Modif Dataugv6

higher/compare_res.py

@@ -2,12 +2,12 @@ from utils import *
 
 if __name__ == "__main__":
 
-    #'''
+    '''
     files=[
         #"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
         #"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
         #"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
-        "res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
+        #"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
     ]
 
     for idx, file in enumerate(files):
@@ -16,7 +16,7 @@ if __name__ == "__main__":
             data = json.load(json_file)
             plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
             #plot_TF_influence(data['Log'], param_names=data['Param_names'])
-    #'''
+    '''
     ## Loss , Acc, Proba = f(epoch) ##
     #plot_compare(filenames=files, fig_name="res/compare")
 
@@ -76,11 +76,11 @@ if __name__ == "__main__":
     '''
 
     #Res print
-    '''
+    #'''
     nb_run=3
     accs = []
     times = []
-    files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Mix1.0-14TFx2-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-%s.json"%str(run) for run in range(nb_run)]
+    files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Mix1-14TFx4-Mag)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
     
     for idx, file in enumerate(files):
         #legend+=str(idx)+'-'+file+'\n'
@@ -90,4 +90,4 @@ if __name__ == "__main__":
         times.append(data['Time'][0])
 
     print(files[0], np.mean(accs), np.std(accs), np.mean(times))
-    '''
+    #'''

higher/datasets.py

@@ -28,9 +28,105 @@ data_test = torchvision.datasets.MNIST(
     "./data", train=False, download=True, transform=torchvision.transforms.ToTensor()
 )
 '''
-data_train = torchvision.datasets.CIFAR10(
-    "./data", train=True, download=True, transform=transform
-)
+
+from torchvision.datasets.vision import VisionDataset
+from PIL import Image
+import augmentation_transforms
+import numpy as np
+
+class AugmentedDataset(VisionDataset):
+    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
+
+        super(AugmentedDataset, self).__init__(root, transform=transform, target_transform=target_transform)
+
+        supervised_dataset = torchvision.datasets.CIFAR10(root, train=train, download=download, transform=transform)
+
+        self.sup_data = supervised_dataset.data
+        self.sup_targets = supervised_dataset.targets
+
+        for idx, img in enumerate(self.sup_data):
+            self.sup_data[idx]= Image.fromarray(img) #to PIL Image
+
+        self.unsup_data=[]
+        self.unsup_targets=[]
+
+        self.data= self.sup_data
+        self.targets= self.sup_targets
+
+
+        self._TF = [
+        'Invert', 'Cutout', 'Sharpness', 'AutoContrast', 'Posterize',
+        'ShearX', 'TranslateX', 'TranslateY', 'ShearY', 'Rotate',
+        'Equalize', 'Contrast', 'Color', 'Solarize', 'Brightness']
+        self._op_list =[]
+        self.prob=0.5
+        for tf in self._TF:
+            for mag in range(1, 10):
+                self._op_list+=[(tf, self.prob, mag)]
+        self._nb_op = len(self._op_list)
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.targets[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        #img = Image.fromarray(img)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def augement_data(self, aug_copy=1):
+
+        policies = []
+        for op_1 in self._op_list:
+            for op_2 in self._op_list:
+                policies += [[op_1, op_2]]
+
+        for idx, image in enumerate(self.sup_data):
+            for _ in range(aug_copy):
+                chosen_policy = policies[np.random.choice(len(policies))]
+                aug_image = augmentation_transforms.apply_policy(chosen_policy, image)
+                #aug_image = augmentation_transforms.cutout_numpy(aug_image)
+
+                self.unsup_data+=[aug_image]
+                self.unsup_targets+=[self.sup_targets[idx]]
+
+        print(type(self.data), type(self.sup_data), type(self.unsup_data))
+        print(len(self.data), len(self.sup_data), len(self.unsup_data))
+        #self.data= self.sup_data+self.unsup_data
+        self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
+        print(len(self.data))
+        self.targets= self.sup_targets+self.unsup_targets
+
+
+    def len_supervised(self):
+        return len(self.sup_data)
+
+    def len_unsupervised(self):
+        return len(self.unsup_data)
+
+    def __len__(self):
+        return len(self.data)
+
+
+data_train = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
+#print(len(data_train))
+#data_train = AugmentedDataset("./data", train=True, download=True, transform=transform)
+#print(len(data_train), data_train.len_supervised(), data_train.len_unsupervised())
+#data_train.augement_data()
+#print(len(data_train), data_train.len_supervised(), data_train.len_unsupervised())
 #data_val = torchvision.datasets.CIFAR10(
 #    "./data", train=True, download=True, transform=transform
 #)
@@ -45,4 +141,4 @@ val_subset_indices=range(int(len(data_train)/2),len(data_train))
 
