这几天觉得自己电脑好卡，之前也没管，今天偶尔看了下，发现有个进程占用了大量内存，这个就是桌面窗口管理器。
关闭virtualbox虚拟机后，总内存占用下降了，但它的没变。
关闭所有应用后，它的占用依旧很高。
尝试网上各种方法都没有效果。
最后想到之前下载过一个内存清理软件，就拿出来试了下，发现效果还不错。
就是这个软件，大家可以试试。
官网：
https://github.com/henrypp/memreduct
下载：
https://github.com/henrypp/memreduct/releases/download/v.3.4/memreduct-3.4-setup.exe
部分
1.IEEE International Microwaves Symposium
2.European Microwave Conference
3.IEEE International Symposium on Antennas and Propagation
4.European Conference on Antennas and Propagation
1.INFOCOM: International Conference on Computer Communications
2.GLOBECOM: IEEE Global Telecommunications Conference
3.ICC:The IEEE International Conference on Communications
4.IEEE ICCV：International Conference on Computer Vision
5.IEEE CVPR：IEEE Conference on Computer Vision and Pattern Recognition
6.ICML：International Conference on Machine Learning
7.IJCAI：International Joint Conference on Artificial Intelligence
8.AAAI: AAAI Conference on Artificial Intelligence
9.ECCV: European Conference on Computer Vision
10.ACM MM: ACM International Conference on Multimedia
11.NIPS：Annual Conference on Neural Information Processing Systems
12.IEEE ICASSP: International Conference on Acoustics, Speech and Signal Processing
13.ACM CHI: The ACM CHI Conference on Human Factors in Computing Systems
14.ACM SIGGRAPH
15.FGR ：International Conference on Face and Gesture Recognition
16.COLING ：International Conference on Computational Linguistics
17.VR：IEEE Virtual Reality Conference
18.MICCAI：Medical Image Computing and Computer-assisted Intervention
19.Interspeech：Interspeech
20.ICLR：International Conference on Learning Representations
21.ACII: Affective Computing and Intelligent Interaction
22.ACL: Annual Meeting of the Association for Computational Linguistics
23.EMNLP: Conference on Empirical Methods in Natural Language Processing
24.NAACL: North American Chapter of the Association for Computational Linguistics
1.GLOBECOM
2.IEEE CVPR：IEEE Conference on Computer Vision and Pattern Recognition
3.AAAI: AAAI Conference on Artificial Intelligence
4.IJCAI：International Joint Conference on Artificial Intelligence
5.ICCV：International Conference on Computer Vision
迁移学习、集成学习、孪生网络、三元损失、知识蒸馏
片段随意摘，可能并无前后逻辑
OpenCV-Contrib-python包来实现。
[51] A.A. Ross, K. Nandakumar and A. Jain, “Handbook of multibiometrics (Vol. 6)”, Springer Science & Business Media, 2006, pp.73-82.
代码
def getPoolingKernel(kernel_size=25):
half_size = float(kernel_size) / 2.0
xc2 = []
for i in range(kernel_size):
xc2.append(half_size - abs(float(i) + 0.5 - half_size))
xc2 = np.array(xc2)
kernel = np.outer(xc2.T, xc2)
kernel = kernel / (half_size ** 2)
return kernel
def get_bin_weight_kernel_size_and_stride(patch_size, num_spatial_bins):
ks = 2 * int(patch_size / (num_spatial_bins + 1));
stride = patch_size // num_spatial_bins
pad = ks // 4
return ks, stride, pad
class SIFTNet(nn.Module):
def CircularGaussKernel(self, kernlen=21, circ=True, sigma_type='hesamp'):
halfSize = float(kernlen) / 2.
r2 = float(halfSize ** 2)
if sigma_type == 'hesamp':
sigma_mul_2 = 0.9 * r2
elif sigma_type == 'vlfeat':
sigma_mul_2 = kernlen ** 2
else:
raise ValueError('Unknown sigma_type', sigma_type, 'try hesamp or vlfeat')
disq = 0
kernel = np.zeros((kernlen, kernlen))
for y in range(kernlen):
for x in range(kernlen):
disq = (y - halfSize + 0.5) ** 2 + (x - halfSize + 0.5) ** 2
kernel[y, x] = math.exp(-disq / sigma_mul_2)
if circ and (disq >= r2):
kernel[y, x] = 0.
