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# Partially from https://github.com/Mael-zys/T2M-GPT
from typing import List, Optional, Union
import torch
import torch.nn as nn
from torch import Tensor, nn
from torch.distributions.distribution import Distribution
from .tools.resnet import Resnet1D
from .tools.quantize_cnn import QuantizeEMAReset, Quantizer, QuantizeEMA, QuantizeReset
from collections import OrderedDict
class VQVae(nn.Module):
def __init__(self,
nfeats: int,
quantizer: str = "ema_reset",
code_num=512,
code_dim=512,
output_emb_width=512,
down_t=3,
stride_t=2,
width=512,
depth=3,
dilation_growth_rate=3,
norm=None,
activation: str = "relu",
**kwargs) -> None:
super().__init__()
self.code_dim = code_dim
self.encoder = Encoder(nfeats,
output_emb_width,
down_t,
stride_t,
width,
depth,
dilation_growth_rate,
activation=activation,
norm=norm)
self.decoder = Decoder(nfeats,
output_emb_width,
down_t,
stride_t,
width,
depth,
dilation_growth_rate,
activation=activation,
norm=norm)
if quantizer == "ema_reset":
self.quantizer = QuantizeEMAReset(code_num, code_dim, mu=0.99)
elif quantizer == "orig":
self.quantizer = Quantizer(code_num, code_dim, beta=1.0)
elif quantizer == "ema":
self.quantizer = QuantizeEMA(code_num, code_dim, mu=0.99)
elif quantizer == "reset":
self.quantizer = QuantizeReset(code_num, code_dim)
def preprocess(self, x):
# (bs, T, Jx3) -> (bs, Jx3, T)
x = x.permute(0, 2, 1)
return x
def postprocess(self, x):
# (bs, Jx3, T) -> (bs, T, Jx3)
x = x.permute(0, 2, 1)
return x
def forward(self, features: Tensor):
# Preprocess
x_in = self.preprocess(features)
# 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 encode(
self,
features: Tensor,
) -> Union[Tensor, Distribution]:
N, T, _ = features.shape
x_in = self.preprocess(features)
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)
# latent, dist
return code_idx, None
def decode(self, z: Tensor):
x_d = self.quantizer.dequantize(z)
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 Encoder(nn.Module):
def __init__(self,
input_emb_width=3,
output_emb_width=512,
down_t=3,
stride_t=2,
width=512,
depth=3,
dilation_growth_rate=3,
activation='relu',
norm=None):
super().__init__()
blocks = []
filter_t, pad_t = stride_t * 2, stride_t // 2
blocks.append(nn.Conv1d(input_emb_width, width, 3, 1, 1))
blocks.append(nn.ReLU())
for i in range(down_t):
input_dim = width
block = nn.Sequential(
nn.Conv1d(input_dim, width, filter_t, stride_t, pad_t),
Resnet1D(width,
depth,
dilation_growth_rate,
activation=activation,
norm=norm),
)
blocks.append(block)
blocks.append(nn.Conv1d(width, output_emb_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x)
class Decoder(nn.Module):
def __init__(self,
input_emb_width=3,
output_emb_width=512,
down_t=3,
stride_t=2,
width=512,
depth=3,
dilation_growth_rate=3,
activation='relu',
norm=None):
super().__init__()
blocks = []
filter_t, pad_t = stride_t * 2, stride_t // 2
blocks.append(nn.Conv1d(output_emb_width, width, 3, 1, 1))
blocks.append(nn.ReLU())
for i in range(down_t):
out_dim = width
block = nn.Sequential(
Resnet1D(width,
depth,
dilation_growth_rate,
reverse_dilation=True,
activation=activation,
norm=norm), nn.Upsample(scale_factor=2,
mode='nearest'),
nn.Conv1d(width, out_dim, 3, 1, 1))
blocks.append(block)
blocks.append(nn.Conv1d(width, width, 3, 1, 1))
blocks.append(nn.ReLU())
blocks.append(nn.Conv1d(width, input_emb_width, 3, 1, 1))
self.model = nn.Sequential(*blocks)
def forward(self, x):
return self.model(x)
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