AntoineH/smart_augmentation
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MaJ example simple + Suppression Nesterov momentum

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Harle, Antoine (Contracteur) 2020-02-17 17:32:54 -05:00
parent b170af076f
commit d53b385c43
4 changed files with 22 additions and 129 deletions
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66
higher/smart_aug/test_dataug.py
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@ -7,58 +7,7 @@ from dataug import *
#from utils import *
from train_utils import *
# Use available TF (see transformations.py)
'''
tf_names = [
## Geometric TF ##
'Identity',
'FlipUD',
'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
#'TranslateXabs',
#'TranslateYabs',
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize',
#Color TF (Common mag scale)
#'+Contrast',
#'+Color',
#'+Brightness',
#'+Sharpness',
#'-Contrast',
#'-Color',
#'-Brightness',
#'-Sharpness',
#'=Posterize',
#'=Solarize',
## Bad Tranformations ##
# Bad Geometric TF #
#'BShearX',
#'BShearY',
#'BTranslateX-',
#'BTranslateX-',
#'BTranslateY',
#'BTranslateY-',
#'BadContrast',
#'BadBrightness',
#'Random',
#'RandBlend'
]
'''
postfix=''
TF_loader=TF_loader()
device = torch.device('cuda') #Select device to use
@ -84,7 +33,7 @@ if __name__ == "__main__":
}
#Parameters
n_inner_iter = 1
epochs = 20
epochs = 2
dataug_epoch_start=0
optim_param={
'Meta':{
@ -96,7 +45,7 @@ if __name__ == "__main__":
'lr':1e-1, #1e-2/1e-1 (ResNet)
'momentum':0.9, #0.9
'decay':0.0005, #0.0005
'nesterov':True,
'nesterov':False, #False (True: Bad behavior w/ Data_aug)
'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential'
}
}
@ -161,7 +110,6 @@ if __name__ == "__main__":
if 'aug_model' in tasks:
tf_config='../config/base_tf_config.json'
tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
#tf_dict = {k: TF_dict[k] for k in tf_names}
torch.cuda.reset_max_memory_allocated() #reset_peak_stats
torch.cuda.reset_max_memory_cached() #reset_peak_stats
@ -170,7 +118,7 @@ if __name__ == "__main__":
model = Higher_model(model, model_name) #run_dist_dataugV3
aug_model = Augmented_model(
Data_augV5(TF_dict=tf_dict,
N_TF=3,
N_TF=1,
mix_dist=0.5,
fixed_prob=False,
fixed_mag=False,
@ -184,10 +132,10 @@ if __name__ == "__main__":
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=1,
print_freq=20,
unsup_loss=1,
hp_opt=False,
save_sample_freq=0)
save_sample_freq=None)
exec_time=time.perf_counter() - t0
max_allocated = torch.cuda.max_memory_allocated()/(1024.0 * 1024.0)
@ -204,7 +152,7 @@ if __name__ == "__main__":
"Param_names": aug_model.TF_names(),
"Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)+postfix
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)