import os
import json
from contextlib import contextmanager
import torch
import numpy as np
from einops import rearrange
import torch.nn.functional as F
import torch.distributed as dist
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from taming.modules.vqvae.quantize import VectorQuantizer as VectorQuantizer
from core.modules.networks.ae_modules import Encoder, Decoder
from core.distributions import DiagonalGaussianDistribution
from utils.utils import instantiate_from_config
from utils.save_video import tensor2videogrids
from core.common import shape_to_str, gather_data
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
) # 1500 # 12000/8
self.count = 0
self.eval_metrics = {}
self.decodes = []
self.save_decode_samples = 2048
if getattr(self.test_args, "cal_metrics", False):
self.EvalLpips = EvalLpips()
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 len(x.shape) == 3:
# x = x[..., None]
# if x.dim() == 4:
# x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
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 test_step(self, batch, batch_idx):
# save z, dec
inputs = self.get_input(batch, self.image_key)
# forward
sample_posterior = True
posterior = self.encode(inputs)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
# logs
if self.test_args.save_z:
torch.save(
z,
os.path.join(
self.root_zs,
f"zs_batch{batch_idx}_rank{self.global_rank}_shape{shape_to_str(z)}.pt",
),
)
if self.test_args.save_reconstruction:
tensor2videogrids(
dec,
self.root_dec,
f"reconstructions_batch{batch_idx}_rank{self.global_rank}_shape{shape_to_str(z)}.mp4",
fps=10,
)
if self.test_args.save_input:
tensor2videogrids(
inputs,
self.root_inputs,
f"inputs_batch{batch_idx}_rank{self.global_rank}_shape{shape_to_str(z)}.mp4",
fps=10,
)
if "save_z" in self.test_args and self.test_args.save_z:
dec_np = (dec.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) + 1) / 2 * 255
dec_np = dec_np.astype(np.uint8)
self.root_dec_np = os.path.join(self.root, "reconstructions_np")
os.makedirs(self.root_dec_np, exist_ok=True)
np.savez(
os.path.join(
self.root_dec_np,
f"reconstructions_batch{batch_idx}_rank{self.global_rank}_shape{shape_to_str(dec_np)}.npz",
),
dec_np,
)
self.count += z.shape[0]
# misc
self.log("batch_idx", batch_idx, prog_bar=True)
self.log_dict(self.eval_metrics, prog_bar=True, logger=True)
torch.cuda.empty_cache()
if self.test_maximum is not None:
if self.count > self.test_maximum:
import sys
sys.exit()
else:
prog = self.count / self.test_maximum * 100
print(f"Test progress: {prog:.2f}% [{self.count}/{self.test_maximum}]")
@rank_zero_only
def on_test_end(self):
if self.test_args.cal_metrics:
psnrs, ssims, ms_ssims, lpipses = [], [], [], []
n_batches = 0
n_samples = 0
overall = {}
for k, v in self.eval_metrics.items():
psnrs.append(v["psnr"])
ssims.append(v["ssim"])
lpipses.append(v["lpips"])
n_batches += 1
n_samples += v["n_samples"]
mean_psnr = sum(psnrs) / len(psnrs)
mean_ssim = sum(ssims) / len(ssims)
# overall['ms_ssim'] = min(ms_ssims)
mean_lpips = sum(lpipses) / len(lpipses)
overall = {
"psnr": mean_psnr,
"ssim": mean_ssim,
"lpips": mean_lpips,
"n_batches": n_batches,
"n_samples": n_samples,
}
overall_t = torch.tensor([mean_psnr, mean_ssim, mean_lpips])
# dump
for k, v in overall.items():
if isinstance(v, torch.Tensor):
overall[k] = float(v)
with open(
os.path.join(self.root, f"reconstruction_metrics.json"), "w"
) as f:
json.dump(overall, f)
f.close()
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
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