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
 dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))
-dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False)
+dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False)

higher/dataug.py

@@ -692,6 +692,208 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
         else:
             return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
 
+import numpy as np
+class Data_augV6(nn.Module): #Optimisation sequentielle
+    def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True):
+        super(Data_augV6, self).__init__()
+        assert len(TF_dict)>0
+        
+        self._data_augmentation = True
+
+        self._TF_dict = TF_dict
+        self._TF= list(self._TF_dict.keys())
+        self._nb_tf= len(self._TF)
+
+        self._N_seqTF = N_TF
+        self._shared_mag = shared_mag
+        self._fixed_mag = fixed_mag
+        
+        self._TF_set_size=3
+        #if self._TF_set_size>self._nb_tf:
+        #    print("Warning : TF sets size higher than number of TF. Reducing set size to %d"%self._nb_tf)
+        #    self._TF_set_size=self._nb_tf
+        assert self._nb_tf>=self._TF_set_size
+        self._TF_sets=[]
+        for i in range(1,self._nb_tf):
+            for j in range(i,self._nb_tf):
+                if i!=j:
+                    self._TF_sets+=[torch.tensor([0, i, j])]
+        #print(self._TF_sets)
+        #self._TF_sets=[torch.tensor([0, i, j]) for i in range(1,self._nb_tf)] #All VS Identity
+        self._TF_schedule = [list(range(len(self._TF_sets))) for _ in range(self._N_seqTF)]
+        for n_tf in range(self._N_seqTF) :
+            TF.random.shuffle(self._TF_schedule[n_tf])
+        #print(self._TF_schedule)
+        self._current_TF_idx=0 #random.randint
+        self._start_prob = 1/self._TF_set_size
+
+
+        self._params = nn.ParameterDict({
+            "prob": nn.Parameter(torch.tensor(self._start_prob).expand(self._nb_tf)), #Proba independantes
+            "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag
+                            else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX]
+        })
+
+        #for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag
+
+        #Distribution
+        self._fixed_prob=fixed_prob
+        self._samples = []
+        self._mix_dist = False
+        if mix_dist != 0.0:
+            self._mix_dist = True
+            self._mix_factor = max(min(mix_dist, 1.0), 0.0)
+
+        #Mag regularisation
+        if not self._fixed_mag:
+            if  self._shared_mag :
+                self._reg_tgt = torch.tensor(TF.PARAMETER_MAX, dtype=torch.float) #Encourage amplitude max
+            else:
+                self._reg_mask=[self._TF.index(t) for t in self._TF if t not in TF.TF_ignore_mag]
+                self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max
+
+    def forward(self, x):
+        self._samples = []
+        if self._data_augmentation:# and TF.random.random() < 0.5:
+            device = x.device
+            batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
+
+            x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
+            
+            for n_tf in range(self._N_seqTF):
+
+                tf_set = self._TF_sets[self._TF_schedule[n_tf][self._current_TF_idx]].to(device)
+                #print(n_tf, tf_set)
+                ## Echantillonage ##
+                uniforme_dist = torch.ones(1,len(tf_set),device=device).softmax(dim=1)
+
+                if not self._mix_dist:
+                    self._distrib = uniforme_dist        
+                else:
+                    prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
+                    curr_prob = torch.index_select(prob, 0, tf_set)
+                    curr_prob = curr_prob /sum(curr_prob) #Contrainte sum(p)=1
+                    self._distrib = (self._mix_factor*curr_prob+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
+
+                cat_distrib= Categorical(probs=torch.ones((batch_size, len(tf_set)), device=device)*self._distrib)
+                sample = cat_distrib.sample()
+                self._samples.append(sample)
+
+                ## Transformations ##
+                x = self.apply_TF(x, sample)
+        return x
+
+    def apply_TF(self, x, sampled_TF):
+        device = x.device
+        batch_size, channels, h, w = x.shape
+        smps_x=[]
+        
+        for sel_idx, tf_idx in enumerate(self._TF_sets[self._current_TF_idx]):
+            mask = sampled_TF==sel_idx #Create selection mask
+            smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim)
