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- from torch import nn
- import torch.nn.functional as F
- import torch
- 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.Module):
- """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,
- bias=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,
- bias=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, :, :], dim=1)
- tcl_pred = F.softmax(cls_res[i][:, 2:, :, :], dim=1)
- outs['level_{}'.format(i)] = torch.cat(
- [tr_pred, tcl_pred, reg_res[i]], dim=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
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