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Dataugv5- Modification des TF pour propagation du gradient (mag)

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Harle, Antoine (Contracteur) 2019-11-18 12:53:23 -05:00
parent 05f81787d6
commit 994d657a28
5 changed files with 94 additions and 21 deletions
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22
higher/dataug.py
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@ -583,19 +583,33 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
def apply_TF(self, x, sampled_TF):
device = x.device
batch_size, channels, h, w = x.shape
smps_x=[]
masks=[]
for tf_idx in range(self._nb_tf):
mask = sampled_TF==tf_idx #Create selection mask
smp_x = x[mask] #torch.masked_select() ?
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"][tf_idx]*10
tf=self._TF[tf_idx]
#print(magnitude)
x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) # Refusionner eviter x[mask] : in place
#x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) # Refusionner eviter x[mask] : in place
smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude)
idx= mask.nonzero()
#print('-'*8)
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 ...
#print(idx.shape, smp_x.shape)
#print(idx[0], tf_idx)
#print(smp_x[0,])
#x=x.view(-1,3*32*32)
#smp_x=smp_x.view(-1,3*32*32)
x=x.scatter(dim=0, index=idx, src=smp_x)
#x=x.view(-1,3,32,32)
#print(x[0,])
return x
def adjust_prob(self, soft=False): #Detach from gradient ?