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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import torch
import torch.nn as nn
import pytorch_lightning as pl
import torch.nn.functional as F
from torch.autograd import grad
# from fightingcv_attention.attention.SelfAttention import ScaledDotProductAttention
import numpy as np
class SDF2Density(pl.LightningModule):
def __init__(self):
super(SDF2Density, self).__init__()
# learnable parameters beta, with initial value 0.1
self.beta = nn.Parameter(torch.tensor(0.1))
def forward(self, sdf):
# use Laplace CDF to compute the probability
# temporally use sigmoid to represent laplace CDF
return 1.0/(self.beta+1e-6)*F.sigmoid(-sdf/(self.beta+1e-6))
class SDF2Occ(pl.LightningModule):
def __init__(self):
super(SDF2Occ, self).__init__()
# learnable parameters beta, with initial value 0.1
self.beta = nn.Parameter(torch.tensor(0.1))
def forward(self, sdf):
# use Laplace CDF to compute the probability
# temporally use sigmoid to represent laplace CDF
return F.sigmoid(-sdf/(self.beta+1e-6))
class DeformationMLP(pl.LightningModule):
def __init__(self,input_dim=64,output_dim=3,activation='LeakyReLU',name=None,opt=None):
super(DeformationMLP, self).__init__()
self.name = name
self.activation = activation
self.activate = nn.LeakyReLU(inplace=True)
# self.mlp = nn.Sequential(
# nn.Conv1d(input_dim+8+1+3, 64, 1),
# nn.LeakyReLU(inplace=True),
# nn.Conv1d(64, output_dim, 1),
# )
channels=[input_dim+8+1+3,128, 64, output_dim]
self.deform_mlp=MLP(filter_channels=channels,
name="if",
res_layers=opt.res_layers,
norm=opt.norm_mlp,
last_op=None) # occupancy
smplx_dim = 10475
k=8
self.per_pt_code = nn.Embedding(smplx_dim,k)
def forward(self, feature,smpl_vis,pts_id, xyz):
'''
feature may include multiple view inputs
args:
feature: [B, C_in, N]
return:
[B, C_out, N] prediction
'''
y = feature
e_code=self.per_pt_code(pts_id).permute(0,2,1) # a code that distinguishes each point on different parts of the body
y=torch.cat([y,xyz,smpl_vis,e_code],1)
y = self.deform_mlp(y)
return y
class MLP(pl.LightningModule):
def __init__(self,
filter_channels,
name=None,
res_layers=[],
norm='group',
last_op=None):
super(MLP, self).__init__()
self.filters = nn.ModuleList()
self.norms = nn.ModuleList()
self.res_layers = res_layers
self.norm = norm
self.last_op = last_op
self.name = name
self.activate = nn.LeakyReLU(inplace=True)
for l in range(0, len(filter_channels) - 1):
if l in self.res_layers:
self.filters.append(
nn.Conv1d(filter_channels[l] + filter_channels[0],
filter_channels[l + 1], 1))
else:
self.filters.append(
nn.Conv1d(filter_channels[l], filter_channels[l + 1], 1))
if l != len(filter_channels) - 2:
if norm == 'group':
self.norms.append(nn.GroupNorm(32, filter_channels[l + 1]))
elif norm == 'batch':
self.norms.append(nn.BatchNorm1d(filter_channels[l + 1]))
elif norm == 'instance':
self.norms.append(nn.InstanceNorm1d(filter_channels[l +
1]))
elif norm == 'weight':
self.filters[l] = nn.utils.weight_norm(self.filters[l],
name='weight')
# print(self.filters[l].weight_g.size(),
# self.filters[l].weight_v.size())
def forward(self, feature):
'''
feature may include multiple view inputs
args:
feature: [B, C_in, N]
return:
[B, C_out, N] prediction
'''
y = feature
tmpy = feature
for i, f in enumerate(self.filters):
y = f(y if i not in self.res_layers else torch.cat([y, tmpy], 1))
if i != len(self.filters) - 1:
if self.norm not in ['batch', 'group', 'instance']:
y = self.activate(y)
else:
y = self.activate(self.norms[i](y))
if self.last_op is not None:
y = self.last_op(y)
return y
# Positional encoding (section 5.1)
class Embedder(pl.LightningModule):
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x : x)
out_dim += d
max_freq = self.kwargs['max_freq_log2']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands = 2.**torch.linspace(0., max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2.**0., 2.**max_freq, steps=N_freqs)
for freq in freq_bands:
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq : p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
