import torch import math from advanced_logger import LogPriority from dataloaders import get_bilateral_dataloaders from models import get_FRCNN_model, Bilateral_model from detection.engine import evaluate_loss, train_one_epoch_simplified def main(cfg, experimenter): LR = cfg['LR'] WEIGHT_DECAY = cfg['WEIGHT_DECAY'] NUM_EPOCHS = cfg['NUM_EPOCHS'] BATCH_SIZE = cfg['BATCH_SIZE'] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") frcnn_model = get_FRCNN_model().to(device) frcnn_model.load_state_dict(torch.load(cfg['FRCNN_MODEL_PATH'])) model = Bilateral_model(frcnn_model).to(device) train_loader, val_loader = get_bilateral_dataloaders(experimenter.config, batch_size = BATCH_SIZE, data_dir = experimenter.config['DATA_DIR']) if cfg["OPTIM"] == "SGD": optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad , model.roi_heads.parameters()),lr=LR,momentum=0.9,weight_decay=WEIGHT_DECAY) elif cfg["OPTIM"] == "ADAM": optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr = LR, weight_decay = WEIGHT_DECAY) elif cfg["OPTIM"] == "ADAGRAD": optimizer = torch.optim.Adagrad(filter(lambda p: p.requires_grad, model.roi_heads.parameters()), lr = LR, weight_decay = WEIGHT_DECAY) for epoch in range(NUM_EPOCHS): experimenter.start_epoch() train_one_epoch_simplified(model, optimizer, train_loader, device, epoch, experimenter = experimenter,optimizer_backbone=None) loss = evaluate_loss(model, device, val_loader, experimenter = experimenter) experimenter.log('Validation Loss: {}'.format(loss), priority = LogPriority.MEDIUM) experimenter.end_epoch(loss, model, device) experimenter.save_model(model) experimenter.generate_predictions(model, device) if __name__ == '__main__': from experimenter import Experimenter import os os.environ['CUDA_VISIBLE_DEVICES'] = '4' cfg_file = 'configs/default.cfg' experimenter = Experimenter(cfg_file) main(experimenter.config['BILATERAL'], experimenter)