correct patience

mupixutils.py

@@ -158,9 +158,9 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
     n_steps = bat_per_epo * n_epochs
     j=None
     train_gen=None
-    best_val_loss = 1e7
+    best_val_loss = float('inf')
+    cur_patience=patience
     current_epoch = 0 + starting_epoch
-    cur_patience=patience.copy()
     history = [],[],[]
     if val_dataset is not None : xval,yval = val_dataset[0],val_dataset[1]
     for i in range(n_steps):
@@ -177,7 +177,7 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
             del trainA, trainB, tab_ix
             # val loss computation
             if val_dataset is not None :
-                print(np.mean(xval[0]),np.mean(yval[0]))
+                # print(np.mean(xval[0]),np.mean(yval[0]))
                 v_res = (g_model.predict((xval/127.5)-1)+1)*127.5
                 mse=(mean_squared_error(v_res,yval)).numpy()
                 ssim=((structural_similarity_index(v_res,yval)).numpy()) 
@@ -194,10 +194,11 @@ def train(d_model, g_model, gan_model, dataset, output_path, val_dataset = None,
                     g_model.save(output_path+"/networks/Generator")
                     d_model.save(output_path+"/networks/Discriminator")
                     imwrite(output_path+"/images/Validation_epoch_%d.tif"%(current_epoch),np.concatenate((xval[0],v_res[0],yval[0]),axis=1))
-                    cur_patience = patience.copy()
+                    cur_patience = patience
                 else:
                     with open(output_path+"/log.txt", 'a') as file:
-                        file.write("Loss did not improve \n")                    
+                            file.write("Loss did not improve : best val_loss was: "+str(best_val_loss)+" and current val_loss is "+str(val_loss)+"\n")
+
                 if cur_patience == 0 : 
                     print("Patience has been reached, training stopped")
                     if g_model.optimizer.learning_rate.numpy()>1e-4:
@@ -309,11 +310,31 @@ def percentile_normalize(image, lower_percentile=1, upper_percentile=99):
     lower_bound = np.percentile(image, lower_percentile)
     upper_bound = np.percentile(image, upper_percentile)
     
+    # Normalisation
+    normalized_image = np.clip(image, lower_bound, upper_bound)
+    normalized_image = 1* (normalized_image - lower_bound) / (upper_bound - lower_bound)
+    
+    return normalized_image
+
+def percentile_normalize_test(image, lower_percentile=1, upper_percentile=99):
+    """
+    Normalise l'image en utilisant la normalisation par centile.
+    
+    :param image: Tableau NumPy représentant l'image
+    :param lower_percentile: Percentile inférieur pour la normalisation (par défaut: 1)
+    :param upper_percentile: Percentile supérieur pour la normalisation (par défaut: 99)
+    :return: Image normalisée
+    """
+    # Calcul des percentiles
+    lower_bound = np.percentile(image, lower_percentile)
+    upper_bound = np.percentile(image, upper_percentile)
+    
     # Normalisation
     normalized_image = np.clip(image, lower_bound, upper_bound)
     normalized_image = 255* (normalized_image - lower_bound) / (upper_bound - lower_bound)
     
     return normalized_image
+
 # 'normalized_image' contiendra l'image normalisée avec des valeurs entre 0 et 1
 
 def dataloading(pathx,pathy, val_size=0.1,tilesize=256,seed=42):
@@ -404,7 +425,7 @@ def test_dataloading(test_data_path, experiment_path, batch_size):
     for filename in image_filenames:
         image_path = os.path.join(test_data_path, filename)
         image = imread(image_path)
-        image_tensor = percentile_normalize(image)[..., None]  # Normalize and add channel
+        image_tensor = percentile_normalize_test(image)[..., None]  # Normalize and add channel
         image_tensor = ((image_tensor / 127.5) - 1)[None, ...]  # Prepare for model
         
         # Predict