def get_embedder(multires=6, i=0):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input' : True,
'input_dims' : 3,
'max_freq_log2' : multires-1,
'num_freqs' : multires,
'log_sampling' : True,
'periodic_fns' : [torch.sin, torch.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj : eo.embed(x)
return embed, embedder_obj.out_dim
# Transformer encoder layer
# uses Embedder to add positional encoding to input points
# uses query points as query, deformed points as key, point features as value for attention
class TransformerEncoderLayer(pl.LightningModule):
def __init__(self, d_model=256, skips=4, multires=6, num_mlp_layers=8, dropout=0.1, opt=None):
super(TransformerEncoderLayer, self).__init__()
embed_fn, input_ch = get_embedder(multires=multires)
self.skips=skips
self.dropout = dropout
D=num_mlp_layers
self.positional_encoding = embed_fn
self.d_model = d_model
triplane_dim=64
opt.mlp_dim[0]=triplane_dim+6+8
opt.mlp_dim_color[0]=triplane_dim+6+8
self.geo_mlp=MLP(filter_channels=opt.mlp_dim,
name="if",
res_layers=opt.res_layers,
norm=opt.norm_mlp,
last_op=nn.Sigmoid()) # occupancy
self.color_mlp=MLP(filter_channels=opt.mlp_dim_color,
name="color_if",
res_layers=opt.res_layers,
norm=opt.norm_mlp,
last_op=nn.Tanh()) # color
self.softmax = nn.Softmax(dim=-1)
def forward(self,query_points,key_points,point_features,smpl_feat,training=True,type='shape'):
# Q=self.positional_encoding(query_points) #[B,N,39]
# K=self.positional_encoding(key_points) #[B,N',39]
# V=point_features.permute(0,2,1) #[B,N',192]
# t=0.1
# #attn_output, attn_output_weights = self.attention(Q.permute(1,0,2), K.permute(1,0,2), V.permute(1,0,2)) #[B,N,192]
# attn_output_weights = torch.bmm(Q, K.transpose(1, 2)) #[B,N,N']
# attn_output_weights = self.softmax(attn_output_weights/t) #[B,N,N']
# # drop out
# attn_output_weights = F.dropout(attn_output_weights, p=self.dropout, training=True)
# # master feature
# attn_output = torch.bmm(attn_output_weights, V) #[B,N,192]
attn_output=point_features # [B,N,192] bary centric interpolation
feature=torch.cat([attn_output,smpl_feat],dim=1)
if type=='shape':
h=feature
h=self.geo_mlp(h) # [B,1,N]
return h
elif type=='color':
#f=self.head(feature) #[B,N,512]
h=feature
h=self.color_mlp(h) # [B,3,N]
return h
elif type=='shape_color':
h_s=feature
h_c=feature
h_s=self.geo_mlp(h_s) # [B,1,N]
h_c=self.color_mlp(h_c) # [B,3,N]
return h_s,h_c
class Swish(pl.LightningModule):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
# # Import pytorch modules
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# Define positional encoding class
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=1000):
super(PositionalEncoding, self).__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) *
-(math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1)]
return x
# # Define model parameters
# d_model = 256 # output size of MLP
# nhead = 8 # number of attention heads
# dim_feedforward = 512 # hidden size of MLP
# num_layers = 2 # number of MLP layers
# num_frequencies = 6 # number of frequencies for positional encoding
# dropout = 0.1 # dropout rate
# # Define model components
# pos_encoder = PositionalEncoding(d_model, num_frequencies) # positional encoding layer
# encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout) # transformer encoder layer
# encoder = nn.TransformerEncoder(encoder_layer, num_layers) # transformer encoder
# mlp_geo = nn.Sequential(nn.Linear(3, d_model), nn.ReLU(), nn.Linear(d_model, d_model)) # MLP for geometry
# mlp_alb = nn.Sequential(nn.Linear(3, d_model), nn.ReLU(), nn.Linear(d_model, d_model)) # MLP for albedo
# head_geo = nn.Sequential(nn.Linear(d_model, d_model), nn.ReLU(), nn.Linear(d_model, 3)) # geometry head
# head_alb = nn.Sequential(nn.Linear(d_model, d_model), nn.ReLU(), nn.Linear(d_model, 3), nn.Sigmoid()) # albedo head
# # Define input tensors
# # deformed body points: (batch_size, num_points, 3)
# x = torch.randn(batch_size, num_points, 3)
# # query point positions: (batch_size, num_queries, 3)
# y = torch.randn(batch_size, num_queries, 3)
# # Map both d
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