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import torch
import torch.nn as nn
import torchvision.transforms as tvf
from .modules import InterestPointModule, CorrespondenceModule
def warp_homography_batch(sources, homographies):
"""
Batch warp keypoints given homographies. From https://github.com/TRI-ML/KP2D.
Parameters
----------
sources: torch.Tensor (B,H,W,C)
Keypoints vector.
homographies: torch.Tensor (B,3,3)
Homographies.
Returns
-------
warped_sources: torch.Tensor (B,H,W,C)
Warped keypoints vector.
"""
B, H, W, _ = sources.shape
warped_sources = []
for b in range(B):
source = sources[b].clone()
source = source.view(-1, 2)
"""
[X, [M11, M12, M13 [x, M11*x + M12*y + M13 [M11, M12 [M13,
Y, = M21, M22, M23 * y, = M21*x + M22*y + M23 = [x, y] * M21, M22 + M23,
Z] M31, M32, M33] 1] M31*x + M32*y + M33 M31, M32].T M33]
"""
source = torch.addmm(homographies[b, :, 2], source, homographies[b, :, :2].t())
source.mul_(1 / source[:, 2].unsqueeze(1))
source = source[:, :2].contiguous().view(H, W, 2)
warped_sources.append(source)
return torch.stack(warped_sources, dim=0)
class PointModel(nn.Module):
def __init__(self, is_test=True):
super(PointModel, self).__init__()
self.is_test = is_test
self.interestpoint_module = InterestPointModule(is_test=self.is_test)
self.correspondence_module = CorrespondenceModule()
self.norm_rgb = tvf.Normalize(mean=[0.5, 0.5, 0.5], std=[0.225, 0.225, 0.225])
def forward(self, *args):
if self.is_test:
img = args[0]
img = self.norm_rgb(img)
score, coord, desc = self.interestpoint_module(img)
return score, coord, desc
else:
source_score, source_coord, source_desc_block = self.interestpoint_module(
args[0]
)
target_score, target_coord, target_desc_block = self.interestpoint_module(
args[1]
)
B, _, H, W = args[0].shape
B, _, hc, wc = source_score.shape
device = source_score.device
# Normalize the coordinates from ([0, h], [0, w]) to ([0, 1], [0, 1]).
source_coord_norm = source_coord.clone()
source_coord_norm[:, 0] = (
source_coord_norm[:, 0] / (float(W - 1) / 2.0)
) - 1.0
source_coord_norm[:, 1] = (
source_coord_norm[:, 1] / (float(H - 1) / 2.0)
) - 1.0
source_coord_norm = source_coord_norm.permute(0, 2, 3, 1)
target_coord_norm = target_coord.clone()
target_coord_norm[:, 0] = (
target_coord_norm[:, 0] / (float(W - 1) / 2.0)
) - 1.0
target_coord_norm[:, 1] = (
target_coord_norm[:, 1] / (float(H - 1) / 2.0)
) - 1.0
target_coord_norm = target_coord_norm.permute(0, 2, 3, 1)
target_coord_warped_norm = warp_homography_batch(source_coord_norm, args[2])
target_coord_warped = target_coord_warped_norm.clone()
# de-normlize the coordinates
target_coord_warped[:, :, :, 0] = (target_coord_warped[:, :, :, 0] + 1) * (
float(W - 1) / 2.0
)
target_coord_warped[:, :, :, 1] = (target_coord_warped[:, :, :, 1] + 1) * (
float(H - 1) / 2.0
)
target_coord_warped = target_coord_warped.permute(0, 3, 1, 2)
# Border mask
border_mask_ori = torch.ones(B, hc, wc)
border_mask_ori[:, 0] = 0
border_mask_ori[:, hc - 1] = 0
border_mask_ori[:, :, 0] = 0
border_mask_ori[:, :, wc - 1] = 0
border_mask_ori = border_mask_ori.gt(1e-3).to(device)
oob_mask2 = (
target_coord_warped_norm[:, :, :, 0].lt(1)
& target_coord_warped_norm[:, :, :, 0].gt(-1)
& target_coord_warped_norm[:, :, :, 1].lt(1)
& target_coord_warped_norm[:, :, :, 1].gt(-1)
)
border_mask = border_mask_ori & oob_mask2
# score
target_score_warped = torch.nn.functional.grid_sample(
target_score, target_coord_warped_norm.detach(), align_corners=False
)
# descriptor
source_desc2 = torch.nn.functional.grid_sample(
source_desc_block[0], source_coord_norm.detach()
)
source_desc3 = torch.nn.functional.grid_sample(
source_desc_block[1], source_coord_norm.detach()
)
source_aware = source_desc_block[2]
source_desc = torch.mul(
source_desc2, source_aware[:, 0, :, :].unsqueeze(1).contiguous()
) + torch.mul(
source_desc3, source_aware[:, 1, :, :].unsqueeze(1).contiguous()
)
target_desc2 = torch.nn.functional.grid_sample(
target_desc_block[0], target_coord_norm.detach()
)
target_desc3 = torch.nn.functional.grid_sample(
target_desc_block[1], target_coord_norm.detach()
)
target_aware = target_desc_block[2]
target_desc = torch.mul(
target_desc2, target_aware[:, 0, :, :].unsqueeze(1).contiguous()
) + torch.mul(
target_desc3, target_aware[:, 1, :, :].unsqueeze(1).contiguous()
)
target_desc2_warped = torch.nn.functional.grid_sample(
target_desc_block[0], target_coord_warped_norm.detach()
)
target_desc3_warped = torch.nn.functional.grid_sample(
target_desc_block[1], target_coord_warped_norm.detach()
)
target_aware_warped = torch.nn.functional.grid_sample(
target_desc_block[2], target_coord_warped_norm.detach()
)
target_desc_warped = torch.mul(
target_desc2_warped,
target_aware_warped[:, 0, :, :].unsqueeze(1).contiguous(),
) + torch.mul(
target_desc3_warped,
target_aware_warped[:, 1, :, :].unsqueeze(1).contiguous(),
)
confidence_matrix = self.correspondence_module(source_desc, target_desc)
confidence_matrix = torch.clamp(confidence_matrix, 1e-12, 1 - 1e-12)
output = {
"source_score": source_score,
"source_coord": source_coord,
"source_desc": source_desc,
"source_aware": source_aware,
"target_score": target_score,
"target_coord": target_coord,
"target_score_warped": target_score_warped,
"target_coord_warped": target_coord_warped,
"target_desc_warped": target_desc_warped,
"target_aware_warped": target_aware_warped,
"border_mask": border_mask,
"confidence_matrix": confidence_matrix,
}
return output
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