+
+            if smp_x.shape[0]!=0: #if there's data to TF
+                magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx]
+                if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param
+
+                tf=self._TF[tf_idx]
+                #print(magnitude)
+
+                #In place
+                #x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
+
+                #Out of place
+                smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude)
+                idx= mask.nonzero()
+                idx= idx.expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
+                x=x.scatter(dim=0, index=idx, src=smp_x)
+                                
+        return x
+
+    def adjust_param(self, soft=False): #Detach from gradient ?
+        if not self._fixed_prob:
+            if soft :
+                self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
+            else:
+                self._params['prob'].data = F.relu(self._params['prob'].data)
+                #self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0)
+                #self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
+
+                self._params['prob'].data[0]=self._start_prob #Fixe p identite
+
+        if not self._fixed_mag:
+            #self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme
+            self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
+
+    def loss_weight(self): #A verifier
+        if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation
+
+        prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
+
+        #Plusieurs TF sequentielles (Attention ne prend pas en compte ordre !)
+        w_loss = torch.zeros((self._samples[0].shape[0],self._TF_set_size), device=self._samples[0].device)
+        for n_tf in range(self._N_seqTF):
+            tmp_w = torch.zeros(w_loss.size(),device=w_loss.device)
+            tmp_w.scatter_(dim=1, index=self._samples[n_tf].view(-1,1), value=1/self._N_seqTF)
+
+            tf_set = self._TF_sets[self._TF_schedule[n_tf][self._current_TF_idx]].to(prob.device)
+            curr_prob = torch.index_select(prob, 0, tf_set)
+            curr_prob = curr_prob /sum(curr_prob) #Contrainte sum(p)=1
+
+            #ATTENTION DISTRIB DIFFERENTE AVEC MIX
+            assert not self._mix_dist
+            w_loss += tmp_w * curr_prob /self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss)
+
+        w_loss = torch.sum(w_loss,dim=1)
+        return w_loss
+
+    def reg_loss(self, reg_factor=0.005):
+        if self._fixed_mag: # or self._fixed_prob: #Pas de regularisation si trop peu de DOF
+            return torch.tensor(0)
+        else:
+            #return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean') 
+            params = self._params['mag'] if self._params['mag'].shape==torch.Size([]) else self._params['mag'][self._reg_mask]
+            return reg_factor * F.mse_loss(params, target=self._reg_tgt.to(params.device), reduction='mean')
+
+    def next_TF_set(self, idx=None):
+        if idx:
+            self._current_TF_idx=idx
+        else:
+            self._current_TF_idx+=1
+            if self._current_TF_idx== len(self._TF_schedule[0]): 
+                self._current_TF_idx=0
+                #for n_tf in range(self._N_seqTF) :
+                #    TF.random.shuffle(self._TF_schedule[n_tf])
+                #print(self._TF_schedule)
+        #print("Current TF :",self._TF_sets[self._current_TF_idx])
+
+    def train(self, mode=None):
+        if mode is None :
+            mode=self._data_augmentation
+        self.augment(mode=mode) #Inutile si mode=None
+        super(Data_augV6, self).train(mode)
+
+    def eval(self):
+        self.train(mode=False)
+
+    def augment(self, mode=True):
+        self._data_augmentation=mode
+
+    def __getitem__(self, key):
+        return self._params[key]
+
+    def __str__(self):
+        dist_param=''
+        if self._fixed_prob: dist_param+='Fx'
+        mag_param='Mag'
+        if self._fixed_mag: mag_param+= 'Fx'
+        if self._shared_mag: mag_param+= 'Sh'
+        if not self._mix_dist:
+            return "Data_augV6(Uniform%s-%dTF(%d)x%d-%s)" % (dist_param, self._nb_tf, self._TF_set_size, self._N_seqTF, mag_param)
+        else:
+            return "Data_augV6(Mix%.1f%s-%dTF(%d)x%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._TF_set_size, self._N_seqTF, mag_param)
+
+
 class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
     def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
         super(RandAug, self).__init__()

