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loss_util.py
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import math
import torch
import torch.nn as nn
import trimesh
import pymesh
import numpy as np
from scipy.sparse import coo_matrix
class LaplacianLoss(nn.Module):
def __init__(self, verts, faces, mask=None):
super(LaplacianLoss, self).__init__()
mesh = trimesh.Trimesh(vertices=verts, faces=faces)
laplacian = trimesh.smoothing.laplacian_calculation(mesh, equal_weight=False)
laplacian_coo = laplacian.tocoo()
indices = torch.LongTensor(np.vstack((laplacian_coo.row, laplacian_coo.col)))
values = torch.FloatTensor(laplacian_coo.data)
shape = laplacian_coo.shape
self.laplacian = torch.sparse.FloatTensor(indices, values, torch.Size(shape)).to_dense().cuda()
if mask is None:
self.mask = torch.tensor((list(range(verts.shape[0])))).long().cuda()
else:
self.mask = torch.tensor(mask).long().cuda()
self.register_buffer('delta_V', self.laplacian @ torch.tensor(mesh.vertices, dtype=torch.float32).cuda())
def forward(self, V_prime):
delta_V_prime = self.laplacian @ V_prime
loss = torch.sum((delta_V_prime[self.mask] - self.delta_V[self.mask]) ** 2)
return loss
class ARAPLoss(nn.Module):
def __init__(self, vertex, faces, average=False):
super(ARAPLoss, self).__init__()
self.nv = vertex.shape[0]
self.nf = faces.shape[0]
self.average = average
laplacian = np.zeros([self.nv, self.nv]).astype(np.float32)
laplacian[faces[:, 0], faces[:, 1]] = 1
laplacian[faces[:, 1], faces[:, 0]] = 1
laplacian[faces[:, 1], faces[:, 2]] = 1
laplacian[faces[:, 2], faces[:, 1]] = 1
laplacian[faces[:, 2], faces[:, 0]] = 1
laplacian[faces[:, 0], faces[:, 2]] = 1
self.register_buffer('laplacian', torch.from_numpy(laplacian).cuda())
def forward(self, dx, x):
# lap: Nv Nv
# dx: N, Nv, 3
diffx = torch.zeros(x.shape[0], x.shape[1], x.shape[1]).cuda()
diffdx = torch.zeros(x.shape[0], x.shape[1], x.shape[1]).cuda()
dx_sub = self.laplacian.matmul(dx) # N, Nv, 3
x_sub = self.laplacian.matmul(x) # N, Nv, 3
dx_diff = dx_sub - dx[:, None]
x_diff = x_sub - x[:, None]
diffdx += dx_diff.pow(2).sum(dim=-1)
diffx += x_diff.pow(2).sum(dim=-1)
diff = (diffx - diffdx).abs()
diff = diff[:, self.laplacian.bool()].mean(dim=1)
return diff
class EdgeLoss(nn.Module):
def __init__(self, faces, average=False, size_factor=1.0):
super(EdgeLoss, self).__init__()
edge_set = set()
for tri in faces:
tri = tri.numpy()
edge_set.add((tri[0], tri[1]))
edge_set.add((tri[1], tri[2]))
edge_set.add((tri[0], tri[2]))
self.edges = torch.tensor(np.array(list(edge_set))).long().cuda()
self.size_factor = size_factor
def forward(self, x):
x = x * self.size_factor
p1 = x[self.edges[:, 0]]
p2 = x[self.edges[:, 1]]
distance = nn.functional.pairwise_distance(p1, p2, p=2)
return torch.std(distance)
class NormLoss(nn.Module):
def __init__(self, norm):
super(NormLoss, self).__init__()
self.norm = norm.cuda()
self.cos = nn.CosineSimilarity(dim=1, eps=1e-6)
def forward(self, x):
cos_theta = 1 - self.cos(x, self.norm).abs()
loss = torch.mean(cos_theta)
return loss
class FlattenLoss(nn.Module):
def __init__(self, faces, threshold=0, average=False):
super(FlattenLoss, self).__init__()
self.nf = faces.shape[0]
self.average = average
self.threshold = threshold
faces = faces.detach().cpu().numpy()
vertices = list(set([tuple(v) for v in np.sort(np.concatenate((faces[:, 0:2], faces[:, 1:3]), axis=0))]))
