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| import torch | |
| from annotator.uniformer.mmcv.cnn import NonLocal2d | |
| from torch import nn | |
| from ..builder import HEADS | |
| from .fcn_head import FCNHead | |
| class DisentangledNonLocal2d(NonLocal2d): | |
| """Disentangled Non-Local Blocks. | |
| Args: | |
| temperature (float): Temperature to adjust attention. Default: 0.05 | |
| """ | |
| def __init__(self, *arg, temperature, **kwargs): | |
| super().__init__(*arg, **kwargs) | |
| self.temperature = temperature | |
| self.conv_mask = nn.Conv2d(self.in_channels, 1, kernel_size=1) | |
| def embedded_gaussian(self, theta_x, phi_x): | |
| """Embedded gaussian with temperature.""" | |
| # NonLocal2d pairwise_weight: [N, HxW, HxW] | |
| pairwise_weight = torch.matmul(theta_x, phi_x) | |
| if self.use_scale: | |
| # theta_x.shape[-1] is `self.inter_channels` | |
| pairwise_weight /= theta_x.shape[-1]**0.5 | |
| pairwise_weight /= self.temperature | |
| pairwise_weight = pairwise_weight.softmax(dim=-1) | |
| return pairwise_weight | |
| def forward(self, x): | |
| # x: [N, C, H, W] | |
| n = x.size(0) | |
| # g_x: [N, HxW, C] | |
| g_x = self.g(x).view(n, self.inter_channels, -1) | |
| g_x = g_x.permute(0, 2, 1) | |
| # theta_x: [N, HxW, C], phi_x: [N, C, HxW] | |
| if self.mode == 'gaussian': | |
| theta_x = x.view(n, self.in_channels, -1) | |
| theta_x = theta_x.permute(0, 2, 1) | |
| if self.sub_sample: | |
| phi_x = self.phi(x).view(n, self.in_channels, -1) | |
| else: | |
| phi_x = x.view(n, self.in_channels, -1) | |
| elif self.mode == 'concatenation': | |
| theta_x = self.theta(x).view(n, self.inter_channels, -1, 1) | |
| phi_x = self.phi(x).view(n, self.inter_channels, 1, -1) | |
| else: | |
| theta_x = self.theta(x).view(n, self.inter_channels, -1) | |
| theta_x = theta_x.permute(0, 2, 1) | |
| phi_x = self.phi(x).view(n, self.inter_channels, -1) | |
| # subtract mean | |
| theta_x -= theta_x.mean(dim=-2, keepdim=True) | |
| phi_x -= phi_x.mean(dim=-1, keepdim=True) | |
| pairwise_func = getattr(self, self.mode) | |
| # pairwise_weight: [N, HxW, HxW] | |
| pairwise_weight = pairwise_func(theta_x, phi_x) | |
| # y: [N, HxW, C] | |
| y = torch.matmul(pairwise_weight, g_x) | |
| # y: [N, C, H, W] | |
| y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, | |
| *x.size()[2:]) | |
| # unary_mask: [N, 1, HxW] | |
| unary_mask = self.conv_mask(x) | |
| unary_mask = unary_mask.view(n, 1, -1) | |
| unary_mask = unary_mask.softmax(dim=-1) | |
| # unary_x: [N, 1, C] | |
| unary_x = torch.matmul(unary_mask, g_x) | |
| # unary_x: [N, C, 1, 1] | |
| unary_x = unary_x.permute(0, 2, 1).contiguous().reshape( | |
| n, self.inter_channels, 1, 1) | |
| output = x + self.conv_out(y + unary_x) | |
| return output | |
| class DNLHead(FCNHead): | |
| """Disentangled Non-Local Neural Networks. | |
| This head is the implementation of `DNLNet | |
| <https://arxiv.org/abs/2006.06668>`_. | |
| Args: | |
| reduction (int): Reduction factor of projection transform. Default: 2. | |
| use_scale (bool): Whether to scale pairwise_weight by | |
| sqrt(1/inter_channels). Default: False. | |
| mode (str): The nonlocal mode. Options are 'embedded_gaussian', | |
| 'dot_product'. Default: 'embedded_gaussian.'. | |
| temperature (float): Temperature to adjust attention. Default: 0.05 | |
| """ | |
| def __init__(self, | |
| reduction=2, | |
| use_scale=True, | |
| mode='embedded_gaussian', | |
| temperature=0.05, | |
| **kwargs): | |
| super(DNLHead, self).__init__(num_convs=2, **kwargs) | |
| self.reduction = reduction | |
| self.use_scale = use_scale | |
| self.mode = mode | |
| self.temperature = temperature | |
| self.dnl_block = DisentangledNonLocal2d( | |
| in_channels=self.channels, | |
| reduction=self.reduction, | |
| use_scale=self.use_scale, | |
| conv_cfg=self.conv_cfg, | |
| norm_cfg=self.norm_cfg, | |
| mode=self.mode, | |
| temperature=self.temperature) | |
| def forward(self, inputs): | |
| """Forward function.""" | |
| x = self._transform_inputs(inputs) | |
| output = self.convs[0](x) | |
| output = self.dnl_block(output) | |
| output = self.convs[1](output) | |
| if self.concat_input: | |
| output = self.conv_cat(torch.cat([x, output], dim=1)) | |
| output = self.cls_seg(output) | |
| return output | |