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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:
# Adapt to checkpoint
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) |