mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
Merge branch 'master' of http://frd-git/scm/axon/smart_augmentation
This commit is contained in:
root
commit
ebee1b789f
5 changed files with 277 additions and 51 deletions
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@ -2,11 +2,12 @@ from utils import *
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if __name__ == "__main__":
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'''
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#'''
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files=[
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
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"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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]
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for idx, file in enumerate(files):
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@ -15,7 +16,7 @@ if __name__ == "__main__":
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data = json.load(json_file)
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plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
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#plot_TF_influence(data['Log'], param_names=data['Param_names'])
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'''
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#'''
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## Loss , Acc, Proba = f(epoch) ##
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#plot_compare(filenames=files, fig_name="res/compare")
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@ -75,6 +76,7 @@ if __name__ == "__main__":
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'''
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#Res print
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'''
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nb_run=3
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accs = []
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times = []
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@ -88,3 +90,4 @@ if __name__ == "__main__":
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times.append(data['Time'][0])
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print(files[0], np.mean(accs), np.std(accs), np.mean(times))
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'''
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168
higher/dataug.py
168
higher/dataug.py
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@ -659,7 +659,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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return w_loss
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def reg_loss(self, reg_factor=0.005):
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if self._fixed_mag:
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if self._fixed_mag: # or self._fixed_prob: #Pas de regularisation si trop peu de DOF
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return torch.tensor(0)
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else:
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#return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean')
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@ -692,6 +692,172 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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else:
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return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
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class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
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super(RandAug, self).__init__()
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self._data_augmentation = True
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self._TF_dict = TF_dict
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self._TF= list(self._TF_dict.keys())
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self._nb_tf= len(self._TF)
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self._N_seqTF = N_TF
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self.mag=nn.Parameter(torch.tensor(float(mag)))
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self._params = nn.ParameterDict({
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"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #pas utilise
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"mag" : nn.Parameter(torch.tensor(float(mag))),
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})
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self._shared_mag = True
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self._fixed_mag = True
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def forward(self, x):
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if self._data_augmentation:# and TF.random.random() < 0.5:
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device = x.device
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batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
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x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
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for _ in range(self._N_seqTF):
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## Echantillonage ## == sampled_ops = np.random.choice(transforms, N)
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uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
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cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*uniforme_dist)
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sample = cat_distrib.sample()
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## Transformations ##
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x = self.apply_TF(x, sample)
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return x
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def apply_TF(self, x, sampled_TF):
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smps_x=[]
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for tf_idx in range(self._nb_tf):
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mask = sampled_TF==tf_idx #Create selection mask
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smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim)
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if smp_x.shape[0]!=0: #if there's data to TF
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magnitude=self._params["mag"].detach()
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tf=self._TF[tf_idx]
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#print(magnitude)
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#In place
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x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
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return x
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def adjust_param(self, soft=False):
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pass #Pas de parametre a opti
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def loss_weight(self):
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return 1 #Pas d'echantillon = pas de ponderation
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def reg_loss(self, reg_factor=0.005):
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return torch.tensor(0) #Pas de regularisation
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def train(self, mode=None):
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if mode is None :
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mode=self._data_augmentation
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self.augment(mode=mode) #Inutile si mode=None
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super(RandAug, self).train(mode)
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def eval(self):
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self.train(mode=False)
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def augment(self, mode=True):
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self._data_augmentation=mode
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def __getitem__(self, key):
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return self._params[key]
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def __str__(self):
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return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
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class RandAugUDA(nn.Module): #RandAugment from UDA (for DA during training)
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
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super(RandAugUDA, self).__init__()
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self._data_augmentation = True
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self._TF_dict = TF_dict
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self._TF= list(self._TF_dict.keys())
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self._nb_tf= len(self._TF)
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self._N_seqTF = N_TF
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self.mag=nn.Parameter(torch.tensor(float(mag)))
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self._params = nn.ParameterDict({
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"prob": nn.Parameter(torch.tensor(0.5).unsqueeze(dim=0)),
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"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX))),
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})
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self._shared_mag = True
