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import jax
_ = jax.device_count() # ugly hack to prevent tpu comms to lock/race or smth smh
from typing import Tuple, Optional
import os
from argparse import ArgumentParser
from flax_trainer import FlaxTrainerUNetPseudo3D
from dataset import load_dataset
def train(
dataset_path: str,
model_path: str,
output_dir: str,
dataset_cache_dir: Optional[str] = None,
from_pt: bool = True,
convert2d: bool = False,
only_temporal: bool = True,
sample_size: Tuple[int, int] = (64, 64),
lr: float = 5e-5,
batch_size: int = 1,
num_frames: int = 24,
epochs: int = 10,
warmup: float = 0.1,
decay: float = 0.0,
weight_decay: float = 1e-2,
log_every_step: int = 50,
save_every_epoch: int = 1,
sample_every_epoch: int = 1,
seed: int = 0,
dtype: str = 'bfloat16',
param_dtype: str = 'float32',
use_memory_efficient_attention: bool = True,
verbose: bool = True,
use_wandb: bool = False
) -> None:
log = lambda x: print(x) if verbose else None
log('\n----------------')
log('Init trainer')
trainer = FlaxTrainerUNetPseudo3D(
model_path = model_path,
from_pt = from_pt,
convert2d = convert2d,
sample_size = sample_size,
seed = seed,
dtype = dtype,
param_dtype = param_dtype,
use_memory_efficient_attention = use_memory_efficient_attention,
verbose = verbose,
only_temporal = only_temporal
)
log('\n----------------')
log('Init dataset')
dataloader = load_dataset(
dataset_path = dataset_path,
model_path = model_path,
cache_dir = dataset_cache_dir,
batch_size = batch_size * trainer.num_devices,
num_frames = num_frames,
num_workers = min(trainer.num_devices * 2, os.cpu_count() - 1),
as_numpy = True,
shuffle = True
)
log('\n----------------')
log('Train')
if use_wandb:
trainer.enable_wandb()
trainer.train(
dataloader = dataloader,
epochs = epochs,
num_frames = num_frames,
log_every_step = log_every_step,
save_every_epoch = save_every_epoch,
sample_every_epoch = sample_every_epoch,
lr = lr,
warmup = warmup,
decay = decay,
weight_decay = weight_decay,
output_dir = output_dir
)
log('\n----------------')
log('Done')
if __name__ == '__main__':
parser = ArgumentParser()
bool_type = lambda x: x.lower() in ['true', '1', 'yes']
parser.add_argument('-v', '--verbose', type = bool_type, default = True)
parser.add_argument('-d', '--dataset_path', required = True)
parser.add_argument('-m', '--model_path', required = True)
parser.add_argument('-o', '--output_dir', required = True)
parser.add_argument('-b', '--batch_size', type = int, default = 1)
parser.add_argument('-f', '--num_frames', type = int, default = 24)
parser.add_argument('-e', '--epochs', type = int, default = 2)
parser.add_argument('--only_temporal', type = bool_type, default = True)
parser.add_argument('--dataset_cache_dir', type = str, default = None)
parser.add_argument('--from_pt', type = bool_type, default = True)
parser.add_argument('--convert2d', type = bool_type, default = False)
parser.add_argument('--lr', type = float, default = 1e-4)
parser.add_argument('--warmup', type = float, default = 0.1)
parser.add_argument('--decay', type = float, default = 0.0)
parser.add_argument('--weight_decay', type = float, default = 1e-2)
parser.add_argument('--sample_size', type = int, nargs = 2, default = [64, 64])
parser.add_argument('--log_every_step', type = int, default = 250)
parser.add_argument('--save_every_epoch', type = int, default = 1)
parser.add_argument('--sample_every_epoch', type = int, default = 1)
parser.add_argument('--seed', type = int, default = 0)
parser.add_argument('--use_memory_efficient_attention', type = bool_type, default = True)
parser.add_argument('--dtype', choices = ['float32', 'bfloat16', 'float16'], default = 'bfloat16')
parser.add_argument('--param_dtype', choices = ['float32', 'bfloat16', 'float16'], default = 'float32')
parser.add_argument('--wandb', type = bool_type, default = False)
args = parser.parse_args()
args.sample_size = tuple(args.sample_size)
if args.verbose:
print(args)
train(
dataset_path = args.dataset_path,
model_path = args.model_path,
from_pt = args.from_pt,
convert2d = args.convert2d,
only_temporal = args.only_temporal,
output_dir = args.output_dir,
dataset_cache_dir = args.dataset_cache_dir,
batch_size = args.batch_size,
num_frames = args.num_frames,
epochs = args.epochs,
lr = args.lr,
warmup = args.warmup,
decay = args.decay,
weight_decay = args.weight_decay,
sample_size = args.sample_size,
seed = args.seed,
dtype = args.dtype,
param_dtype = args.param_dtype,
use_memory_efficient_attention = args.use_memory_efficient_attention,
log_every_step = args.log_every_step,
save_every_epoch = args.save_every_epoch,
sample_every_epoch = args.sample_every_epoch,
verbose = args.verbose,
use_wandb = args.wandb
)
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