AntoineH/smart_augmentation
1
0
Fork 0
mirror of https://github.com/AntoineHX/smart_augmentation.git synced 2025-05-04 12:10:45 +02:00
Code Issues Projects Releases Packages Wiki Activity

Borne mag + Regularisation mag

Browse source
This commit is contained in:
Harle, Antoine (Contracteur) 2019-11-19 15:37:29 -05:00
parent f4bdd9bca5
commit 64282bda3a
10 changed files with 43 additions and 228 deletions
Download patch file Download diff file Expand all files Collapse all files

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)