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import pytorch_lightning as pl
import hydra
from omegaconf import DictConfig
import remfx.utils as utils
import torch
from remfx.models import RemFXChainInference
log = utils.get_logger(__name__)
@hydra.main(version_base=None, config_path="../cfg", config_name="config.yaml")
def main(cfg: DictConfig):
# Apply seed for reproducibility
if cfg.seed:
pl.seed_everything(cfg.seed)
log.info(f"Instantiating datamodule <{cfg.datamodule._target_}>.")
datamodule = hydra.utils.instantiate(cfg.datamodule, _convert_="partial")
log.info("Instantiating Chain Inference Models")
models = {}
for effect in cfg.ckpts:
model = hydra.utils.instantiate(cfg.ckpts[effect].model, _convert_="partial")
ckpt_path = cfg.ckpts[effect].ckpt_path
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
state_dict = torch.load(ckpt_path, map_location=device)["state_dict"]
model.load_state_dict(state_dict)
model.to(device)
models[effect] = model
classifier = None
if "classifier" in cfg:
log.info(f"Instantiating classifier <{cfg.classifier._target_}>.")
classifier = hydra.utils.instantiate(cfg.classifier, _convert_="partial")
ckpt_path = cfg.classifier_ckpt
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
state_dict = torch.load(ckpt_path, map_location=device)["state_dict"]
classifier.load_state_dict(state_dict)
classifier.to(device)
callbacks = []
if "callbacks" in cfg:
for _, cb_conf in cfg["callbacks"].items():
if "_target_" in cb_conf:
log.info(f"Instantiating callback <{cb_conf._target_}>.")
callbacks.append(hydra.utils.instantiate(cb_conf, _convert_="partial"))
logger = hydra.utils.instantiate(cfg.logger, _convert_="partial")
log.info(f"Instantiating trainer <{cfg.trainer._target_}>.")
cfg.trainer.accelerator = "gpu" if torch.cuda.is_available() else "cpu"
trainer = hydra.utils.instantiate(
cfg.trainer, callbacks=callbacks, logger=logger, _convert_="partial"
)
log.info("Logging hyperparameters!")
utils.log_hyperparameters(
config=cfg,
model=model,
datamodule=datamodule,
trainer=trainer,
callbacks=callbacks,
logger=logger,
)
log.info("Instantiating Inference Model")
inference_model = RemFXChainInference(
models,
sample_rate=cfg.sample_rate,
num_bins=cfg.num_bins,
effect_order=cfg.inference_effects_ordering,
classifier=classifier,
shuffle_effect_order=cfg.inference_effects_shuffle,
use_all_effect_models=cfg.inference_use_all_effect_models,
)
trainer.test(model=inference_model, datamodule=datamodule)
if __name__ == "__main__":
main()
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