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+++ venv/lib/python3.10/site-packages/selfcontact/body_segmentation.py
@@ -14,6 +14,8 @@
#
# Contact: ps-license@tuebingen.mpg.de
+from pathlib import Path
+
import torch
import trimesh
import torch.nn as nn
@@ -22,6 +24,17 @@
from .utils.mesh import winding_numbers
+
+def load_pkl(path):
+ with open(path, "rb") as fin:
+ return pickle.load(fin)
+
+
+def save_pkl(obj, path):
+ with open(path, "wb") as fout:
+ pickle.dump(obj, fout)
+
+
class BodySegment(nn.Module):
def __init__(self,
name,
@@ -63,9 +76,17 @@
self.register_buffer('segment_faces', segment_faces)
# create vector to select vertices form faces
- tri_vidx = []
- for ii in range(faces.max().item()+1):
- tri_vidx += [torch.nonzero(faces==ii)[0].tolist()]
+ segments_folder = Path(segments_folder)
+ tri_vidx_path = segments_folder / "tri_vidx.pkl"
+ if not tri_vidx_path.is_file():
+ tri_vidx = []
+ for ii in range(faces.max().item()+1):
+ tri_vidx += [torch.nonzero(faces==ii)[0].tolist()]
+
+ save_pkl(tri_vidx, tri_vidx_path)
+ else:
+ tri_vidx = load_pkl(tri_vidx_path)
+
self.register_buffer('tri_vidx', torch.tensor(tri_vidx))
def create_band_faces(self):
@@ -149,7 +170,7 @@
self.segmentation = {}
for idx, name in enumerate(names):
self.segmentation[name] = BodySegment(name, faces, segments_folder,
- model_type).to('cuda')
+ model_type).to(device)
def batch_has_self_isec_verts(self, vertices):
"""
+++ venv/lib/python3.10/site-packages/selfcontact/selfcontact.py
@@ -41,6 +41,7 @@
test_segments=True,
compute_hd=False,
buffer_geodists=False,
+ device="cuda",
):
super().__init__()
@@ -95,7 +96,7 @@
if self.test_segments:
sxseg = pickle.load(open(segments_bounds_path, 'rb'))
self.segments = BatchBodySegment(
- [x for x in sxseg.keys()], faces, segments_folder, self.model_type
+ [x for x in sxseg.keys()], faces, segments_folder, self.model_type, device=device,
)
# load regressor to get high density mesh
@@ -106,7 +107,7 @@
torch.tensor(hd_operator['values']),
torch.Size(hd_operator['size']))
self.register_buffer('hd_operator',
- torch.tensor(hd_operator).float())
+ hd_operator.clone().detach().float())
with open(point_vert_corres_path, 'rb') as f:
hd_geovec = pickle.load(f)['faces_vert_is_sampled_from']
@@ -135,9 +136,13 @@
# split because of memory into two chunks
exterior = torch.zeros((bs, nv), device=vertices.device,
dtype=torch.bool)
- exterior[:, :5000] = winding_numbers(vertices[:,:5000,:],
+ exterior[:, :3000] = winding_numbers(vertices[:,:3000,:],
triangles).le(0.99)
- exterior[:, 5000:] = winding_numbers(vertices[:,5000:,:],
+ exterior[:, 3000:6000] = winding_numbers(vertices[:,3000:6000,:],
+ triangles).le(0.99)
+ exterior[:, 6000:9000] = winding_numbers(vertices[:,6000:9000,:],
+ triangles).le(0.99)
+ exterior[:, 9000:] = winding_numbers(vertices[:,9000:,:],
triangles).le(0.99)
# check if intersections happen within segments
@@ -173,9 +178,13 @@
# split because of memory into two chunks
exterior = torch.zeros((bs, np), device=points.device,
dtype=torch.bool)
- exterior[:, :6000] = winding_numbers(points[:,:6000,:],
+ exterior[:, :3000] = winding_numbers(points[:,:3000,:],
+ triangles).le(0.99)
+ exterior[:, 3000:6000] = winding_numbers(points[:,3000:6000,:],
triangles).le(0.99)
- exterior[:, 6000:] = winding_numbers(points[:,6000:,:],
+ exterior[:, 6000:9000] = winding_numbers(points[:,6000:9000,:],
+ triangles).le(0.99)
+ exterior[:, 9000:] = winding_numbers(points[:,9000:,:],
triangles).le(0.99)
return exterior
@@ -371,6 +380,23 @@
return hd_v2v_mins, hd_exteriors, hd_points, hd_faces_in_contacts
+ def verts_in_contact(self, vertices, return_idx=False):
+
+ # get pairwise distances of vertices
+ v2v = self.get_pairwise_dists(vertices, vertices, squared=True)
+
+ # mask v2v with eucledean and geodesic dsitance
+ euclmask = v2v < self.euclthres**2
+ mask = euclmask * self.geomask
+
+ # find closes vertex in contact
+ in_contact = mask.sum(1) > 0
+
+ if return_idx:
+ in_contact = torch.where(in_contact)
+
+ return in_contact
+
class SelfContactSmall(nn.Module):
+++ venv/lib/python3.10/site-packages/selfcontact/utils/mesh.py
@@ -82,7 +82,7 @@
if valid_vals > 0:
loss = (mask * dists).sum() / valid_vals
else:
- loss = torch.Tensor([0]).cuda()
+ loss = mask.new_tensor([0])
return loss
def batch_index_select(inp, dim, index):
@@ -103,6 +103,7 @@
xx = torch.bmm(x, x.transpose(2, 1))
yy = torch.bmm(y, y.transpose(2, 1))
zz = torch.bmm(x, y.transpose(2, 1))
+ use_cuda = x.device.type == "cuda"
if use_cuda:
dtype = torch.cuda.LongTensor
else:
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