| """ Bilinear-Attention-Transform and Non-Local Attention |
| |
| Paper: `Non-Local Neural Networks With Grouped Bilinear Attentional Transforms` |
| - https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html |
| Adapted from original code: https://github.com/BA-Transform/BAT-Image-Classification |
| """ |
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
|
|
| from .conv_bn_act import ConvNormAct |
| from .helpers import make_divisible |
| from .trace_utils import _assert |
|
|
|
|
| class NonLocalAttn(nn.Module): |
| """Spatial NL block for image classification. |
| |
| This was adapted from https://github.com/BA-Transform/BAT-Image-Classification |
| Their NonLocal impl inspired by https://github.com/facebookresearch/video-nonlocal-net. |
| """ |
|
|
| def __init__(self, in_channels, use_scale=True, rd_ratio=1/8, rd_channels=None, rd_divisor=8, **kwargs): |
| super(NonLocalAttn, self).__init__() |
| if rd_channels is None: |
| rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor) |
| self.scale = in_channels ** -0.5 if use_scale else 1.0 |
| self.t = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True) |
| self.p = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True) |
| self.g = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True) |
| self.z = nn.Conv2d(rd_channels, in_channels, kernel_size=1, stride=1, bias=True) |
| self.norm = nn.BatchNorm2d(in_channels) |
| self.reset_parameters() |
|
|
| def forward(self, x): |
| shortcut = x |
|
|
| t = self.t(x) |
| p = self.p(x) |
| g = self.g(x) |
|
|
| B, C, H, W = t.size() |
| t = t.view(B, C, -1).permute(0, 2, 1) |
| p = p.view(B, C, -1) |
| g = g.view(B, C, -1).permute(0, 2, 1) |
|
|
| att = torch.bmm(t, p) * self.scale |
| att = F.softmax(att, dim=2) |
| x = torch.bmm(att, g) |
|
|
| x = x.permute(0, 2, 1).reshape(B, C, H, W) |
| x = self.z(x) |
| x = self.norm(x) + shortcut |
|
|
| return x |
|
|
| def reset_parameters(self): |
| for name, m in self.named_modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_( |
| m.weight, mode='fan_out', nonlinearity='relu') |
| if len(list(m.parameters())) > 1: |
| nn.init.constant_(m.bias, 0.0) |
| elif isinstance(m, nn.BatchNorm2d): |
| nn.init.constant_(m.weight, 0) |
| nn.init.constant_(m.bias, 0) |
| elif isinstance(m, nn.GroupNorm): |
| nn.init.constant_(m.weight, 0) |
| nn.init.constant_(m.bias, 0) |
|
|
|
|
| class BilinearAttnTransform(nn.Module): |
|
|
| def __init__(self, in_channels, block_size, groups, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d): |
| super(BilinearAttnTransform, self).__init__() |
|
|
| self.conv1 = ConvNormAct(in_channels, groups, 1, act_layer=act_layer, norm_layer=norm_layer) |
| self.conv_p = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(block_size, 1)) |
| self.conv_q = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(1, block_size)) |
| self.conv2 = ConvNormAct(in_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer) |
| self.block_size = block_size |
| self.groups = groups |
| self.in_channels = in_channels |
|
|
| def resize_mat(self, x, t: int): |
| B, C, block_size, block_size1 = x.shape |
| _assert(block_size == block_size1, '') |
| if t <= 1: |
| return x |
| x = x.view(B * C, -1, 1, 1) |
| x = x * torch.eye(t, t, dtype=x.dtype, device=x.device) |
| x = x.view(B * C, block_size, block_size, t, t) |
| x = torch.cat(torch.split(x, 1, dim=1), dim=3) |
| x = torch.cat(torch.split(x, 1, dim=2), dim=4) |
| x = x.view(B, C, block_size * t, block_size * t) |
| return x |
|
|
| def forward(self, x): |
| _assert(x.shape[-1] % self.block_size == 0, '') |
| _assert(x.shape[-2] % self.block_size == 0, '') |
| B, C, H, W = x.shape |
| out = self.conv1(x) |
| rp = F.adaptive_max_pool2d(out, (self.block_size, 1)) |
| cp = F.adaptive_max_pool2d(out, (1, self.block_size)) |
| p = self.conv_p(rp).view(B, self.groups, self.block_size, self.block_size).sigmoid() |
| q = self.conv_q(cp).view(B, self.groups, self.block_size, self.block_size).sigmoid() |
| p = p / p.sum(dim=3, keepdim=True) |
| q = q / q.sum(dim=2, keepdim=True) |
| p = p.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size( |
| 0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous() |
| p = p.view(B, C, self.block_size, self.block_size) |
| q = q.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size( |
| 0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous() |
| q = q.view(B, C, self.block_size, self.block_size) |
| p = self.resize_mat(p, H // self.block_size) |
| q = self.resize_mat(q, W // self.block_size) |
| y = p.matmul(x) |
| y = y.matmul(q) |
|
|
| y = self.conv2(y) |
| return y |
|
|
|
|
| class BatNonLocalAttn(nn.Module): |
| """ BAT |
| Adapted from: https://github.com/BA-Transform/BAT-Image-Classification |
| """ |
|
|
| def __init__( |
| self, in_channels, block_size=7, groups=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8, |
| drop_rate=0.2, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, **_): |
| super().__init__() |
| if rd_channels is None: |
| rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor) |
| self.conv1 = ConvNormAct(in_channels, rd_channels, 1, act_layer=act_layer, norm_layer=norm_layer) |
| self.ba = BilinearAttnTransform(rd_channels, block_size, groups, act_layer=act_layer, norm_layer=norm_layer) |
| self.conv2 = ConvNormAct(rd_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer) |
| self.dropout = nn.Dropout2d(p=drop_rate) |
|
|
| def forward(self, x): |
| xl = self.conv1(x) |
| y = self.ba(xl) |
| y = self.conv2(y) |
| y = self.dropout(y) |
| return y + x |
|
|
