AntoineH/smart_augmentation
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Modif solarize (Tjrs pas differentiable...)

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Harle, Antoine (Contracteur) 2019-11-27 17:19:51 -05:00
parent 4a7e73088d
commit d822f8f92e
5 changed files with 56 additions and 42 deletions
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28
higher/dataug.py
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@ -692,7 +692,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
else:
return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh
class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
super(RandAug, self).__init__()
@ -773,9 +773,9 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh
def __str__(self):
return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
class RandAugUDA(nn.Module): #RandAugment from UDA (for DA during training)
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
super(RandAug, self).__init__()
super(RandAugUDA, self).__init__()
self._data_augmentation = True
@ -786,7 +786,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
self.mag=nn.Parameter(torch.tensor(float(mag)))
self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.tensor(0.5)),
"prob": nn.Parameter(torch.tensor(0.5).unsqueeze(dim=0)),
"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX))),
})
self._shared_mag = True
@ -794,12 +794,10 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
self._op_list =[]
for tf in self._TF:
for mag in range(0.1, self._params['mag'], 0.1):
op_list+=[(tf, self._params['prob'], mag)]
for mag in range(1, int(self._params['mag']*10), 1):
self._op_list+=[(tf, self._params['prob'].item(), mag/10)]
self._nb_op = len(self._op_list)
print(self._op_list)
def forward(self, x):
if self._data_augmentation:# and TF.random.random() < 0.5:
device = x.device
@ -821,16 +819,16 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
smps_x=[]
for op_idx in range(self._nb_op):
mask = sampled_TF==tf_idx #Create selection mask
mask = sampled_TF==op_idx #Create selection mask
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
if TF.random.random() < self.op_list[op_idx][1]:
magnitude=self.op_list[op_idx][2]
tf=self.op_list[op_idx][0]
if TF.random.random() < self._op_list[op_idx][1]:
magnitude=self._op_list[op_idx][2]
tf=self._op_list[op_idx][0]
#In place
x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
x[mask]=self._TF_dict[tf](x=smp_x, mag=torch.tensor(magnitude, device=x.device))
return x
@ -847,7 +845,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
if mode is None :
mode=self._data_augmentation
self.augment(mode=mode) #Inutile si mode=None
super(RandAug, self).train(mode)
super(RandAugUDA, self).train(mode)
def eval(self):
self.train(mode=False)
@ -859,7 +857,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
return self._params[key]
def __str__(self):
return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
return "RandAugUDA(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
class Augmented_model(nn.Module):
def __init__(self, data_augmenter, model):