from typing import Dict
import torch
import torch.nn as nn
from kornia.testing import KORNIA_CHECK_SHAPE
from kornia.utils.helpers import map_location_to_cpu
urls: Dict[str, str] = {}
urls["liberty"] = "https://github.com/vbalnt/tfeat/raw/master/pretrained-models/tfeat-liberty.params" # pylint: disable
urls[
"notredame"
] = "https://github.com/vbalnt/tfeat/raw/master/pretrained-models/tfeat-notredame.params" # pylint: disable
urls[
"yosemite"
] = "https://github.com/vbalnt/tfeat/raw/master/pretrained-models/tfeat-yosemite.params" # pylint: disable
[docs]class TFeat(nn.Module):
r"""Module, which computes TFeat descriptors of given grayscale patches of 32x32.
This is based on the original code from paper "Learning local feature descriptors
with triplets and shallow convolutional neural networks".
See :cite:`TFeat2016` for more details
Args:
pretrained: Download and set pretrained weights to the model.
Returns:
torch.Tensor: TFeat descriptor of the patches.
Shape:
- Input: :math:`(B, 1, 32, 32)`
- Output: :math:`(B, 128)`
Examples:
>>> input = torch.rand(16, 1, 32, 32)
>>> tfeat = TFeat()
>>> descs = tfeat(input) # 16x128
"""
patch_size = 32
def __init__(self, pretrained: bool = False) -> None:
super().__init__()
self.features = nn.Sequential(
nn.InstanceNorm2d(1, affine=False),
nn.Conv2d(1, 32, kernel_size=7),
nn.Tanh(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=6),
nn.Tanh(),
)
self.descr = nn.Sequential(nn.Linear(64 * 8 * 8, 128), nn.Tanh())
# use torch.hub to load pretrained model
if pretrained:
pretrained_dict = torch.hub.load_state_dict_from_url(urls['liberty'], map_location=map_location_to_cpu)
self.load_state_dict(pretrained_dict, strict=True)
self.eval()
def forward(self, input: torch.Tensor) -> torch.Tensor:
KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
x = self.features(input)
x = x.view(x.size(0), -1)
x = self.descr(x)
return x