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from types import SimpleNamespace
from typing import Callable, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import nn
from utils import rotation_matrix_from_ortho6d
try:
from flash_attn.modules.mha import FlashCrossAttention
except ModuleNotFoundError:
FlashCrossAttention = None
if FlashCrossAttention or hasattr(F, "scaled_dot_product_attention"):
FLASH_AVAILABLE = True
else:
FLASH_AVAILABLE = False
torch.backends.cudnn.deterministic = True
torch.set_float32_matmul_precision('medium')
def normalize_keypoints(kpts, intrinsics):
# kpts: (B, M, 2)
# intrinsics: (B, 3, 3)
b, m, _ = kpts.shape
kpts = torch.cat([kpts, torch.ones((b, m, 1), device=kpts.device)], dim=2)
kpts = intrinsics.inverse() @ kpts.mT
kpts = kpts.mT
kpts = kpts[..., :2]
return kpts
# @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
def cosine_similarity(x, y):
sim = torch.einsum('...id,...jd->...ij', x / x.norm(2, -1, keepdim=True), y / y.norm(2, -1, keepdim=True))
sim = (sim + 1) / 2
return sim
def pad_to_length(x: torch.Tensor, length: int) -> Tuple[torch.Tensor]:
if length <= x.shape[-2]:
return x, torch.ones_like(x[..., :1], dtype=torch.bool)
pad = torch.ones(
*x.shape[:-2], length - x.shape[-2], x.shape[-1], device=x.device, dtype=x.dtype
)
y = torch.cat([x, pad], dim=-2)
mask = torch.zeros(*y.shape[:-1], 1, dtype=torch.bool, device=x.device)
mask[..., : x.shape[-2], :] = True
return y, mask
def gather(x: torch.Tensor, indices: torch.tensor):
b, _, n = x.shape
bs = torch.arange(b).reshape(b, 1, 1)
ns = torch.arange(n)
return x[bs, indices.unsqueeze(-1), ns]
class Attention(nn.Module):
def __init__(self, allow_flash: bool = True) -> None:
super().__init__()
if allow_flash and not FLASH_AVAILABLE:
warnings.warn(
"FlashAttention is not available. For optimal speed, "
"consider installing torch >= 2.0 or flash-attn.",
stacklevel=2,
)
self.enable_flash = allow_flash and FLASH_AVAILABLE
self.has_sdp = hasattr(F, "scaled_dot_product_attention")
if allow_flash and FlashCrossAttention:
self.flash_ = FlashCrossAttention()
if self.has_sdp:
torch.backends.cuda.enable_flash_sdp(allow_flash)
def forward(self, q, k, v, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.enable_flash and q.device.type == "cuda":
# use torch 2.0 scaled_dot_product_attention with flash
if self.has_sdp:
args = [x.contiguous() for x in [q, k, v]]
v = F.scaled_dot_product_attention(*args, attn_mask=mask).to(q.dtype)
return v if mask is None else v.nan_to_num()
else:
assert mask is None
q, k, v = [x.transpose(-2, -3).contiguous() for x in [q, k, v]]
m = self.flash_(q, torch.stack([k, v], 2))
return m.transpose(-2, -3).to(q.dtype).clone()
elif self.has_sdp:
args = [x.contiguous() for x in [q, k, v]]
v = F.scaled_dot_product_attention(*args, attn_mask=mask)
return v if mask is None else v.nan_to_num()
else:
s = q.shape[-1] ** -0.5
sim = torch.einsum("...id,...jd->...ij", q, k) * s
if mask is not None:
sim.masked_fill(~mask, -float("inf"))
attn = F.softmax(sim, -1)
return torch.einsum("...ij,...jd->...id", attn, v)
class SelfBlock(nn.Module):
def __init__(
self, embed_dim: int, num_heads: int, bias: bool = True
) -> None:
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
assert self.embed_dim % num_heads == 0
self.head_dim = self.embed_dim // num_heads
