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|
| import pdb |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from nets.sampler import FullSampler |
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|
|
| class CosimLoss(nn.Module): |
| """Try to make the repeatability repeatable from one image to the other.""" |
|
|
| def __init__(self, N=16): |
| nn.Module.__init__(self) |
| self.name = f"cosim{N}" |
| self.patches = nn.Unfold(N, padding=0, stride=N // 2) |
|
|
| def extract_patches(self, sal): |
| patches = self.patches(sal).transpose(1, 2) |
| patches = F.normalize(patches, p=2, dim=2) |
| return patches |
|
|
| def forward(self, repeatability, aflow, **kw): |
| B, two, H, W = aflow.shape |
| assert two == 2 |
|
|
| |
| sali1, sali2 = repeatability |
| grid = FullSampler._aflow_to_grid(aflow) |
| sali2 = F.grid_sample(sali2, grid, mode="bilinear", padding_mode="border") |
|
|
| patches1 = self.extract_patches(sali1) |
| patches2 = self.extract_patches(sali2) |
| cosim = (patches1 * patches2).sum(dim=2) |
| return 1 - cosim.mean() |
|
|
|
|
| class PeakyLoss(nn.Module): |
| """Try to make the repeatability locally peaky. |
| |
| Mechanism: we maximize, for each pixel, the difference between the local mean |
| and the local max. |
| """ |
|
|
| def __init__(self, N=16): |
| nn.Module.__init__(self) |
| self.name = f"peaky{N}" |
| assert N % 2 == 0, "N must be pair" |
| self.preproc = nn.AvgPool2d(3, stride=1, padding=1) |
| self.maxpool = nn.MaxPool2d(N + 1, stride=1, padding=N // 2) |
| self.avgpool = nn.AvgPool2d(N + 1, stride=1, padding=N // 2) |
|
|
| def forward_one(self, sali): |
| sali = self.preproc(sali) |
| return 1 - (self.maxpool(sali) - self.avgpool(sali)).mean() |
|
|
| def forward(self, repeatability, **kw): |
| sali1, sali2 = repeatability |
| return (self.forward_one(sali1) + self.forward_one(sali2)) / 2 |
|
|