我修改的jupyterlab
jxhoh opened this issue · 4 comments
import os
os.chdir("..")
from copy import deepcopy
import torch
import cv2
import numpy as np
import matplotlib.cm as cm
from src.utils.plotting import make_matching_figure
from src.utils.plotting import make_matching_figure_2
print(torch.cuda.is_available())
from src.loftr import LoFTR,default_cfg
_default_cfg = deepcopy(default_cfg)
_default_cfg['coarse']['temp_bug_fix'] = True # set to False when using the old ckpt
_default_cfg['coarse']['npe'] = [832, 832, 832, 832]
print(_default_cfg)
matcher = LoFTR(config=_default_cfg)
matcher.load_state_dict(torch.load('D:\jx\ai\featue_match\ELOFTR\EfficientLoFTR-main\eloftr_outdoor.ckpt')['state_dict'])
matcher.eval().cuda()
Load example images
img0_pth = "D:\jx\ai\imageRdataset\models-master\models-master\research\delf\delf\python\examples\data\oxford5k_images\Snipaste_2024-03-14_17-34-54_r.jpg"
img1_pth = "D:\jx\ai\imageRdataset\models-master\models-master\research\delf\delf\python\examples\data\oxford5k_images\Snipaste_2024-03-14_17-31-37.jpg"
#img0_pth = "D:\jx\data\040-1-014231-514-004490.jpg"
#img1_pth = "D:\jx\data\040-2-014231-514-004490.jpg"
img0_raw = cv2.imread(img0_pth, cv2.IMREAD_GRAYSCALE)
img1_raw = cv2.imread(img1_pth, cv2.IMREAD_GRAYSCALE)
img0_raw = cv2.resize(img0_raw, (640, 480))
img1_raw = cv2.resize(img1_raw, (640, 480))
img0 = torch.from_numpy(img0_raw)[None][None].cuda() / 255.
img1 = torch.from_numpy(img1_raw)[None][None].cuda() / 255.
batch = {'image0': img0, 'image1': img1}
Inference with LoFTR and get prediction
with torch.no_grad():
matcher(batch)
mkpts0 = batch['mkpts0_f'].cpu().numpy()
mkpts1 = batch['mkpts1_f'].cpu().numpy()
mconf = batch['mconf'].cpu().numpy()
Draw
color = cm.jet(mconf)
text = [
'LoFTR',
'Matches: {}'.format(len(mkpts0)),
]
fig = make_matching_figure_2(img0_raw, img1_raw, mkpts0, mkpts1, color, text=text,path="D:\jx\ai\imageRdataset\models-master\models-master\research\delf\delf\python\examples\data\oxford5k_images\result_1.jpg")
配置文件的内容我就这样写了
from yacs.config import CfgNode as CN
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return yacs_cfg
return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}
############## ↓ LoFTR Pipeline ↓ ##############
_CN = CN()
_CN.BACKBONE_TYPE = 'RepVGG'
_CN.ALIGN_CORNER = False
_CN.RESOLUTION = (8, 1)
_CN.FINE_WINDOW_SIZE = 8 # window_size in fine_level, must be even
_CN.FP16 = False
_CN.REPLACE_NAN = False
_CN.EVAL_TIMES = 1
1. LoFTR-backbone (local feature CNN) config
_CN.BACKBONE = CN()
_CN.BACKBONE.BLOCK_DIMS = [64, 128, 256] # s1, s2, s3
2. LoFTR-coarse module config
_CN.COARSE = CN()
_CN.COARSE.D_MODEL = 256
_CN.COARSE.D_FFN = 256
_CN.COARSE.NHEAD = 8
_CN.COARSE.LAYER_NAMES = ['self', 'cross'] * 4
_CN.COARSE.AGG_SIZE0 = 4
_CN.COARSE.AGG_SIZE1 = 4
_CN.COARSE.NO_FLASH = False
_CN.COARSE.ROPE = True
_CN.COARSE.NPE = None
3. Coarse-Matching config
