from model import * from dataug import * #from utils import * from train_utils import * 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 #Color TF (Common mag scale) #'+Contrast', #'+Color', #'+Brightness', #'+Sharpness', #'-Contrast', #'-Color', #'-Brightness', #'-Sharpness', #'=Posterize', #'=Solarize', 'BShearX', 'BShearY', 'BTranslateX-', 'BTranslateX-', 'BTranslateY', 'BTranslateY-', 'BadContrast', 'BadBrightness', 'Random', 'RandBlend' #Non fonctionnel #'Auto_Contrast', #Pas opti pour des batch (Super lent) #'Equalize', ] device = torch.device('cuda') if device == torch.device('cpu'): device_name = 'CPU' else: device_name = torch.cuda.get_device_name(device) ########################################## if __name__ == "__main__": tasks={ #'classic', #'aug_dataset', 'aug_model' } n_inner_iter = 1 epochs = 150 dataug_epoch_start=0 optim_param={ 'Meta':{ 'optim':'Adam', 'lr':1e-2, #1e-2 }, 'Inner':{ 'optim': 'SGD', 'lr':1e-2, #1e-2 'momentum':0.9, #0.9 } } model = LeNet(3,10) #model = ResNet(num_classes=10) #Lents #model = MobileNetV2(num_classes=10) #model = WideResNet(num_classes=10, wrn_size=32) model = Higher_model(model) #run_dist_dataugV3 #### Classic #### if 'classic' in tasks: t0 = time.process_time() model = model.to(device) print("{} on {} for {} epochs".format(str(model), device_name, epochs)) log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=1) #log= train_classic_higher(model=model, epochs=epochs) exec_time=time.process_time() - 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['Inner'], "Device": device_name, "Log": log} print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{} epochs".format(str(model),epochs) with open("res/log/%s.json" % filename, "w+") as f: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') plot_res(log, fig_name="res/"+filename) print('Execution Time : %.00f '%(exec_time)) print('-'*9) #### Augmented Dataset #### if 'aug_dataset' in tasks: t0 = time.process_time() #data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2))) #data_train_aug.augement_data(aug_copy=30) #print(data_train_aug) #dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True) #xs, ys = next(iter(dl_train)) #viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug))) #model = model.to(device) #print("{} on {} for {} epochs".format(str(model), device_name, epochs)) #log= train_classic(model=model, epochs=epochs, print_freq=10) ##log= train_classic_higher(model=model, epochs=epochs) data_train_aug = AugmentedDatasetV2("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2))) data_train_aug.augement_data(aug_copy=1) print(data_train_aug) unsup_ratio = 5 dl_unsup = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE*unsup_ratio, shuffle=True, num_workers=num_workers, pin_memory=pin_memory) unsup_xs, sup_xs, ys = next(iter(dl_unsup)) viz_sample_data(imgs=sup_xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug))) viz_sample_data(imgs=unsup_xs, labels=ys, fig_name='samples/data_sample_{}_unsup'.format(str(data_train_aug))) model = model.to(device) print("{} on {} for {} epochs".format(str(model), device_name, epochs)) log= train_UDA(model=model, dl_unsup=dl_unsup, epochs=epochs, opt_param=optim_param, print_freq=10) exec_time=time.process_time() - 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['Inner'], "Device": device_name, "Param_names": data_train_aug._TF, "Log": log} print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1]) filename = "{}-{}-{} epochs".format(str(data_train_aug),str(model),epochs) with open("res/log/%s.json" % filename, "w+") as f: json.dump(out, f, indent=True) print('Log :\"',f.name, '\" saved !') plot_res(log, fig_name="res/"+filename) print('Execution Time : %.00f '%(exec_time)) print('-'*9) #### Augmented Model #### if 'aug_model' in tasks: t0 = time.process_time() tf_dict = {k: TF.TF_dict[k] for k in tf_names} aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), 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=1, KLdiv=True, hp_opt=True, loss_patience=None) exec_time=time.process_time() - 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)+"-opt_hp" with open("res/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/"+filename, param_names=aug_model.TF_names()) except: print("Failed to plot res") print('Execution Time : %.00f '%(exec_time)) print('-'*9)