return kernel
def __repr__(self):
return self.__class__.__name__ + '(' + 'num_ang_bins=' + str(self.num_ang_bins) + \
', ' + 'num_spatial_bins=' + str(self.num_spatial_bins) + \
', ' + 'patch_size=' + str(self.patch_size) + \
', ' + 'rootsift=' + str(self.rootsift) + \
', ' + 'sigma_type=' + str(self.sigma_type) + \
', ' + 'mask_type=' + str(self.mask_type) + \
', ' + 'clipval=' + str(self.clipval) + ')'
def __init__(self,
patch_size=65,
num_ang_bins=8,
num_spatial_bins=4,
clipval=0.2,
rootsift=False,
mask_type='CircularGauss',
sigma_type='hesamp'):
super(SIFTNet, self).__init__()
self.eps = 1e-10
self.num_ang_bins = num_ang_bins
self.num_spatial_bins = num_spatial_bins
self.clipval = clipval
self.rootsift = rootsift
self.mask_type = mask_type
self.patch_size = patch_size
self.sigma_type = sigma_type
if self.mask_type == 'CircularGauss':
self.gk = torch.from_numpy(
self.CircularGaussKernel(kernlen=patch_size, circ=True, sigma_type=sigma_type).astype(np.float32))
elif self.mask_type == 'Gauss':
self.gk = torch.from_numpy(
self.CircularGaussKernel(kernlen=patch_size, circ=False, sigma_type=sigma_type).astype(np.float32))
elif self.mask_type == 'Uniform':
self.gk = torch.ones(patch_size, patch_size).float() / float(patch_size * patch_size)
else:
raise ValueError(self.mask_type, 'is unknown mask type')
self.bin_weight_kernel_size, self.bin_weight_stride, self.pad = get_bin_weight_kernel_size_and_stride(
patch_size, num_spatial_bins)
self.gx = nn.Conv2d(1, 1, kernel_size=(1, 3), bias=False)
self.gx.weight.data = torch.tensor(np.array([[[[-1, 0, 1]]]], dtype=np.float32))
self.gy = nn.Conv2d(1, 1, kernel_size=(3, 1), bias=False)
self.gy.weight.data = torch.from_numpy(np.array([[[[-1], [0], [1]]]], dtype=np.float32))
nw = getPoolingKernel(kernel_size=self.bin_weight_kernel_size)
self.pk = nn.Conv2d(1, 1, kernel_size=(nw.shape[0], nw.shape[1]),
stride=(self.bin_weight_stride, self.bin_weight_stride),
padding=(self.pad, self.pad),
bias=False)
new_weights = np.array(nw.reshape((1, 1, nw.shape[0], nw.shape[1])))
self.pk.weight.data = torch.from_numpy(new_weights.astype(np.float32))
return
def forward(self, x):
gx = self.gx(F.pad(x, (1, 1, 0, 0), 'replicate'))
gy = self.gy(F.pad(x, (0, 0, 1, 1), 'replicate'))
mag = torch.sqrt(gx * gx + gy * gy + self.eps)
ori = torch.atan2(gy, gx + self.eps)
mag = mag * self.gk.expand_as(mag).to(mag.device)
o_big = (ori + 2.0 * math.pi) / (2.0 * math.pi) * float(self.num_ang_bins)
bo0_big_ = torch.floor(o_big)
wo1_big_ = o_big - bo0_big_
bo0_big = bo0_big_ % self.num_ang_bins
bo1_big = (bo0_big + 1) % self.num_ang_bins
wo0_big = (1.0 - wo1_big_) * mag
wo1_big = wo1_big_ * mag
ang_bins = []
for i in range(0, self.num_ang_bins):
out = self.pk((bo0_big == i).float() * wo0_big + (bo1_big == i).float() * wo1_big)
ang_bins.append(out)
ang_bins = torch.cat(ang_bins, 1)
ang_bins = ang_bins.view(ang_bins.size(0), -1)
ang_bins = F.normalize(ang_bins, p=2)
ang_bins = torch.clamp(ang_bins, 0., float(self.clipval))
ang_bins = F.normalize(ang_bins, p=2)
if self.rootsift:
ang_bins = torch.sqrt(F.normalize(ang_bins, p=1) + 1e-10)
return ang_bins
class SIFTNetFeature2D:
def __init__(self):
self.model = SIFTNet(patch_size=32, mask_type='Gauss', sigma_type='hesamp')
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
self.model.cuda()
self.model.eval()
def patch2des_once(self, patches):
if len(patches.shape) == 4:
new_patches = []
for i in range(len(patches)):
new_patches.append(cv2.cvtColor(patches[i] * 255, cv2.COLOR_BGR2GRAY) / 255.0)
new_patches = torch.from_numpy(numpy.array(new_patches).astype(np.float32)).to(device)
data_a = new_patches.permute(0, 2, 1).unsqueeze(dim=1)
else:
data_a = torch.from_numpy(numpy.array(patches).astype(np.float32)).to(device).unsqueeze(dim=1)
with torch.no_grad():
out_a = self.model(data_a)
return out_a
def compute(self, patches):
batch_size = 128
dim = 128
descriptors_for_net = np.zeros((len(patches), dim), dtype=np.float32)
for i in range(0, len(patches), batch_size):
data_a = patches[i: i + batch_size, :, :].astype(np.float32)
out_a = self.patch2des_once(data_a)
descriptors_for_net[i: i + batch_size] = out_a.cpu().detach().numpy().reshape(-1, dim)
return descriptors_for_net / np.max(np.abs(descriptors_for_net))用法
sift_net = SIFTNetFeature2D()
sift_net.compute(patches)效果
比OpenCV的经典SIFT效果略差。
其他
当然也可以直接使用kornia写好的，文档网址：
https://kornia.readthedocs.io/en/latest/_modules/kornia/feature/siftdesc.html#SIFTDescriptor
本文只提供网络图，具体原理内容请移步对应论文
论文：
https://www.sciencedirect.com/science/article/pii/S0925231219315140
代码：https://github.com/QinJinghui/MSFFRN