_CN.MATCH_COARSE = CN()
_CN.MATCH_COARSE.THR = 0.1
_CN.MATCH_COARSE.BORDER_RM = 2
_CN.MATCH_COARSE.DSMAX_TEMPERATURE = 0.1
_CN.MATCH_COARSE.TRAIN_COARSE_PERCENT = 0.2 # training tricks: save GPU memory
_CN.MATCH_COARSE.TRAIN_PAD_NUM_GT_MIN = 200 # training tricks: avoid DDP deadlock
_CN.MATCH_COARSE.SPARSE_SPVS = True
_CN.MATCH_COARSE.SKIP_SOFTMAX = False
_CN.MATCH_COARSE.FP16MATMUL = False
4. Fine-Matching config
_CN.MATCH_FINE = CN()
_CN.MATCH_FINE.SPARSE_SPVS = True
_CN.MATCH_FINE.LOCAL_REGRESS_TEMPERATURE = 1.0
_CN.MATCH_FINE.LOCAL_REGRESS_SLICEDIM = 8
5. LoFTR Losses
-- # coarse-level
_CN.LOSS = CN()
_CN.LOSS.COARSE_TYPE = 'focal' # ['focal', 'cross_entropy']
_CN.LOSS.COARSE_WEIGHT = 1.0
_CN.LOSS.COARSE_SIGMOID_WEIGHT = 1.0
_CN.LOSS.LOCAL_WEIGHT = 0.5
_CN.LOSS.COARSE_OVERLAP_WEIGHT = False
_CN.LOSS.FINE_OVERLAP_WEIGHT = False
_CN.LOSS.FINE_OVERLAP_WEIGHT2 = False
-- - -- # focal loss (coarse)
_CN.LOSS.FOCAL_ALPHA = 0.25
_CN.LOSS.FOCAL_GAMMA = 2.0
_CN.LOSS.POS_WEIGHT = 1.0
_CN.LOSS.NEG_WEIGHT = 1.0
-- # fine-level
_CN.LOSS.FINE_TYPE = 'l2_with_std' # ['l2_with_std', 'l2']
_CN.LOSS.FINE_WEIGHT = 1.0
_CN.LOSS.FINE_CORRECT_THR = 1.0 # for filtering valid fine-level gts (some gt matches might fall out of the fine-level window)
default_cfg = lower_config(_CN)
不知道有没有问题 我jupyter跑出来的结果倒是没问题，就是有时候点太多了
精度感觉没有那么高
感谢！点确实有点多，请问有没有筛选的方法，去除一些精度比较低的点对？
感谢！点确实有点多，请问有没有筛选的方法，去除一些精度比较低的点对？
可以在程序里面调那个阈值，有个配置文件中的thr，我跟了一遍代码就出来了
抱歉没有及时回复，我们已经更新一个jupyter notebook demo，上面你提供的jupyterlab的模型调用是对的，但是有几个需要修改的地方 (增加重参数化，THR设为0.2，MATCH_FINE.LOCAL_REGRESS_TEMPERATURE设为10.0)，建议直接使用我们更新的demo！
关于点太多的问题，这是一个非常好的发现！事实上，我们推荐full和opt. model分别使用thr=0.2和thr=25，你也可以根据实际情况自己调节。复现脚本给的(full thr=0.1, opt. thr=20)只是为了准确复现论文里AUC数值，我们在这里列出两组thr在MegaDepth和ScanNet测试集上的精度，以便更好理解和使用我们的模型。另外，你也可以看到我们的project page里的可视化demo的左下角，所有ours (full) model都是使用thr=0.2作为阈值的。
| thr. (full/opt.) | Megadepth AUC[5/10/20] | ScanNet AUC[5/10/20] |
|---|---|---|
| Reproduce paper (0.1/20) | Full(56.4/72.2/83.5) / Opt.(55.4/71.4/82.9) | Full(19.2/37.0/53.6) / Opt.(17.4/34.4/51.2) |
| Recommended (0.2/25) | Full(55.6/71.7/83.2) / Opt.(55.5/71.6/83.0) | Full(19.5/37.5/54.1) / Opt.(18.5/35.8/52.2) |
抱歉没有及时回复，我们已经更新一个jupyter notebook demo，上面你提供的jupyterlab的模型调用是对的，但是有几个需要修改的地方 (增加重参数化，THR设为0.2，MATCH_FINE.LOCAL_REGRESS_TEMPERATURE设为10.0)，建议直接使用我们更新的demo！
关于点太多的问题，这是一个非常好的发现！事实上，我们推荐full和opt. model分别使用thr=0.2和thr=25，你也可以根据实际情况自己调节。复现脚本给的(full thr=0.1, opt. thr=20)只是为了准确复现论文里AUC数值，我们在这里列出两组thr在MegaDepth和ScanNet测试集上的精度，以便更好理解和使用我们的模型。另外，你也可以看到我们的project page里的可视化demo的左下角，所有ours (full) model都是使用thr=0.2作为阈值的。
thr. (full/opt.) Megadepth AUC[5/10/20] ScanNet AUC[5/10/20]
Reproduce paper (0.1/20) Full(56.4/72.2/83.5) / Opt.(55.4/71.4/82.9) Full(19.2/37.0/53.6) / Opt.(17.4/34.4/51.2)
Recommended (0.2/25) Full(55.6/71.7/83.2) / Opt.(55.5/71.6/83.0) Full(19.5/37.5/54.1) / Opt.(18.5/35.8/52.2)
okok，感谢答复