""" Script to run series of experiments. """ from dataug import * #from utils import * from train_utils import * import torchvision.models as models model_list={models.resnet: ['resnet18', 'resnet50','wide_resnet50_2']} #lr=0.1 optim_param={ 'Meta':{ 'optim':'Adam', 'lr':1e-2, #1e-2 }, 'Inner':{ 'optim': 'SGD', 'lr':1e-2, #1e-2 #1e-1 for ResNet 'momentum':0.9, #0.9 } } res_folder="../res/benchmark/CIFAR10/" #res_folder="../res/HPsearch/" epochs= 200 dataug_epoch_start=0 nb_run= 3 # Use available TF (see transformations.py) tf_names = [ ## Geometric TF ## 'Identity', 'FlipUD', 'FlipLR', 'Rotate', 'TranslateX', 'TranslateY', 'ShearX', 'ShearY', ## Color TF (Expect image in the range of [0, 1]) ## 'Contrast', 'Color', 'Brightness', 'Sharpness', 'Posterize', 'Solarize', #=>Image entre [0,1] #Pas opti pour des batch ## Bad Tranformations ## # Bad Geometric TF # #'BShearX', #'BShearY', #'BTranslateX-', #'BTranslateX-', #'BTranslateY', #'BTranslateY-', #'BadContrast', #'BadBrightness', #'Random', #'RandBlend' ] tf_dict = {k: TF.TF_dict[k] for k in tf_names} device = torch.device('cuda') 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 ### ''' 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_cached() #reset_peak_stats t0 = time.perf_counter() model = getattr(model_type, model_name)(pretrained=False) 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=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), 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_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_cached, "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) ''' ### Benchmark - RandAugment ### 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_cached() #reset_peak_stats t0 = time.perf_counter() model = getattr(model_type, model_name)(pretrained=False).to(device) print("RandAugment(N{}-M{})-{} 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_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_cached, "Rand_Aug": rand_aug, "Log": log} print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "RandAugment(N{}-M{})-{}-{} 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) #plot_resV2(log, fig_name=res_folder+filename) print('Execution Time : %.00f '%(exec_time)) print('-'*9) ### HP Search ### ''' from LeNet import * inner_its = [1] dist_mix = [0.0, 0.5, 0.8, 1.0] N_seq_TF= [3, 2, 4] 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) 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) '''