Spaces:
Running
on
T4
Running
on
T4
File size: 1,471 Bytes
9e275b8 |
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 |
import json
import torch
import torch.nn as nn
from Preprocessing.Codec.env import AttrDict
from Preprocessing.Codec.models import Encoder
from Preprocessing.Codec.models import Generator
from Preprocessing.Codec.models import Quantizer
class VQVAE(nn.Module):
def __init__(self,
config_path,
ckpt_path,
with_encoder=False):
super(VQVAE, self).__init__()
ckpt = torch.load(ckpt_path, map_location=torch.device('cpu'))
with open(config_path) as f:
data = f.read()
json_config = json.loads(data)
self.h = AttrDict(json_config)
self.quantizer = Quantizer(self.h)
self.generator = Generator(self.h)
self.generator.load_state_dict(ckpt['generator'])
self.quantizer.load_state_dict(ckpt['quantizer'])
if with_encoder:
self.encoder = Encoder(self.h)
self.encoder.load_state_dict(ckpt['encoder'])
def forward(self, x):
# x is the codebook
# x.shape (B, T, Nq)
quant_emb = self.quantizer.embed(x)
return self.generator(quant_emb)
def encode(self, x):
batch_size = x.size(0)
if len(x.shape) == 3 and x.shape[-1] == 1:
x = x.squeeze(-1)
c = self.encoder(x.unsqueeze(1))
q, loss_q, c = self.quantizer(c)
c = [code.reshape(batch_size, -1) for code in c]
# shape: [N, T, 4]
return torch.stack(c, -1)
|