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https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 20:20:46 +02:00
Changes since Teledyne
This commit is contained in:
Antoine Harlé
parent
03ffd7fe05
commit
b89dac9084
185 changed files with 16668 additions and 484 deletions
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@ -2,22 +2,14 @@
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"""
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import sys
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from LeNet 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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from transformations import TF_loader
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# from arg_parser import *
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postfix='-MetaScheduler2'
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TF_loader=TF_loader()
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device = torch.device('cuda') #Select device to use
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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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torch.backends.cudnn.benchmark = True #Faster if same input size #Not recommended for reproductibility
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#Increase reproductibility
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@ -27,46 +19,68 @@ np.random.seed(0)
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##########################################
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if __name__ == "__main__":
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#Task to perform
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tasks={
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#'classic',
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'aug_model'
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}
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args = parser.parse_args()
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print(args)
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res_folder=args.res_folder
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postfix=args.postfix
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if args.dtype == 'FP32':
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def_type=torch.float32
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elif args.dtype == 'FP16':
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# def_type=torch.float16 #Default : float32
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def_type=torch.bfloat16
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else:
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raise Exception('dtype not supported :', args.dtype)
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torch.set_default_dtype(def_type) #Default : float32
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device = torch.device(args.device) #Select device to use
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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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#Parameters
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n_inner_iter = 1
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epochs = 100
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n_inner_iter = args.K
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epochs = args.epochs
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dataug_epoch_start=0
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Nb_TF_seq=3
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Nb_TF_seq= args.N
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optim_param={
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'Meta':{
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'optim':'Adam',
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'lr':1e-3, #1e-2
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'epoch_start': 2, #0 / 2 (Resnet?)
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'reg_factor': 0.001,
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'scheduler': 'multiStep', #None, 'multiStep'
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'lr':args.mlr,
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'epoch_start': args.meta_epoch_start, #0 / 2 (Resnet?)
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'reg_factor': args.mag_reg,
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'scheduler': None, #None, 'multiStep'
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},
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'Inner':{
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'optim': 'SGD',
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'lr':1e-1, #1e-2/1e-1 (ResNet)
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'momentum':0.9, #0.9
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'decay':0.0005, #0.0005
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'nesterov':False, #False (True: Bad behavior w/ Data_aug)
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'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential'
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'lr':args.lr, #1e-2/1e-1 (ResNet)
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'momentum':args.momentum, #0.9
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'weight_decay':args.decay, #0.0005
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'nesterov':args.nesterov, #False (True: Bad behavior w/ Data_aug)
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'scheduler': args.scheduler, #None, 'cosine', 'multiStep', 'exponential'
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'warmup':{
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'multiplier': args.warmup, #2 #+ batch_size => + mutliplier #No warmup = 0
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'epochs': 5
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}
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}
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}
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#Models
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#model = LeNet(3,10)
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#model = ResNet(num_classes=10)
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import torchvision.models as models
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#model=models.resnet18()
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model_name = 'resnet18' #'wide_resnet50_2' #'resnet18' #str(model)
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model = getattr(models.resnet, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes))
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#Info params
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F1=True
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sample_save=None
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print_f= epochs/4
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#Load network
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model, model_name= load_model(args.model, num_classes=len(dl_train.dataset.classes), pretrained=args.pretrained)
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#### Classic ####
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if 'classic' in tasks:
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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if not args.augment:
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if device_name != 'CPU':
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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t0 = time.perf_counter()
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model = model.to(device)
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@ -74,12 +88,17 @@ if __name__ == "__main__":
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print("{} on {} for {} epochs{}".format(model_name, device_name, epochs, postfix))
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#print("RandAugment(N{}-M{:.2f})-{} on {} for {} epochs{}".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs, postfix))
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log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=10)
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log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=print_f)
