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"""
"XFeat: Accelerated Features for Lightweight Image Matching, CVPR 2024."
https://www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24/
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import time
class BasicLayer(nn.Module):
"""
Basic Convolutional Layer: Conv2d -> BatchNorm -> ReLU
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=False):
super().__init__()
self.layer = nn.Sequential(
nn.Conv2d( in_channels, out_channels, kernel_size, padding = padding, stride=stride, dilation=dilation, bias = bias),
nn.BatchNorm2d(out_channels, affine=False),
nn.ReLU(inplace = True),
)
def forward(self, x):
return self.layer(x)
class XFeatModel(nn.Module):
"""
Implementation of architecture described in
"XFeat: Accelerated Features for Lightweight Image Matching, CVPR 2024."
"""
def __init__(self):
super().__init__()
self.norm = nn.InstanceNorm2d(1)
########### ⬇️ CNN Backbone & Heads ⬇️ ###########
self.skip1 = nn.Sequential( nn.AvgPool2d(4, stride = 4),
nn.Conv2d (1, 24, 1, stride = 1, padding=0) )
self.block1 = nn.Sequential(
BasicLayer( 1, 4, stride=1),
BasicLayer( 4, 8, stride=2),
BasicLayer( 8, 8, stride=1),
BasicLayer( 8, 24, stride=2),
)
self.block2 = nn.Sequential(
BasicLayer(24, 24, stride=1),
BasicLayer(24, 24, stride=1),
)
self.block3 = nn.Sequential(
BasicLayer(24, 64, stride=2),
BasicLayer(64, 64, stride=1),
BasicLayer(64, 64, 1, padding=0),
)
self.block4 = nn.Sequential(
BasicLayer(64, 64, stride=2),
BasicLayer(64, 64, stride=1),
BasicLayer(64, 64, stride=1),
)
self.block5 = nn.Sequential(
BasicLayer( 64, 128, stride=2),
BasicLayer(128, 128, stride=1),
BasicLayer(128, 128, stride=1),
BasicLayer(128, 64, 1, padding=0),
)
self.block_fusion = nn.Sequential(
BasicLayer(64, 64, stride=1),
BasicLayer(64, 64, stride=1),
nn.Conv2d (64, 64, 1, padding=0)
)
self.heatmap_head = nn.Sequential(
BasicLayer(64, 64, 1, padding=0),
BasicLayer(64, 64, 1, padding=0),
nn.Conv2d (64, 1, 1),
nn.Sigmoid()
)
self.keypoint_head = nn.Sequential(
BasicLayer(64, 64, 1, padding=0),
BasicLayer(64, 64, 1, padding=0),
BasicLayer(64, 64, 1, padding=0),
nn.Conv2d (64, 65, 1),
)
########### ⬇️ Fine Matcher MLP ⬇️ ###########
self.fine_matcher = nn.Sequential(
nn.Linear(128, 512),
nn.BatchNorm1d(512, affine=False),
nn.ReLU(inplace = True),
nn.Linear(512, 512),
nn.BatchNorm1d(512, affine=False),
nn.ReLU(inplace = True),
nn.Linear(512, 512),
nn.BatchNorm1d(512, affine=False),
nn.ReLU(inplace = True),
nn.Linear(512, 512),
nn.BatchNorm1d(512, affine=False),
nn.ReLU(inplace = True),
nn.Linear(512, 64),
)
def _unfold2d(self, x, ws = 2):
"""
Unfolds tensor in 2D with desired ws (window size) and concat the channels
"""
B, C, H, W = x.shape
x = x.unfold(2, ws , ws).unfold(3, ws,ws) \
.reshape(B, C, H//ws, W//ws, ws**2)
return x.permute(0, 1, 4, 2, 3).reshape(B, -1, H//ws, W//ws)
def forward(self, x):
"""
input:
x -> torch.Tensor(B, C, H, W) grayscale or rgb images
return:
feats -> torch.Tensor(B, 64, H/8, W/8) dense local features
keypoints -> torch.Tensor(B, 65, H/8, W/8) keypoint logit map
heatmap -> torch.Tensor(B, 1, H/8, W/8) reliability map
"""
#dont backprop through normalization
with torch.no_grad():
x = x.mean(dim=1, keepdim = True)
x = self.norm(x)
#main backbone
x1 = self.block1(x)
x2 = self.block2(x1 + self.skip1(x))
x3 = self.block3(x2)
x4 = self.block4(x3)
x5 = self.block5(x4)
#pyramid fusion
x4 = F.interpolate(x4, (x3.shape[-2], x3.shape[-1]), mode='bilinear')
x5 = F.interpolate(x5, (x3.shape[-2], x3.shape[-1]), mode='bilinear')
feats = self.block_fusion( x3 + x4 + x5 )
#heads
heatmap = self.heatmap_head(feats) # Reliability map
keypoints = self.keypoint_head(self._unfold2d(x, ws=8)) #Keypoint map logits
return feats, keypoints, heatmap
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