self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias)
self.inner_attn = Attention()
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.ffn = nn.Sequential(
nn.Linear(2 * embed_dim, 2 * embed_dim),
nn.LayerNorm(2 * embed_dim, elementwise_affine=True),
nn.GELU(),
nn.Linear(2 * embed_dim, embed_dim),
)
def forward(
self,
x: torch.Tensor,
encoding: torch.Tensor,
mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
qkv = self.Wqkv(x)
qkv = qkv.unflatten(-1, (self.num_heads, -1, 3)).transpose(1, 2)
q, k, v = qkv[..., 0], qkv[..., 1], qkv[..., 2]
q += encoding
k += encoding
context = self.inner_attn(q, k, v, mask=mask)
message = self.out_proj(context.transpose(1, 2).flatten(start_dim=-2))
return x + self.ffn(torch.cat([x, message], -1))
class CrossBlock(nn.Module):
def __init__(
self, embed_dim: int, num_heads: int, bias: bool = True
) -> None:
super().__init__()
self.heads = num_heads
dim_head = embed_dim // num_heads
self.scale = dim_head**-0.5
inner_dim = dim_head * num_heads
self.to_qk = nn.Linear(embed_dim, inner_dim, bias=bias)
self.to_v = nn.Linear(embed_dim, inner_dim, bias=bias)
self.to_out = nn.Linear(inner_dim, embed_dim, bias=bias)
self.ffn = nn.Sequential(
nn.Linear(2 * embed_dim, 2 * embed_dim),
nn.LayerNorm(2 * embed_dim, elementwise_affine=True),
nn.GELU(),
nn.Linear(2 * embed_dim, embed_dim),
)
# self.reg_attn = nn.Identity()
# self.reg_sim = nn.Identity()
def map_(self, func: Callable, x0: torch.Tensor, x1: torch.Tensor):
return func(x0), func(x1)
def forward(
self, x0: torch.Tensor, x1: torch.Tensor, match: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> List[torch.Tensor]:
qk0, qk1 = self.map_(self.to_qk, x0, x1)
v0, v1 = self.map_(self.to_v, x0, x1)
qk0, qk1, v0, v1 = map(
lambda t: t.unflatten(-1, (self.heads, -1)).transpose(1, 2),
(qk0, qk1, v0, v1),
)
qk0, qk1 = qk0 * self.scale**0.5, qk1 * self.scale**0.5
sim = torch.einsum("bhid, bhjd -> bhij", qk0, qk1)
if mask is not None:
sim = sim.masked_fill(~mask.unsqueeze(1), -float("inf"))
assert len(match.shape) == 3
match = match.unsqueeze(1)
sim = sim * match
# sim = self.reg_attn(sim)
# match = self.reg_sim(match)
attn01 = F.softmax(sim, dim=-1)
attn10 = F.softmax(sim.transpose(-2, -1).contiguous(), dim=-1)
m0 = torch.einsum("bhij, bhjd -> bhid", attn01, v1)
m1 = torch.einsum("bhji, bhjd -> bhid", attn10.transpose(-2, -1), v0)
if mask is not None:
m0, m1 = m0.nan_to_num(), m1.nan_to_num()
m0, m1 = self.map_(lambda t: t.transpose(1, 2).flatten(start_dim=-2), m0, m1)
m0, m1 = self.map_(self.to_out, m0, m1)
x0 = x0 + self.ffn(torch.cat([x0, m0], -1))
x1 = x1 + self.ffn(torch.cat([x1, m1], -1))
return x0, x1
class TransformerLayer(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
self.self_attn = SelfBlock(*args, **kwargs)
self.cross_attn = CrossBlock(*args, **kwargs)
def forward(
self,
desc0,
desc1,
encoding0,
encoding1,
match,
mask0: Optional[torch.Tensor] = None,
mask1: Optional[torch.Tensor] = None,
):
if mask0 is not None and mask1 is not None:
return self.masked_forward(desc0, desc1, encoding0, encoding1, match, mask0, mask1)
else:
desc0 = self.self_attn(desc0, encoding0)
desc1 = self.self_attn(desc1, encoding1)
return self.cross_attn(desc0, desc1, match)
# This part is compiled and allows padding inputs
def masked_forward(self, desc0, desc1, encoding0, encoding1, match, mask0, mask1):
mask = mask0 & mask1.transpose(-1, -2)
