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import os
import sys
import torch
import torch.nn as nn
import numpy as np
import math
import cv2
os.environ['CUDA_VISIBLE_DEVICES']='1'
import kornia as K
sys.path.append(os.path.join(os.path.dirname(__file__),'..'))
from models.model import LiftFeatSPModel
from models.interpolator import InterpolateSparse2d
from utils.config import featureboost_config
class NonMaxSuppression(torch.nn.Module):
def __init__(self, rep_thr=0.1, top_k=4096):
super(NonMaxSuppression,self).__init__()
self.max_filter = torch.nn.MaxPool2d(kernel_size=5, stride=1, padding=2)
self.rep_thr = rep_thr
self.top_k=top_k
def NMS(self, x, threshold = 0.05, kernel_size = 5):
B, _, H, W = x.shape
pad=kernel_size//2
local_max = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=pad)(x)
pos = (x == local_max) & (x > threshold)
pos_batched = [k.nonzero()[..., 1:].flip(-1) for k in pos]
pad_val = max([len(x) for x in pos_batched])
pos = torch.zeros((B, pad_val, 2), dtype=torch.long, device=x.device)
#Pad kpts and build (B, N, 2) tensor
for b in range(len(pos_batched)):
pos[b, :len(pos_batched[b]), :] = pos_batched[b]
return pos
def forward(self, score):
pos = self.NMS(score,self.rep_thr)
return pos
def load_model(model, weight_path):
pretrained_weights = torch.load(weight_path)
model_keys = set(model.state_dict().keys())
pretrained_keys = set(pretrained_weights.keys())
missing_keys = model_keys - pretrained_keys
unexpected_keys = pretrained_keys - model_keys
if missing_keys:
print("Missing keys in pretrained weights:", missing_keys)
else:
print("No missing keys in pretrained weights.")
if unexpected_keys:
print("Unexpected keys in pretrained weights:", unexpected_keys)
else:
print("No unexpected keys in pretrained weights.")
if not missing_keys and not unexpected_keys:
model.load_state_dict(pretrained_weights)
print("Pretrained weights loaded successfully.")
else:
model.load_state_dict(pretrained_weights, strict=False)
print("There were issues with the keys.")
return model
def load_torch_image(fname, device=torch.device('cpu')):
img = K.image_to_tensor(cv2.imread(fname), False).float() / 255.
img = K.color.bgr_to_rgb(img.to(device))
image=cv2.imread(fname)
H,W,C=image.shape[0],image.shape[1],image.shape[2]
_H=math.ceil(H/32)*32
_W=math.ceil(W/32)*32
pad_h=_H-H
pad_w=_W-W
image=cv2.copyMakeBorder(image,0,pad_h,0,pad_w,cv2.BORDER_CONSTANT,None,(0, 0, 0))
pad_info=[0,pad_h,0,pad_w]
image = K.image_to_tensor(image, False).float() / 255.
image = image.to(device)
return image,pad_info
class LiftFeat(nn.Module):
def __init__(self,weight,top_k=4096,detect_threshold=0.1):
super().__init__()
self.net=LiftFeatSPModel(featureboost_config)
self.top_k=top_k
self.sampler=InterpolateSparse2d('bicubic')
self.net=load_model(self.net,weight)
self.detector=NonMaxSuppression(rep_thr=detect_threshold)
@torch.inference_mode()
def extract(self,image,pad_info):
B,_,_H1,_W1=image.shape
M1,K1,D1=self.net.forward1(image)
refine_M=self.net.forward2(M1,K1,D1)
refine_M=refine_M.reshape(M1.shape[0],M1.shape[2],M1.shape[3],-1).permute(0,3,1,2)
refine_M=torch.nn.functional.normalize(refine_M,2,dim=1)
descs_map=refine_M
# descs_map=M1
scores=torch.softmax(K1,dim=1)[:,:64]
heatmap=scores.permute(0,2,3,1).reshape(scores.shape[0],scores.shape[2],scores.shape[3],8,8)
heatmap=heatmap.permute(0,1,3,2,4).reshape(scores.shape[0],1,scores.shape[2]*8,scores.shape[3]*8)
pos=self.detector(heatmap)
kpts=pos.squeeze(0)
mask_w=kpts[...,0]<(_W1-pad_info[-1])
kpts=kpts[mask_w]
mask_h=kpts[..., 1]<(_H1-pad_info[1])
kpts=kpts[mask_h]
descs=self.sampler(descs_map,kpts.unsqueeze(0),_H1,_W1)
descs=torch.nn.functional.normalize(descs,p=2,dim=1)
descs=descs.squeeze(0)
return {
'descriptors':descs,
'keypoints':kpts
}
def match_liftfeat(self, img1, pad_info1, img2, pad_info2, min_cossim=-1):
# import pdb;pdb.set_trace()
data1=self.extract(img1, pad_info1)
data2=self.extract(img2, pad_info2)
kpts1,feats1=data1['keypoints'],data1['descriptors']
kpts2,feats2=data2['keypoints'],data2['descriptors']
cossim = feats1 @ feats2.t()
cossim_t = feats2 @ feats1.t()
_, match12 = cossim.max(dim=1)
_, match21 = cossim_t.max(dim=1)
idx0 = torch.arange(len(match12), device=match12.device)
mutual = match21[match12] == idx0
if min_cossim > 0:
cossim, _ = cossim.max(dim=1)
good = cossim > min_cossim
idx0 = idx0[mutual & good]
idx1 = match12[mutual & good]
else:
idx0 = idx0[mutual]
idx1 = match12[mutual]
mkpts1,mkpts2=kpts1[idx0],kpts2[idx1]
return mkpts1, mkpts2
weight=os.path.join(os.path.dirname(__file__),'../weights/LiftFeat.pth')
liftfeat=LiftFeat(weight)
save_file=os.path.join(os.path.dirname(__file__),'../weights/LiftFeat.pt')
liftfeat_script=torch.jit.script(liftfeat)
liftfeat_script.save(save_file)
# checkpoint = {
# 'model_name': 'LiftFeat',
# 'model_args': {
# 'top_k': 4096,
# 'detect_threshold': 0.1
# },
# 'state_dict': liftfeat.state_dict()
# }
# torch.save(checkpoint,os.path.join(os.path.dirname(__file__),'../weights/LiftFeat.ckpt'))
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