Changement permission fichiers + Simplification utilisation Augmented_dataset

higher/datasets.py

@@ -125,10 +125,11 @@ class AugmentedDataset(VisionDataset):
                 aug_image = augmentation_transforms.apply_policy(chosen_policy, image, use_mean_std=False) #Cast en float image
                 #aug_image = augmentation_transforms.cutout_numpy(aug_image)
 
-                self.unsup_data+=[aug_image]
+                self.unsup_data+=[(aug_image*255.).astype(self.sup_data.dtype)]#Cast float image to uint8
                 self.unsup_targets+=[self.sup_targets[idx]]
 
-        self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
+        #self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
+        self.unsup_data=np.array(self.unsup_data)
         self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
         self.targets= np.concatenate((self.sup_targets, self.unsup_targets), axis=0)
 
@@ -159,15 +160,13 @@ train_subset_indices=range(int(len(data_train)/2))
 val_subset_indices=range(int(len(data_train)/2),len(data_train))
 #train_subset_indices=range(BATCH_SIZE*10)
 #val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
-
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
 
 ### Augmented Dataset ###
-data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
-data_train_aug.augement_data(aug_copy=1)
-print(data_train_aug)
-
-dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
+#data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
+#data_train_aug.augement_data(aug_copy=10)
+#print(data_train_aug)
+#dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
 
 
 dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))