HardNetPS

This version of HardNet is trained on PS dataset by Mitra et.al. with torch package and then converted to PyTorch.

The structure of the network has minor changes, use this definition:

class HardNetPS(nn.Module):
   def __init__(self):
        super(HardNetPS, self).__init__()
        self.features = nn.Sequential(
        nn.Conv2d(1, 32, kernel_size=3, padding=1, bias = True),
        nn.BatchNorm2d(32, affine=True),
        nn.ReLU(),
        nn.Conv2d(32, 32, kernel_size=3, padding=1, bias = True),
        nn.BatchNorm2d(32, affine=True),
        nn.ReLU(),
        nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias = True),
        nn.BatchNorm2d(64, affine=True),
        nn.ReLU(),
        nn.Conv2d(64, 64, kernel_size=3, padding=1, bias = True),
        nn.BatchNorm2d(64, affine=True),
        nn.ReLU(),
        nn.Conv2d(64, 128, kernel_size=3, stride=2,padding=1, bias = True),
        nn.BatchNorm2d(128, affine=True),
        nn.ReLU(),
        nn.Conv2d(128, 128, kernel_size=3, padding=1, bias = True),
        nn.BatchNorm2d(128, affine=True),
        nn.ReLU(),
        nn.Conv2d(128, 128, kernel_size=8, bias = True)
    )
    return
    def input_norm(self,x):
        flat = x.view(x.size(0), -1)
        mp = torch.mean(flat, dim=1)
        sp = torch.std(flat, dim=1) + 1e-7
        return (x - mp.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).expand_as(x)) / sp.unsqueeze(-1).unsqueeze(-1).unsqueeze(1).expand_as(x)

def forward(self, input):
    x_features = self.features(self.input_norm(input))
    x = x_features.view(x_features.size(0), -1)
    return L2Norm()(x)

If you use this weights, please cite:

@ARTICLE{2018arXiv180101466M,
   author = {{Mitra}, R. and {Doiphode}, N. and {Gautam}, U. and {Narayan}, S. and 
    {Ahmed}, S. and {Chandran}, S. and {Jain}, A.},
    title = "{A Large Dataset for Improving Patch Matching}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1801.01466},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition},
     year = 2018,
    month = jan,
   adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180101466M},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}