Spaces:
Running
on
L4
Running
on
L4
File size: 5,335 Bytes
2fbcf51 |
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 |
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
|