|
import torch |
|
from diffusers import ModelMixin, ConfigMixin |
|
from torch import nn |
|
import os |
|
import json |
|
import pytorch_lightning as pl |
|
from diffusers.configuration_utils import ConfigMixin |
|
from diffusers.models.modeling_utils import ModelMixin |
|
from typing import Optional, Union |
|
import glob |
|
|
|
|
|
class VideoBaseAE(ModelMixin, ConfigMixin): |
|
config_name = "config.json" |
|
|
|
def __init__(self, *args, **kwargs) -> None: |
|
super().__init__(*args, **kwargs) |
|
|
|
def encode(self, x: torch.Tensor, *args, **kwargs): |
|
pass |
|
|
|
def decode(self, encoding: torch.Tensor, *args, **kwargs): |
|
pass |
|
|
|
@property |
|
def num_training_steps(self) -> int: |
|
"""Total training steps inferred from datamodule and devices.""" |
|
if self.trainer.max_steps: |
|
return self.trainer.max_steps |
|
|
|
limit_batches = self.trainer.limit_train_batches |
|
batches = len(self.train_dataloader()) |
|
batches = min(batches, limit_batches) if isinstance(limit_batches, int) else int(limit_batches * batches) |
|
|
|
num_devices = max(1, self.trainer.num_gpus, self.trainer.num_processes) |
|
if self.trainer.tpu_cores: |
|
num_devices = max(num_devices, self.trainer.tpu_cores) |
|
|
|
effective_accum = self.trainer.accumulate_grad_batches * num_devices |
|
return (batches // effective_accum) * self.trainer.max_epochs |
|
|
|
@classmethod |
|
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): |
|
ckpt_files = glob.glob(os.path.join(pretrained_model_name_or_path, '*.ckpt')) |
|
if ckpt_files: |
|
|
|
last_ckpt_file = ckpt_files[-1] |
|
config_file = os.path.join(pretrained_model_name_or_path, cls.config_name) |
|
model = cls.from_config(config_file) |
|
model.init_from_ckpt(last_ckpt_file) |
|
return model |
|
else: |
|
return super().from_pretrained(pretrained_model_name_or_path, **kwargs) |
