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import torch
import torch.nn as nn
import torch.nn.functional as F
from contextlib import contextmanager
from lib.model_zoo.common.get_model import get_model, register
# from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from .autokl_modules import Encoder, Decoder
from .distributions import DiagonalGaussianDistribution
from .autokl_utils import LPIPSWithDiscriminator
@register('autoencoderkl')
class AutoencoderKL(nn.Module):
def __init__(self,
ddconfig,
lossconfig,
embed_dim,):
super().__init__()
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
if lossconfig is not None:
self.loss = LPIPSWithDiscriminator(**lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
@torch.no_grad()
def encode(self, x, out_posterior=False):
return self.encode_trainable(x, out_posterior)
def encode_trainable(self, x, out_posterior=False):
x = x*2-1
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if out_posterior:
return posterior
else:
return posterior.sample()
@torch.no_grad()
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
dec = torch.clamp((dec+1)/2, 0, 1)
return dec
def decode_trainable(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
dec = (dec+1)/2
return dec
def apply_model(self, input, sample_posterior=True):
posterior = self.encode_trainable(input, out_posterior=True)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode_trainable(z)
return dec, posterior
def get_input(self, batch, k):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
return x
def forward(self, x, optimizer_idx, global_step):
reconstructions, posterior = self.apply_model(x)
if optimizer_idx == 0:
# train encoder+decoder+logvar
aeloss, log_dict_ae = self.loss(x, reconstructions, posterior, optimizer_idx, global_step=global_step,
last_layer=self.get_last_layer(), split="train")
return aeloss, log_dict_ae
if optimizer_idx == 1:
# train the discriminator
discloss, log_dict_disc = self.loss(x, reconstructions, posterior, optimizer_idx, global_step=global_step,
last_layer=self.get_last_layer(), split="train")
return discloss, log_dict_disc
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
last_layer=self.get_last_layer(), split="val")
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
last_layer=self.get_last_layer(), split="val")
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr, betas=(0.5, 0.9))
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
@torch.no_grad()
def log_images(self, batch, only_inputs=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if not only_inputs:
xrec, posterior = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
log["reconstructions"] = xrec
log["inputs"] = x
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
return x
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