AntoineH/smart_augmentation
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Fix etat Train/Eval pour augmentation differee (Retester !)

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Harle, Antoine (Contracteur) 2020-01-20 17:09:31 -05:00
parent 2d6d2f7397
commit d21a6bbf5c
4 changed files with 38 additions and 29 deletions
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14
higher/train_utils.py
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@ -827,6 +827,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
device = next(model.parameters()).device
log = []
dl_val_it = iter(dl_val)
val_loss=None
high_grad_track = True
if inner_it == 0: #No HP optimization
@ -909,10 +910,8 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
if(high_grad_track and i>0 and i%inner_it==0): #Perform Meta step
#print("meta")
val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss()
#print_graph(val_loss) #to visualize computational graph
val_loss.backward()
torch.nn.utils.clip_grad_norm_(model['data_aug'].parameters(), max_norm=10, norm_type=2) #Prevent exploding grad with RNN
@ -920,7 +919,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
meta_opt.step()
#Adjust Hyper-parameters
model['data_aug'].adjust_param(soft=True) #Contrainte sum(proba)=1
model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
if hp_opt:
for param_group in diffopt.param_groups:
for param in list(opt_param['Inner'].keys())[1:]:
@ -949,6 +948,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
accuracy, test_loss =test(model)
model.train()
print(model['data_aug']._data_augmentation)
#### Log ####
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={
@ -989,7 +989,13 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=0, dataug_epoch_start
print('Starting Data Augmention...')
dataug_epoch_start = epoch
model.augment(mode=True)
if inner_it != 0: high_grad_track = True
if inner_it != 0: #Rebuild diffopt if needed
high_grad_track = True
diffopt = model['model'].get_diffopt(
inner_opt,
grad_callback=(lambda grads: clip_norm(grads, max_norm=10)),
track_higher_grads=high_grad_track)
#Data sample saving
try: