third_party/DKM/dkm/models/model_zoo/DKMv3.py

import torch

from torch import nn
from ..dkm import *
from ..encoders import *


def DKMv3(
    weights,
    h,
    w,
    symmetric=True,
    sample_mode="threshold_balanced",
    device=None,
    **kwargs
):
    if device is None:
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    gp_dim = 256
    dfn_dim = 384
    feat_dim = 256
    coordinate_decoder = DFN(
        internal_dim=dfn_dim,
        feat_input_modules=nn.ModuleDict(
            {
                "32": nn.Conv2d(512, feat_dim, 1, 1),
                "16": nn.Conv2d(512, feat_dim, 1, 1),
            }
        ),
        pred_input_modules=nn.ModuleDict(
            {
                "32": nn.Identity(),
                "16": nn.Identity(),
            }
        ),
        rrb_d_dict=nn.ModuleDict(
            {
                "32": RRB(gp_dim + feat_dim, dfn_dim),
                "16": RRB(gp_dim + feat_dim, dfn_dim),
            }
        ),
        cab_dict=nn.ModuleDict(
            {
                "32": CAB(2 * dfn_dim, dfn_dim),
                "16": CAB(2 * dfn_dim, dfn_dim),
            }
        ),
        rrb_u_dict=nn.ModuleDict(
            {
                "32": RRB(dfn_dim, dfn_dim),
                "16": RRB(dfn_dim, dfn_dim),
            }
        ),
        terminal_module=nn.ModuleDict(
            {
                "32": nn.Conv2d(dfn_dim, 3, 1, 1, 0),
                "16": nn.Conv2d(dfn_dim, 3, 1, 1, 0),
            }
        ),
    )
    dw = True
    hidden_blocks = 8
    kernel_size = 5
    displacement_emb = "linear"
    conv_refiner = nn.ModuleDict(
        {
            "16": ConvRefiner(
                2 * 512 + 128 + (2 * 7 + 1) ** 2,
                2 * 512 + 128 + (2 * 7 + 1) ** 2,
                3,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=128,
                local_corr_radius=7,
                corr_in_other=True,
            ),
            "8": ConvRefiner(
                2 * 512 + 64 + (2 * 3 + 1) ** 2,
                2 * 512 + 64 + (2 * 3 + 1) ** 2,
                3,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=64,
                local_corr_radius=3,
                corr_in_other=True,
            ),
            "4": ConvRefiner(
                2 * 256 + 32 + (2 * 2 + 1) ** 2,
                2 * 256 + 32 + (2 * 2 + 1) ** 2,
                3,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=32,
                local_corr_radius=2,
                corr_in_other=True,
            ),
            "2": ConvRefiner(
                2 * 64 + 16,
                128 + 16,
                3,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=16,
            ),
            "1": ConvRefiner(
                2 * 3 + 6,
                24,
                3,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=6,
            ),
        }
    )
    kernel_temperature = 0.2
    learn_temperature = False
    no_cov = True
    kernel = CosKernel
    only_attention = False
    basis = "fourier"
    gp32 = GP(
        kernel,
        T=kernel_temperature,
        learn_temperature=learn_temperature,
        only_attention=only_attention,
        gp_dim=gp_dim,
        basis=basis,
        no_cov=no_cov,
    )
    gp16 = GP(
        kernel,
        T=kernel_temperature,
        learn_temperature=learn_temperature,
        only_attention=only_attention,
        gp_dim=gp_dim,
        basis=basis,
        no_cov=no_cov,
    )
    gps = nn.ModuleDict({"32": gp32, "16": gp16})
    proj = nn.ModuleDict(
        {"16": nn.Conv2d(1024, 512, 1, 1), "32": nn.Conv2d(2048, 512, 1, 1)}
    )
    decoder = Decoder(coordinate_decoder, gps, proj, conv_refiner, detach=True)

    encoder = ResNet50(pretrained=False, high_res=False, freeze_bn=False)
    matcher = RegressionMatcher(
        encoder,
        decoder,
        h=h,
        w=w,
        name="DKMv3",
        sample_mode=sample_mode,
        symmetric=symmetric,
        **kwargs
    ).to(device)
    res = matcher.load_state_dict(weights)
    return matcher