vert_face = {}
for k, v in enumerate(faces):
for vx in v:
if vx not in vert_face.keys():
vert_face[vx] = [k]
else:
vert_face[vx].append(k)
v0s = np.array([v[0] for v in vertices], 'int32')
v1s = np.array([v[1] for v in vertices], 'int32')
v2s = []
v3s = []
idx = 0
nosin_list = []
for v0, v1 in zip(v0s, v1s):
count = 0
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) > 2:
continue
# for face in faces:
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) == 2:
nosin_list.append(idx)
for faceid in sorted(list(set(vert_face[v0]) & set(vert_face[v1]))):
face = faces[faceid]
if v0 in face and v1 in face:
v = np.copy(face)
v = v[v != v0]
v = v[v != v1]
if count == 0:
v2s.append(int(v[0]))
count += 1
else:
v3s.append(int(v[0]))
idx += 1
v2s = np.array(v2s, 'int32')
v3s = np.array(v3s, 'int32')
v0s = v0s[nosin_list]
v1s = v1s[nosin_list]
v2s = v2s[nosin_list]
self.register_buffer('v0s', torch.from_numpy(v0s).long())
self.register_buffer('v1s', torch.from_numpy(v1s).long())
self.register_buffer('v2s', torch.from_numpy(v2s).long())
self.register_buffer('v3s', torch.from_numpy(v3s).long())
def forward(self, vertices, eps=1e-6):
# make v0s, v1s, v2s, v3s
vertices = vertices.unsqueeze(0)
batch_size = vertices.shape[0]
#print(self.v0s.shape)
v0s = vertices[:, self.v0s, :]
v1s = vertices[:, self.v1s, :]
v2s = vertices[:, self.v2s, :]
v3s = vertices[:, self.v3s, :]
a1 = v1s - v0s
b1 = v2s - v0s
a1l2 = a1.pow(2).sum(-1)
b1l2 = b1.pow(2).sum(-1)
a1l1 = (a1l2 + eps).sqrt()
b1l1 = (b1l2 + eps).sqrt()
ab1 = (a1 * b1).sum(-1)
cos1 = ab1 / (a1l1 * b1l1 + eps)
sin1 = (1 - cos1.pow(2) + eps).sqrt()
c1 = a1 * (ab1 / (a1l2 + eps))[:, :, None]
cb1 = b1 - c1
cb1l1 = b1l1 * sin1
a2 = v1s - v0s
b2 = v3s - v0s
a2l2 = a2.pow(2).sum(-1)
b2l2 = b2.pow(2).sum(-1)
a2l1 = (a2l2 + eps).sqrt()
b2l1 = (b2l2 + eps).sqrt()
ab2 = (a2 * b2).sum(-1)
cos2 = ab2 / (a2l1 * b2l1 + eps)
sin2 = (1 - cos2.pow(2) + eps).sqrt()
c2 = a2 * (ab2 / (a2l2 + eps))[:, :, None]
cb2 = b2 - c2
cb2l1 = b2l1 * sin2
cos = (cb1 * cb2).sum(-1) / (cb1l1 * cb2l1 + eps)
dims = tuple(range(cos.ndimension())[1:])
threshold = math.cos(self.threshold * math.pi / 180)
cos = torch.where(cos > threshold, -1, cos)
loss = (cos + 1).pow(2).sum(dims)
#print((cos + 1).pow(2).shape)
if self.average:
return loss.sum() / batch_size
else:
return loss
class FlattenLoss_v2(nn.Module):
def __init__(self, variables, mask_list=[], pre_mask = [], ex_mask=[]):
super(FlattenLoss_v2, self).__init__()
self.variables = variables
#print(self.variables["neighbor_indices_ori"])
max_ns = max(len(lst) for lst in self.variables["neighbor_indices_ori"])
self.neighbor_num = torch.tensor([len(lst) for lst in self.variables["neighbor_indices_ori"]]).cuda()
# min_ns = min(len(lst) for lst in neighbor_indices)
# print(max_ns, min_ns)
mask = []
for i, lst in enumerate(self.variables["neighbor_indices_ori"]):
m = [1] * self.neighbor_num[i]
if len(m) < max_ns:
m.extend([0] * (max_ns - len(lst)))
mask.append(m.copy())
self.mask = torch.tensor(mask).cuda().requires_grad_(False)
self.mask = self.mask.unsqueeze(-1).repeat([1, 1, 3])
#print(self.mask.shape)
#print(self.neighbor_num)
self.loss = nn.MSELoss()
self.region_mask = []
for r in mask_list:
self.region_mask += self.variables["facial_regions"]["region_masks"][r].tolist()
self.region_mask += pre_mask