higher/test_dataug.py

@@ -5,21 +5,21 @@ from train_utils import *
 
 tf_names = [
     ## Geometric TF ##
-    #'Identity',
-    #'FlipUD',
-    #'FlipLR',
-    #'Rotate',
-    #'TranslateX',
-    #'TranslateY',
-    #'ShearX',
-    #'ShearY',
+    'Identity',
+    'FlipUD',
+    'FlipLR',
+    'Rotate',
+    'TranslateX',
+    'TranslateY',
+    'ShearX',
+    'ShearY',
 
     ## Color TF (Expect image in the range of [0, 1]) ##
-    #'Contrast',
-    #'Color',
-    #'Brightness',
-    #'Sharpness',
-    #'Posterize',
+    'Contrast',
+    'Color',
+    'Brightness',
+    'Sharpness',
+    'Posterize',
     'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
 
     #Color TF (Common mag scale)
@@ -44,10 +44,10 @@ tf_names = [
     #'BadTranslateY',
     #'BadTranslateY_neg',
 
-    #'BadColor',
-    #'BadSharpness',
-    #'BadContrast',
-    #'BadBrightness',
+    'BadColor',
+    'BadSharpness',
+    'BadContrast',
+    'BadBrightness',
 
     #Non fonctionnel
     #'Auto_Contrast', #Pas opti pour des batch (Super lent)
@@ -65,7 +65,7 @@ else:
 if __name__ == "__main__":
 
     n_inner_iter = 10
-    epochs = 1
+    epochs = 100
     dataug_epoch_start=0
 
     #### Classic ####
@@ -95,12 +95,12 @@ 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=0.0, fixed_prob=True, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
+    aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=True, 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=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
     #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
     print(str(aug_model), 'on', device_name)
     #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
-    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=None)
+    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None)
 
     ####
     print('-'*9)

higher/train_utils.py

@@ -618,6 +618,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
 
                 meta_opt.step()
                 model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
+                model['data_aug'].next_TF_set()
 
                 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)
@@ -651,7 +652,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
             print('TF Proba :', model['data_aug']['prob'].data)
             #print('proba grad',model['data_aug']['prob'].grad)
             print('TF Mag :', model['data_aug']['mag'].data)
-            print('Mag grad',model['data_aug']['mag'].grad)
+            #print('Mag grad',model['data_aug']['mag'].grad)
             #print('Reg loss:', model['data_aug'].reg_loss().item())
         #############
         #### Log ####

higher/transformations.py

@@ -46,7 +46,7 @@ TF_dict={ #Dataugv5 #AutoAugment
   'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
   'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
   'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
-  'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] #Pas opti pour des batch
+  'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
   
   #Non fonctionnel
   #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
@@ -70,7 +70,7 @@ TF_dict={ #Dataugv5
   'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
   'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
   'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
-  'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] #Pas opti pour des batch
+  'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
 
   #Color TF (Common mag scale)
   '+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
@@ -82,7 +82,7 @@ TF_dict={ #Dataugv5
   '-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
   '-Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
   '=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] #Pas opti pour des batch
+  '=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]
   
 
   'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))),
@@ -295,8 +295,7 @@ def equalize(x): #PAS OPTIMISE POUR DES BATCH
 
   return float_image(x)
 
-def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
-  # Optimisation : Mask direct sur toute les donnees (Mask = (B,C,H,W)> (B))
+def solarize(x, thresholds):
   batch_size, channels, h, w = x.shape
   #imgs=[]
   #for idx, t in enumerate(thresholds): #Operation par image