File size: 6,019 Bytes
4409449 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
# 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)
|