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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9
higher/compare_res.py
View file

@ -2,11 +2,12 @@ from utils import *
if __name__ == "__main__": if __name__ == "__main__":
''' #'''
files=[ files=[
#"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json", #"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
#"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json", #"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json", #"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
] ]
for idx, file in enumerate(files): for idx, file in enumerate(files):
@ -15,7 +16,7 @@ if __name__ == "__main__":
data = json.load(json_file) data = json.load(json_file)
plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names']) plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
#plot_TF_influence(data['Log'], param_names=data['Param_names']) #plot_TF_influence(data['Log'], param_names=data['Param_names'])
''' #'''
## Loss , Acc, Proba = f(epoch) ## ## Loss , Acc, Proba = f(epoch) ##
#plot_compare(filenames=files, fig_name="res/compare") #plot_compare(filenames=files, fig_name="res/compare")
@ -75,6 +76,7 @@ if __name__ == "__main__":
''' '''
#Res print #Res print
'''
nb_run=3 nb_run=3
accs = [] accs = []
times = [] times = []
@ -88,3 +90,4 @@ if __name__ == "__main__":
times.append(data['Time'][0]) times.append(data['Time'][0])
print(files[0], np.mean(accs), np.std(accs), np.mean(times)) print(files[0], np.mean(accs), np.std(accs), np.mean(times))
'''

28
higher/dataug.py
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@ -692,7 +692,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
else: else:
return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param) 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): def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
super(RandAug, self).__init__() super(RandAug, self).__init__()
@ -773,9 +773,9 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh
def __str__(self): def __str__(self):
return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag) 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): 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 self._data_augmentation = True
@ -786,7 +786,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
self.mag=nn.Parameter(torch.tensor(float(mag))) self.mag=nn.Parameter(torch.tensor(float(mag)))
self._params = nn.ParameterDict({ 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))), "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX))),
}) })
self._shared_mag = True self._shared_mag = True
@ -794,12 +794,10 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
self._op_list =[] self._op_list =[]
for tf in self._TF: for tf in self._TF:
for mag in range(0.1, self._params['mag'], 0.1): for mag in range(1, int(self._params['mag']*10), 1):
op_list+=[(tf, self._params['prob'], mag)] self._op_list+=[(tf, self._params['prob'].item(), mag/10)]
self._nb_op = len(self._op_list) self._nb_op = len(self._op_list)
print(self._op_list)
def forward(self, x): def forward(self, x):
if self._data_augmentation:# and TF.random.random() < 0.5: if self._data_augmentation:# and TF.random.random() < 0.5:
device = x.device device = x.device
@ -821,16 +819,16 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
smps_x=[] smps_x=[]
for op_idx in range(self._nb_op): 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) 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 smp_x.shape[0]!=0: #if there's data to TF
if TF.random.random() < self.op_list[op_idx][1]: if TF.random.random() < self._op_list[op_idx][1]:
magnitude=self.op_list[op_idx][2] magnitude=self._op_list[op_idx][2]
tf=self.op_list[op_idx][0] tf=self._op_list[op_idx][0]
#In place #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 return x
@ -847,7 +845,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
if mode is None : if mode is None :
mode=self._data_augmentation mode=self._data_augmentation
self.augment(mode=mode) #Inutile si mode=None self.augment(mode=mode) #Inutile si mode=None
super(RandAug, self).train(mode) super(RandAugUDA, self).train(mode)
def eval(self): def eval(self):
self.train(mode=False) self.train(mode=False)
@ -859,7 +857,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
return self._params[key] return self._params[key]
def __str__(self): 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): class Augmented_model(nn.Module):
def __init__(self, data_augmenter, model): def __init__(self, data_augmenter, model):

37
higher/test_dataug.py
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@ -5,21 +5,21 @@ from train_utils import *
tf_names = [ tf_names = [
## Geometric TF ## ## Geometric TF ##
'Identity', #'Identity',
'FlipUD', #'FlipUD',
'FlipLR', #'FlipLR',
'Rotate', #'Rotate',
'TranslateX', #'TranslateX',
'TranslateY', #'TranslateY',
'ShearX', #'ShearX',
'ShearY', #'ShearY',
## Color TF (Expect image in the range of [0, 1]) ## ## Color TF (Expect image in the range of [0, 1]) ##
'Contrast', #'Contrast',
'Color', #'Color',
'Brightness', #'Brightness',
'Sharpness', #'Sharpness',
'Posterize', #'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch 'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#Color TF (Common mag scale) #Color TF (Common mag scale)
@ -48,6 +48,7 @@ tf_names = [
#'BadSharpness', #'BadSharpness',
#'BadContrast', #'BadContrast',
#'BadBrightness', #'BadBrightness',
#Non fonctionnel #Non fonctionnel
#'Auto_Contrast', #Pas opti pour des batch (Super lent) #'Auto_Contrast', #Pas opti pour des batch (Super lent)
#'Equalize', #'Equalize',
@ -63,8 +64,8 @@ else:
########################################## ##########################################
if __name__ == "__main__": if __name__ == "__main__":
n_inner_iter = 0 n_inner_iter = 10
epochs = 150 epochs = 1
dataug_epoch_start=0 dataug_epoch_start=0
#### Classic #### #### Classic ####
@ -94,12 +95,12 @@ if __name__ == "__main__":
t0 = time.process_time() t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names} tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict #tf_dict = TF.TF_dict
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device) aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=True, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device) #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device) #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
print(str(aug_model), 'on', device_name) print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs) #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, loss_patience=None) log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=None)
#### ####
print('-'*9) print('-'*9)

2
higher/train_utils.py
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@ -651,7 +651,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
print('TF Proba :', model['data_aug']['prob'].data) print('TF Proba :', model['data_aug']['prob'].data)
#print('proba grad',model['data_aug']['prob'].grad) #print('proba grad',model['data_aug']['prob'].grad)
print('TF Mag :', model['data_aug']['mag'].data) print('TF Mag :', model['data_aug']['mag'].data)
#print('Mag grad',model['data_aug']['mag'].grad) print('Mag grad',model['data_aug']['mag'].grad)
#print('Reg loss:', model['data_aug'].reg_loss().item()) #print('Reg loss:', model['data_aug'].reg_loss().item())
############# #############
#### Log #### #### Log ####

22
higher/transformations.py
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@ -298,12 +298,12 @@ def equalize(x): #PAS OPTIMISE POUR DES BATCH
def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
# Optimisation : Mask direct sur toute les donnees (Mask = (B,C,H,W)> (B)) # Optimisation : Mask direct sur toute les donnees (Mask = (B,C,H,W)> (B))
batch_size, channels, h, w = x.shape batch_size, channels, h, w = x.shape
imgs=[] #imgs=[]
for idx, t in enumerate(thresholds): #Operation par image #for idx, t in enumerate(thresholds): #Operation par image
mask = x[idx] > t #Perte du gradient # mask = x[idx] > t #Perte du gradient
#In place #In place
inv_x = 1-x[idx][mask] # inv_x = 1-x[idx][mask]
x[idx][mask]=inv_x # x[idx][mask]=inv_x
# #
#Out of place #Out of place
@ -316,6 +316,18 @@ def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
#idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... #idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
#x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs)) #x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
# #
thresholds = thresholds.unsqueeze(dim=1).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(thresholds.grad_fn)
x=torch.where(x>thresholds,1-x, x)
#print(mask.grad_fn)
#x=x.min(thresholds)
#inv_x = 1-x[mask]
#x=x.where(x<thresholds,1-x)
#x[mask]=inv_x
#x=x.masked_scatter(mask, inv_x)
return x return x
#https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818 #https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818