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self._fixed_mag = True
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self._op_list =[]
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for tf in self._TF:
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for mag in range(1, int(self._params['mag']*10), 1):
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self._op_list+=[(tf, self._params['prob'].item(), mag/10)]
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self._nb_op = len(self._op_list)
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def forward(self, x):
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if self._data_augmentation:# and TF.random.random() < 0.5:
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device = x.device
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batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
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x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
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for _ in range(self._N_seqTF):
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## Echantillonage ## == sampled_ops = np.random.choice(transforms, N)
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uniforme_dist = torch.ones(1, self._nb_op, device=device).softmax(dim=1)
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cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_op), device=device)*uniforme_dist)
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sample = cat_distrib.sample()
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## Transformations ##
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x = self.apply_TF(x, sample)
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return x
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def apply_TF(self, x, sampled_TF):
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smps_x=[]
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for op_idx in range(self._nb_op):
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mask = sampled_TF==op_idx #Create selection mask
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smp_x = x[mask] #torch.masked_select() ? (Necessite d'expand le mask au meme dim)
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if smp_x.shape[0]!=0: #if there's data to TF
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if TF.random.random() < self._op_list[op_idx][1]:
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magnitude=self._op_list[op_idx][2]
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tf=self._op_list[op_idx][0]
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#In place
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x[mask]=self._TF_dict[tf](x=smp_x, mag=torch.tensor(magnitude, device=x.device))
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return x
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def adjust_param(self, soft=False):
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pass #Pas de parametre a opti
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def loss_weight(self):
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return 1 #Pas d'echantillon = pas de ponderation
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def reg_loss(self, reg_factor=0.005):
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return torch.tensor(0) #Pas de regularisation
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def train(self, mode=None):
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if mode is None :
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mode=self._data_augmentation
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self.augment(mode=mode) #Inutile si mode=None
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super(RandAugUDA, self).train(mode)
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def eval(self):
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self.train(mode=False)
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def augment(self, mode=True):
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self._data_augmentation=mode
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def __getitem__(self, key):
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return self._params[key]
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def __str__(self):
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return "RandAugUDA(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
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class Augmented_model(nn.Module):
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def __init__(self, data_augmenter, model):
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@ -5,14 +5,34 @@ from train_utils import *
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tf_names = [
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## Geometric TF ##
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'Identity',
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'FlipUD',
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'FlipLR',
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'Rotate',
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'TranslateX',
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'TranslateY',
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'ShearX',
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'ShearY',
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#'Identity',
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#'FlipUD',
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#'FlipLR',
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#'Rotate',
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#'TranslateX',
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#'TranslateY',
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#'ShearX',
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#'ShearY',
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## Color TF (Expect image in the range of [0, 1]) ##
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#'Contrast',
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#'Color',
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#'Brightness',
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#'Sharpness',
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#'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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#Color TF (Common mag scale)
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#'+Contrast',
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#'+Color',
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#'+Brightness',
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#'+Sharpness',
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#'-Contrast',
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#'-Color',
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#'-Brightness',
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#'-Sharpness',
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#'=Posterize',
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#'=Solarize',
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#'BRotate',
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#'BTranslateX',
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@ -24,13 +44,10 @@ tf_names = [
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#'BadTranslateY',
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#'BadTranslateY_neg',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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'Color',
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'Brightness',
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'Sharpness',
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'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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#'BadColor',
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#'BadSharpness',
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#'BadContrast',
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#'BadBrightness',
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#Non fonctionnel
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#'Auto_Contrast', #Pas opti pour des batch (Super lent)
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@ -48,7 +65,7 @@ else:
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if __name__ == "__main__":
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n_inner_iter = 10
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epochs = 100
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epochs = 1
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dataug_epoch_start=0
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#### Classic ####
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@ -74,15 +91,16 @@ if __name__ == "__main__":
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print('-'*9)
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'''
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#### Augmented Model ####
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'''
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#'''
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t0 = time.process_time()
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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#tf_dict = TF.TF_dict
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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=False), LeNet(3,10)).to(device)
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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)
