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from itertools import accumulate
import random
from omegaconf import DictConfig, open_dict
import torch
from einops import rearrange
from lightning.pytorch.utilities.types import STEP_OUTPUT, OptimizerLRScheduler
from algorithms.common.base_pytorch_algo import BasePytorchAlgo
from algorithms.common.metrics.video import (
VideoMetric,
SharedVideoMetricModelRegistry,
)
from utils.distributed_utils import is_rank_zero, broadcast_from_zero
from utils.logging_utils import log_video
from ..common.losses import LPIPSWithDiscriminator3D, warmup
from .model import VideoVAE
class VideoVAETrainer(BasePytorchAlgo):
def __init__(
self,
cfg: DictConfig,
):
self.lr = cfg.lr
self.disc_start = cfg.loss.disc_start
self.warmup_steps = cfg.training.warmup_steps
self.gradient_clip_val = cfg.training.gradient_clip_val
self.video_length_probs = list(accumulate(cfg.training.video_length_probs))
assert self.video_length_probs[-1] == 1.0, "video_length_probs must sum to 1"
self.video_lengths = cfg.training.video_lengths
self.validation_video_lengths = cfg.validation.video_lengths
self.num_logged_videos = [0] * len(self.validation_video_lengths)
super().__init__(cfg)
def _build_model(self):
with open_dict(self.cfg):
for key, value in self.cfg.model.items():
if isinstance(value, list):
self.cfg.model[key] = tuple(value)
self.vae = VideoVAE(**self.cfg.model)
self.loss = LPIPSWithDiscriminator3D(**self.cfg.loss)
self.metrics_registry = SharedVideoMetricModelRegistry()
self.metrics = torch.nn.ModuleList(
[
VideoMetric(
registry=self.metrics_registry,
metric_types=self.cfg.logging.metrics,
)
for video_length in self.validation_video_lengths
]
)
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
super().on_load_checkpoint(checkpoint)
new_state_dict = {}
for key, value in self.state_dict().items():
if key.startswith("metrics"):
new_state_dict[key] = value
else:
new_state_dict[key] = checkpoint["state_dict"][key]
checkpoint["state_dict"] = new_state_dict
for state in checkpoint["optimizer_states"]:
if "opt" in state:
state = state["opt"]
for pg in state["param_groups"]:
pg["lr"] = self.cfg.lr
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
# save model config to enable loading the model from checkpoint only
checkpoint["model_cfg"] = self.cfg.model
def _load_ema_weights_to_state_dict(self, checkpoint: dict) -> None:
if (
checkpoint.get("pretrained_ema", False)
and len(checkpoint["optimizer_states"]) == 0
):
# NOTE: for lightweight EMA-only ckpts for releasing pretrained models,
# we already have EMA weights in the state_dict
return
vae_ema_weights = checkpoint["optimizer_states"][0]["ema"]
vae_parameter_keys = ["vae." + k for k, _ in self.vae.named_parameters()]
assert len(vae_ema_weights) == len(vae_parameter_keys)
for key, weight in zip(vae_parameter_keys, vae_ema_weights):
checkpoint["state_dict"][key] = weight
def configure_optimizers(self) -> OptimizerLRScheduler:
self.automatic_optimization = False
optimizer_vae = torch.optim.Adam(
self.vae.parameters(),
lr=self.lr,
betas=self.cfg.training.optimizer_beta,
)
optimizer_disc = torch.optim.Adam(
self.loss.discriminator.parameters(),
lr=self.lr,
betas=self.cfg.training.optimizer_beta,
)
return [optimizer_vae, optimizer_disc], []
def on_after_batch_transfer(
self, batch: Dict[str, torch.Tensor], dataloader_idx: int = 0
) -> torch.Tensor:
x = batch["videos"]
return self._rearrange_and_normalize(x)
def training_step(
self,
batch: torch.Tensor,
batch_idx: int,
namespace: str = "training",
video_length: Optional[int] = None,
):
is_training = namespace == "training"
batch = self._randomly_crop_video(
batch, video_length=video_length, random_start=is_training
)
recons, posterior = self.vae(batch)
if is_training:
optimizer_vae, optimizer_disc = self.optimizers()
warmup_info = self._compute_warmup()
# Optimize VAE
vae_loss, vae_loss_dict = self.loss(
inputs=batch,
reconstructions=recons,
posteriors=posterior,
optimizer_idx=0,
global_step=self.global_step,
last_layer=self.vae.get_last_layer(),
namespace=f"{namespace}_vae",
)
if is_training:
self._optimizer_step(optimizer_vae, vae_loss, warmup_info)
self._log_losses(f"{namespace}_vae", vae_loss, vae_loss_dict, is_training)
# Optimize Discriminator
disc_loss, disc_loss_dict = self.loss(
inputs=batch,
reconstructions=recons,
posteriors=posterior,
optimizer_idx=1,
global_step=self.global_step,
last_layer=None,
namespace=f"{namespace}_disc",
)
