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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch | |
| import torch.nn.functional as F | |
| from mmcv.runner import BaseModule | |
| from torch import nn | |
| from mmocr.models.builder import NECKS | |
| class UpBlock(BaseModule): | |
| """Upsample block for DRRG and TextSnake.""" | |
| def __init__(self, in_channels, out_channels, init_cfg=None): | |
| super().__init__(init_cfg=init_cfg) | |
| assert isinstance(in_channels, int) | |
| assert isinstance(out_channels, int) | |
| self.conv1x1 = nn.Conv2d( | |
| in_channels, in_channels, kernel_size=1, stride=1, padding=0) | |
| self.conv3x3 = nn.Conv2d( | |
| in_channels, out_channels, kernel_size=3, stride=1, padding=1) | |
| self.deconv = nn.ConvTranspose2d( | |
| out_channels, out_channels, kernel_size=4, stride=2, padding=1) | |
| def forward(self, x): | |
| x = F.relu(self.conv1x1(x)) | |
| x = F.relu(self.conv3x3(x)) | |
| x = self.deconv(x) | |
| return x | |
| class FPN_UNet(BaseModule): | |
| """The class for implementing DRRG and TextSnake U-Net-like FPN. | |
| DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape | |
| Text Detection <https://arxiv.org/abs/2003.07493>`_. | |
| TextSnake: `A Flexible Representation for Detecting Text of Arbitrary | |
| Shapes <https://arxiv.org/abs/1807.01544>`_. | |
| Args: | |
| in_channels (list[int]): Number of input channels at each scale. The | |
| length of the list should be 4. | |
| out_channels (int): The number of output channels. | |
| init_cfg (dict or list[dict], optional): Initialization configs. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| init_cfg=dict( | |
| type='Xavier', | |
| layer=['Conv2d', 'ConvTranspose2d'], | |
| distribution='uniform')): | |
| super().__init__(init_cfg=init_cfg) | |
| assert len(in_channels) == 4 | |
| assert isinstance(out_channels, int) | |
| blocks_out_channels = [out_channels] + [ | |
| min(out_channels * 2**i, 256) for i in range(4) | |
| ] | |
| blocks_in_channels = [blocks_out_channels[1]] + [ | |
| in_channels[i] + blocks_out_channels[i + 2] for i in range(3) | |
| ] + [in_channels[3]] | |
| self.up4 = nn.ConvTranspose2d( | |
| blocks_in_channels[4], | |
| blocks_out_channels[4], | |
| kernel_size=4, | |
| stride=2, | |
| padding=1) | |
| self.up_block3 = UpBlock(blocks_in_channels[3], blocks_out_channels[3]) | |
| self.up_block2 = UpBlock(blocks_in_channels[2], blocks_out_channels[2]) | |
| self.up_block1 = UpBlock(blocks_in_channels[1], blocks_out_channels[1]) | |
| self.up_block0 = UpBlock(blocks_in_channels[0], blocks_out_channels[0]) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x (list[Tensor] | tuple[Tensor]): A list of four tensors of shape | |
| :math:`(N, C_i, H_i, W_i)`, representing C2, C3, C4, C5 | |
| features respectively. :math:`C_i` should matches the number in | |
| ``in_channels``. | |
| Returns: | |
| Tensor: Shape :math:`(N, C, H, W)` where :math:`H=4H_0` and | |
| :math:`W=4W_0`. | |
| """ | |
| c2, c3, c4, c5 = x | |
| x = F.relu(self.up4(c5)) | |
| x = torch.cat([x, c4], dim=1) | |
| x = F.relu(self.up_block3(x)) | |
| x = torch.cat([x, c3], dim=1) | |
| x = F.relu(self.up_block2(x)) | |
| x = torch.cat([x, c2], dim=1) | |
| x = F.relu(self.up_block1(x)) | |
| x = self.up_block0(x) | |
| # the output should be of the same height and width as backbone input | |
| return x | |