[datasets] made sure that id labels are native python int, since otherwise it...

[datasets] made sure that id labels are native python int, since otherwise it could cause problem in the concatenation of datasets

bob/learn/pytorch/datasets/utils.py

@@ -41,15 +41,24 @@ def map_labels(raw_labels, start_index=0):
   
   for i in range(len(possible_labels)):
     l = possible_labels[i]
-    labels[numpy.where(labels==l)[0]] = i + start_index
-
-  return labels
+    labels[numpy.where(labels==l)[0]] = i + start_index 
+
+  # -----
+  # map back to native int, resolve the problem with dataset concatenation
+  # it does: line 78 is now ok
+  # for some reason, it was not working when the type of id labels were numpy.int64 ...
+  labels_int = []
+  for i in range(len(labels)):
+    labels_int.append(labels[i].item())
+  
+  return labels_int
 
 
 from torch.utils.data import Dataset
 import bob.io.base
 import bob.io.image
 
+
 class ConcatDataset(Dataset):
   """
   Class to concatenate two or more datasets for DR-GAN training
@@ -64,14 +73,8 @@ class ConcatDataset(Dataset):
     self.transform = datasets[0].transform
     self.data_files = sum((d.data_files for d in datasets), [])
     self.pose_labels = sum((d.pose_labels for d in datasets), [])
+    self.id_labels = sum((d.id_labels for d in datasets), [])
   
-    # TODO: for an unknown reason, the above does not work for id_labels - Guillaume HEUSCH, 01-12-2017
-    self.id_labels = []
-    for d in datasets:
-      self.id_labels.append(d.id_labels)
-      print "type of id labels = {}".format(type(d.id_labels[0]))
-    print len(self.id_labels)
-
   def __len__(self):
       """
         return the length of the dataset (i.e. nb of examples)