best_model_wts = copy.deepcopy(model.state_dict()) print() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) # 가장 나은 모델 가중치를 불러옴 model.load_state_dict(best_model_wts) return model if __name__ == "__main__": freeze_support() model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features model_ft.fc = torch.nn.Linear(num_ftrs, 2) learning_rate = 0.001 criterion = torch.nn.CrossEntropyLoss() optimizer_ft = torch.optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9) exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) if is_cuda: model_ft = model_ft.cuda()
# przypisanie kolorów wierzchołką zbioru # @timing def main(n): time1 = time.time() for node in n: colors_of_nodes[node] = get_color_for_node(node) time2 = time.time() myfile = open("test.txt", "a") myfile.write('%s: \t %0.4f s\n' % ("main", time2 - time1)) myfile.close() return colors_of_nodes if __name__ == "__main__": #wykonanie symulacji for cores in range(1, 5): n = G.nodes() n = [n[i::cores] for i in range(cores)] with Pool(cores) as p: freeze_support() time1 = time.time() print(p.map(main, n)) time2 = time.time() myfile = open("test.txt", "a") myfile.write('%s: \t %0.4f s\n' % (cores, time2 - time1)) myfile.close()