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Borne mag + Regularisation mag

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Harle, Antoine (Contracteur) 2019-11-19 15:37:29 -05:00
parent f4bdd9bca5
commit 64282bda3a
10 changed files with 43 additions and 228 deletions
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4
higher/datasets.py
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@ -38,8 +38,8 @@ data_test = torchvision.datasets.CIFAR10(
"./data", train=False, download=True, transform=transform
)
#'''
#train_subset_indices=range(int(len(data_train)/2))
train_subset_indices=range(BATCH_SIZE*10)
train_subset_indices=range(int(len(data_train)/2))
#train_subset_indices=range(BATCH_SIZE*10)
val_subset_indices=range(int(len(data_train)/2),len(data_train))
dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))

27
higher/dataug.py
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@ -114,7 +114,7 @@ class Data_augV2(nn.Module): #Methode exacte
return kornia.warp_affine(x, M, dsize=(x.shape[2], x.shape[3])) #dsize=(h, w)
def adjust_prob(self): #Detach from gradient ?
def adjust_param(self): #Detach from gradient ?
self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0)
#print('proba',self._params['prob'])
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
@ -262,7 +262,7 @@ class Data_augV3(nn.Module): #Echantillonage uniforme/Mixte
# warp the original image by the found transform
return kornia.warp_perspective(x, M, dsize=(h, w))
def adjust_prob(self, soft=False): #Detach from gradient ?
def adjust_param(self, soft=False): #Detach from gradient ?
if soft :
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
@ -478,7 +478,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
'''
return x
def adjust_prob(self, soft=False): #Detach from gradient ?
def adjust_param(self, soft=False): #Detach from gradient ?
if soft :
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
@ -549,15 +549,22 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(0.5) if self._shared_mag
else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]/10
else torch.tensor(0.5).expand(self._nb_tf)), #[0, PARAMETER_MAX]
})
self._samples = []
#Distribution
self._samples = []
self._mix_dist = False
if mix_dist != 0.0:
self._mix_dist = True
self._mix_factor = max(min(mix_dist, 1.0), 0.0)
#Mag regularisation
if not self._fixed_mag:
ignore={'Identity', 'FlipUD', 'FlipLR', 'Solarize', 'Posterize'}
self._reg_mask=[self._TF.index(t) for t in self._TF if t not in ignore]
self._reg_tgt = torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max
def forward(self, x):
if self._data_augmentation:
device = x.device
@ -610,18 +617,17 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
return x
def adjust_prob(self, soft=False): #Detach from gradient ?
def adjust_param(self, soft=False): #Detach from gradient ?
if soft :
self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
else:
#self._params['prob'].clamp(min=0.0,max=1.0)
self._params['prob'].data = F.relu(self._params['prob'].data)
#self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0)
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
#self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme
self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
def loss_weight(self):
# 1 seule TF
@ -642,6 +648,9 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
w_loss = torch.sum(w_loss,dim=1)
return w_loss
def reg_loss(self, reg_factor=0.005):
#return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean')
return reg_factor * F.mse_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt.to(self._params['mag'].device), reduction='mean')
def train(self, mode=None):
if mode is None :

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higher/res/Aug_mod(Data_augV4(Uniform-1 TF)-LeNet)-2 epochs (dataug:0)- 0 in_it.png
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higher/res/Aug_mod(Data_augV4(Uniform-2 TF)-LeNet)-2 epochs (dataug:0)- 0 in_it.png
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72
higher/res/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-2 epochs (dataug:0)- 10 in_it.json
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@ -1,72 +0,0 @@
{
"Accuracy": 20.8,
"Time": [
51.4427050715,
0.4778038694999971
],
"Device": "TITAN RTX",
"Param_names": [
"Identity",
"FlipUD",
"FlipLR",
"Rotate",
"TranslateX",
"TranslateY",
"ShearX",
"ShearY",
"Contrast",
"Color",
"Brightness",
"Sharpness",
"Posterize",
"Solarize"
],
"Log": [
{
"epoch": 1,
"train_loss": 2.3032476902008057,
"val_loss": 2.2924728393554688,
"acc": 11.1,
"time": 51.920508941,
"param": [
0.07925213128328323,
0.08312409371137619,
0.08779778331518173,
0.0853320062160492,
0.08577536046504974,
0.057290591299533844,
0.0774931013584137,
0.08246791362762451,
0.047001805156469345,
0.07887403666973114,
0.05897113308310509,
0.05021947622299194,
0.07581018656492233,
0.050590354949235916
]
},
{
"epoch": 2,
"train_loss": 2.171858787536621,
"val_loss": 2.078795909881592,
"acc": 20.8,
"time": 50.96490120200001,
"param": [
0.07892196625471115,
0.07488056272268295,
0.08041033148765564,
0.09144628793001175,
0.09114645421504974,
0.055715303868055344,
0.0672164335846901,
0.07994510233402252,
0.05105787515640259,
0.09191103279590607,
0.07849953323602676,
0.07014491409063339,
0.07624118775129318,
0.012463102117180824
]
}
]
}

