AntoineH/smart_augmentation
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Ajout des TF multiples sequentielles

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Harle, Antoine (Contracteur) 2019-11-11 17:01:15 -05:00
parent caf5fad470
commit c6dd26c29a
204 changed files with 54 additions and 35 deletions
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58
higher/dataug.py
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@ -326,12 +326,14 @@ class Data_augV4(nn.Module): #Transformations avec mask
self._TF=list(self._mag_fct.keys())
self._nb_tf= len(self._TF)
self._N_TF = N_TF
self._fixed_mag=5 #[0, PARAMETER_MAX]
self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
})
self._sample = []
self._samples = []
self._mix_dist = False
if mix_dist != 0.0:
@ -342,23 +344,26 @@ class Data_augV4(nn.Module): #Transformations avec mask
if self._data_augmentation:
device = x.device
batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
## Echantillonage ##
uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
if not self._mix_dist:
self._distrib = uniforme_dist
else:
self._distrib = (self._mix_factor*self._params["prob"]+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
print(self.distrib.shape)
cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib)
self._sample = cat_distrib.sample()
## Transformations ##
x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
x = self.apply_TF(x, self._sample)
self._samples = []
for _ in range(self._N_TF):
## Echantillonage ##
uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1)
if not self._mix_dist:
self._distrib = uniforme_dist
else:
self._distrib = (self._mix_factor*self._params["prob"]+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
print(self.distrib.shape)
cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib)
sample = cat_distrib.sample()
self._samples.append(sample)
## Transformations ##
x = self.apply_TF(x, sample)
return x
'''
def compute_TF_matrix(self, magnitude=None, sample_info= None):
@ -482,12 +487,25 @@ class Data_augV4(nn.Module): #Transformations avec mask
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
def loss_weight(self):
w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device)
w_loss.scatter_(1, self._sample.view(-1,1), 1)
# 1 seule TF
#self._sample = self._samples[-1]
#w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device)
#w_loss.scatter_(dim=1, index=self._sample.view(-1,1), value=1)
#w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss)
#w_loss = torch.sum(w_loss,dim=1)
#Plusieurs TF sequentielles
w_loss = torch.zeros((self._samples[0].shape[0],self._nb_tf), device=self._samples[0].device)
for sample in self._samples:
tmp_w = torch.zeros(w_loss.size(),device=w_loss.device)
tmp_w.scatter_(dim=1, index=sample.view(-1,1), value=1/self._N_TF)
w_loss += tmp_w
w_loss = w_loss * self._params["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 train(self, mode=None):
if mode is None :
mode=self._data_augmentation
@ -505,9 +523,9 @@ class Data_augV4(nn.Module): #Transformations avec mask
def __str__(self):
if not self._mix_dist:
return "Data_augV4(Uniform-%d TF)" % self._nb_tf
return "Data_augV4(Uniform-%d TF x %d)" % (self._nb_tf, self._N_TF)
else:
return "Data_augV4(Mix %.1f-%d TF)" % (self._mix_factor, self._nb_tf)
return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_TF)
class Augmented_model(nn.Module):
def __init__(self, data_augmenter, model):