if is_training:
self._optimizer_step(optimizer_disc, disc_loss, warmup_info)
self._log_losses(f"{namespace}_disc", disc_loss, disc_loss_dict, is_training)
return {
"gts": self._rearrange_and_unnormalize(batch),
"recons": self._rearrange_and_unnormalize(recons),
}
def on_validation_epoch_start(self) -> None:
self.num_logged_videos = [0] * len(self.validation_video_lengths)
def on_validation_epoch_end(self, namespace: str = "validation") -> None:
# Log metrics
for video_length, metrics in zip(self.validation_video_lengths, self.metrics):
self.log_dict(
metrics.log(f"{namespace}_{video_length}"),
on_step=False,
on_epoch=True,
prog_bar=True,
)
def on_test_epoch_start(self) -> None:
self.on_validation_epoch_start()
def on_test_epoch_end(self) -> None:
self.on_validation_epoch_end(namespace="test")
def validation_step(
self, batch: torch.Tensor, batch_idx: int, namespace: str = "validation"
) -> STEP_OUTPUT:
for video_length_idx, video_length in enumerate(self.validation_video_lengths):
new_namespace = f"{namespace}_{video_length}"
metrics = self.metrics[video_length_idx]
num_logged_videos = self.num_logged_videos[video_length_idx]
output_dict = self.training_step(
batch, batch_idx, new_namespace, video_length
)
# Update metrics
gts, recons = output_dict["gts"], output_dict["recons"]
metrics(recons, gts)
# Log ground truth and reconstruction videos
gts, recons = self.gather_data((gts, recons))
if not (
is_rank_zero
and self.logger
and num_logged_videos < self.cfg.logging.max_num_videos
):
continue
num_videos_to_log = min(
self.cfg.logging.max_num_videos - num_logged_videos,
gts.shape[1],
)
gts, recons = map(
lambda x: x[:num_videos_to_log],
(gts, recons),
)
log_video(
recons,
gts,
step=None if new_namespace.startswith("test") else self.global_step,
namespace=f"{new_namespace}_vis",
logger=self.logger.experiment,
indent=num_logged_videos,
)
self.num_logged_videos[video_length_idx] += num_videos_to_log
def test_step(self, batch: torch.Tensor, batch_idx: int) -> STEP_OUTPUT:
return self.validation_step(batch, batch_idx, namespace="test")
def _log_losses(
self,
namespace: str,
loss: torch.Tensor,
loss_dict: Dict[str, torch.Tensor],
on_step: bool = True,
):
if self.global_step % self.cfg.logging.loss_freq > 1:
return
loss_dict = {
k: v.to(self.device) for k, v in loss_dict.items()
} # to enable gathering across devices
self.log(
f"{namespace}/loss",
loss,
on_step=on_step,
on_epoch=not on_step,
prog_bar=True,
sync_dist=True,
)
self.log_dict(
loss_dict,
on_step=on_step,
on_epoch=not on_step,
prog_bar=False,
sync_dist=True,
)
def _optimizer_step(
self,
optimizer: torch.optim.Optimizer,
loss: torch.Tensor,
warmup_info: Tuple[bool, float],
) -> None:
should_warmup, lr_scale = warmup_info
optimizer.zero_grad()
self.manual_backward(loss)
if self.gradient_clip_val is not None:
self.clip_gradients(optimizer, gradient_clip_val=self.gradient_clip_val)
if should_warmup:
optimizer = warmup(optimizer, self.lr, lr_scale)
optimizer.step()
def _compute_warmup(self) -> Tuple[bool, float]:
should_warmup, lr_scale = False, 1.0
if self.global_step < self.warmup_steps:
should_warmup = True
lr_scale = float(self.global_step + 1) / self.warmup_steps
elif (
self.global_step >= self.disc_start - 1
and self.global_step < self.disc_start + self.warmup_steps
):
should_warmup = True
lr_scale = float(self.global_step - self.disc_start + 1) / self.warmup_steps
return should_warmup, min(lr_scale, 1.0)
def _rearrange_and_normalize(self, x: torch.Tensor) -> torch.Tensor:
x = rearrange(x, "b t c h w -> b c t h w")
return 2.0 * x - 1.0
def _rearrange_and_unnormalize(self, x: torch.Tensor) -> torch.Tensor:
x = 0.5 * x + 0.5
return rearrange(x, "b c t h w -> b t c h w")
def _randomly_crop_video(
self,
x: torch.Tensor,
video_length: Optional[int] = None,
random_start: bool = True,
) -> torch.Tensor:
"""
Randomly crop the video to a random temporal length, if not provided.
Same length across all GPUs.
"""
if video_length is None:
rand = broadcast_from_zero(
lambda: torch.zeros(1, device=self.device),
lambda: torch.rand(1, device=self.device),
).item()
for i, prob in enumerate(self.video_length_probs):
if rand < prob:
video_length = self.video_lengths[i]
break
crop_start = random.randint(0, x.size(2) - video_length) if random_start else 0
x = x[:, :, crop_start : crop_start + video_length]
assert x.size(2) == video_length, "Cropped video length does not match"
return x
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