""" Script to run series of experiments. """ from dataug import * #from utils import * from train_utils import * from transformations import TF_loader import torchvision.models as models from LeNet import * #model_list={models.resnet: ['resnet18', 'resnet50','wide_resnet50_2']} #lr=0.1 model_list={models.resnet: ['wide_resnet50_2']} optim_param={ 'Meta':{ 'optim':'Adam', 'lr':5e-3, #1e-2 'epoch_start': 2, #0 / 2 (Resnet?) 'reg_factor': 0.001, 'scheduler': None, #None, 'multiStep' }, 'Inner':{ 'optim': 'SGD', 'lr':1e-2, #1e-2/1e-1 (ResNet) 'momentum':0.9, #0.9 'decay':0.0005, #0.0005 'nesterov':False, #False (True: Bad behavior w/ Data_aug) 'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential' } } res_folder="../res/benchmark/CIFAR10/" #res_folder="../res/benchmark/MNIST/" #res_folder="../res/HPsearch/" epochs= 200 dataug_epoch_start=0 nb_run= 3 tf_config='../config/bad_tf_config.json' #'../config/wide_tf_config.json'#'../config/base_tf_config.json' TF_loader=TF_loader() tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config) device = torch.device('cuda:1') if device == torch.device('cpu'): device_name = 'CPU' else: device_name = torch.cuda.get_device_name(device) torch.backends.cudnn.benchmark = True #Faster if same input size #Not recommended for reproductibility #Increase reproductibility torch.manual_seed(0) np.random.seed(0) ########################################## if __name__ == "__main__": ### Benchmark ### ''' inner_its = [0] dist_mix = [0.5] N_seq_TF= [3] mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant) for model_type in model_list.keys(): for model_name in model_list[model_type]: for run in range(nb_run): for n_inner_iter in inner_its: for n_tf in N_seq_TF: for dist in dist_mix: for m_setup in mag_setup: torch.cuda.reset_max_memory_allocated() #reset_peak_stats torch.cuda.reset_max_memory_cached() #reset_peak_stats t0 = time.perf_counter() model = getattr(model_type, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes)) #model_name = 'LeNet' #model = LeNet(3,10) model = Higher_model(model, model_name) #run_dist_dataugV3 if n_inner_iter!=0: aug_model = Augmented_model( Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=False, fixed_mag=m_setup[0], shared_mag=m_setup[1], TF_ignore_mag=tf_ignore_mag), model).to(device) else: aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=n_tf), model).to(device) print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter)) log= run_dist_dataugV3(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, opt_param=optim_param, print_freq=epochs/4, unsup_loss=1, hp_opt=False, save_sample_freq=None) exec_time=time.perf_counter() - t0 max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0) max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB #### print('-'*9) times = [x["time"] for x in log] out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Memory": [max_allocated, max_cached], "TF_config": tf_config, "Param_names": aug_model.TF_names(), "Log": log} print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{} epochs (dataug:{})- {} in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter, run) with open(res_folder+"log/%s.json" % filename, "w+") as f: try: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') except: print("Failed to save logs :",f.name) try: plot_resV2(log, fig_name=res_folder+filename, param_names=aug_model.TF_names()) except: print("Failed to plot res") print(sys.exc_info()[1]) print('Execution Time : %.00f '%(exec_time)) print('-'*9) ''' ### Benchmark - RandAugment/Vanilla ### #''' for model_type in model_list.keys(): for model_name in model_list[model_type]: for run in range(nb_run): torch.cuda.reset_max_memory_allocated() #reset_peak_stats torch.cuda.reset_max_memory_cached() #reset_peak_stats t0 = time.perf_counter() model = getattr(model_type, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes)).to(device) print("{} on {} for {} epochs".format(model_name, device_name, epochs)) #print("RandAugment(N{}-M{:.2f})-{} on {} for {} epochs".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs)) log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=epochs/4) exec_time=time.perf_counter() - t0 max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0) max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB #### print('-'*9) times = [x["time"] for x in log] out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Memory": [max_allocated, max_cached], #"Rand_Aug": rand_aug, "Log": log} print(model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{} epochs -{}-basicDA".format(model_name,epochs, run) #print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) #filename = "RandAugment(N{}-M{:.2f})-{}-{} epochs -{}".format(rand_aug['N'],rand_aug['M'],model_name,epochs, run) with open(res_folder+"log/%s.json" % filename, "w+") as f: try: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') except: print("Failed to save logs :",f.name) print(sys.exc_info()[1]) try: plot_resV2(log, fig_name=res_folder+filename, param_names=aug_model.TF_names()) except: print("Failed to plot res") print(sys.exc_info()[1]) print('Execution Time : %.00f '%(exec_time)) print('-'*9) #''' ### HP Search ### ''' from LeNet import * inner_its = [1] dist_mix = [1.0]#[0.0, 0.5, 0.8, 1.0] N_seq_TF= [5, 6] mag_setup = [(True, True), (False, False)] #(FxSh, Independant) #prob_setup = [True, False] try: os.mkdir(res_folder) os.mkdir(res_folder+"log/") except FileExistsError: pass for n_inner_iter in inner_its: for n_tf in N_seq_TF: for dist in dist_mix: #for i in TF_nb: for m_setup in mag_setup: #for p_setup in prob_setup: p_setup=False for run in range(nb_run): t0 = time.perf_counter() model = getattr(models.resnet, 'resnet18')(pretrained=False, num_classes=len(dl_train.dataset.classes)) #model = LeNet(3,10) model = Higher_model(model) #run_dist_dataugV3 aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device) #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter)) log= run_dist_dataugV3(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, opt_param=optim_param, print_freq=epochs/4, unsup_loss=1, hp_opt=False, save_sample_freq=None) exec_time=time.perf_counter() - t0 #### print('-'*9) times = [x["time"] for x in log] out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log} print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{} epochs (dataug:{})- {} in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter, run) with open(res_folder+"log/%s.json" % filename, "w+") as f: try: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') except: print("Failed to save logs :",f.name) print('Execution Time : %.00f '%(exec_time)) print('-'*9) '''