self.region_mask = list(set(self.region_mask) - set(ex_mask))
if len(self.region_mask) == 0:
self.region_mask = [idx for idx in range(self.mask.shape[0])]
self.region_mask = list(set(self.region_mask))
self.region_mask = torch.from_numpy(np.array(self.region_mask)).cuda()
def forward(self, vertices):
#neighbor_num = torch.tensor([len(lst) for lst in self.variables["neighbor_indices_ori"]]).cuda()
neighbor_pos = vertices[self.variables["neighbor_indices"]] * self.mask
pos_sum = torch.sum(neighbor_pos, dim=1)
ave_pos = pos_sum / self.neighbor_num.unsqueeze(1)
#print(self.loss(ave_pos, vertices))
#print(self.mask.shape, neighbor_pos.shape, ave_pos.shape)
return self.loss(ave_pos[self.region_mask], vertices[self.region_mask])
class SoftFlattenLoss(nn.Module):
def __init__(self, faces, threshold=180, average=False):
super(SoftFlattenLoss, self).__init__()
self.nf = faces.shape[0]
self.average = average
self.threshold = threshold
faces = faces.detach().cpu().numpy()
vertices = list(set([tuple(v) for v in np.sort(np.concatenate((faces[:, 0:2], faces[:, 1:3]), axis=0))]))
vert_face = {}
for k, v in enumerate(faces):
for vx in v:
if vx not in vert_face.keys():
vert_face[vx] = [k]
else:
vert_face[vx].append(k)
v0s = np.array([v[0] for v in vertices], 'int32')
v1s = np.array([v[1] for v in vertices], 'int32')
v2s = []
v3s = []
idx = 0
nosin_list = []
for v0, v1 in zip(v0s, v1s):
count = 0
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) > 2:
continue
# for face in faces:
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) == 2:
nosin_list.append(idx)
for faceid in sorted(list(set(vert_face[v0]) & set(vert_face[v1]))):
face = faces[faceid]
if v0 in face and v1 in face:
v = np.copy(face)
v = v[v != v0]
v = v[v != v1]
if count == 0:
v2s.append(int(v[0]))
count += 1
else:
v3s.append(int(v[0]))
idx += 1
v2s = np.array(v2s, 'int32')
v3s = np.array(v3s, 'int32')
v0s = v0s[nosin_list]
v1s = v1s[nosin_list]
v2s = v2s[nosin_list]
self.register_buffer('v0s', torch.from_numpy(v0s).long())
self.register_buffer('v1s', torch.from_numpy(v1s).long())
self.register_buffer('v2s', torch.from_numpy(v2s).long())
self.register_buffer('v3s', torch.from_numpy(v3s).long())
def forward(self, vertices, cos_init=None, eps=1e-6):
# make v0s, v1s, v2s, v3s
vertices = vertices.unsqueeze(0)
batch_size = vertices.shape[0]
#print(self.v0s.shape)
v0s = vertices[:, self.v0s, :]
v1s = vertices[:, self.v1s, :]
v2s = vertices[:, self.v2s, :]
v3s = vertices[:, self.v3s, :]
a1 = v1s - v0s
b1 = v2s - v0s
a1l2 = a1.pow(2).sum(-1)
b1l2 = b1.pow(2).sum(-1)
a1l1 = (a1l2 + eps).sqrt()
b1l1 = (b1l2 + eps).sqrt()
ab1 = (a1 * b1).sum(-1)
cos1 = ab1 / (a1l1 * b1l1 + eps)
sin1 = (1 - cos1.pow(2) + eps).sqrt()
c1 = a1 * (ab1 / (a1l2 + eps))[:, :, None]
cb1 = b1 - c1
cb1l1 = b1l1 * sin1
a2 = v1s - v0s
b2 = v3s - v0s
a2l2 = a2.pow(2).sum(-1)
b2l2 = b2.pow(2).sum(-1)
a2l1 = (a2l2 + eps).sqrt()
b2l1 = (b2l2 + eps).sqrt()
ab2 = (a2 * b2).sum(-1)
cos2 = ab2 / (a2l1 * b2l1 + eps)
sin2 = (1 - cos2.pow(2) + eps).sqrt()
c2 = a2 * (ab2 / (a2l2 + eps))[:, :, None]
cb2 = b2 - c2
cb2l1 = b2l1 * sin2
cos = (cb1 * cb2).sum(-1) / (cb1l1 * cb2l1 + eps)
cos_ori = cos.detach().clone()
dims = tuple(range(cos.ndimension())[1:])
#threshold = math.cos(self.threshold * math.pi / 180)
#cos = torch.where(cos < threshold, -1, cos)
if cos_init is not None:
