VTimeLLM/flash-attention/training/src/eval.py

from typing import List, Optional
from pathlib import Path

import torch

import hydra
from omegaconf import OmegaConf, DictConfig
from pytorch_lightning import (
    Callback,
    LightningDataModule,
    LightningModule,
    Trainer,
    seed_everything,
)
from pytorch_lightning.loggers import LightningLoggerBase

from src.utils import utils

log = utils.get_logger(__name__)


def remove_prefix(text: str, prefix: str):
    if text.startswith(prefix):
        return text[len(prefix) :]
    return text  # or whatever


def load_checkpoint(path, device='cpu'):
    path = Path(path).expanduser()
    if path.is_dir():
        path /= 'last.ckpt'
    # dst = f'cuda:{torch.cuda.current_device()}'
    log.info(f'Loading checkpoint from {str(path)}')
    state_dict = torch.load(path, map_location=device)
    # T2T-ViT checkpoint is nested in the key 'state_dict_ema'
    if state_dict.keys() == {'state_dict_ema'}:
        state_dict = state_dict['state_dict_ema']
    # Swin checkpoint is nested in the key 'model'
    if state_dict.keys() == {'model'}:
        state_dict = state_dict['model']
    # Lightning checkpoint contains extra stuff, we only want the model state dict
    if 'pytorch-lightning_version' in state_dict:
        state_dict = {remove_prefix(k, 'model.'): v for k, v in state_dict['state_dict'].items()}
    return state_dict


def evaluate(config: DictConfig) -> None:
    """Example of inference with trained model.
    It loads trained image classification model from checkpoint.
    Then it loads example image and predicts its label.
    """

    # load model from checkpoint
    # model __init__ parameters will be loaded from ckpt automatically
    # you can also pass some parameter explicitly to override it

    # We want to add fields to config so need to call OmegaConf.set_struct
    OmegaConf.set_struct(config, False)

    # load model
    checkpoint_type = config.eval.get('checkpoint_type', 'pytorch')
    if checkpoint_type not in ['lightning', 'pytorch']:
        raise NotImplementedError(f'checkpoint_type ${checkpoint_type} not supported')

    if checkpoint_type == 'lightning':
        cls = hydra.utils.get_class(config.task._target_)
        model = cls.load_from_checkpoint(checkpoint_path=config.eval.ckpt)
    elif checkpoint_type == 'pytorch':
        model_cfg = config.model_pretrained if 'model_pretrained' in config else None
        trained_model: LightningModule = hydra.utils.instantiate(config.task, cfg=config,
                                                                 model_cfg=model_cfg,
                                                                 _recursive_=False)
        if 'ckpt' in config.eval:
            load_return = trained_model.model.load_state_dict(
                load_checkpoint(config.eval.ckpt, device=trained_model.device), strict=False
            )
            log.info(load_return)
        if 'model_pretrained' in config:
            ...
        else:
            model = trained_model

    datamodule: LightningDataModule = hydra.utils.instantiate(config.datamodule)
    # datamodule: LightningDataModule = model._datamodule
    datamodule.prepare_data()
    datamodule.setup()

    # print model hyperparameters
    log.info(f'Model hyperparameters: {model.hparams}')

    # Init Lightning callbacks
    callbacks: List[Callback] = []
    if "callbacks" in config:
        for _, cb_conf in config["callbacks"].items():
            if cb_conf is not None and "_target_" in cb_conf:
                log.info(f"Instantiating callback <{cb_conf._target_}>")
                callbacks.append(hydra.utils.instantiate(cb_conf))

    # Init Lightning loggers
    logger: List[LightningLoggerBase] = []
    if "logger" in config:
        for _, lg_conf in config["logger"].items():
            if lg_conf is not None and "_target_" in lg_conf:
                log.info(f"Instantiating logger <{lg_conf._target_}>")
                logger.append(hydra.utils.instantiate(lg_conf))

    # Init Lightning trainer
    log.info(f"Instantiating trainer <{config.trainer._target_}>")
    trainer: Trainer = hydra.utils.instantiate(
        config.trainer, callbacks=callbacks, logger=logger,  _convert_="partial"
    )

    # Evaluate the model
    log.info("Starting evaluation!")
    if config.eval.get('run_val', True):
        trainer.validate(model=model, datamodule=datamodule)
    if config.eval.get('run_test', True):
        trainer.test(model=model, datamodule=datamodule)

    # Make sure everything closed properly
    log.info("Finalizing!")
    utils.finish(
        config=config,
        model=model,
        datamodule=datamodule,
        trainer=trainer,
        callbacks=callbacks,
        logger=logger,
    )