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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.einops import rearrange
INF = 1e9
def mask_border(m, b: int, v):
"""Mask borders with value
Args:
m (torch.Tensor): [N, H0, W0, H1, W1]
b (int)
v (m.dtype)
"""
if b <= 0:
return
m[:, :b] = v
m[:, :, :b] = v
m[:, :, :, :b] = v
m[:, :, :, :, :b] = v
m[:, -b:] = v
m[:, :, -b:] = v
m[:, :, :, -b:] = v
m[:, :, :, :, -b:] = v
def mask_border_with_padding(m, bd, v, p_m0, p_m1):
if bd <= 0:
return
m[:, :bd] = v
m[:, :, :bd] = v
m[:, :, :, :bd] = v
m[:, :, :, :, :bd] = v
h0s, w0s = p_m0.sum(1).max(-1)[0].int(), p_m0.sum(-1).max(-1)[0].int()
h1s, w1s = p_m1.sum(1).max(-1)[0].int(), p_m1.sum(-1).max(-1)[0].int()
for b_idx, (h0, w0, h1, w1) in enumerate(zip(h0s, w0s, h1s, w1s)):
m[b_idx, h0 - bd :] = v
m[b_idx, :, w0 - bd :] = v
m[b_idx, :, :, h1 - bd :] = v
m[b_idx, :, :, :, w1 - bd :] = v
def compute_max_candidates(p_m0, p_m1):
"""Compute the max candidates of all pairs within a batch
Args:
p_m0, p_m1 (torch.Tensor): padded masks
"""
h0s, w0s = p_m0.sum(1).max(-1)[0], p_m0.sum(-1).max(-1)[0]
h1s, w1s = p_m1.sum(1).max(-1)[0], p_m1.sum(-1).max(-1)[0]
max_cand = torch.sum(torch.min(torch.stack([h0s * w0s, h1s * w1s], -1), -1)[0])
return max_cand
class CoarseMatching(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# general config
self.thr = config["thr"]
self.border_rm = config["border_rm"]
# -- # for trainig fine-level LoFTR
self.train_coarse_percent = config["train_coarse_percent"]
self.train_pad_num_gt_min = config["train_pad_num_gt_min"]
# we provide 2 options for differentiable matching
self.match_type = config["match_type"]
if self.match_type == "dual_softmax":
self.temperature = config["dsmax_temperature"]
elif self.match_type == "sinkhorn":
try:
from .superglue import log_optimal_transport
except ImportError:
raise ImportError("download superglue.py first!")
self.log_optimal_transport = log_optimal_transport
self.bin_score = nn.Parameter(
torch.tensor(config["skh_init_bin_score"], requires_grad=True)
)
self.skh_iters = config["skh_iters"]
self.skh_prefilter = config["skh_prefilter"]
else:
raise NotImplementedError()
def forward(self, data):
"""
Args:
data (dict)
Update:
data (dict): {
'b_ids' (torch.Tensor): [M'],
'i_ids' (torch.Tensor): [M'],
'j_ids' (torch.Tensor): [M'],
'gt_mask' (torch.Tensor): [M'],
'mkpts0_c' (torch.Tensor): [M, 2],
'mkpts1_c' (torch.Tensor): [M, 2],
'mconf' (torch.Tensor): [M]}
NOTE: M' != M during training.
