AntoineH/smart_augmentation
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Test WRN Brutus

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Harle, Antoine (Contracteur) 2019-12-04 16:34:02 -05:00
parent b26fbcd2a2
commit fa5bc72616
6 changed files with 72 additions and 20 deletions
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26
higher/datasets.py
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@ -65,21 +65,26 @@ class AugmentedDataset(VisionDataset):
self._TF = [
'Invert',
'Cutout',
'Sharpness',
'AutoContrast',
'Posterize',
'ShearX',
## Geometric TF ##
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
'Rotate',
'Equalize',
'Cutout',
## Color TF ##
'Contrast',
'Color',
'Solarize',
'Brightness'
'Brightness',
'Sharpness',
#'Posterize',
#'Solarize',
'Invert',
'AutoContrast',
'Equalize',
]
self._op_list =[]
self.prob=0.5
@ -119,6 +124,7 @@ class AugmentedDataset(VisionDataset):
for idx, image in enumerate(self.sup_data):
if (idx/self.dataset_info['sup'])%0.2==0: print("Augmenting data... ", idx,"/", self.dataset_info['sup'])
#if idx==10000:break
for _ in range(aug_copy):
chosen_policy = policies[np.random.choice(len(policies))]

2
higher/model.py
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@ -94,6 +94,8 @@ class NetworkBlock(nn.Module):
def forward(self, x):
return self.layer(x)
#wrn_size: 32 = WRN-28-2 ? 160 = WRN-28-10
class WideResNet(nn.Module):
#def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
def __init__(self, num_classes, wrn_size, depth=28, dropRate=0.0):

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higher/res/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-LeNet)-200 epochs (dataug:0)- 1 in_it.png
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46
higher/test_brutus.py
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@ -33,6 +33,49 @@ else:
##########################################
if __name__ == "__main__":
n_inner_iter = 1
epochs = 200
dataug_epoch_start=0
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
t0 = time.process_time()
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
print(str(aug_model), 'on', device_name)
#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=None, loss_patience=None)
exec_time=time.process_time() - t0
####
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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
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:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
####
t0 = time.process_time()
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
print(str(aug_model), 'on', device_name)
#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=None, loss_patience=None)
exec_time=time.process_time() - t0
####
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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
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:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
'''
res_folder="res/brutus-tests/"
epochs= 150
inner_its = [1]
@ -80,4 +123,5 @@ if __name__ == "__main__":
print('Log :\"',f.name, '\" saved !')
#plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
print('-'*9)
print('-'*9)
'''

12
higher/test_dataug.py
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@ -109,12 +109,12 @@ if __name__ == "__main__":
t0 = time.process_time()
data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
data_train_aug.augement_data(aug_copy=10)
data_train_aug.augement_data(aug_copy=30)
print(data_train_aug)
dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
#xs, ys = next(iter(dl_train))
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
xs, ys = next(iter(dl_train))
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
model = LeNet(3,10).to(device)
#model = WideResNet(num_classes=10, wrn_size=16).to(device)
@ -149,9 +149,9 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), 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(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
print(str(aug_model), 'on', device_name)
#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)

6
higher/train_utils.py
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@ -625,9 +625,9 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
model_copy(src=fmodel, dst=model)
optim_copy(dopt=diffopt, opt=inner_opt)
if epoch>50:
meta_opt.step()
model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
#if epoch>50:
meta_opt.step()
model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
#model['data_aug'].next_TF_set()
fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)