xhs-note-crawling/python/py/Lib/site-packages/kornia/feature/sosnet.py

# LICENSE HEADER MANAGED BY add-license-header
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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Dict

import torch
from torch import nn

from kornia.core.check import KORNIA_CHECK_SHAPE

urls: Dict[str, str] = {}
urls["lib"] = "https://github.com/yuruntian/SOSNet/raw/master/sosnet-weights/sosnet_32x32_liberty.pth"
urls["hp_a"] = "https://github.com/yuruntian/SOSNet/raw/master/sosnet-weights/sosnet_32x32_hpatches_a.pth"


class SOSNet(nn.Module):
    r"""128-dimensional SOSNet model definition for 32x32 patches.

    This is based on the original code from paper
    "SOSNet:Second Order Similarity Regularization for Local Descriptor Learning".

    Args:
        pretrained: Download and set pretrained weights to the model.

    Shape:
        - Input: :math:`(B, 1, 32, 32)`
        - Output: :math:`(B, 128)`

    Examples:
        >>> input = torch.rand(8, 1, 32, 32)
        >>> sosnet = SOSNet()
        >>> descs = sosnet(input) # 8x128

    """

    patch_size = 32

    def __init__(self, pretrained: bool = False) -> None:
        super().__init__()
        self.layers = nn.Sequential(
            nn.InstanceNorm2d(1, affine=False),
            nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(32, affine=False),
            nn.ReLU(),
            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(32, affine=False),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64, affine=False),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64, affine=False),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128, affine=False),
            nn.ReLU(),
            nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(128, affine=False),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Conv2d(128, 128, kernel_size=8, bias=False),
            nn.BatchNorm2d(128, affine=False),
        )
        self.desc_norm = nn.Sequential(nn.LocalResponseNorm(256, alpha=256.0, beta=0.5, k=0.0))
        # load pretrained model
        if pretrained:
            pretrained_dict = torch.hub.load_state_dict_from_url(urls["lib"], map_location=torch.device("cpu"))
            self.load_state_dict(pretrained_dict, strict=True)
        self.eval()

    def forward(self, input: torch.Tensor, eps: float = 1e-10) -> torch.Tensor:
        KORNIA_CHECK_SHAPE(input, ["B", "1", "32", "32"])
        descr = self.desc_norm(self.layers(input) + eps)
        descr = descr.view(descr.size(0), -1)
        return descr