Upload selfcontact.diff

patches/selfcontact.diff

@@ -0,0 +1,159 @@
++++ 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: