AntoineH/smart_augmentation
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work in progress - validation brutus res

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Harle, Antoine (Contracteur) 2019-12-04 10:05:59 -05:00
parent f8cc38dd6b
commit bb59c7de25
8 changed files with 4278 additions and 4278 deletions
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23
higher/dataug.py
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@ -694,7 +694,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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):
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=None, fixed_mag=True, shared_mag=True):
super(Data_augV6, self).__init__()
assert len(TF_dict)>0
@ -708,8 +708,9 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
self._shared_mag = shared_mag
self._fixed_mag = fixed_mag
self._TF_set_size=3
self._fixed_TF=[0]
self._TF_set_size = prob_set_size if prob_set_size else self._nb_tf
self._fixed_TF=[0] #Identite
assert self._TF_set_size>=len(self._fixed_TF)
if self._TF_set_size>self._nb_tf:
@ -723,7 +724,6 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
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):
@ -739,12 +739,12 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
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])
#print(self._TF_schedule)
self._current_TF_idx=0 #random.randint
self._start_prob = 1/self._TF_set_size
@ -881,12 +881,11 @@ class Data_augV6(nn.Module): #Optimisation sequentielle
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])
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])
def train(self, mode=None):
if mode is None :