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Modification/Suppression des BadTF

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Harle, Antoine (Contracteur) 2020-01-10 16:29:10 -05:00
parent 23351ec13c
commit 3425ba2ceb
2 changed files with 14 additions and 18 deletions
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7
higher/train_utils.py
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@ -746,7 +746,10 @@ def run_dist_dataugV2(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
#if epoch>50: #if epoch>50:
meta_opt.step() meta_opt.step()
model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1 model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
#model['data_aug'].next_TF_set() try: #Dataugv6
model['data_aug'].next_TF_set()
except:
pass
fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True) fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track) diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track)
@ -754,7 +757,7 @@ def run_dist_dataugV2(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
tf = time.process_time() tf = time.process_time()
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch)) #viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
#viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch)) #viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch), weight_labels=model['data_aug'].loss_weight())
if(not high_grad_track): if(not high_grad_track):
countcopy+=1 countcopy+=1

23
higher/transformations.py
View file

@ -84,25 +84,18 @@ TF_dict={ #Dataugv5
'=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient '=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
'=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] '=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=0))),
'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=1))),
'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=0))),
'BTranslateX-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=0))),
'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=1))),
'BTranslateY-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=1))),
'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))), 'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9*2, maxval=2*4))),
'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=0))),
'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=1))),
'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=0))),
'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=1))),
'BadTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=0))),
'BadTranslateX_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=0))),
'BadTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=1))),
'BadTranslateY_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=1))),
'BadColor':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
'BadSharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))), 'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
'Random':(lambda x, mag: torch.rand_like(x)), 'Random':(lambda x, mag: torch.rand_like(x)),
'RandBlend': (lambda x, mag: blend(x,torch.rand_like(x), alpha=torch.tensor(0.5,device=mag.device).expand(x.shape[0]))), 'RandBlend': (lambda x, mag: blend(x,torch.rand_like(x), alpha=torch.tensor(0.7,device=mag.device).expand(x.shape[0]))),
#Non fonctionnel #Non fonctionnel
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent) #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)