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#log= train_classic_higher(model=model, epochs=epochs)
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exec_time=time.perf_counter() - t0
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max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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if device_name != 'CPU':
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max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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else:
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max_allocated = 0.0
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max_cached=0.0
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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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@ -94,7 +113,7 @@ if __name__ == "__main__":
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filename = "{}-{} epochs".format(model_name,epochs)+postfix
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#print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
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#filename = "RandAugment(N{}-M{:.2f})-{}-{} epochs".format(rand_aug['N'],rand_aug['M'],model_name,epochs)+postfix
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with open("../res/log/%s.json" % filename, "w+") as f:
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with open(res_folder+"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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@ -103,7 +122,7 @@ if __name__ == "__main__":
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print(sys.exc_info()[1])
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try:
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plot_resV2(log, fig_name="../res/"+filename)
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plot_resV2(log, fig_name=res_folder+filename, f1=F1)
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except:
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print("Failed to plot res")
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print(sys.exc_info()[1])
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@ -112,55 +131,63 @@ if __name__ == "__main__":
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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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tf_config='../config/invScale_wide_tf_config.json'#'../config/base_tf_config.json'
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
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else:
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# tf_config='../config/invScale_wide_tf_config.json'#'../config/invScale_wide_tf_config.json'#'../config/base_tf_config.json'
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(args.tf_config)
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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if device_name != 'CPU':
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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torch.cuda.reset_max_memory_cached() #reset_peak_stats
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t0 = time.perf_counter()
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model = Higher_model(model, model_name) #run_dist_dataugV3
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dataug_mod = 'Data_augV8' if args.learn_seq else 'Data_augV5'
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if n_inner_iter !=0:
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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globals()[dataug_mod](TF_dict=tf_dict,
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N_TF=Nb_TF_seq,
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mix_dist=0.5,
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temp=args.temp,
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fixed_prob=False,
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fixed_mag=False,
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shared_mag=False,
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fixed_mag=args.fixed_mag,
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shared_mag=args.shared_mag,
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TF_ignore_mag=tf_ignore_mag), model).to(device)
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else:
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=Nb_TF_seq), model).to(device)
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print("{} on {} for {} epochs - {} inner_it{}".format(str(aug_model), device_name, epochs, n_inner_iter, postfix))
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log= run_dist_dataugV3(model=aug_model,
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log, aug_acc = 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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unsup_loss=1,
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hp_opt=False,
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save_sample_freq=None)
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augment_loss=args.augment_loss,
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hp_opt=False, #False #['lr', 'momentum', 'weight_decay']
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print_freq=print_f,
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save_sample_freq=sample_save)
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exec_time=time.perf_counter() - t0
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max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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if device_name != 'CPU':
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max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
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max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
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else:
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max_allocated = 0.0
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max_cached = 0.0
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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]),
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"Aug_Accuracy": [args.augment_loss, aug_acc],
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"Time": (np.mean(times),np.std(times), exec_time),
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'Optimizer': optim_param,
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"Device": device_name,
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"Memory": [max_allocated, max_cached],
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"TF_config": tf_config,
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"TF_config": args.tf_config,
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"Param_names": aug_model.TF_names(),
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"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)+postfix
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with open("../res/log/%s.json" % filename, "w+") as f:
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print(str(aug_model),": acc", out["Accuracy"], "/ aug_acc", out["Aug_Accuracy"][1] , "in:", out["Time"][0], "+/-", out["Time"][1])
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filename = "{}-{}_epochs-{}_in_it-AL{}".format(str(aug_model),epochs,n_inner_iter,args.augment_loss)+postfix
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with open(res_folder+"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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@ -168,7 +195,7 @@ if __name__ == "__main__":
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print("Failed to save logs :",f.name)
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print(sys.exc_info()[1])
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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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plot_resV2(log, fig_name=res_folder+filename, param_names=aug_model.TF_names(), f1=F1)
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except:
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print("Failed to plot res")
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print(sys.exc_info()[1])
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