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)
# self.load_state_dict(sd, strict=True)
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:
# train encoder+decoder+logvar
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:
# train the discriminator
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:
# 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 = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x
class IdentityFirstStage(torch.nn.Module):
def __init__(self, *args, vq_interface=False, **kwargs):
self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
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