95
higher/res/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-3 epochs (dataug:0)- 0 in_it.json
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@ -1,95 +0,0 @@
{
"Accuracy": 31.369999999999997,
"Time": [
38.67262149066667,
0.4140408795968137
],
"Device": "TITAN RTX",
"Param_names": [
"Identity",
"FlipUD",
"FlipLR",
"Rotate",
"TranslateX",
"TranslateY",
"ShearX",
"ShearY",
"Contrast",
"Color",
"Brightness",
"Sharpness",
"Posterize",
"Solarize"
],
"Log": [
{
"epoch": 1,
"train_loss": 2.2571041584014893,
"val_loss": 2.212921142578125,
"acc": 20.169999999999998,
"time": 38.788926192000005,
"param": [
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774
]
},
{
"epoch": 2,
"train_loss": 2.212834358215332,
"val_loss": 2.043567180633545,
"acc": 25.009999999999998,
"time": 38.117478509,
"param": [
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774
]
},
{
"epoch": 3,
"train_loss": 2.091825008392334,
"val_loss": 1.9359350204467773,
"acc": 31.369999999999997,
"time": 39.111459771,
"param": [
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774,
0.0714285746216774
]
}
]
}

34
higher/res/log/LeNet-3 epochs.json
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@ -1,34 +0,0 @@
{
"Accuracy": 39.2,
"Time": [
3.9452463850000012,
0.2891758564900622
],
"Device": "TITAN RTX",
"Log": [
{
"epoch": 0,
"train_loss": 2.109266757965088,
"val_loss": 2.1106348037719727,
"acc": 22.3,
"time": 4.312489993,
"param": null
},
{
"epoch": 1,
"train_loss": 1.7782783508300781,
"val_loss": 1.8776130676269531,
"acc": 33.76,
"time": 3.605794182000002,
"param": null
},
{
"epoch": 2,
"train_loss": 1.8152618408203125,
"val_loss": 1.6963396072387695,
"acc": 39.2,
"time": 3.9174549800000023,
"param": null
}
]
}

19
higher/test_dataug.py
View file

@ -5,9 +5,9 @@ from train_utils import *
tf_names = [
## Geometric TF ##
#'Identity',
#'FlipUD',
#'FlipLR',
'Identity',
'FlipUD',
'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
@ -37,8 +37,8 @@ else:
##########################################
if __name__ == "__main__":
n_inner_iter = 1
epochs = 2
n_inner_iter = 10
epochs = 200
dataug_epoch_start=0
#### Classic ####
@ -57,7 +57,7 @@ if __name__ == "__main__":
print('-'*9)
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Log": log}
print(str(model),": acc", out["Accuracy"], "in (ms):", out["Time"][0], "+/-", out["Time"][1])
print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
with open("res/log/%s.json" % "{}-{} epochs".format(str(model),epochs), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
@ -68,7 +68,7 @@ if __name__ == "__main__":
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.5, 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=False, shared_mag=False), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
@ -79,12 +79,13 @@ if __name__ == "__main__":
print('-'*9)
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in (s?):", out["Time"][0], "+/-", out["Time"][1])
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
with open("res/log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('Execution Time : %.00f (s?)'%(time.process_time() - t0))
print('TF influence', TF_influence(log))
print('Execution Time : %.00f '%(time.process_time() - t0))
print('-'*9)
#'''
#### TF number tests ####

15
higher/train_utils.py
View file

@ -528,7 +528,7 @@ def run_dist_dataug(model, epochs=1, inner_it=1, dataug_epoch_start=0):
optim_copy(dopt=diffopt, opt=inner_opt)
meta_opt.step()
model['data_aug'].adjust_prob() #Contrainte sum(proba)=1
model['data_aug'].adjust_param() #Contrainte sum(proba)=1
print("Copy ", countcopy)
return log
@ -588,7 +588,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
loss = F.cross_entropy(logits, ys, reduction='none') # no need to call loss.backwards()
if fmodel._data_augmentation: #Weight loss
w_loss = fmodel['data_aug'].loss_weight().to(device)
w_loss = fmodel['data_aug'].loss_weight()#.to(device)
loss = loss * w_loss
loss = loss.mean()
#'''
@ -605,7 +605,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
if(high_grad_track and i%inner_it==0): #Perform Meta step
#print("meta")
#Peu utile si high_grad_track = False
val_loss = compute_vaLoss(model=fmodel, dl_it=dl_val_it, dl=dl_val)
val_loss = compute_vaLoss(model=fmodel, dl_it=dl_val_it, dl=dl_val) + fmodel['data_aug'].reg_loss()
#print_graph(val_loss)
@ -616,15 +616,15 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
optim_copy(dopt=diffopt, opt=inner_opt)
meta_opt.step()
model['data_aug'].adjust_prob(soft=False) #Contrainte sum(proba)=1
model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track)
tf = time.process_time()
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
#viz_sample_data(imgs=model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
if(not high_grad_track):
countcopy+=1
@ -643,7 +643,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
if(print_freq and epoch%print_freq==0):
print('-'*9)
print('Epoch : %d/%d'%(epoch,epochs))
print('Time : %.00f s'%(tf - t0))
print('Time : %.00f'%(tf - t0))
print('Train loss :',loss.item(), '/ val loss', val_loss.item())
print('Accuracy :', accuracy)
print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start))
@ -651,6 +651,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
#print('proba grad',model['data_aug']['prob'].grad)
print('TF Mag :', model['data_aug']['mag'].data)
#print('Mag grad',model['data_aug']['mag'].grad)
print('Reg loss:', model['data_aug'].reg_loss().item())
#############
#### Log ####
#print(type(model['data_aug']) is dataug.Data_augV5)

5
higher/utils.py
View file

@ -254,6 +254,11 @@ def print_torch_mem(add_info=''):
torch.cuda.max_memory_cached()/ mega_bytes)
print(string)
def TF_influence(log):
proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
return np.mean(proba, axis=1)*np.mean(mag, axis=1) #Pourrait etre interessant de multiplier avant le mean
class loss_monitor(): #Voir https://github.com/pytorch/ignite
def __init__(self, patience, end_train=1):