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import warnings
import torch.nn as nn
from roma.models.matcher import *
from roma.models.transformer import Block, TransformerDecoder, MemEffAttention
from roma.models.encoders import *
def roma_model(
resolution, upsample_preds, device=None, weights=None, dinov2_weights=None, **kwargs
):
# roma weights and dinov2 weights are loaded seperately, as dinov2 weights are not parameters
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
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,
)
h, w = resolution
symmetric = True
attenuate_cert = True
matcher = RegressionMatcher(
encoder,
decoder,
h=h,
w=w,
upsample_preds=upsample_preds,
symmetric=symmetric,
attenuate_cert=attenuate_cert,
**kwargs
).to(device)
matcher.load_state_dict(weights)
return matcher
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