AntoineH/smart_augmentation
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Brutus

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Harle, Antoine (Contracteur) 2020-01-13 18:02:36 -05:00
parent 53bd421670
commit e291bc2e44
9 changed files with 55 additions and 44 deletions
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17
higher/compare_res.py
View file

@ -2,13 +2,12 @@ from utils import *
if __name__ == "__main__":
'''
#'''
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(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-0.json",
"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-1.json",
"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.json",
"res/log/Aug_mod(Data_augV5(Mix0.8-23TFx4-Mag)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-0.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-1.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.json",
#"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
]
@ -18,7 +17,7 @@ if __name__ == "__main__":
data = json.load(json_file)
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'])
'''
#'''
## Loss , Acc, Proba = f(epoch) ##
#plot_compare(filenames=files, fig_name="res/compare")
@ -78,7 +77,7 @@ if __name__ == "__main__":
'''
#Res print
#'''
'''
nb_run=3
accs = []
times = []
@ -93,4 +92,4 @@ if __name__ == "__main__":
print(idx, data['Accuracy'])
print(files[0], np.mean(accs), np.std(accs), np.mean(times))
#'''
'''

8
higher/dataug.py
View file

@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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, fixed_mag=True, shared_mag=True, ):
super(Data_augV5, self).__init__()
assert len(TF_dict)>0
@ -548,8 +548,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
#self._fixed_mag=5 #[0, PARAMETER_MAX]
self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag
else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX]
"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)/2) if self._shared_mag
else torch.tensor(float(TF.PARAMETER_MAX)/2).expand(self._nb_tf)), #[0, PARAMETER_MAX]
})
#for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag
@ -633,7 +633,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
if not self._fixed_mag:
self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX) #Bloque une fois au extreme
self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
#self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
def loss_weight(self):

18
higher/test_brutus.py
View file

@ -93,15 +93,15 @@ if __name__ == "__main__":
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
'''
res_folder="res/brutus-tests/"
res_folder="res/brutus-tests2/"
epochs= 150
inner_its = [1]
dist_mix = [0.0, 0.5, 0.8, 1.0]
dataug_epoch_starts= [0]
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [2, 3]
mag_setup = [(True,True), (False, False)]
N_seq_TF= [2, 3, 4]
mag_setup = [(True,True), (False, False)] #(Fixed, Shared)
#prob_setup = [True, False]
nb_run= 3
@ -118,12 +118,14 @@ if __name__ == "__main__":
#for i in TF_nb:
for m_setup in mag_setup:
#for p_setup in prob_setup:
p_setup=True
p_setup=False
for run in range(nb_run):
if n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True): continue #Autres setup inutiles sans meta-opti
if (n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True)) or (p_setup and dist!=0.0): continue #Autres setup inutiles sans meta-opti
#keys = list(TF.TF_dict.keys())[0:i]
#ntf_dict = {k: TF.TF_dict[k] for k in keys}
t0 = time.process_time()
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
@ -143,9 +145,9 @@ if __name__ == "__main__":
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("res/log/%s.json" % filename, "w+") as f:
filename = "{}-{} epochs (dataug:{})- {} in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter, run)
with open(res_folder+"log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('-'*9)
'''
#'''

10
higher/test_dataug.py
View file

@ -19,8 +19,8 @@ tf_names = [
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#'Posterize',
#'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#Color TF (Common mag scale)
#'+Contrast',
@ -66,7 +66,7 @@ if __name__ == "__main__":
#'aug_dataset',
'aug_model'
}
n_inner_iter = 10
n_inner_iter = 1
epochs = 100
dataug_epoch_start=0
optim_param={
@ -168,7 +168,7 @@ if __name__ == "__main__":
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
@ -187,7 +187,7 @@ if __name__ == "__main__":
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), 'Optimizer': optim_param, "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)+"demi_mag"
with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')

1
higher/utils.py
View file

@ -90,6 +90,7 @@ def plot_resV2(log, fig_name='res', param_names=None):
ax[0, 2].set_title('Mag =f(epoch)')
ax[0, 2].stackplot(epochs, mag, labels=param_names)
#ax[0, 2].plot(epochs, np.array(mag).T, label=param_names)
ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
ax[1, 2].set_title('Mag =f(TF)')