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feature_l2net.py
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feature_l2net.py
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"""
* This file is part of PYSLAM
* Adapted from https://github.com/vcg-uvic/image-matching-benchmark-baselines/blob/master/third_party/l2net_config/l2net_model.py, see licence therein.
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
# adapted from https://github.com/vcg-uvic/image-matching-benchmark-baselines/blob/master/third_party/l2net_config/l2net_model.py
import config
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import time
import os
import cv2
import math
import numpy as np
from utils_features import extract_patches_tensor, extract_patches_array, extract_patches_array_cpp
kVerbose = True
class L2Norm(nn.Module):
def __init__(self):
super(L2Norm,self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sqrt(torch.sum(x * x, dim = 1) + self.eps)
x= x / norm.unsqueeze(-1).expand_as(x)
return x
class L1Norm(nn.Module):
def __init__(self):
super(L1Norm,self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.sum(torch.abs(x), dim = 1) + self.eps
x= x / norm.expand_as(x)
return x
class L2Net(nn.Module):
def __init__(self):
super(L2Net, self).__init__()
self.eps = 1e-10
self.features = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=True),
nn.BatchNorm2d(32, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
nn.BatchNorm2d(32, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=True),
nn.BatchNorm2d(64, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=True),
nn.BatchNorm2d(64, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=True),
nn.BatchNorm2d(128, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=True),
nn.BatchNorm2d(128, affine=True, eps=self.eps),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=8, bias=True),
nn.BatchNorm2d(128, affine=True, eps=self.eps),
)
return
def input_norm(self, x):
# matlab norm
z = x.contiguous().transpose(2, 3).contiguous().view(x.size(0),-1)
x_minus_mean = z.transpose(0,1)-z.mean(1)
sp = torch.std(z,1).detach()
norm_inp = x_minus_mean/(sp+1e-12)
norm_inp = norm_inp.transpose(0, 1).view(-1, 1, x.size(2), x.size(3)).transpose(2,3)
return norm_inp
def forward(self, input):
norm_img = self.input_norm(input)
x_features = self.features(norm_img)
return nn.LocalResponseNorm(256,1*256,0.5,0.5)(x_features).view(input.size(0),-1)
# interface for pySLAM
class L2NetFeature2D:
def __init__(self, do_cuda=True):
print('Using L2NetFeature2D')
self.model_base_path = config.cfg.root_folder + '/thirdparty/l2net/'
self.model_weights_path = self.model_base_path + 'l2net_ported_weights_lib+.pth'
#print('model_weights_path:',self.model_weights_path)
# get pre-trained image mean
# l2net_weights = sio.loadmat(args.matlab_weights_path)
# imgMean = l2net_weights['pixMean']
self.do_cuda = do_cuda & torch.cuda.is_available()
print('cuda:',self.do_cuda)
device = torch.device("cuda:0" if self.do_cuda else "cpu")
torch.set_grad_enabled(False)
# mag_factor is how many times the original keypoint scale
# is enlarged to generate a patch from a keypoint
self.mag_factor = 1.0
# inference batch size
self.batch_size = 512
self.process_all = True # process all the patches at once
print('==> Loading pre-trained network.')
self.model = L2Net()
self.checkpoint = torch.load(self.model_weights_path)
#self.model.load_state_dict(self.checkpoint['state_dict'])
self.model.load_state_dict(self.checkpoint)
if self.do_cuda:
self.model.cuda()
print('Extracting on GPU')
else:
print('Extracting on CPU')
self.model = model.cpu()
self.model.eval()
print('==> Successfully loaded pre-trained network.')
def compute_des_batches(self, patches):
n_batches = int(len(patches) / self.batch_size) + 1
descriptors_for_net = np.zeros((len(patches), 128))
for i in range(0, len(patches), self.batch_size):
data_a = patches[i: i + self.batch_size, :, :, :].astype(np.float32)
data_a = torch.from_numpy(data_a)
if self.do_cuda:
data_a = data_a.cuda()
data_a = Variable(data_a)
# compute output
with torch.no_grad():
out_a = self.model(data_a)
descriptors_for_net[i: i + self.batch_size,:] = out_a.data.cpu().numpy().reshape(-1, 128)
return descriptors_for_net
def compute_des(self, patches):
patches = torch.from_numpy(patches).float()
patches = torch.unsqueeze(patches,1)
if self.do_cuda:
patches = patches.cuda()
with torch.no_grad():
descrs = self.model(patches)
return descrs.detach().cpu().numpy().reshape(-1, 128)
def compute(self, img, kps, mask=None): #mask is a fake input
num_kps = len(kps)
des = []
if num_kps>0:
if not self.process_all:
# compute descriptor for each patch
patches = extract_patches_tensor(img, kps, patch_size=32, mag_factor=self.mag_factor)
des = self.compute_des_batches(patches).astype(np.float32)
else:
# compute descriptor by feeeding the full patch tensor to the network
t = time.time()
if False:
# use python code
patches = extract_patches_array(img, kps, patch_size=32, mag_factor=self.mag_factor)
else:
# use faster cpp code
patches = extract_patches_array_cpp(img, kps, patch_size=32, mag_factor=self.mag_factor)
patches = np.asarray(patches)
if kVerbose:
print('patches.shape:',patches.shape)
if kVerbose:
print('patch elapsed: ', time.time()-t)
des = self.compute_des(patches)
if kVerbose:
print('descriptor: L2NET, #features: ', len(kps), ', frame res: ', img.shape[0:2])
return kps, des