CLIPSampler/main.py

import os
import torch
from config import Config
from src.model import SaliencyNet
from torch.utils.data import DataLoader
from src.train import SalientFrameSamplerTrainer
from dataloaders.t2v_dataloader import VideoDescriptionDataloader

def load_weights(path, which_epoch):
    weights, last_epoch = None, None
    available_models = [name for name in os.listdir(path) if name.endswith(".pt")]

    if available_models:
        last_epoch = max([int(name[6:-3]) for name in available_models])
        last_epoch = min(last_epoch, which_epoch)
        weights = torch.load(os.path.join(path, f'epoch_{last_epoch}.pt'))

    return weights, last_epoch

def set_seeds(seed: int):
    os.environ['PYTHONHASHSEED'] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.benchmark = False 
    torch.backends.cudnn.deterministic = True

def main(cfg):

    set_seeds(cfg.seed)

    model = SaliencyNet(cfg)
    
    model.to(cfg.device)
    
    if cfg.load_complete_model:
        weights, last_epoch = load_weights(cfg.trained_model_path, cfg.trained_model_epoch)
        if weights:
            model.load_state_dict(weights)
            print(f'Complete model -{cfg.backbone} trained on {cfg.similarity_matrix_loss} and {cfg.saliency_matching_loss} lossese from epoch #{last_epoch}')

    elif cfg.load_backbone:
        weights, last_epoch = load_weights(cfg.trained_backbone_path, cfg.trained_backbone_epoch)
        if weights:
            model.pretrained.load_state_dict(weights)
            print(f'{cfg.backbone} backbone trained on {cfg.similarity_matrix_loss} loss loaded from epoch #{last_epoch}')
        
    else:
        print(f'{cfg.backbone} backbone loaded from scratch.')
    
    train_dataset = VideoDescriptionDataloader(cfg, data_type='train')
    val_dataset = VideoDescriptionDataloader(cfg, data_type='valid')

    train_dataloader = DataLoader(train_dataset, shuffle=True, num_workers=cfg.workers)
    val_dataloader = DataLoader(val_dataset, shuffle=False, num_workers=cfg.workers)
    
    trainer = SalientFrameSamplerTrainer(model, train_dataloader, val_dataloader, cfg)
    trainer.train()

if __name__ == "__main__":
    torch.multiprocessing.set_start_method("spawn")
    cfg = Config().args

    main(cfg)