Spaces:
Runtime error
Runtime error
File size: 6,554 Bytes
74d6764 |
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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
import itertools
import torch
import torch.nn as nn
import pose_estimation
class MSE(nn.Module):
def __init__(self, ignore=None):
super().__init__()
self.mse = torch.nn.MSELoss(reduction="none")
self.ignore = ignore if ignore is not None else []
def forward(self, y_pred, y_data):
loss = self.mse(y_pred, y_data)
if len(self.ignore) > 0:
loss[self.ignore] *= 0
return loss.sum() / (len(loss) - len(self.ignore))
class Parallel(nn.Module):
def __init__(self, skeleton, ignore=None, ground_parallel=None):
super().__init__()
self.skeleton = skeleton
if ignore is not None:
self.ignore = set(ignore)
else:
self.ignore = set()
self.ground_parallel = ground_parallel if ground_parallel is not None else []
self.parallel_in_3d = []
self.cos = None
def forward(self, y_pred3d, y_data, z, spine_j, global_step=0):
y_pred = y_pred3d[:, :2]
rleg, lleg = spine_j
Lcon2d = Lcount = 0
if hasattr(self, "contact_2d"):
for c2d in self.contact_2d:
for (
(src_1, dst_1, t_1),
(src_2, dst_2, t_2),
) in itertools.combinations(c2d, 2):
a_1 = torch.lerp(y_data[src_1], y_data[dst_1], t_1)
a_2 = torch.lerp(y_data[src_2], y_data[dst_2], t_2)
a = a_2 - a_1
b_1 = torch.lerp(y_pred[src_1], y_pred[dst_1], t_1)
b_2 = torch.lerp(y_pred[src_2], y_pred[dst_2], t_2)
b = b_2 - b_1
lcon2d = ((a - b) ** 2).sum()
Lcon2d = Lcon2d + lcon2d
Lcount += 1
if Lcount > 0:
Lcon2d = Lcon2d / Lcount
Ltan = Lpar = Lcos = Lcount = 0
Lspine = 0
for i, bone in enumerate(self.skeleton):
if bone in self.ignore:
continue
src, dst = bone
b = y_data[dst] - y_data[src]
t = nn.functional.normalize(b, dim=0)
n = torch.stack([-t[1], t[0]])
if src == 10 and dst == 11: # right leg
a = rleg
elif src == 13 and dst == 14: # left leg
a = lleg
else:
a = y_pred[dst] - y_pred[src]
bone_name = f"{pose_estimation.KPS[src]}_{pose_estimation.KPS[dst]}"
c = a - b
lcos_loc = ltan_loc = lpar_loc = 0
if self.cos is not None:
if bone not in [
(1, 2), # Neck + Right Shoulder
(1, 5), # Neck + Left Shoulder
(9, 10), # Hips + Right Upper Leg
(9, 13), # Hips + Left Upper Leg
]:
a = y_pred[dst] - y_pred[src]
l2d = torch.norm(a, dim=0)
l3d = torch.norm(y_pred3d[dst] - y_pred3d[src], dim=0)
lcos = self.cos[i]
lcos_loc = (l2d / l3d - lcos) ** 2
Lcos = Lcos + lcos_loc
lpar_loc = ((a / l2d) * n).sum() ** 2
Lpar = Lpar + lpar_loc
else:
ltan_loc = ((c * t).sum()) ** 2
Ltan = Ltan + ltan_loc
lpar_loc = (c * n).sum() ** 2
Lpar = Lpar + lpar_loc
Lcount += 1
if Lcount > 0:
Ltan = Ltan / Lcount
Lcos = Lcos / Lcount
Lpar = Lpar / Lcount
Lspine = Lspine / Lcount
Lgr = Lcount = 0
for (src, dst), value in self.ground_parallel:
bone = y_pred[dst] - y_pred[src]
bone = nn.functional.normalize(bone, dim=0)
l = (torch.abs(bone[0]) - value) ** 2
Lgr = Lgr + l
Lcount += 1
if Lcount > 0:
Lgr = Lgr / Lcount
Lstraight3d = Lcount = 0
for (i, j), (k, l) in self.parallel_in_3d:
a = z[j] - z[i]
a = nn.functional.normalize(a, dim=0)
b = z[l] - z[k]
b = nn.functional.normalize(b, dim=0)
lo = (((a * b).sum() - 1) ** 2).sum()
Lstraight3d = Lstraight3d + lo
Lcount += 1
b = y_data[1] - y_data[8]
b = nn.functional.normalize(b, dim=0)
if Lcount > 0:
Lstraight3d = Lstraight3d / Lcount
return Ltan, Lcos, Lpar, Lspine, Lgr, Lstraight3d, Lcon2d
class MimickedSelfContactLoss(nn.Module):
def __init__(self, geodesics_mask):
super().__init__()
"""
Loss that lets vertices in contact on presented mesh attract vertices that are close.
"""
# geodesic distance mask
self.register_buffer("geomask", geodesics_mask)
def forward(
self,
presented_contact,
vertices,
v2v=None,
contact_mode="dist_tanh",
contact_thresh=1,
):
contactloss = 0.0
if v2v is None:
# compute pairwise distances
verts = vertices.contiguous()
nv = verts.shape[1]
v2v = verts.squeeze().unsqueeze(1).expand(
nv, nv, 3
) - verts.squeeze().unsqueeze(0).expand(nv, nv, 3)
v2v = torch.norm(v2v, 2, 2)
# loss for self-contact from mimic'ed pose
if len(presented_contact) > 0:
# without geodesic distance mask, compute distances
# between each pair of verts in contact
with torch.no_grad():
cvertstobody = v2v[presented_contact, :]
cvertstobody = cvertstobody[:, presented_contact]
maskgeo = self.geomask[presented_contact, :]
maskgeo = maskgeo[:, presented_contact]
weights = torch.ones_like(cvertstobody).to(verts.device)
weights[~maskgeo] = float("inf")
min_idx = torch.min((cvertstobody + 1) * weights, 1)[1]
min_idx = presented_contact[min_idx.cpu().numpy()]
v2v_min = v2v[presented_contact, min_idx]
# tanh will not pull vertices that are ~more than contact_thres far apart
if contact_mode == "dist_tanh":
contactloss = contact_thresh * torch.tanh(v2v_min / contact_thresh)
contactloss = contactloss.mean()
else:
contactloss = v2v_min.mean()
return contactloss
|