AntoineH/smart_augmentation
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Log et utils pour Dataugv5

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Harle, Antoine (Contracteur) 2019-11-18 16:48:51 -05:00
parent 860d9f1bbb
commit f4bdd9bca5
6 changed files with 57 additions and 45 deletions
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23
higher/compare_res.py
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@ -2,21 +2,18 @@ from utils import *
if __name__ == "__main__": if __name__ == "__main__":
#### Comparison #### '''
## Loss , Acc, Proba = f(epoch) ##
files=[ files=[
#"res/log/LeNet-100 epochs.json", "res/log/Aug_mod(Data_augV5(Mix0.5-11TFx1-MagSh)-LeNet)-2 epochs (dataug:0)- 1 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-4 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
#"res/log/Aug_mod(Data_augV3(Uniform-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
#"res/log/Aug_mod(Data_augV4(Mix 0,5-3 TF)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
#"res/log/Aug_mod(Data_augV4(Mix 0.5-3 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-3 TF)-LeNet)-100 epochs (dataug:0)- 10 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 10 in_it.json",
#"res/log/Aug_mod(Data_augV4(Uniform-10 TF)-LeNet)-100 epochs (dataug:50)- 0 in_it.json",
] ]
for idx, file in enumerate(files):
#legend+=str(idx)+'-'+file+'\n'
with open(file) as json_file:
data = json.load(json_file)
plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
'''
## Loss , Acc, Proba = f(epoch) ##
#plot_compare(filenames=files, fig_name="res/compare") #plot_compare(filenames=files, fig_name="res/compare")
## Acc, Time, Epochs = f(n_tf) ## ## Acc, Time, Epochs = f(n_tf) ##

4
higher/datasets.py
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@ -38,8 +38,8 @@ data_test = torchvision.datasets.CIFAR10(
"./data", train=False, download=True, transform=transform "./data", train=False, download=True, transform=transform
) )
#''' #'''
train_subset_indices=range(int(len(data_train)/2)) #train_subset_indices=range(int(len(data_train)/2))
#train_subset_indices=range(BATCH_SIZE*10) train_subset_indices=range(BATCH_SIZE*10)
val_subset_indices=range(int(len(data_train)/2),len(data_train)) 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)) dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))

29
higher/dataug.py
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@ -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) 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) class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, shared_mag=True): def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_mag=True, shared_mag=True):
super(Data_augV5, self).__init__() super(Data_augV5, self).__init__()
assert len(TF_dict)>0 assert len(TF_dict)>0
@ -543,14 +543,14 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._N_seqTF = N_TF self._N_seqTF = N_TF
self._shared_mag = shared_mag self._shared_mag = shared_mag
self._fixed_mag = fixed_mag
#self._fixed_mag=5 #[0, PARAMETER_MAX] #self._fixed_mag=5 #[0, PARAMETER_MAX]
self._params = nn.ParameterDict({ self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(0.5) if shared_mag "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]/10
}) })
self._samples = [] self._samples = []
self._mix_dist = False self._mix_dist = False
@ -594,23 +594,19 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
if smp_x.shape[0]!=0: #if there's data to TF if smp_x.shape[0]!=0: #if there's data to TF
magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx] magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx]
if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param
tf=self._TF[tf_idx] tf=self._TF[tf_idx]
#print(magnitude) #print(magnitude)
#x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) # Refusionner eviter x[mask] : in place #In place
smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude) #x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
#Out of place
smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude)
idx= mask.nonzero() idx= mask.nonzero()
#print('-'*8)
idx= idx.expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... idx= idx.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(idx.shape, smp_x.shape)
#print(idx[0], tf_idx)
#print(smp_x[0,])
#x=x.view(-1,3*32*32)
#smp_x=smp_x.view(-1,3*32*32)
x=x.scatter(dim=0, index=idx, src=smp_x) x=x.scatter(dim=0, index=idx, src=smp_x)
#x=x.view(-1,3,32,32)
#print(x[0,])
return x return x
@ -663,10 +659,13 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
return self._params[key] return self._params[key]
def __str__(self): def __str__(self):
mag_param='Mag'
if self._fixed_mag: mag_param+= 'Fx'
if self._shared_mag: mag_param+= 'Sh'
if not self._mix_dist: if not self._mix_dist:
return "Data_augV5(Uniform-%d TF x %d)" % (self._nb_tf, self._N_seqTF) return "Data_augV5(Uniform-%dTFx%d-%s)" % (self._nb_tf, self._N_seqTF, mag_param)
else: else:
return "Data_augV5(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF) return "Data_augV5(Mix%.1f-%dTFx%d-%s)" % (self._mix_factor, self._nb_tf, self._N_seqTF, mag_param)
class Augmented_model(nn.Module): class Augmented_model(nn.Module):

