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import torch.nn as nn
import torch
import numpy as np
from .skeleton_DME import SkeletonConv, SkeletonPool, find_neighbor, build_edge_topology
from .skeleton import SkeletonResidual
from .decoders import VQDecoderV3
class LocalEncoder(nn.Module):
def __init__(self, args, topology):
super(LocalEncoder, self).__init__()
args.channel_base = 6
args.activation = "tanh"
args.use_residual_blocks = True
args.z_dim = 1024
args.temporal_scale = 8
args.kernel_size = 4
args.num_layers = args.vae_layer
args.skeleton_dist = 2
args.extra_conv = 0
# check how to reflect in 1d
args.padding_mode = "constant"
args.skeleton_pool = "mean"
args.upsampling = "linear"
self.topologies = [topology]
self.channel_base = [args.channel_base]
self.channel_list = []
self.edge_num = [len(topology)]
self.pooling_list = []
self.layers = nn.ModuleList()
self.args = args
# self.convs = []
kernel_size = args.kernel_size
kernel_even = False if kernel_size % 2 else True
padding = (kernel_size - 1) // 2
bias = True
self.grow = args.vae_grow
for i in range(args.num_layers):
self.channel_base.append(self.channel_base[-1] * self.grow[i])
for i in range(args.num_layers):
seq = []
neighbour_list = find_neighbor(self.topologies[i], args.skeleton_dist)
in_channels = self.channel_base[i] * self.edge_num[i]
out_channels = self.channel_base[i + 1] * self.edge_num[i]
if i == 0:
self.channel_list.append(in_channels)
self.channel_list.append(out_channels)
last_pool = True if i == args.num_layers - 1 else False
# (T, J, D) => (T, J', D)
pool = SkeletonPool(
edges=self.topologies[i],
pooling_mode=args.skeleton_pool,
channels_per_edge=out_channels // len(neighbour_list),
last_pool=last_pool,
)
if args.use_residual_blocks:
# (T, J, D) => (T/2, J', 2D)
seq.append(
SkeletonResidual(
self.topologies[i],
neighbour_list,
joint_num=self.edge_num[i],
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=2,
padding=padding,
padding_mode=args.padding_mode,
bias=bias,
extra_conv=args.extra_conv,
pooling_mode=args.skeleton_pool,
activation=args.activation,
last_pool=last_pool,
)
)
else:
for _ in range(args.extra_conv):
# (T, J, D) => (T, J, D)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=in_channels,
joint_num=self.edge_num[i],
kernel_size=kernel_size - 1 if kernel_even else kernel_size,
stride=1,
padding=padding,
padding_mode=args.padding_mode,
bias=bias,
)
)
seq.append(nn.PReLU() if args.activation == "relu" else nn.Tanh())
# (T, J, D) => (T/2, J, 2D)
seq.append(
SkeletonConv(
neighbour_list,
in_channels=in_channels,
out_channels=out_channels,
joint_num=self.edge_num[i],
kernel_size=kernel_size,
stride=2,
padding=padding,
padding_mode=args.padding_mode,
bias=bias,
add_offset=False,
in_offset_channel=3 * self.channel_base[i] // self.channel_base[0],
)
)
# self.convs.append(seq[-1])
seq.append(pool)
seq.append(nn.PReLU() if args.activation == "relu" else nn.Tanh())
self.layers.append(nn.Sequential(*seq))
self.topologies.append(pool.new_edges)
self.pooling_list.append(pool.pooling_list)
self.edge_num.append(len(self.topologies[-1]))
# in_features = self.channel_base[-1] * len(self.pooling_list[-1])
# in_features *= int(args.temporal_scale / 2)
# self.reduce = nn.Linear(in_features, args.z_dim)
# self.mu = nn.Linear(in_features, args.z_dim)
# self.logvar = nn.Linear(in_features, args.z_dim)
def forward(self, input):
# bs, n, c = input.shape[0], input.shape[1], input.shape[2]
output = input.permute(0, 2, 1) # input.reshape(bs, n, -1, 6)
for layer in self.layers:
output = layer(output)
# output = output.view(output.shape[0], -1)
output = output.permute(0, 2, 1)
return output
def reparameterize(mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
class VAEConv(nn.Module):
def __init__(self, args):
super(VAEConv, self).__init__()
# self.encoder = VQEncoderV3(args)
# self.decoder = VQDecoderV3(args)
self.fc_mu = nn.Linear(args.vae_length, args.vae_length)
self.fc_logvar = nn.Linear(args.vae_length, args.vae_length)
self.variational = args.variational
def forward(self, inputs):
pre_latent = self.encoder(inputs)
mu, logvar = None, None
if self.variational:
mu = self.fc_mu(pre_latent)
logvar = self.fc_logvar(pre_latent)
pre_latent = reparameterize(mu, logvar)
rec_pose = self.decoder(pre_latent)
return {
"poses_feat": pre_latent,
"rec_pose": rec_pose,
"pose_mu": mu,
"pose_logvar": logvar,
}
def map2latent(self, inputs):
pre_latent = self.encoder(inputs)
if self.variational:
mu = self.fc_mu(pre_latent)
logvar = self.fc_logvar(pre_latent)
pre_latent = reparameterize(mu, logvar)
return pre_latent
def decode(self, pre_latent):
rec_pose = self.decoder(pre_latent)
return rec_pose
class VAESKConv(VAEConv):
def __init__(self, args, model_save_path="./emage/"):
# args = args()
super(VAESKConv, self).__init__(args)
smpl_fname = model_save_path + "smplx_models/smplx/SMPLX_NEUTRAL_2020.npz"
smpl_data = np.load(smpl_fname, encoding="latin1")
parents = smpl_data["kintree_table"][0].astype(np.int32)
edges = build_edge_topology(parents)
self.encoder = LocalEncoder(args, edges)
self.decoder = VQDecoderV3(args)
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