mask0 = mask0 & mask0.transpose(-1, -2)
mask1 = mask1 & mask1.transpose(-1, -2)
desc0 = self.self_attn(desc0, encoding0, mask0)
desc1 = self.self_attn(desc1, encoding1, mask1)
return self.cross_attn(desc0, desc1, match, mask)
class RelPose(nn.Module):
default_conf = {
"name": "RelPose", # just for interfacing
"input_dim": 256, # input descriptor dimension (autoselected from weights)
"descriptor_dim": 256,
"add_scale_ori": False,
"n_layers": 3,
"num_heads": 4,
"pct_pruning": 0,
"task": "scene",
"mp": False, # enable mixed precision
"weights": None,
}
required_data_keys = ["image0", "image1"]
features = {
"superpoint": {
"input_dim": 256,
},
"disk": {
"input_dim": 128,
},
"aliked": {
"input_dim": 128,
},
"sift": {
"input_dim": 128,
# "add_scale_ori": True,
},
}
def __init__(self, features="superpoint", **conf) -> None:
super().__init__()
self.conf = conf = SimpleNamespace(**{**self.default_conf, **conf})
if features is not None:
if features not in self.features:
raise ValueError(
f"Unsupported features: {features} not in "
f"{{{','.join(self.features)}}}"
)
for k, v in self.features[features].items():
setattr(conf, k, v)
if conf.input_dim != conf.descriptor_dim:
self.input_proj = nn.Linear(conf.input_dim, conf.descriptor_dim, bias=True)
else:
self.input_proj = nn.Identity()
head_dim = conf.descriptor_dim // conf.num_heads
self.posenc = nn.Linear(
2 + 2 * self.conf.add_scale_ori, head_dim
)
h, n, d = conf.num_heads, conf.n_layers, conf.descriptor_dim
self.transformers = nn.ModuleList(
[TransformerLayer(d, h) for _ in range(n)]
)
self.rotation_regressor = nn.Sequential(
nn.Linear(conf.descriptor_dim*2, conf.descriptor_dim),
nn.ReLU(),
nn.Linear(conf.descriptor_dim, conf.descriptor_dim//2),
nn.ReLU(),
nn.Linear(conf.descriptor_dim//2, 6),
)
self.translation_regressor = nn.Sequential(
nn.Linear(conf.descriptor_dim*2, conf.descriptor_dim),
nn.ReLU(),
nn.Linear(conf.descriptor_dim, conf.descriptor_dim//2),
nn.ReLU(),
nn.Linear(conf.descriptor_dim//2, 3),
)
# self.reg_kpts0 = nn.Identity()
# self.reg_kpts1 = nn.Identity()
# static lengths LightGlue is compiled for (only used with torch.compile)
self.static_lengths = None
def compile(
self, mode="reduce-overhead", static_lengths=[256, 512, 768, 1024, 1280, 1536]
):
for i in range(self.conf.n_layers):
self.transformers[i].masked_forward = torch.compile(
self.transformers[i].masked_forward, mode=mode, fullgraph=True
)
self.static_lengths = static_lengths
def forward(self, data: dict) -> dict:
"""
Match keypoints and descriptors between two images
Input (dict):
image0: dict
keypoints: [B x M x 2]
descriptors: [B x M x D]
image: [B x C x H x W] or image_size: [B x 2]
image1: dict
keypoints: [B x N x 2]
descriptors: [B x N x D]
image: [B x C x H x W] or image_size: [B x 2]
Output
"""
with torch.autocast(enabled=self.conf.mp, device_type="cuda"):
return self._forward(data)
def _forward(self, data: dict) -> dict:
for key in self.required_data_keys:
assert key in data, f"Missing key {key} in data"
data0, data1 = data["image0"], data["image1"]
kpts0, kpts1 = data0["keypoints"], data1["keypoints"]
intrinsic0, intrinsic1 = data0["intrinsics"], data1["intrinsics"]
b, m, _ = kpts0.shape
b, n, _ = kpts1.shape
if self.conf.add_scale_ori:
kpts0 = torch.cat(
[kpts0] + [data0[k].unsqueeze(-1) for k in ("scales", "oris")], -1
)
kpts1 = torch.cat(
