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