| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from contextlib import contextmanager |
| from .common.get_model import get_model, register |
|
|
| |
|
|
| 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: |
| |
| 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: |
| |
| 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: |
| |
| 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 |
|
|
