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import shutil
import os
import argparse
import yaml
import torch
from audioldm_train.utilities.data.dataset_original_mos4 import AudioDataset as AudioDataset
from torch.utils.data import DataLoader
from pytorch_lightning import seed_everything
from audioldm_train.utilities.tools import get_restore_step
from audioldm_train.utilities.model_util import instantiate_from_config
from audioldm_train.utilities.tools import build_dataset_json_from_list
def infer(dataset_key, configs, config_yaml_path, exp_group_name, exp_name):
seed_everything(0)
if "precision" in configs.keys():
torch.set_float32_matmul_precision(configs["precision"])
log_path = configs["log_directory"]
if "dataloader_add_ons" in configs["data"].keys():
dataloader_add_ons = configs["data"]["dataloader_add_ons"]
else:
dataloader_add_ons = []
val_dataset = AudioDataset(
configs, split="test", add_ons=dataloader_add_ons, dataset_json=dataset_key
)
val_loader = DataLoader(
val_dataset,
batch_size=1,
)
try:
config_reload_from_ckpt = configs["reload_from_ckpt"]
except:
config_reload_from_ckpt = None
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)
os.makedirs(checkpoint_path, exist_ok=True)
shutil.copy(config_yaml_path, wandb_path)
# /disk1/changli/jiqun_training_checkpoints/checkpoints/
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
latent_diffusion = instantiate_from_config(configs["model"])
latent_diffusion.set_log_dir(log_path, exp_group_name, exp_name)
guidance_scale = configs["model"]["params"]["evaluation_params"][
"unconditional_guidance_scale"
]
ddim_sampling_steps = configs["model"]["params"]["evaluation_params"][
"ddim_sampling_steps"
]
n_candidates_per_samples = configs["model"]["params"]["evaluation_params"][
"n_candidates_per_samples"
]
# resume_from_checkpoint = ""
checkpoint = torch.load(resume_from_checkpoint)
latent_diffusion.load_state_dict(checkpoint["state_dict"],strict=False)
latent_diffusion.eval()
latent_diffusion = latent_diffusion.cuda()
latent_diffusion.generate_sample(
val_loader,
unconditional_guidance_scale=guidance_scale,
ddim_steps=ddim_sampling_steps,
n_gen=n_candidates_per_samples,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config_yaml",
type=str,
required=False,
help="path to config .yaml file",
)
parser.add_argument(
"-l",
"--list_inference",
type=str,
required=False,
help="The filelist that contain captions (and optionally filenames)",
)
parser.add_argument(
"-reload_from_ckpt",
"--reload_from_ckpt",
type=str,
required=False,
default=None,
help="the checkpoint path for the model",
)
args = parser.parse_args()
# import pdb
# pdb.set_trace()
assert torch.cuda.is_available(), "CUDA is not available"
config_yaml = args.config_yaml
dataset_key = build_dataset_json_from_list(args.list_inference)
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 args.reload_from_ckpt is not None:
config_yaml["reload_from_ckpt"] = args.reload_from_ckpt
infer(dataset_key, config_yaml, config_yaml_path, exp_group_name, exp_name) |