AntoineH/smart_augmentation
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Modification du early stopping (sur test data...)

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Harle, Antoine (Contracteur) 2019-11-13 12:03:54 -05:00
parent bc8e5f2817
commit 198fb06065
3 changed files with 15 additions and 19 deletions
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26
higher/train_utils.py
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@ -6,12 +6,6 @@ import higher
from datasets import *
from utils import *
#Variables a definir
#dl_train = None
#dl_val = None
#dl_test = None
#device = torch.device('cuda')
def test(model):
device = next(model.parameters()).device
model.eval()
@ -21,13 +15,13 @@ def test(model):
pred = model.forward(features)
return pred.argmax(dim=1).eq(labels).sum().item() / dl_test.batch_size * 100
def compute_vaLoss(model, dl_val_it):
def compute_loss(model, dl_it, dl):
device = next(model.parameters()).device
try:
xs_val, ys_val = next(dl_val_it)
xs_val, ys_val = next(dl_it)
except StopIteration: #Fin epoch val
dl_val_it = iter(dl_val)
xs_val, ys_val = next(dl_val_it)
dl_val_it = iter(dl)
xs_val, ys_val = next(dl_it)
xs_val, ys_val = xs_val.to(device), ys_val.to(device)
try:
@ -528,6 +522,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
countcopy=0
val_loss=torch.tensor(0) #Necessaire si pas de metastep sur une epoch
dl_val_it = iter(dl_val)
dl_test_it = iter(dl_test) #ATTENTION A UTILISER SEULEMT POUR EARLY STOP
meta_opt = torch.optim.Adam(model['data_aug'].parameters(), lr=1e-2)
inner_opt = torch.optim.SGD(model['model'].parameters(), lr=1e-2, momentum=0.9)
@ -542,7 +537,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
val_loss_monitor= None
if loss_patience != None :
if dataug_epoch_start==-1: val_loss_monitor = loss_monitor(patience=loss_patience, end_train=2) #1st limit = dataug start
else: val_loss_monitor = loss_monitor(patience=loss_patience) #Val loss monitor
else: val_loss_monitor = loss_monitor(patience=loss_patience) #Val loss monitor (Not on val data : used by Dataug... => Test data)
model.train()
@ -594,7 +589,7 @@ 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_val_it=dl_val_it)
val_loss = compute_loss(model=fmodel, dl_it=dl_val_it, dl=dl_val)
#print_graph(val_loss)
@ -619,7 +614,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
countcopy+=1
model_copy(src=fmodel, dst=model)
optim_copy(dopt=diffopt, opt=inner_opt)
val_loss = compute_vaLoss(model=fmodel, dl_val_it=dl_val_it)
val_loss = compute_loss(model=fmodel, dl_it=dl_val_it, dl=dl_val)
#Necessaire pour reset higher (Accumule les fast_param meme avec track_higher_grads = False)
fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
@ -652,9 +647,10 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
log.append(data)
#############
if val_loss_monitor :
val_loss_monitor.register(val_loss.item())
model.eval()
val_loss_monitor.register(compute_loss(model, dl_it=dl_test_it, dl=dl_test))#val_loss.item())
if val_loss_monitor.end_training(): break #Stop training
model.train()
if not model.is_augmenting() and (epoch == dataug_epoch_start or (val_loss_monitor and val_loss_monitor.limit_reached()==1)):
print('Starting Data Augmention...')