| import os |
| from contextlib import contextmanager |
| import torch |
| import numpy as np |
| from einops import rearrange |
| import torch.nn.functional as F |
| import pytorch_lightning as pl |
| from lvdm.modules.networks.ae_modules import Encoder, Decoder |
| from lvdm.distributions import DiagonalGaussianDistribution |
| from utils.utils import instantiate_from_config |
|
|
|
|
| class AutoencoderKL(pl.LightningModule): |
| def __init__(self, |
| ddconfig, |
| lossconfig, |
| embed_dim, |
| ckpt_path=None, |
| ignore_keys=[], |
| image_key="image", |
| colorize_nlabels=None, |
| monitor=None, |
| test=False, |
| logdir=None, |
| input_dim=4, |
| test_args=None, |
| ): |
| super().__init__() |
| self.image_key = image_key |
| self.encoder = Encoder(**ddconfig) |
| self.decoder = Decoder(**ddconfig) |
| self.loss = instantiate_from_config(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 |
| self.input_dim = input_dim |
| self.test = test |
| self.test_args = test_args |
| self.logdir = logdir |
| if colorize_nlabels is not None: |
| assert type(colorize_nlabels)==int |
| self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) |
| if monitor is not None: |
| self.monitor = monitor |
| if ckpt_path is not None: |
| self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) |
| if self.test: |
| self.init_test() |
| |
| def init_test(self,): |
| self.test = True |
| save_dir = os.path.join(self.logdir, "test") |
| if 'ckpt' in self.test_args: |
| ckpt_name = os.path.basename(self.test_args.ckpt).split('.ckpt')[0] + f'_epoch{self._cur_epoch}' |
| self.root = os.path.join(save_dir, ckpt_name) |
| else: |
| self.root = save_dir |
| if 'test_subdir' in self.test_args: |
| self.root = os.path.join(save_dir, self.test_args.test_subdir) |
|
|
| self.root_zs = os.path.join(self.root, "zs") |
| self.root_dec = os.path.join(self.root, "reconstructions") |
| self.root_inputs = os.path.join(self.root, "inputs") |
| os.makedirs(self.root, exist_ok=True) |
|
|
| if self.test_args.save_z: |
| os.makedirs(self.root_zs, exist_ok=True) |
| if self.test_args.save_reconstruction: |
| os.makedirs(self.root_dec, exist_ok=True) |
| if self.test_args.save_input: |
| os.makedirs(self.root_inputs, exist_ok=True) |
| assert(self.test_args is not None) |
| self.test_maximum = getattr(self.test_args, 'test_maximum', None) |
| self.count = 0 |
| self.eval_metrics = {} |
| self.decodes = [] |
| self.save_decode_samples = 2048 |
|
|
| def init_from_ckpt(self, path, ignore_keys=list()): |
| sd = torch.load(path, map_location="cpu") |
| try: |
| self._cur_epoch = sd['epoch'] |
| sd = sd["state_dict"] |
| except: |
| self._cur_epoch = 'null' |
| keys = list(sd.keys()) |
| for k in keys: |
| for ik in ignore_keys: |
| if k.startswith(ik): |
| print("Deleting key {} from state_dict.".format(k)) |
| del sd[k] |
| self.load_state_dict(sd, strict=False) |
| |
| print(f"Restored from {path}") |
|
|
| def encode(self, x, **kwargs): |
| |
| h = self.encoder(x) |
| moments = self.quant_conv(h) |
| posterior = DiagonalGaussianDistribution(moments) |
| return posterior |
|
|
| def decode(self, z, **kwargs): |
| z = self.post_quant_conv(z) |
| dec = self.decoder(z) |
| return dec |
|
|
| def forward(self, input, sample_posterior=True): |
| posterior = self.encode(input) |
| if sample_posterior: |
| z = posterior.sample() |
| else: |
| z = posterior.mode() |
| dec = self.decode(z) |
| return dec, posterior |
|
|
| def get_input(self, batch, k): |
| x = batch[k] |
| if x.dim() == 5 and self.input_dim == 4: |
| b,c,t,h,w = x.shape |
| self.b = b |
| self.t = t |
| x = rearrange(x, 'b c t h w -> (b t) c h w') |
|
|
| return x |
|
|
| def training_step(self, batch, batch_idx, optimizer_idx): |
| inputs = self.get_input(batch, self.image_key) |
| reconstructions, posterior = self(inputs) |
|
|
| if optimizer_idx == 0: |
| |
| aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, |
| last_layer=self.get_last_layer(), split="train") |
| self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) |
| self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) |
| return aeloss |
|
|
| if optimizer_idx == 1: |
| |
| discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, |
| last_layer=self.get_last_layer(), split="train") |
|
|
| self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) |
| self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) |
| return discloss |
|
|
| 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 |
|
|
| class IdentityFirstStage(torch.nn.Module): |
| def __init__(self, *args, vq_interface=False, **kwargs): |
| self.vq_interface = vq_interface |
| super().__init__() |
|
|
| def encode(self, x, *args, **kwargs): |
| return x |
|
|
| def decode(self, x, *args, **kwargs): |
| return x |
|
|
| def quantize(self, x, *args, **kwargs): |
| if self.vq_interface: |
| return x, None, [None, None, None] |
| return x |
|
|
| def forward(self, x, *args, **kwargs): |
| return x |
