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