2
higher/test_dataug.py
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@ -68,7 +68,7 @@ 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=0.5, shared_mag=True), LeNet(3,10)).to(device) 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_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).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) 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)

7
higher/train_utils.py
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@ -650,9 +650,11 @@ 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)
############# #############
#### Log #### #### Log ####
#print(type(model['data_aug']) is dataug.Data_augV5)
param = [{'p': p.item(), 'm':model['data_aug']['mag'].item()} for p in model['data_aug']['prob']] if model['data_aug']._shared_mag else [{'p': p.item(), 'm': m.item()} for p, m in zip(model['data_aug']['prob'], model['data_aug']['mag'])]
data={ data={
"epoch": epoch, "epoch": epoch,
"train_loss": loss.item(), "train_loss": loss.item(),
@ -660,7 +662,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
"acc": accuracy, "acc": accuracy,
"time": tf - t0, "time": tf - t0,
"param": [p.item() for p in model['data_aug']['prob']], "param": param #if isinstance(model['data_aug'], Data_augV5)
#else [p.item() for p in model['data_aug']['prob']],
} }
log.append(data) log.append(data)
############# #############

37
higher/utils.py
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@ -52,28 +52,41 @@ def plot_resV2(log, fig_name='res', param_names=None):
epochs = [x["epoch"] for x in log] epochs = [x["epoch"] for x in log]
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15, 15)) fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(30, 15))
ax[0, 0].set_title('Loss') ax[0, 0].set_title('Loss')
ax[0, 0].plot(epochs,[x["train_loss"] for x in log], label='Train') ax[0, 0].plot(epochs,[x["train_loss"] for x in log], label='Train')
ax[0, 0].plot(epochs,[x["val_loss"] for x in log], label='Val') ax[0, 0].plot(epochs,[x["val_loss"] for x in log], label='Val')
ax[0, 0].legend() ax[0, 0].legend()
ax[0, 1].set_title('Acc') ax[1, 0].set_title('Acc')
ax[0, 1].plot(epochs,[x["acc"] for x in log]) ax[1, 0].plot(epochs,[x["acc"] for x in log])
if log[0]["param"]!= None: if log[0]["param"]!= None:
ax[1, 1].set_title('Prob')
if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])] if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])]
proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])] #proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])]
ax[1, 1].stackplot(epochs, proba, labels=param_names) proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])]
ax[1, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])]
ax[1, 0].set_title('Mean prob') ax[0, 1].set_title('Prob =f(epoch)')
mean = np.mean([x["param"] for x in log], axis=0) ax[0, 1].stackplot(epochs, proba, labels=param_names)
std = np.std([x["param"] for x in log], axis=0) #ax[0, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
ax[1, 0].bar(param_names, mean, yerr=std)
plt.sca(ax[1, 0]), plt.xticks(rotation=90) ax[1, 1].set_title('Prob =f(TF)')
mean = np.mean(proba, axis=1)
std = np.std(proba, axis=1)
ax[1, 1].bar(param_names, mean, yerr=std)
plt.sca(ax[1, 1]), plt.xticks(rotation=90)
ax[0, 2].set_title('Mag =f(epoch)')
ax[0, 2].stackplot(epochs, mag, labels=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)')
mean = np.mean(mag, axis=1)
std = np.std(mag, axis=1)
ax[1, 2].bar(param_names, mean, yerr=std)
plt.sca(ax[1, 2]), plt.xticks(rotation=90)
fig_name = fig_name.replace('.',',') fig_name = fig_name.replace('.',',')