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#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)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
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print(str(aug_model), 'on', device_name)
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#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
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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)
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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)
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####
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print('-'*9)
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@ -98,9 +116,9 @@ if __name__ == "__main__":
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print('Execution Time : %.00f '%(time.process_time() - t0))
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print('-'*9)
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'''
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#### TF tests ####
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#'''
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#### TF tests ####
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'''
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res_folder="res/brutus-tests/"
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epochs= 150
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inner_its = [1, 10]
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@ -150,4 +168,4 @@ if __name__ == "__main__":
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#plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
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print('-'*9)
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#'''
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'''
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@ -540,6 +540,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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val_loss=torch.tensor(0) #Necessaire si pas de metastep sur une epoch
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dl_val_it = iter(dl_val)
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#if inner_it!=0:
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meta_opt = torch.optim.Adam(model['data_aug'].parameters(), lr=1e-2)
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inner_opt = torch.optim.SGD(model['model'].parameters(), lr=1e-2, momentum=0.9)
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@ -650,7 +651,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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print('TF Proba :', model['data_aug']['prob'].data)
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#print('proba grad',model['data_aug']['prob'].grad)
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print('TF Mag :', model['data_aug']['mag'].data)
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#print('Mag grad',model['data_aug']['mag'].grad)
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print('Mag grad',model['data_aug']['mag'].grad)
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#print('Reg loss:', model['data_aug'].reg_loss().item())
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#############
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#### Log ####
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@ -680,5 +681,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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model.augment(mode=True)
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if inner_it != 0: high_grad_track = True
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viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
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viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
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#print("Copy ", countcopy)
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return log
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@ -64,6 +64,27 @@ TF_dict={ #Dataugv5
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'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
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'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'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
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#Color TF (Common mag scale)
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'+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'+Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
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'-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
|
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'-Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
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'=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
|
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'=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
|
||||
|
||||
|
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'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))),
|
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'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=0))),
|
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'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=1))),
|
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|
|
@ -74,14 +95,11 @@ TF_dict={ #Dataugv5
|
|||
'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))),
|
||||
|
||||
## Color TF (Expect image in the range of [0, 1]) ##
|
||||
'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
|
||||
'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
|
||||
'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
|
||||
|
||||
'BadColor':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
|
||||
'BadSharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
|
||||
'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
|
||||
'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))),
|
||||
|
||||
#Non fonctionnel
|
||||
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
|
||||
|
|
@ -111,10 +129,15 @@ def float_image(int_image):
|
|||
# return random.uniform(minval, real_max)
|
||||
|
||||
def rand_floats(size, mag, maxval, minval=None): #[(-maxval,minval), maxval]
|
||||
real_max = float_parameter(mag, maxval=maxval)
|
||||
if not minval : minval = -real_max
|
||||
real_mag = float_parameter(mag, maxval=maxval)
|
||||
if not minval : minval = -real_mag
|
||||
#return random.uniform(minval, real_max)
|
||||
return minval +(real_max-minval) * torch.rand(size, device=mag.device)
|
||||
return minval + (real_mag-minval) * torch.rand(size, device=mag.device) #[min_val, real_mag]
|
||||
|
||||
def invScale_rand_floats(size, mag, maxval, minval):
|
||||
#Mag=[0,PARAMETER_MAX] => [PARAMETER_MAX, 0] = [maxval, minval]
|
||||
real_mag = float_parameter(float(PARAMETER_MAX) - mag, maxval=maxval-minval)+minval
|
||||
return real_mag + (maxval-real_mag) * torch.rand(size, device=mag.device) #[real_mag, max_val]
|
||||
|
||||
def zero_stack(tensor, zero_pos):
|
||||
if zero_pos==0:
|
||||
|
|
@ -139,7 +162,7 @@ def float_parameter(level, maxval):
|
|||
#return float(level) * maxval / PARAMETER_MAX
|
||||
return (level * maxval / PARAMETER_MAX)#.to(torch.float)
|
||||
|
||||
def int_parameter(level, maxval): #Perte de gradient
|
||||
#def int_parameter(level, maxval): #Perte de gradient
|
||||
"""Helper function to scale `val` between 0 and maxval .
|
||||
Args:
|
||||
level: Level of the operation that will be between [0, `PARAMETER_MAX`].
|
||||
|
|
@ -149,7 +172,7 @@ def int_parameter(level, maxval): #Perte de gradient
|
|||
An int that results from scaling `maxval` according to `level`.
|
||||
"""
|
||||
#return int(level * maxval / PARAMETER_MAX)
|
||||
return (level * maxval / PARAMETER_MAX)
|
||||
# return (level * maxval / PARAMETER_MAX)
|
||||
|
||||
def flipLR(x):
|
||||
device = x.device
|
||||
|
|
@ -275,24 +298,36 @@ def equalize(x): #PAS OPTIMISE POUR DES BATCH
|
|||
def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
|
||||
# Optimisation : Mask direct sur toute les donnees (Mask = (B,C,H,W)> (B))
|
||||
batch_size, channels, h, w = x.shape
|
||||
imgs=[]
|
||||
for idx, t in enumerate(thresholds): #Operation par image
|
||||
mask = x[idx] > t #Perte du gradient
|
||||
#imgs=[]
|
||||
#for idx, t in enumerate(thresholds): #Operation par image
|
||||
# mask = x[idx] > t #Perte du gradient
|
||||
#In place
|
||||
#inv_x = 1-x[idx][mask]
|
||||
#x[idx][mask]=inv_x
|
||||
# inv_x = 1-x[idx][mask]
|
||||
# x[idx][mask]=inv_x
|
||||
#
|
||||
|
||||
#Out of place
|
||||
im = x[idx]
|
||||
inv_x = 1-im[mask]
|
||||
# im = x[idx]
|
||||
# inv_x = 1-im[mask]
|
||||
|
||||
imgs.append(im.masked_scatter(mask,inv_x))
|
||||
# imgs.append(im.masked_scatter(mask,inv_x))
|
||||
|
||||
idxs=torch.tensor(range(x.shape[0]), device=x.device)
|
||||
idxs=idxs.unsqueeze(dim=1).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=idxs, src=torch.stack(imgs))
|
||||
#idxs=torch.tensor(range(x.shape[0]), device=x.device)
|
||||
#idxs=idxs.unsqueeze(dim=1).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=idxs, src=torch.stack(imgs))
|
||||
#
|
||||
|
||||
thresholds = thresholds.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
|
||||
#print(thresholds.grad_fn)
|
||||
x=torch.where(x>thresholds,1-x, x)
|
||||
#print(mask.grad_fn)
|
||||
|
||||
#x=x.min(thresholds)
|
||||
#inv_x = 1-x[mask]
|
||||
#x=x.where(x<thresholds,1-x)
|
||||
#x[mask]=inv_x
|
||||
#x=x.masked_scatter(mask, inv_x)
|
||||
|
||||
return x
|
||||
|
||||
#https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue