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Support taille arbitraire de sets dans Dataugv6

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Harle, Antoine (Contracteur) 2019-12-02 08:22:24 -05:00
parent 6f9cb2cd68
commit f8cc38dd6b
4 changed files with 2176 additions and 2155 deletions
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43
higher/dataug.py
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@ -709,17 +709,38 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
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._fixed_TF=[0]
assert self._TF_set_size>=len(self._fixed_TF)
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
## Genenerate TF sets ##
if self._TF_set_size==len(self._fixed_TF):
print("Warning : using only fixed set of TF : ", self._fixed_TF)
self._TF_sets=torch.tensor([self._fixed_TF])
else:
def generate_TF_sets(n_TF, set_size, idx_prefix=[]):
TF_sets=[]
print(set_size, idx_prefix)
if len(idx_prefix)!=0:
if set_size>2:
for i in range(idx_prefix[-1]+1, n_TF):
TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size-1, idx_prefix=idx_prefix+[i])
else:
#if i not in idx_prefix:
TF_sets+=[torch.tensor(idx_prefix+[i]) for i in range(idx_prefix[-1]+1, n_TF)]
elif set_size>1:
for i in range(0, n_TF):
TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size, idx_prefix=[i])
else:
TF_sets+=[torch.tensor([i]) for i in range(0, n_TF)]
return TF_sets
self._TF_sets=generate_TF_sets(self._nb_tf, self._TF_set_size, self._fixed_TF)
## Plan TF learning schedule ##
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])