reg_res[i]], axis=1) | |
| else: | |
| preds = [[cls_res[i], reg_res[i]] for i in range(level_num)] | |
| outs['levels'] = preds | |
| return outs | |
| def forward_single(self, x): | |
| cls_predict = self.out_conv_cls(x) | |
| reg_predict = self.out_conv_reg(x) | |
| return cls_predict, reg_predict | |