File size: 4,304 Bytes
5085882 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 |
import shutil
import os
import argparse
import yaml
import torch
from audioldm_train.utilities.data.dataset_original_mos1 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) |