#cos = torch.where(abs(torch.arccos(cos) - torch.arccos(cos_init)) < self.threshold * torch.pi / 180, -1, cos)
loss = (1 - torch.cos(abs(torch.arccos(cos) - torch.arccos(cos_init)))).sum()
else:
loss = (cos + 1).pow(2).sum(dims)
#loss = (cos + 1).pow(2).sum(dims)
#print((cos + 1).pow(2).shape)
if self.average:
return loss.sum() / batch_size
else:
return loss, cos_ori
class SoftFlattenLoss_v2(nn.Module):
def __init__(self, faces, threshold=180, average=False):
super(SoftFlattenLoss_v2, self).__init__()
self.nf = faces.shape[0]
self.average = average
self.threshold = threshold
faces = faces.detach().cpu().numpy()
vertices = list(set([tuple(v) for v in np.sort(np.concatenate((faces[:, 0:2], faces[:, 1:3]), axis=0))]))
vert_face = {}
for k, v in enumerate(faces):
for vx in v:
if vx not in vert_face.keys():
vert_face[vx] = [k]
else:
vert_face[vx].append(k)
v0s = np.array([v[0] for v in vertices], 'int32')
v1s = np.array([v[1] for v in vertices], 'int32')
v2s = []
v3s = []
idx = 0
nosin_list = []
for v0, v1 in zip(v0s, v1s):
count = 0
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) > 2:
continue
# for face in faces:
if len(sorted(list(set(vert_face[v0]) & set(vert_face[v1])))) == 2:
nosin_list.append(idx)
for faceid in sorted(list(set(vert_face[v0]) & set(vert_face[v1]))):
face = faces[faceid]
if v0 in face and v1 in face:
v = np.copy(face)
v = v[v != v0]
v = v[v != v1]
if count == 0:
v2s.append(int(v[0]))
count += 1
else:
v3s.append(int(v[0]))
idx += 1
v2s = np.array(v2s, 'int32')
v3s = np.array(v3s, 'int32')
v0s = v0s[nosin_list]
v1s = v1s[nosin_list]
v2s = v2s[nosin_list]
self.register_buffer('v0s', torch.from_numpy(v0s).long())
self.register_buffer('v1s', torch.from_numpy(v1s).long())
self.register_buffer('v2s', torch.from_numpy(v2s).long())
self.register_buffer('v3s', torch.from_numpy(v3s).long())
def forward(self, vertices, cos_init=None, eps=1e-6):
# make v0s, v1s, v2s, v3s
vertices = vertices.unsqueeze(0)
batch_size = vertices.shape[0]
#print(self.v0s.shape)
v0s = vertices[:, self.v0s, :]
v1s = vertices[:, self.v1s, :]
v2s = vertices[:, self.v2s, :]
v3s = vertices[:, self.v3s, :]
a1 = v1s - v0s
b1 = v2s - v0s
a1l2 = a1.pow(2).sum(-1)
b1l2 = b1.pow(2).sum(-1)
a1l1 = (a1l2 + eps).sqrt()
b1l1 = (b1l2 + eps).sqrt()
ab1 = (a1 * b1).sum(-1)
cos1 = ab1 / (a1l1 * b1l1 + eps)
sin1 = (1 - cos1.pow(2) + eps).sqrt()
c1 = a1 * (ab1 / (a1l2 + eps))[:, :, None]
cb1 = b1 - c1
cb1l1 = b1l1 * sin1
a2 = v1s - v0s
b2 = v3s - v0s
a2l2 = a2.pow(2).sum(-1)
b2l2 = b2.pow(2).sum(-1)
a2l1 = (a2l2 + eps).sqrt()
b2l1 = (b2l2 + eps).sqrt()
ab2 = (a2 * b2).sum(-1)
cos2 = ab2 / (a2l1 * b2l1 + eps)
sin2 = (1 - cos2.pow(2) + eps).sqrt()
c2 = a2 * (ab2 / (a2l2 + eps))[:, :, None]
cb2 = b2 - c2
cb2l1 = b2l1 * sin2
cos = (cb1 * cb2).sum(-1) / (cb1l1 * cb2l1 + eps)
cos_ori = cos.detach().clone()
dims = tuple(range(cos.ndimension())[1:])
#threshold = math.cos(self.threshold * math.pi / 180)
#cos = torch.where(cos < threshold, -1, cos)
if cos_init is not None:
#cos = torch.where(abs(torch.arccos(cos) - torch.arccos(cos_init)) < self.threshold * torch.pi / 180, -1, cos)
loss = (1 - torch.cos(abs(torch.arccos(cos) - torch.arccos(cos_init)))).pow(2).sum(dims)
else:
loss = (cos + 1).pow(2).sum(dims)
#loss = (cos + 1).pow(2).sum(dims)
#print((cos + 1).pow(2).shape)
if self.average:
return loss.sum() / batch_size
else:
return loss, cos_ori