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import torch
from torch import nn
from torchvision.models import resnet
from typing import Optional, Callable
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels,
gate: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None):
super().__init__()
if gate is None:
self.gate = nn.ReLU(inplace=True)
else:
self.gate = gate
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self.conv1 = resnet.conv3x3(in_channels, out_channels)
self.bn1 = norm_layer(out_channels)
self.conv2 = resnet.conv3x3(out_channels, out_channels)
self.bn2 = norm_layer(out_channels)
def forward(self, x):
x = self.gate(self.bn1(self.conv1(x))) # B x in_channels x H x W
x = self.gate(self.bn2(self.conv2(x))) # B x out_channels x H x W
return x
# copied from torchvision\models\resnet.py#27->BasicBlock
class ResBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
gate: Optional[Callable[..., nn.Module]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None
) -> None:
super(ResBlock, self).__init__()
if gate is None:
self.gate = nn.ReLU(inplace=True)
else:
self.gate = gate
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('ResBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in ResBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = resnet.conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.conv2 = resnet.conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x: torch.Tensor) -> torch.Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.gate(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.gate(out)
return out
class ALNet(nn.Module):
def __init__(self, c1: int = 32, c2: int = 64, c3: int = 128, c4: int = 128, dim: int = 128,
single_head: bool = True,
):
super().__init__()
self.gate = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool4 = nn.MaxPool2d(kernel_size=4, stride=4)
self.block1 = ConvBlock(3, c1, self.gate, nn.BatchNorm2d)
self.block2 = ResBlock(inplanes=c1, planes=c2, stride=1,
downsample=nn.Conv2d(c1, c2, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
self.block3 = ResBlock(inplanes=c2, planes=c3, stride=1,
downsample=nn.Conv2d(c2, c3, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
self.block4 = ResBlock(inplanes=c3, planes=c4, stride=1,
downsample=nn.Conv2d(c3, c4, 1),
gate=self.gate,
norm_layer=nn.BatchNorm2d)
# ================================== feature aggregation
self.conv1 = resnet.conv1x1(c1, dim // 4)
self.conv2 = resnet.conv1x1(c2, dim // 4)
self.conv3 = resnet.conv1x1(c3, dim // 4)
self.conv4 = resnet.conv1x1(dim, dim // 4)
self.upsample2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
self.upsample8 = nn.Upsample(scale_factor=8, mode='bilinear', align_corners=True)
self.upsample32 = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=True)
# ================================== detector and descriptor head
self.single_head = single_head
if not self.single_head:
self.convhead1 = resnet.conv1x1(dim, dim)
self.convhead2 = resnet.conv1x1(dim, dim + 1)
def forward(self, image):
# ================================== feature encoder
x1 = self.block1(image) # B x c1 x H x W
x2 = self.pool2(x1)
x2 = self.block2(x2) # B x c2 x H/2 x W/2
x3 = self.pool4(x2)
x3 = self.block3(x3) # B x c3 x H/8 x W/8
x4 = self.pool4(x3)
x4 = self.block4(x4) # B x dim x H/32 x W/32
# ================================== feature aggregation
x1 = self.gate(self.conv1(x1)) # B x dim//4 x H x W
x2 = self.gate(self.conv2(x2)) # B x dim//4 x H//2 x W//2
x3 = self.gate(self.conv3(x3)) # B x dim//4 x H//8 x W//8
x4 = self.gate(self.conv4(x4)) # B x dim//4 x H//32 x W//32
x2_up = self.upsample2(x2) # B x dim//4 x H x W
x3_up = self.upsample8(x3) # B x dim//4 x H x W
x4_up = self.upsample32(x4) # B x dim//4 x H x W
x1234 = torch.cat([x1, x2_up, x3_up, x4_up], dim=1)
# ================================== detector and descriptor head
if not self.single_head:
x1234 = self.gate(self.convhead1(x1234))
x = self.convhead2(x1234) # B x dim+1 x H x W
descriptor_map = x[:, :-1, :, :]
scores_map = torch.sigmoid(x[:, -1, :, :]).unsqueeze(1)
return scores_map, descriptor_map
if __name__ == '__main__':
from thop import profile
net = ALNet(c1=16, c2=32, c3=64, c4=128, dim=128, single_head=True)
image = torch.randn(1, 3, 640, 480)
flops, params = profile(net, inputs=(image,), verbose=False)
print('{:<30} {:<8} GFLops'.format('Computational complexity: ', flops / 1e9))
print('{:<30} {:<8} KB'.format('Number of parameters: ', params / 1e3))
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