AntoineH/smart_augmentation
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Minor improvement (RandAug)

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Harle, Antoine (Contracteur) 2020-01-30 11:21:25 -05:00
parent 6bba069d8a
commit 561b71b30a
5 changed files with 50 additions and 179 deletions
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25
higher/smart_aug/test_dataug.py
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@ -53,10 +53,6 @@ tf_names = [
#'Random',
#'RandBlend'
#Non fonctionnel
#'Auto_Contrast', #Pas opti pour des batch (Super lent)
#'Equalize',
]
@ -67,6 +63,12 @@ if device == torch.device('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__":
@ -78,7 +80,7 @@ if __name__ == "__main__":
}
#Parameters
n_inner_iter = 1
epochs = 1
epochs = 150
dataug_epoch_start=0
optim_param={
'Meta':{
@ -95,9 +97,8 @@ if __name__ == "__main__":
#Models
model = LeNet(3,10)
#model = ResNet(num_classes=10)
#Lents
#model = MobileNetV2(num_classes=10)
#model = WideResNet(num_classes=10, wrn_size=32)
#import torchvision.models as models
#model=models.resnet18()
#### Classic ####
if 'classic' in tasks:
@ -105,7 +106,7 @@ if __name__ == "__main__":
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(model=model, opt_param=optim_param, epochs=epochs, print_freq=20)
#log= train_classic_higher(model=model, epochs=epochs)
exec_time=time.process_time() - t0
@ -130,11 +131,10 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
model = Higher_model(model) #run_dist_dataugV3
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(Data_augV5(TF_dict=tf_dict, N_TF=2, 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_simple_smartaug(model=aug_model, epochs=epochs, inner_it=n_inner_iter, opt_param=optim_param)
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,
@ -142,7 +142,8 @@ if __name__ == "__main__":
opt_param=optim_param,
print_freq=1,
unsup_loss=1,
hp_opt=False)
hp_opt=False,
save_sample_freq=None)
exec_time=time.process_time() - t0
####