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import torch.nn as nn
import torch
import numpy as np
from .skeleton import ResidualBlock, SkeletonResidual, residual_ratio, SkeletonConv, SkeletonPool, find_neighbor, build_edge_topology
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
class ResBlock(nn.Module):
def __init__(self, channel):
super(ResBlock, self).__init__()
self.model = nn.Sequential(
nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(channel, channel, kernel_size=3, stride=1, padding=1),
)
def forward(self, x):
residual = x
out = self.model(x)
out += residual
return out
class VQDecoderV3(nn.Module):
def __init__(self, args):
super(VQDecoderV3, self).__init__()
n_up = args.vae_layer
channels = []
for i in range(n_up-1):
channels.append(args.vae_length)
channels.append(args.vae_length)
channels.append(args.vae_test_dim)
input_size = args.vae_length
n_resblk = 2
assert len(channels) == n_up + 1
if input_size == channels[0]:
layers = []
else:
layers = [nn.Conv1d(input_size, channels[0], kernel_size=3, stride=1, padding=1)]
for i in range(n_resblk):
layers += [ResBlock(channels[0])]
# channels = channels
for i in range(n_up):
layers += [
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv1d(channels[i], channels[i+1], kernel_size=3, stride=1, padding=1),
nn.LeakyReLU(0.2, inplace=True)
]
layers += [nn.Conv1d(channels[-1], channels[-1], kernel_size=3, stride=1, padding=1)]
self.main = nn.Sequential(*layers)
# self.main.apply(init_weight)
def forward(self, inputs):
inputs = inputs.permute(0, 2, 1)
outputs = self.main(inputs).permute(0, 2, 1)
return outputs
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) |