openmusic / audioldm_train /train /latent_diffusion.py
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import sys
# sys.path.append("src")
import shutil
import os
os.environ["TOKENIZERS_PARALLELISM"] = "true"
import argparse
import yaml
import torch
from tqdm import tqdm
from pytorch_lightning.strategies.ddp import DDPStrategy
from audioldm_train.modules.latent_diffusion.ddpm import LatentDiffusion
from torch.utils.data import WeightedRandomSampler
from torch.utils.data import DataLoader
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
from audioldm_train.utilities.tools import (
listdir_nohidden,
get_restore_step,
copy_test_subset_data,
)
import wandb
from audioldm_train.utilities.model_util import instantiate_from_config
import logging
logging.basicConfig(level=logging.WARNING)
def convert_path(path):
parts = path.decode().split("/")[-4:]
base = ""
result = "/".join(parts)
def print_on_rank0(msg):
if torch.distributed.get_rank() == 0:
print(msg)
def main(configs, config_yaml_path, exp_group_name, exp_name, perform_validation):
print("MAIN START")
# cpth = "/train20/intern/permanent/changli7/dataset_ptm/test_dataset/dataset/audioset/zip_audios/unbalanced_train_segments/unbalanced_train_segments_part9/Y7fmOlUlwoNg.wav"
# convert_path(cpth)
if "seed" in configs.keys():
seed_everything(configs["seed"])
else:
print("SEED EVERYTHING TO 0")
seed_everything(1234)
if "precision" in configs.keys():
torch.set_float32_matmul_precision(
configs["precision"]
) # highest, high, medium
log_path = configs["log_directory"]
batch_size = configs["model"]["params"]["batchsize"]
train_lmdb_path = configs["train_path"]["train_lmdb_path"]
train_key_path = [_ + '/data_key.key' for _ in train_lmdb_path]
val_lmdb_path = configs["val_path"]["val_lmdb_path"]
val_key_path = configs["val_path"]["val_key_path"]
#try:
mos_path = configs["mos_path"]
from audioldm_train.utilities.data.hhhh import AudioDataset
dataset = AudioDataset(config=configs, lmdb_path=train_lmdb_path, key_path=train_key_path, mos_path=mos_path)
loader = DataLoader(
dataset,
batch_size=batch_size,
num_workers=8,
pin_memory=True,
shuffle=True,
)
print(
"The length of the dataset is %s, the length of the dataloader is %s, the batchsize is %s"
% (len(dataset), len(loader), batch_size)
)
try:
val_dataset = AudioDataset(config=configs, lmdb_path=val_lmdb_path, key_path=val_key_path, mos_path=mos_path)
except:
val_dataset = AudioDataset(config=configs, lmdb_path=val_lmdb_path, key_path=val_key_path)
val_loader = DataLoader(
val_dataset,
batch_size=8,
)
# Copy test data
import os
test_data_subset_folder = os.path.join(
os.path.dirname(configs["log_directory"]),
"testset_data",
"tmp",
)
os.makedirs(test_data_subset_folder, exist_ok=True)
# copy to test:
# import pdb
# pdb.set_trace()
# for i in range(len(val_dataset.keys)):
# key_tmp = val_dataset.keys[i].decode()
# cmd = "cp {} {}".format(key_tmp, os.path.join(test_data_subset_folder))
# os.system(cmd)
try:
config_reload_from_ckpt = configs["reload_from_ckpt"]
except:
config_reload_from_ckpt = None
try:
limit_val_batches = configs["step"]["limit_val_batches"]
except:
limit_val_batches = None
validation_every_n_epochs = configs["step"]["validation_every_n_epochs"]
save_checkpoint_every_n_steps = configs["step"]["save_checkpoint_every_n_steps"]
max_steps = configs["step"]["max_steps"]
save_top_k = configs["step"]["save_top_k"]
checkpoint_path = os.path.join(log_path, exp_group_name, exp_name, "checkpoints")
wandb_path = os.path.join(log_path, exp_group_name, exp_name)
checkpoint_callback = ModelCheckpoint(
dirpath=checkpoint_path,
monitor="global_step",
mode="max",
filename="checkpoint-fad-{val/frechet_inception_distance:.2f}-global_step={global_step:.0f}",
every_n_train_steps=save_checkpoint_every_n_steps,
save_top_k=save_top_k,