"""
conf_matrix = data["conf_matrix"]
# predict coarse matches from conf_matrix
data.update(**self.get_coarse_match(conf_matrix, data))
@torch.no_grad()
def get_coarse_match(self, conf_matrix, data):
"""
Args:
conf_matrix (torch.Tensor): [N, L, S]
data (dict): with keys ['hw0_i', 'hw1_i', 'hw0_c', 'hw1_c']
Returns:
coarse_matches (dict): {
'b_ids' (torch.Tensor): [M'],
'i_ids' (torch.Tensor): [M'],
'j_ids' (torch.Tensor): [M'],
'gt_mask' (torch.Tensor): [M'],
'm_bids' (torch.Tensor): [M],
'mkpts0_c' (torch.Tensor): [M, 2],
'mkpts1_c' (torch.Tensor): [M, 2],
'mconf' (torch.Tensor): [M]}
"""
axes_lengths = {
"h0c": data["hw0_c"][0],
"w0c": data["hw0_c"][1],
"h1c": data["hw1_c"][0],
"w1c": data["hw1_c"][1],
}
_device = conf_matrix.device
# 1. confidence thresholding
mask = conf_matrix > self.thr
mask = rearrange(
mask, "b (h0c w0c) (h1c w1c) -> b h0c w0c h1c w1c", **axes_lengths
)
if "mask0" not in data:
mask_border(mask, self.border_rm, False)
else:
mask_border_with_padding(
mask, self.border_rm, False, data["mask0"], data["mask1"]
)
mask = rearrange(
mask, "b h0c w0c h1c w1c -> b (h0c w0c) (h1c w1c)", **axes_lengths
)
# 2. mutual nearest
mask = (
mask
* (conf_matrix == conf_matrix.max(dim=2, keepdim=True)[0])
* (conf_matrix == conf_matrix.max(dim=1, keepdim=True)[0])
)
# 3. find all valid coarse matches
# this only works when at most one `True` in each row
mask_v, all_j_ids = mask.max(dim=2)
b_ids, i_ids = torch.where(mask_v)
j_ids = all_j_ids[b_ids, i_ids]
mconf = conf_matrix[b_ids, i_ids, j_ids]
# 4. Random sampling of training samples for fine-level LoFTR
# (optional) pad samples with gt coarse-level matches
if self.training:
# NOTE:
# The sampling is performed across all pairs in a batch without manually balancing
# #samples for fine-level increases w.r.t. batch_size
if "mask0" not in data:
num_candidates_max = mask.size(0) * max(mask.size(1), mask.size(2))
else:
num_candidates_max = compute_max_candidates(
data["mask0"], data["mask1"]
)
num_matches_train = int(num_candidates_max * self.train_coarse_percent)
num_matches_pred = len(b_ids)
assert (
self.train_pad_num_gt_min < num_matches_train
), "min-num-gt-pad should be less than num-train-matches"
# pred_indices is to select from prediction
if num_matches_pred <= num_matches_train - self.train_pad_num_gt_min:
pred_indices = torch.arange(num_matches_pred, device=_device)
else:
pred_indices = torch.randint(
num_matches_pred,
(num_matches_train - self.train_pad_num_gt_min,),
device=_device,
)
# gt_pad_indices is to select from gt padding. e.g. max(3787-4800, 200)
gt_pad_indices = torch.randint(
len(data["spv_b_ids"]),
(max(num_matches_train - num_matches_pred, self.train_pad_num_gt_min),),
device=_device,
)
mconf_gt = torch.zeros(
len(data["spv_b_ids"]), device=_device
) # set conf of gt paddings to all zero
b_ids, i_ids, j_ids, mconf = map(
lambda x, y: torch.cat([x[pred_indices], y[gt_pad_indices]], dim=0),
*zip(
[b_ids, data["spv_b_ids"]],
[i_ids, data["spv_i_ids"]],
[j_ids, data["spv_j_ids"]],
[mconf, mconf_gt],
)
)
# These matches select patches that feed into fine-level network
coarse_matches = {"b_ids": b_ids, "i_ids": i_ids, "j_ids": j_ids}
# 4. Update with matches in original image resolution
scale = data["hw0_i"][0] / data["hw0_c"][0]
scale0 = scale * data["scale0"][b_ids] if "scale0" in data else scale
scale1 = scale * data["scale1"][b_ids] if "scale1" in data else scale
mkpts0_c = (
torch.stack([i_ids % data["hw0_c"][1], i_ids // data["hw0_c"][1]], dim=1)
* scale0
)
mkpts1_c = (
torch.stack([j_ids % data["hw1_c"][1], j_ids // data["hw1_c"][1]], dim=1)
* scale1
)
# These matches is the current prediction (for visualization)
coarse_matches.update(
{
"gt_mask": mconf == 0,
"m_bids": b_ids[mconf != 0], # mconf == 0 => gt matches
"mkpts0_c": mkpts0_c[mconf != 0],
"mkpts1_c": mkpts1_c[mconf != 0],
"mconf": mconf[mconf != 0],
}
)
return coarse_matches
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