def main(): dataset = DatasetLoader() dataset.transform_load() # create network, modify, and set parameters net = Net(dataset) net.build() net.set_params() if os.path.exists(cfg.MODEL.FILENAME): net.model = torch.load(cfg.MODEL.FILENAME) evaluate(net, dataset)
import os from dataloader import DatasetLoader from network import Net from test import evaluate from train import train from config import cfg from utils import plot_loss, plot_accuracy import torch dataset = DatasetLoader() dataset.transform_load() # create network, modify, and set parameters net = Net(dataset) net.build() net.set_params() print("net =", net) #evaluate(net, dataset) if not os.path.exists(cfg.MODEL.DIR): os.makedirs(cfg.MODEL.DIR) if cfg.MODEL.CONTINUE_TRAINING and os.path.exists(cfg.MODEL.FILENAME): net.model = torch.load(cfg.MODEL.FILENAME) net, history = train(net, dataset) print("net =", net)