auto_insert_metric_name=False,
save_last=False,
)
os.makedirs(checkpoint_path, exist_ok=True)
# shutil.copy(config_yaml_path, wandb_path)
if len(os.listdir(checkpoint_path)) > 0:
print("Load checkpoint from path: %s" % checkpoint_path)
restore_step, n_step = get_restore_step(checkpoint_path)
resume_from_checkpoint = os.path.join(checkpoint_path, restore_step)
print("Resume from checkpoint", resume_from_checkpoint)
elif config_reload_from_ckpt is not None:
resume_from_checkpoint = config_reload_from_ckpt
print("Reload ckpt specified in the config file %s" % resume_from_checkpoint)
else:
print("Train from scratch")
resume_from_checkpoint = None
devices = torch.cuda.device_count()
latent_diffusion = instantiate_from_config(configs["model"])
latent_diffusion.set_log_dir(log_path, exp_group_name, exp_name)
wandb_logger = WandbLogger(
save_dir=wandb_path,
project=configs["project"],
config=configs,
name="%s/%s" % (exp_group_name, exp_name),
)
latent_diffusion.test_data_subset_path = test_data_subset_folder
print("==> Save checkpoint every %s steps" % save_checkpoint_every_n_steps)
print("==> Perform validation every %s epochs" % validation_every_n_epochs)
trainer = Trainer(
accelerator="auto",
devices="auto",
logger=wandb_logger,
max_steps=max_steps,
num_sanity_val_steps=1,
limit_val_batches=limit_val_batches,
check_val_every_n_epoch=validation_every_n_epochs,
strategy=DDPStrategy(find_unused_parameters=True),
gradient_clip_val=2.0,callbacks=[checkpoint_callback],num_nodes=1,
)
trainer.fit(latent_diffusion, loader, val_loader, ckpt_path=resume_from_checkpoint)
################################################################################################################
# if(resume_from_checkpoint is not None):
# ckpt = torch.load(resume_from_checkpoint)["state_dict"]
# key_not_in_model_state_dict = []
# size_mismatch_keys = []
# state_dict = latent_diffusion.state_dict()
# print("Filtering key for reloading:", resume_from_checkpoint)
# print("State dict key size:", len(list(state_dict.keys())), len(list(ckpt.keys())))
# for key in tqdm(list(ckpt.keys())):
# if(key not in state_dict.keys()):
# key_not_in_model_state_dict.append(key)
# del ckpt[key]
# continue
# if(state_dict[key].size() != ckpt[key].size()):
# del ckpt[key]
# size_mismatch_keys.append(key)
# if(len(key_not_in_model_state_dict) != 0 or len(size_mismatch_keys) != 0):
# print("⛳", end=" ")
# print("==> Warning: The following key in the checkpoint is not presented in the model:", key_not_in_model_state_dict)
# print("==> Warning: These keys have different size between checkpoint and current model: ", size_mismatch_keys)
# latent_diffusion.load_state_dict(ckpt, strict=False)
# if(perform_validation):
# trainer.validate(latent_diffusion, val_loader)
# trainer.fit(latent_diffusion, loader, val_loader)
################################################################################################################
if __name__ == "__main__":
print("ok")
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config_yaml",
type=str,
required=False,
help="path to config .yaml file",
)
parser.add_argument("--val", action="store_true")
args = parser.parse_args()
perform_validation = args.val
assert torch.cuda.is_available(), "CUDA is not available"
config_yaml = args.config_yaml
exp_name = os.path.basename(config_yaml.split(".")[0])
exp_group_name = os.path.basename(os.path.dirname(config_yaml))
config_yaml_path = os.path.join(config_yaml)
config_yaml = yaml.load(open(config_yaml_path, "r"), Loader=yaml.FullLoader)
if perform_validation:
config_yaml["model"]["params"]["cond_stage_config"][
"crossattn_audiomae_generated"
]["params"]["use_gt_mae_output"] = False
config_yaml["step"]["limit_val_batches"] = None
main(config_yaml, config_yaml_path, exp_group_name, exp_name, perform_validation)