File size: 2,559 Bytes
b24c748 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import torch
import torch.nn as nn
from feature_extraction import ViTEncoder
from coarse_point_matching import CoarsePointMatching
from fine_point_matching import FinePointMatching
from transformer import GeometricStructureEmbedding
from model_utils import sample_pts_feats
from vis_utils import visualize_points_3d, features_to_colors, visualize_two_sets_3d
class Net(nn.Module):
def __init__(self, cfg):
super(Net, self).__init__()
self.cfg = cfg
self.coarse_npoint = cfg.coarse_npoint
self.fine_npoint = cfg.fine_npoint
self.feature_extraction = ViTEncoder(cfg.feature_extraction, self.fine_npoint)
self.geo_embedding = GeometricStructureEmbedding(cfg.geo_embedding)
self.coarse_point_matching = CoarsePointMatching(cfg.coarse_point_matching)
self.fine_point_matching = FinePointMatching(cfg.fine_point_matching)
def forward(self, end_points):
dense_pm, dense_fm, dense_po, dense_fo, radius = self.feature_extraction(end_points)
# visualize_two_sets_3d(
# dense_po.squeeze(0).cpu().numpy(),
# dense_po.squeeze(0).cpu().numpy(),
# "dense", s=1)
# breakpoint()
# pre-compute geometric embeddings for geometric transformer
bg_point = torch.ones(dense_pm.size(0),1,3).float().to(dense_pm.device) * 100
sparse_pm, sparse_fm, fps_idx_m = sample_pts_feats(
dense_pm, dense_fm, self.coarse_npoint, return_index=True
)
geo_embedding_m = self.geo_embedding(torch.cat([bg_point, sparse_pm], dim=1))
sparse_po, sparse_fo, fps_idx_o = sample_pts_feats(
dense_po, dense_fo, self.coarse_npoint, return_index=True
)
geo_embedding_o = self.geo_embedding(torch.cat([bg_point, sparse_po], dim=1))
# coarse_point_matching
end_points, points = self.coarse_point_matching(
sparse_pm, sparse_fm, geo_embedding_m,
sparse_po, sparse_fo, geo_embedding_o,
radius, end_points,
)
# breakpoint()
end_points, _ = self.fine_point_matching(
dense_pm, dense_fm, geo_embedding_m, fps_idx_m,
dense_po, dense_fo, geo_embedding_o, fps_idx_o,
radius, end_points)
return end_points, points
# end_points_coarse['init_t'] = end_points_coarse['init_t'] * (radius.reshape(-1, 1)+1e-6)
# return end_points_coarse, points
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