mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 04:00:46 +02:00
205 lines
No EOL
7 KiB
Python
Executable file
205 lines
No EOL
7 KiB
Python
Executable file
from model import *
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from dataug import *
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#from utils import *
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from train_utils import *
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tf_names = [
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## Geometric TF ##
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'Identity',
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'FlipUD',
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'FlipLR',
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'Rotate',
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'TranslateX',
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'TranslateY',
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'ShearX',
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'ShearY',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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'Color',
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'Brightness',
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'Sharpness',
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'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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#Color TF (Common mag scale)
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#'+Contrast',
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#'+Color',
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#'+Brightness',
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#'+Sharpness',
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#'-Contrast',
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#'-Color',
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#'-Brightness',
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#'-Sharpness',
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#'=Posterize',
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#'=Solarize',
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#'BShearX',
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#'BShearY',
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#'BTranslateX-',
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#'BTranslateX-',
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#'BTranslateY',
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#'BTranslateY-',
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#'BadContrast',
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#'BadBrightness',
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#'Random',
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#'RandBlend'
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#Non fonctionnel
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#'Auto_Contrast', #Pas opti pour des batch (Super lent)
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#'Equalize',
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]
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device = torch.device('cuda')
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if device == torch.device('cpu'):
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device_name = 'CPU'
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else:
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device_name = torch.cuda.get_device_name(device)
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##########################################
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if __name__ == "__main__":
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tasks={
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#'classic',
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#'aug_dataset',
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'aug_model'
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}
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n_inner_iter = 1
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epochs = 150
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dataug_epoch_start=0
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optim_param={
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'Meta':{
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'optim':'Adam',
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'lr':1e-2, #1e-2
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},
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'Inner':{
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'optim': 'SGD',
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'lr':1e-2, #1e-2
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'momentum':0.9, #0.9
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}
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}
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model = LeNet(3,10)
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#model = ResNet(num_classes=10)
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#Lents
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#model = MobileNetV2(num_classes=10)
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#model = WideResNet(num_classes=10, wrn_size=32)
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#### Classic ####
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if 'classic' in tasks:
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t0 = time.process_time()
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model = model.to(device)
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print("{} on {} for {} epochs".format(str(model), device_name, epochs))
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log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=1)
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#log= train_classic_higher(model=model, epochs=epochs)
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exec_time=time.process_time() - t0
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####
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{} epochs".format(str(model),epochs)
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with open("res/log/%s.json" % filename, "w+") as f:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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plot_res(log, fig_name="res/"+filename)
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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#### Augmented Dataset ####
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if 'aug_dataset' in tasks:
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t0 = time.process_time()
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#data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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#data_train_aug.augement_data(aug_copy=30)
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#print(data_train_aug)
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#dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
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#xs, ys = next(iter(dl_train))
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#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
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#model = model.to(device)
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#print("{} on {} for {} epochs".format(str(model), device_name, epochs))
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#log= train_classic(model=model, epochs=epochs, print_freq=10)
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##log= train_classic_higher(model=model, epochs=epochs)
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data_train_aug = AugmentedDatasetV2("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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data_train_aug.augement_data(aug_copy=1)
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print(data_train_aug)
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unsup_ratio = 5
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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)
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unsup_xs, sup_xs, ys = next(iter(dl_unsup))
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viz_sample_data(imgs=sup_xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
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viz_sample_data(imgs=unsup_xs, labels=ys, fig_name='samples/data_sample_{}_unsup'.format(str(data_train_aug)))
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model = model.to(device)
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print("{} on {} for {} epochs".format(str(model), device_name, epochs))
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log= train_UDA(model=model, dl_unsup=dl_unsup, epochs=epochs, opt_param=optim_param, print_freq=10)
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exec_time=time.process_time() - t0
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####
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{}-{} epochs".format(str(data_train_aug),str(model),epochs)
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with open("res/log/%s.json" % filename, "w+") as f:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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plot_res(log, fig_name="res/"+filename)
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9)
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#### Augmented Model ####
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if 'aug_model' in tasks:
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t0 = time.process_time()
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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model = Higher_model(model) #run_dist_dataugV3
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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)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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log= run_dist_dataugV3(model=aug_model,
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epochs=epochs,
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inner_it=n_inner_iter,
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dataug_epoch_start=dataug_epoch_start,
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opt_param=optim_param,
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print_freq=20,
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KLdiv=True,
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hp_opt=False)
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exec_time=time.process_time() - t0
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####
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print('-'*9)
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times = [x["time"] for x in log]
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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}
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print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
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with open("res/log/%s.json" % filename, "w+") as f:
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try:
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json.dump(out, f, indent=True)
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print('Log :\"',f.name, '\" saved !')
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except:
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print("Failed to save logs :",f.name)
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try:
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plot_resV2(log, fig_name="res/"+filename, param_names=aug_model.TF_names())
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except:
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print("Failed to plot res")
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print('Execution Time : %.00f '%(exec_time))
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print('-'*9) |