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