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

Borne mag + Regularisation mag

Browse source
This commit is contained in:
Harle, Antoine (Contracteur) 2019-11-19 15:37:29 -05:00
parent f4bdd9bca5
commit 64282bda3a
10 changed files with 43 additions and 228 deletions
Download patch file Download diff file Expand all files Collapse all files

19
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',
@ -37,8 +37,8 @@ else:
##########################################
if __name__ == "__main__":
n_inner_iter = 1
epochs = 2
n_inner_iter = 10
epochs = 200
dataug_epoch_start=0
#### Classic ####
@ -57,7 +57,7 @@ if __name__ == "__main__":
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)), "Device": device_name, "Log": log}
print(str(model),": acc", out["Accuracy"], "in (ms):", out["Time"][0], "+/-", out["Time"][1])
print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
with open("res/log/%s.json" % "{}-{} epochs".format(str(model),epochs), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
@ -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=1, mix_dist=0.5, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=False, shared_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)
@ -79,12 +79,13 @@ if __name__ == "__main__":
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)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in (s?):", out["Time"][0], "+/-", out["Time"][1])
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
with open("res/log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('Execution Time : %.00f (s?)'%(time.process_time() - t0))
print('TF influence', TF_influence(log))
print('Execution Time : %.00f '%(time.process_time() - t0))
print('-'*9)
#'''
#### TF number tests ####