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import torch
import torch.nn.functional as F
import pytorch_lightning as pl
from main import instantiate_from_config
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
class VQModelDual(pl.LightningModule):
def __init__(self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image1_key="image1",
image2_key="image2",
colorize_nlabels=None,
monitor=None,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
):
super().__init__()
self.image1_key = image1_key
self.image2_key = image2_key
self.encoder = {}
self.decoder = {}
self.quantize = {}
self.quant_conv = {}
self.post_quant_conv = {}
self.loss = {}
for i in range(2):
self.encoder[i+1] = Encoder(**ddconfig)
self.decoder[i+1] = Decoder(**ddconfig)
self.quantize[i+1] = VectorQuantizer(n_embed, embed_dim, beta=0.25,
remap=remap, sane_index_shape=sane_index_shape)
self.quant_conv[i+1] = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv[i+1] = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
self.loss[i+1] = instantiate_from_config(lossconfig)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
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, model_key):
h = self.encoder[model_key](x)
h = self.quant_conv[model_key](h)
quant, emb_loss, info = self.quantize[model_key](h)
return quant, emb_loss, info
def decode(self, quant, model_key):
quant = self.post_quant_conv[model_key](quant)
dec = self.decoder[model_key](quant)
return dec
def decode_code(self, code_b, model_key):
quant_b = self.quantize[model_key].embed_code(code_b)
dec = self.decode(quant_b,model_key)
return dec
def forward(self, input, model_key):
quant, diff, _ = self.encode(input, model_key)
dec = self.decode(quant, model_key)
return dec, diff
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)
return x.float()
def training_step(self, batch, batch_idx, optimizer_idx):
breakpoint()
x1 = self.get_input(batch, self.image_key1)
x2 = self.get_input(batch, self.image_key2)
xrec1, qloss1 = self.forward(x1, model_key=1)
xrec2, qloss2 = self.forward(x2, model_key=2)
if optimizer_idx == 0:
# autoencoder 1
aeloss1, log_dict_ae1 = self.loss[1](qloss1, x1, xrec1, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/aeloss1", aeloss1, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae1, prog_bar=False, logger=True, on_step=True, on_epoch=True)
# autoencoder 2
aeloss2, log_dict_ae2 = self.loss[2](qloss2, x2, xrec2, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/aeloss2", aeloss2, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae2, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return aeloss1 + aeloss2
if optimizer_idx == 1:
# discriminator 1
discloss1, log_dict_disc1 = self.loss[1](qloss1, x1, xrec1, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/discloss1", discloss1, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc1, prog_bar=False, logger=True, on_step=True, on_epoch=True)
# discriminator 2
discloss2, log_dict_disc2 = self.loss[2](qloss2, x2, xrec2, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("train/discloss", discloss2, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc2, prog_bar=False, logger=True, on_step=True, on_epoch=True)
return discloss1 + discloss2
def validation_step(self, batch, batch_idx):
breakpoint()
x1 = self.get_input(batch, self.image_key1)
x2 = self.get_input(batch, self.image_key2)
xrec1, qloss1 = self.forward(x1, model_key=1)
xrec2, qloss2 = self.forward(x2, model_key=2)
aeloss1, log_dict_ae1 = self.loss[1](qloss1, x1, xrec1, 0, self.global_step,
last_layer=self.get_last_layer(model_key=1), split="val")
aeloss2, log_dict_ae2 = self.loss[2](qloss2, x2, xrec2, 0, self.global_step,
last_layer=self.get_last_layer(model_key=2), split="val")
discloss1, log_dict_disc1 = self.loss[1](qloss1, x1, xrec1, 1, self.global_step,
last_layer=self.get_last_layer(model_key=1), split="val")
discloss2, log_dict_disc2 = self.loss[2](qloss2, x2, xrec2, 1, self.global_step,
last_layer=self.get_last_layer(model_key=2), split="val")
rec_loss1 = log_dict_ae1["val/rec_loss"]
rec_loss2 = log_dict_ae2["val/rec_loss"]
self.log("val/rec_loss1", rec_loss1,
prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
self.log("val/rec_loss2", rec_loss2,
prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
self.log("val/aeloss1", aeloss1,
prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
self.log("val/aeloss2", aeloss2,
prog_bar=True, logger=True, on_step=True, on_epoch=True, sync_dist=True)
self.log_dict(log_dict_ae1)
self.log_dict(log_dict_disc1)
self.log_dict(log_dict_ae2)
self.log_dict(log_dict_disc2)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder[1].parameters())+
list(self.decoder[1].parameters())+
list(self.quantize[1].parameters())+
list(self.quant_conv[1].parameters())+
list(self.post_quant_conv[1].parameters())+
list(self.encoder[2].parameters())+
list(self.decoder[2].parameters())+
list(self.quantize[2].parameters())+
list(self.quant_conv[2].parameters())+
list(self.post_quant_conv[2].parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(list(self.loss[1].discriminator.parameters())+
list(self.loss[2].discriminator.parameters()),
lr=lr, betas=(0.5, 0.9))
return [opt_ae, opt_disc], []
def get_last_layer(self, model_key):
return self.decoder[model_key].conv_out.weight
def log_images(self, batch, **kwargs):
log = dict()
## log 1
x = self.get_input(batch, self.image_key1)
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["inputs1"] = x
log["reconstructions1"] = xrec
## log 2
x = self.get_input(batch, self.image_key2)
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["inputs2"] = x
log["reconstructions2"] = 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.*(x-x.min())/(x.max()-x.min()) - 1.
return x |