Modification du early stopping (sur test data...)

higher/datasets.py

@@ -3,8 +3,8 @@ from torch.utils.data import SubsetRandomSampler
 import torchvision
 
 BATCH_SIZE = 300
-#TEST_SIZE = 300
-TEST_SIZE = 10000
+TEST_SIZE = 300
+#TEST_SIZE = 10000
 
 #ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
 #transform_train = torchvision.transforms.Compose([

higher/test_dataug.py

@@ -37,8 +37,8 @@ else:
 ##########################################
 if __name__ == "__main__":
 
-    n_inner_iter = 0
-    epochs = 100
+    n_inner_iter = 10
+    epochs = 200
     dataug_epoch_start=0
 
     #### Classic ####

higher/train_utils.py

@@ -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...')