Spaces:
Paused
Paused
File size: 3,242 Bytes
8c02843 |
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 |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class SinusoidalPositionEmbeddings(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, time):
device = time.device
half_dim = self.dim // 2
embeddings = math.log(10000) / (half_dim - 1)
embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)
embeddings = time[:, None] * embeddings[None, :]
embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
return embeddings
class DropoutSampler(torch.nn.Module):
def __init__(self, num_features, num_outputs, dropout_rate = 0.5):
super(DropoutSampler, self).__init__()
self.linear = nn.Linear(num_features, num_features)
self.linear2 = nn.Linear(num_features, num_features)
self.predict = nn.Linear(num_features, num_outputs)
self.num_features = num_features
self.num_outputs = num_outputs
self.dropout_rate = dropout_rate
def forward(self, x):
x = F.relu(self.linear(x))
if self.dropout_rate > 0:
x = F.dropout(x, self.dropout_rate)
x = F.relu(self.linear2(x))
return self.predict(x)
class EncoderMLP(torch.nn.Module):
def __init__(self, in_dim, out_dim, pt_dim=3, uses_pt=True):
super(EncoderMLP, self).__init__()
self.uses_pt = uses_pt
self.output = out_dim
d5 = int(in_dim)
d6 = int(2 * self.output)
d7 = self.output
self.encode_position = nn.Sequential(
nn.Linear(pt_dim, in_dim),
nn.LayerNorm(in_dim),
nn.ReLU(),
nn.Linear(in_dim, in_dim),
nn.LayerNorm(in_dim),
nn.ReLU(),
)
d5 = 2 * in_dim if self.uses_pt else in_dim
self.fc_block = nn.Sequential(
nn.Linear(int(d5), d6),
nn.LayerNorm(int(d6)),
nn.ReLU(),
nn.Linear(int(d6), d6),
nn.LayerNorm(int(d6)),
nn.ReLU(),
nn.Linear(d6, d7))
def forward(self, x, pt=None):
if self.uses_pt:
if pt is None: raise RuntimeError('did not provide pt')
y = self.encode_position(pt)
x = torch.cat([x, y], dim=-1)
return self.fc_block(x)
class MeanEncoder(torch.nn.Module):
def __init__(self, input_channels=3, use_xyz=True, output=512, scale=0.04, factor=1):
super(MeanEncoder, self).__init__()
self.uses_rgb = False
self.dim = 3
def forward(self, xyz, f=None):
# Fix shape
if f is not None:
if len(f.shape) < 3:
f = f.transpose(0,1).contiguous()
f = f[None]
else:
f = f.transpose(1,2).contiguous()
if len(xyz.shape) == 3:
center = torch.mean(xyz, dim=1)
elif len(xyz.shape) == 2:
center = torch.mean(xyz, dim=0)
else:
raise RuntimeError('not sure what to do with points of shape ' + str(xyz.shape))
assert(xyz.shape[-1]) == 3
assert(center.shape[-1]) == 3
return center, center |