submodules/RoMa/romatch/models/model_zoo/roma_models.py

import warnings
import torch.nn as nn
import torch
from romatch.models.matcher import *
from romatch.models.transformer import Block, TransformerDecoder, MemEffAttention
from romatch.models.encoders import *
from romatch.models.tiny import TinyRoMa

def tiny_roma_v1_model(weights = None, freeze_xfeat=False, exact_softmax=False, xfeat = None):
    model = TinyRoMa(
        xfeat = xfeat,
        freeze_xfeat=freeze_xfeat, 
        exact_softmax=exact_softmax)
    if weights is not None:
        model.load_state_dict(weights)
    return model

def roma_model(resolution, upsample_preds, device = None, weights=None, dinov2_weights=None, amp_dtype: torch.dtype=torch.float16, **kwargs):
    # romatch weights and dinov2 weights are loaded seperately, as dinov2 weights are not parameters
    #torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul TODO: these probably ruin stuff, should be careful
    #torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
    warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
    gp_dim = 512
    feat_dim = 512
    decoder_dim = gp_dim + feat_dim
    cls_to_coord_res = 64
    coordinate_decoder = TransformerDecoder(
        nn.Sequential(*[Block(decoder_dim, 8, attn_class=MemEffAttention) for _ in range(5)]), 
        decoder_dim, 
        cls_to_coord_res**2 + 1,
        is_classifier=True,
        amp = True,
        pos_enc = False,)
    dw = True
    hidden_blocks = 8
    kernel_size = 5
    displacement_emb = "linear"
    disable_local_corr_grad = True
    
    conv_refiner = nn.ModuleDict(
        {
            "16": ConvRefiner(
                2 * 512+128+(2*7+1)**2,
                2 * 512+128+(2*7+1)**2,
                2 + 1,
                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,
                amp = True,
                disable_local_corr_grad = disable_local_corr_grad,
                bn_momentum = 0.01,
            ),
            "8": ConvRefiner(
                2 * 512+64+(2*3+1)**2,
                2 * 512+64+(2*3+1)**2,
                2 + 1,
                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,
                amp = True,
                disable_local_corr_grad = disable_local_corr_grad,
                bn_momentum = 0.01,
            ),
            "4": ConvRefiner(
                2 * 256+32+(2*2+1)**2,
                2 * 256+32+(2*2+1)**2,
                2 + 1,
                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,
                amp = True,
                disable_local_corr_grad = disable_local_corr_grad,
                bn_momentum = 0.01,
            ),
            "2": ConvRefiner(
                2 * 64+16,
                128+16,
                2 + 1,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks=hidden_blocks,
                displacement_emb=displacement_emb,
                displacement_emb_dim=16,
                amp = True,
                disable_local_corr_grad = disable_local_corr_grad,
                bn_momentum = 0.01,
            ),
            "1": ConvRefiner(
                2 * 9 + 6,
                24,
                2 + 1,
                kernel_size=kernel_size,
                dw=dw,
                hidden_blocks = hidden_blocks,
                displacement_emb = displacement_emb,
                displacement_emb_dim = 6,
                amp = True,
                disable_local_corr_grad = disable_local_corr_grad,
                bn_momentum = 0.01,
            ),
        }
    )
    kernel_temperature = 0.2
    learn_temperature = False
    no_cov = True
    kernel = CosKernel
    only_attention = False
    basis = "fourier"
    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({"16": gp16})
    proj16 = nn.Sequential(nn.Conv2d(1024, 512, 1, 1), nn.BatchNorm2d(512))
    proj8 = nn.Sequential(nn.Conv2d(512, 512, 1, 1), nn.BatchNorm2d(512))
    proj4 = nn.Sequential(nn.Conv2d(256, 256, 1, 1), nn.BatchNorm2d(256))
    proj2 = nn.Sequential(nn.Conv2d(128, 64, 1, 1), nn.BatchNorm2d(64))
    proj1 = nn.Sequential(nn.Conv2d(64, 9, 1, 1), nn.BatchNorm2d(9))
    proj = nn.ModuleDict({
        "16": proj16,
        "8": proj8,
        "4": proj4,
        "2": proj2,
        "1": proj1,
        })
    displacement_dropout_p = 0.0
    gm_warp_dropout_p = 0.0
    decoder = Decoder(coordinate_decoder, 
                      gps, 
                      proj, 
                      conv_refiner, 
                      detach=True, 
                      scales=["16", "8", "4", "2", "1"], 
                      displacement_dropout_p = displacement_dropout_p,
                      gm_warp_dropout_p = gm_warp_dropout_p)
    
    encoder = CNNandDinov2(
        cnn_kwargs = dict(
            pretrained=False,
            amp = True),
        amp = True,
        use_vgg = True,
        dinov2_weights = dinov2_weights,
        amp_dtype=amp_dtype,
    )
    h,w = resolution
    symmetric = True
    attenuate_cert = True
    sample_mode = "threshold_balanced"
    matcher = RegressionMatcher(encoder, decoder, h=h, w=w, upsample_preds=upsample_preds, 
                                symmetric = symmetric, attenuate_cert = attenuate_cert, sample_mode = sample_mode, **kwargs).to(device)
    matcher.load_state_dict(weights)
    return matcher