DL-Art-School/codes/models/vqvae/vqvae_3.py

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import torch
from torch import nn
from torch.nn import functional as F
import torch.distributed as distributed
from models.vqvae.vqvae import ResBlock, Quantize
from trainer.networks import register_model
from utils.util import checkpoint, opt_get
# Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper.
class UpsampleConv(nn.Module):
def __init__(self, in_filters, out_filters, kernel_size, padding):
super().__init__()
self.conv = nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding)
def forward(self, x):
up = torch.nn.functional.interpolate(x, scale_factor=2)
return self.conv(up)
class Encoder(nn.Module):
def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride):
super().__init__()
if stride == 4:
blocks = [
nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
nn.LeakyReLU(inplace=True),
nn.Conv2d(channel // 2, channel, 5, stride=2, padding=2),
nn.LeakyReLU(inplace=True),
nn.Conv2d(channel, channel, 3, padding=1),
]
elif stride == 2:
blocks = [
nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2),
nn.LeakyReLU(inplace=True),
nn.Conv2d(channel // 2, channel, 3, padding=1),
]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel))
blocks.append(nn.LeakyReLU(inplace=True))
self.blocks = nn.Sequential(*blocks)
def forward(self, input):
return self.blocks(input)
class Decoder(nn.Module):
def __init__(
self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride
):
super().__init__()
blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)]
for i in range(n_res_block):
blocks.append(ResBlock(channel, n_res_channel))
blocks.append(nn.LeakyReLU(inplace=True))
if stride == 4:
blocks.extend(
[
UpsampleConv(channel, channel // 2, 5, padding=2),
nn.LeakyReLU(inplace=True),
UpsampleConv(
channel // 2, out_channel, 5, padding=2
),
]
)
elif stride == 2:
blocks.append(
UpsampleConv(channel, out_channel, 5, padding=2)
)
self.blocks = nn.Sequential(*blocks)
def forward(self, input):
return self.blocks(input)
class VQVAE3(nn.Module):
def __init__(
self,
in_channel=3,
channel=128,
n_res_block=2,
n_res_channel=32,
codebook_dim=64,
codebook_size=512,
decay=0.99,
):
super().__init__()
self.initial_conv = nn.Sequential(*[nn.Conv2d(in_channel, 32, 3, padding=1),
nn.LeakyReLU(inplace=True)])
self.enc_b = Encoder(32, channel, n_res_block, n_res_channel, stride=4)
self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2)
self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
self.quantize_t = Quantize(codebook_dim, codebook_size)
self.dec_t = Decoder(
codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2
)
self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
self.quantize_b = Quantize(codebook_dim, codebook_size)
self.upsample_t = UpsampleConv(
codebook_dim, codebook_dim, 5, padding=2
)
self.dec = Decoder(
codebook_dim + codebook_dim,
32,
channel,
n_res_block,
n_res_channel,
stride=4,
)
self.final_conv = nn.Conv2d(32, in_channel, 3, padding=1)
def forward(self, input):
quant_t, quant_b, diff, _, _ = self.encode(input)
dec = self.decode(quant_t, quant_b)
return dec, diff
def encode(self, input):
fea = self.initial_conv(input)
enc_b = checkpoint(self.enc_b, fea)
enc_t = checkpoint(self.enc_t, enc_b)
quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
quant_t, diff_t, id_t = self.quantize_t(quant_t)
quant_t = quant_t.permute(0, 3, 1, 2)
diff_t = diff_t.unsqueeze(0)
dec_t = checkpoint(self.dec_t, quant_t)
enc_b = torch.cat([dec_t, enc_b], 1)
quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
quant_b, diff_b, id_b = self.quantize_b(quant_b)
quant_b = quant_b.permute(0, 3, 1, 2)
diff_b = diff_b.unsqueeze(0)
return quant_t, quant_b, diff_t + diff_b, id_t, id_b
def decode(self, quant_t, quant_b):
upsample_t = self.upsample_t(quant_t)
quant = torch.cat([upsample_t, quant_b], 1)
dec = checkpoint(self.dec, quant)
dec = checkpoint(self.final_conv, dec)
return dec
def decode_code(self, code_t, code_b):
quant_t = self.quantize_t.embed_code(code_t)
quant_t = quant_t.permute(0, 3, 1, 2)
quant_b = self.quantize_b.embed_code(code_b)
quant_b = quant_b.permute(0, 3, 1, 2)
dec = self.decode(quant_t, quant_b)
return dec
@register_model
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def register_vqvae3(opt_net, opt):
kw = opt_get(opt_net, ['kwargs'], {})
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return VQVAE3(**kw)
if __name__ == '__main__':
v = VQVAE3()
print(v(torch.randn(1,3,128,128))[0].shape)