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import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from celle_taming_main import instantiate_from_config
from taming.modules.diffusionmodules.model import Encoder, Decoder
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from taming.modules.vqvae.quantize import GumbelQuantize
from taming.modules.vqvae.quantize import EMAVectorQuantizer
class VQModel(pl.LightningModule):
def __init__(
self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
):
super().__init__()
self.image_key = image_key
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
self.quantize = VectorQuantizer(
n_embed,
embed_dim,
beta=0.25,
remap=remap,
sane_index_shape=sane_index_shape,
)
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.image_key = image_key
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
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
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):
h = self.encoder(x)
h = self.quant_conv(h)
quant, emb_loss, info = self.quantize(h)
return quant, emb_loss, info
def decode(self, quant):
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
def decode_code(self, code_b):
quant_b = self.quantize.embed_code(code_b)
dec = self.decode(quant_b)
return dec
def forward(self, input):
quant, diff, _ = self.encode(input)
dec = self.decode(quant)
return dec, diff
def get_input(self, batch, k):
if k == "mixed":
keys = ["nucleus", "target"]
index = torch.randint(low=0, high=2, size=(1,), dtype=int).item()
k = keys[index]
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
# x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
return x
def training_step(self, batch, batch_idx=None, optimizer_idx=0):
if type(batch) == dict:
x = self.get_input(batch, self.image_key)
else:
x = batch
xrec, qloss = self(
x,
)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
self.log(
"train/aeloss",
aeloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
self.log(
"train/discloss",
discloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
self.log_dict(
log_dict_disc,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return discloss
def validation_step(self, batch, batch_idx):
if type(batch) == dict:
x = self.get_input(batch, self.image_key)
else:
x = batch
xrec, qloss = self(x)
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
0,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
1,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
# rec_loss = log_dict_ae["val/rec_loss"]
# self.log(
# "val/rec_loss",
# rec_loss,
# prog_bar=True,
# logger=True,
# on_step=True,
# on_epoch=True,
# sync_dist=True,
# )
# self.log(
# "val/aeloss",
# aeloss,
# prog_bar=True,
# logger=True,
# on_step=True,
# on_epoch=True,
# sync_dist=True,
# )
for key, value in log_dict_disc.items():
if key in log_dict_ae:
log_dict_ae[key].extend(value)
else:
log_dict_ae[key] = value
self.log_dict(log_dict_ae, sync_dist=True)
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.quantize.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
def log_images(self, batch, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
xrec, _ = 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["inputs"] = x
log["reconstructions"] = xrec
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 VQSegmentationModel(VQModel):
def __init__(self, n_labels, *args, **kwargs):
super().__init__(*args, **kwargs)
self.register_buffer("colorize", torch.randn(3, n_labels, 1, 1))
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(
list(self.encoder.parameters())
+ list(self.decoder.parameters())
+ list(self.quantize.parameters())
+ list(self.quant_conv.parameters())
+ list(self.post_quant_conv.parameters()),
lr=lr,
betas=(0.5, 0.9),
)
return opt_ae
def training_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="train")
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return aeloss
def validation_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, split="val")
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
total_loss = log_dict_ae["val/total_loss"]
self.log(
"val/total_loss",
total_loss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return aeloss
@torch.no_grad()
def log_images(self, batch, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
xrec, _ = self(x)
if x.shape[1] > 3:
# colorize with random projection
assert xrec.shape[1] > 3
# convert logits to indices
xrec = torch.argmax(xrec, dim=1, keepdim=True)
xrec = F.one_hot(xrec, num_classes=x.shape[1])
xrec = xrec.squeeze(1).permute(0, 3, 1, 2).float()
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["inputs"] = x
log["reconstructions"] = xrec
return log
class VQNoDiscModel(VQModel):
def __init__(
self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
):
super().__init__(
ddconfig=ddconfig,
lossconfig=lossconfig,
n_embed=n_embed,
embed_dim=embed_dim,
ckpt_path=ckpt_path,
ignore_keys=ignore_keys,
image_key=image_key,
colorize_nlabels=colorize_nlabels,
)
def training_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="train")
output = pl.TrainResult(minimize=aeloss)
output.log(
"train/aeloss",
aeloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
output.log_dict(
log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True
)
return output
def validation_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, self.global_step, split="val")
rec_loss = log_dict_ae["val/rec_loss"]
output = pl.EvalResult(checkpoint_on=rec_loss)
output.log(
"val/rec_loss",
rec_loss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
output.log(
"val/aeloss",
aeloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
output.log_dict(log_dict_ae)
return output
def configure_optimizers(self):
optimizer = torch.optim.Adam(
list(self.encoder.parameters())
+ list(self.decoder.parameters())
+ list(self.quantize.parameters())
+ list(self.quant_conv.parameters())
+ list(self.post_quant_conv.parameters()),
lr=self.learning_rate,
betas=(0.5, 0.9),
)
return optimizer
class GumbelVQ(VQModel):
def __init__(
self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
temperature_scheduler_config,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
kl_weight=1e-8,
remap=None,
):
z_channels = ddconfig["z_channels"]
super().__init__(
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=ignore_keys,
image_key=image_key,
colorize_nlabels=colorize_nlabels,
monitor=monitor,
)
self.loss.n_classes = n_embed
self.vocab_size = n_embed
self.quantize = GumbelQuantize(
z_channels,
embed_dim,
n_embed=n_embed,
kl_weight=kl_weight,
temp_init=1.0,
remap=remap,
)
self.temperature_scheduler = instantiate_from_config(
temperature_scheduler_config
) # annealing of temp
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def temperature_scheduling(self):
self.quantize.temperature = self.temperature_scheduler(self.global_step)
def encode_to_prequant(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode_code(self, code_b):
raise NotImplementedError
def training_step(self, batch, batch_idx=None, optimizer_idx=0):
self.temperature_scheduling()
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
self.log(
"temperature",
self.quantize.temperature,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
self.log_dict(
log_dict_disc,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
sync_dist=True,
)
return discloss
def validation_step(self, batch, batch_idx):
x = self.get_input(batch, self.image_key)
xrec, qloss = self(x)
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
0,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
1,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
rec_loss = log_dict_ae["val/rec_loss"]
self.log(
"val/rec_loss",
rec_loss,
prog_bar=True,
logger=True,
on_step=False,
on_epoch=True,
sync_dist=True,
)
self.log(
"val/aeloss",
aeloss,
prog_bar=True,
logger=True,
on_step=False,
on_epoch=True,
sync_dist=True,
)
self.log_dict(log_dict_ae, sync_dist=True)
self.log_dict(log_dict_disc, sync_dist=True)
return self.log_dict
def log_images(self, batch, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
# encode
h = self.encoder(x)
h = self.quant_conv(h)
quant, _, _ = self.quantize(h)
# decode
x_rec = self.decode(quant)
log["inputs"] = x
log["reconstructions"] = x_rec
return log
class EMAVQ(VQModel):
def __init__(
self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
colorize_nlabels=None,
monitor=None,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
):
super().__init__(
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=ignore_keys,
image_key=image_key,
colorize_nlabels=colorize_nlabels,
monitor=monitor,
)
self.quantize = EMAVectorQuantizer(
n_embed=n_embed, embedding_dim=embed_dim, beta=0.25, remap=remap
)
def configure_optimizers(self):
lr = self.learning_rate
# Remove self.quantize from parameter list since it is updated via EMA
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], []
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