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import torch.nn as nn
from models.encdec import Encoder, Decoder
from models.quantize_cnn import QuantizeEMAReset, Quantizer, QuantizeEMA, QuantizeReset
class VQVAE_251(nn.Module):
def __init__(self,
args,
nb_code=1024,
code_dim=512,
output_emb_width=512,
down_t=3,
stride_t=2,
width=512,
depth=3,
dilation_growth_rate=3,
activation='relu',
norm=None):
super().__init__()
self.code_dim = code_dim
self.num_code = nb_code
self.quant = args.quantizer
self.encoder = Encoder(251 if args.dataname == 'kit' else 263, output_emb_width, down_t, stride_t, width, depth, dilation_growth_rate, activation=activation, norm=norm)
self.decoder = Decoder(251 if args.dataname == 'kit' else 263, output_emb_width, down_t, stride_t, width, depth, dilation_growth_rate, activation=activation, norm=norm)
if args.quantizer == "ema_reset":
self.quantizer = QuantizeEMAReset(nb_code, code_dim, args)
elif args.quantizer == "orig":
self.quantizer = Quantizer(nb_code, code_dim, 1.0)
elif args.quantizer == "ema":
self.quantizer = QuantizeEMA(nb_code, code_dim, args)
elif args.quantizer == "reset":
self.quantizer = QuantizeReset(nb_code, code_dim, args)
def preprocess(self, x):
# (bs, T, Jx3) -> (bs, Jx3, T)
x = x.permute(0,2,1).float()
return x
def postprocess(self, x):
# (bs, Jx3, T) -> (bs, T, Jx3)
x = x.permute(0,2,1)
return x
def encode(self, x):
N, T, _ = x.shape
x_in = self.preprocess(x)
x_encoder = self.encoder(x_in)
x_encoder = self.postprocess(x_encoder)
x_encoder = x_encoder.contiguous().view(-1, x_encoder.shape[-1]) # (NT, C)
code_idx = self.quantizer.quantize(x_encoder)
code_idx = code_idx.view(N, -1)
return code_idx
def forward(self, x):
x_in = self.preprocess(x)
# Encode
x_encoder = self.encoder(x_in)
## quantization
x_quantized, loss, perplexity = self.quantizer(x_encoder)
## decoder
x_decoder = self.decoder(x_quantized)
x_out = self.postprocess(x_decoder)
return x_out, loss, perplexity
def forward_decoder(self, x):
x_d = self.quantizer.dequantize(x)
x_d = x_d.view(1, -1, self.code_dim).permute(0, 2, 1).contiguous()
# decoder
x_decoder = self.decoder(x_d)
x_out = self.postprocess(x_decoder)
return x_out
class HumanVQVAE(nn.Module):
def __init__(self,
args,
nb_code=512,
code_dim=512,
output_emb_width=512,
down_t=3,
stride_t=2,
width=512,
depth=3,
dilation_growth_rate=3,
activation='relu',
norm=None):
super().__init__()
self.nb_joints = 21 if args.dataname == 'kit' else 22
self.vqvae = VQVAE_251(args, nb_code, code_dim, output_emb_width, down_t, stride_t, width, depth, dilation_growth_rate, activation=activation, norm=norm)
def encode(self, x):
b, t, c = x.size()
quants = self.vqvae.encode(x) # (N, T)
return quants
def forward(self, x):
x_out, loss, perplexity = self.vqvae(x)
return x_out, loss, perplexity
def forward_decoder(self, x):
x_out = self.vqvae.forward_decoder(x)
return x_out
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