AntoineH/smart_augmentation
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Changement Translation pour taille relative image

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Harle, Antoine (Contracteur) 2020-02-05 12:06:11 -05:00
parent a26252feea
commit a3bf82c7ca
2 changed files with 18 additions and 12 deletions
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24
higher/smart_aug/test_dataug.py
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@ -19,13 +19,16 @@ tf_names = [
'ShearX',
'ShearY',
#'TranslateXabs',
#'TranslateYabs',
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
'Solarize',
#Color TF (Common mag scale)
#'+Contrast',
@ -79,7 +82,7 @@ if __name__ == "__main__":
}
#Parameters
n_inner_iter = 1
epochs = 2
epochs = 150
dataug_epoch_start=0
optim_param={
'Meta':{
@ -94,12 +97,12 @@ if __name__ == "__main__":
}
#Models
#model = LeNet(3,10)
model = LeNet(3,10)
#model = ResNet(num_classes=10)
import torchvision.models as models
#import torchvision.models as models
#model=models.resnet18()
model_name = 'resnet18' #'wide_resnet50_2' #'resnet18' #str(model)
model = getattr(models.resnet, model_name)(pretrained=False)
model_name = str(model) #'wide_resnet50_2' #'resnet18' #str(model)
#model = getattr(models.resnet, model_name)(pretrained=False)
#### Classic ####
if 'classic' in tasks:
@ -143,11 +146,12 @@ if __name__ == "__main__":
#### Augmented Model ####
if 'aug_model' in tasks:
torch.cuda.reset_max_memory_cached() #reset_peak_stats
t0 = time.perf_counter()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
model = Higher_model(model, model_name) #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=1, mix_dist=0.5, 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))
@ -159,10 +163,10 @@ if __name__ == "__main__":
print_freq=1,
unsup_loss=1,
hp_opt=False,
save_sample_freq=None)
save_sample_freq=1)
exec_time=time.perf_counter() - t0
max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved()
max_cached = torch.cuda.max_memory_cached()/(1024.0 * 1024.0) #torch.cuda.max_memory_reserved() #MB
####
print('-'*9)
times = [x["time"] for x in log]
@ -174,7 +178,7 @@ 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)+"(CV)"
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)+"(CV0.1)"
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)

6
higher/smart_aug/transformations.py
View file

@ -38,8 +38,10 @@ TF_dict={ #Dataugv5+
'FlipUD' : (lambda x, mag: flipUD(x)),
'FlipLR' : (lambda x, mag: flipLR(x)),
'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[1]*0.33), zero_pos=0))),
'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[2]*0.33), zero_pos=1))),
'TranslateXabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
'TranslateYabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),