[kpts1] + [data1[k].unsqueeze(-1) for k in ("scales", "oris")], -1
)
desc0 = data0["descriptors"].detach().contiguous()
desc1 = data1["descriptors"].detach().contiguous()
assert desc0.shape[-1] == self.conf.input_dim
assert desc1.shape[-1] == self.conf.input_dim
mask0, mask1 = None, None
c = max(m, n)
do_compile = self.static_lengths and c <= max(self.static_lengths)
if do_compile:
kn = min([k for k in self.static_lengths if k >= c])
desc0, mask0 = pad_to_length(desc0, kn)
desc1, mask1 = pad_to_length(desc1, kn)
kpts0, _ = pad_to_length(kpts0, kn)
kpts1, _ = pad_to_length(kpts1, kn)
matchability = cosine_similarity(desc0, desc1)
assert self.conf.pct_pruning >= 0 and self.conf.pct_pruning < 1
if self.conf.pct_pruning > 0:
ind0, ind1 = self.get_pruned_indices(matchability, self.conf.pct_pruning)
matchability = gather(matchability, ind0)
matchability = gather(matchability.mT, ind1).mT
desc0 = gather(desc0, ind0)
desc1 = gather(desc1, ind1)
kpts0 = gather(kpts0, ind0)
kpts1 = gather(kpts1, ind1)
if self.conf.task == "object":
bbox = data0["bbox"] # (B, 4)
ind0, mask0 = self.get_prompted_indices(kpts0, bbox)
matchability[:, 0] = torch.zeros_like(matchability[:, 0], device=matchability.device)
desc0[:, 0] = torch.zeros_like(desc0[:, 0], device=desc0.device)
kpts0[:, 0] = torch.zeros_like(kpts0[:, 0], device=kpts0.device)
matchability = gather(matchability, ind0)
desc0 = gather(desc0, ind0)
kpts0 = gather(kpts0, ind0)
desc0 = self.input_proj(desc0)
desc1 = self.input_proj(desc1)
# kpts0 = self.reg_kpts0(kpts0)
# kpts1 = self.reg_kpts1(kpts1)
# cache positional embeddings
kpts0 = normalize_keypoints(kpts0, intrinsic0)
kpts1 = normalize_keypoints(kpts1, intrinsic1)
encoding0 = self.posenc(kpts0).unsqueeze(-3)
encoding1 = self.posenc(kpts1).unsqueeze(-3)
for i in range(self.conf.n_layers):
desc0, desc1 = self.transformers[i](
desc0, desc1, encoding0, encoding1, match=matchability, mask0=mask0, mask1=mask1,
)
desc0, desc1 = desc0[..., :m, :], desc1[..., :n, :]
if self.conf.task == 'object':
n_kpts0 = mask0.sum(1, keepdim=True)
n_kpts0 = torch.clip(n_kpts0, min=1)
desc0 = (desc0 * mask0.unsqueeze(-1)).sum(1) / n_kpts0
desc1 = desc1.mean(1)
else:
desc0, desc1 = desc0.mean(1), desc1.mean(1)
feat = torch.cat([desc0, desc1], 1)
R = self.rotation_regressor(feat)
R = rotation_matrix_from_ortho6d(R)
t = self.translation_regressor(feat)
return R, t
def get_pruned_indices(self, match, pct_pruning):
matching_scores0 = match.mean(-1)
matching_scores1 = match.mean(-2)
num_pruning0 = int(pct_pruning * matching_scores0.size(-1))
num_pruning1 = int(pct_pruning * matching_scores1.size(-1))
_, indices0 = matching_scores0.sort()
_, indices1 = matching_scores1.sort()
indices0 = indices0[:, num_pruning0:]
indices1 = indices1[:, num_pruning1:]
return indices0, indices1
def get_prompted_indices(self, kpts, bbox):
# kpts: (B, M, 2)
# bbox: (B, 4) - (x, y, x, y)
x, y = kpts[..., 0], kpts[..., 1]
mask = (x >= bbox[:, 0].unsqueeze(-1)) & (x <= bbox[:, 2].unsqueeze(-1))
mask &= (y >= bbox[:, 1].unsqueeze(-1)) & (y <= bbox[:, 3].unsqueeze(-1))
mask_sorted, indices = mask.long().sort(descending=True)
indices *= mask_sorted
indices = indices[:, :mask_sorted.sum(-1).max()]
mask_sorted = mask_sorted[:, :mask_sorted.sum(-1).max()]
return indices, mask_sorted
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