AntoineH/smart_augmentation
1
0
Fork 0
mirror of https://github.com/AntoineHX/smart_augmentation.git synced 2025-05-04 12:10:45 +02:00
Code Issues Projects Releases Packages Wiki Activity

Dataugv5- Modification des TF pour propagation du gradient (mag)

Browse source
This commit is contained in:
Harle, Antoine (Contracteur) 2019-11-18 12:53:23 -05:00
parent 05f81787d6
commit 994d657a28
5 changed files with 94 additions and 21 deletions
Download patch file Download diff file Expand all files Collapse all files

10
higher/test_dataug.py
View file

@ -5,9 +5,9 @@ from train_utils import *
tf_names = [
## Geometric TF ##
'Identity',
'FlipUD',
'FlipLR',
#'Identity',
#'FlipUD',
#'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
@ -37,7 +37,7 @@ else:
##########################################
if __name__ == "__main__":
n_inner_iter = 10
n_inner_iter = 1
epochs = 2
dataug_epoch_start=0
@ -68,7 +68,7 @@ if __name__ == "__main__":
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, glob_mag=False), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)