Modified the data_folder to be more generic

Hey @ageorge,

It's a good practice to open a merge request each time you intend to work on branch. Doing this way we can discuss changes in an easier way.

Concerning what you did, are you sure that it still works with Olegs stuff ?

Thanks

bob/learn/pytorch/datasets/data_folder.py

@@ -12,16 +12,13 @@ import torch.utils.data as data
 import os
 
 import random
-random.seed( a = 7 )
 
-import PIL
+random.seed( a = 7 )
 
 import numpy as np
 
 from torchvision import transforms
 
-import torch
-
 import h5py
 
 
@@ -36,7 +33,7 @@ def get_file_names_and_labels(files, data_folder, extension = ".hdf5", hldi_type
 
     files : [File]
         A list of files objects defined in the High Level Database Interface
-        of the particular datbase.
+        of the particular database.
 
     data_folder : str
         A directory containing the training data.
@@ -65,11 +62,11 @@ def get_file_names_and_labels(files, data_folder, extension = ".hdf5", hldi_type
 
             if f.attack_type is None:
 
-                label = 0
+                label = 1
 
             else:
 
-                label = 1
+                label = 0
 
             file_name = os.path.join(data_folder, f.path + extension)
 
@@ -93,17 +90,34 @@ def get_file_names_and_labels(files, data_folder, extension = ".hdf5", hldi_type
 class DataFolder(data.Dataset):
     """
     A generic data loader compatible with Bob High Level Database Interfaces
-    (HLDI). Only HLDI's of bob.pad.face are currently supported.
+    (HLDI). Only HLDI's of ``bob.pad.face`` are currently supported.
+
+    The basic functionality is composed of two steps: load the data from hdf5
+    file, and transform it using user defined transformation function.
+
+    Two types of user defined transformations are supported:
+
+    1. An instance of ``Compose`` transformation class from ``torchvision``
+    package.
+
+    2. A custom transformation function, which takes numpy.ndarray as input,
+    and returns a transformed Tensor. The dimensionality of the output tensor
+    must match the format expected by the network to be trained.
+
+    Note: if no special transformation is needed, the ``transform``
+    must at least convert an input numpy array to Tensor.
 
     Attributes
     ----------
 
     data_folder : str
-        A directory containing the training data.
+        A directory containing the training data. Note, that the training data
+        must be stored as a FrameContainers written to the hdf5 files. Other
+        formats are currently not supported.
 
     transform : object
-        A function/transform that  takes in a PIL image, and returns a
-        transformed version. E.g, ``transforms.RandomCrop``. Default: None.
+        A function ``transform`` takes an input numpy.ndarray sample/image,
+        and returns a transformed version as a Tensor. Default: None.
 
     extension : str
         Extension of the data files. Default: ".hdf5".
@@ -155,8 +169,8 @@ class DataFolder(data.Dataset):
             A directory containing the training data.
 
         transform : object
-            A function/transform that  takes in a PIL image, and returns a
-            transformed version. E.g, ``transforms.RandomCrop``. Default: None.
+            A function ``transform`` takes an input numpy.ndarray sample/image,
+            and returns a transformed version as a Tensor. Default: None.
 
         extension : str
             Extension of the data files. Default: ".hdf5".
@@ -227,20 +241,21 @@ class DataFolder(data.Dataset):
     #==========================================================================
     def __getitem__(self, index):
         """
-        Returns an image, possibly transformed, and a target class given index.
+        Returns a **transformed** sample/image and a target class, given index.
+        Two types of transformations are handled, see the doc-string of the
+        class.
 
         Attributes
         ----------
 
-        index : int.
+        index : int
             An index of the sample to return.
 
         Returns
         -------
 
-        pil_img : Tensor or PIL Image
-            If ``self.transform`` is defined the output is the torch.Tensor,
-            otherwise the output is an instance of the PIL.Image.Image class.
+        np_img : Tensor
+            Transformed sample.
 
         target : int
             Index of the class.
@@ -254,28 +269,30 @@ class DataFolder(data.Dataset):
 
         if isinstance(self.transform, transforms.Compose): # if an instance of torchvision composed transformation
 
-            if len(img_array.shape) == 3: # for color images
+            if len(img_array.shape) == 3: # for color or multi-channel images
 
                 img_array_tr = np.swapaxes(img_array, 1, 2)
                 img_array_tr = np.swapaxes(img_array_tr, 0, 2)
 
-                pil_img = PIL.Image.fromarray( img_array_tr ) # convert to PIL from array of size (H x W x 3)
+                np_img =img_array_tr.copy()  # np_img is numpy.ndarray of shape HxWxC
 
             else: # for gray-scale images
 
-                pil_img = PIL.Image.fromarray( img_array, 'L' ) # convert to PIL from array of size (H x W)
+                np_img=np.expand_dims(img_array_tr,2) # np_img is numpy.ndarray of size HxWx1
+
 
             if self.transform is not None:
 
-                pil_img = self.transform(pil_img)
+                np_img = self.transform(np_img) # after this transformation np_img should be a tensor
 
         else: # if custom transformation function is given
 
             img_array_transformed = self.transform(img_array)
 
-            return torch.Tensor(img_array_transformed).unsqueeze(0), target # convert array to Tensor, also return target
+            return img_array_transformed, target
+            # NOTE: make sure ``img_array_transformed`` converted to Tensor in your custom ``transform`` function.
 
-        return pil_img, target
+        return np_img, target
 
 
     #==========================================================================
@@ -289,6 +306,3 @@ class DataFolder(data.Dataset):
         """
         return len(self.file_names_labels_keys)
 
-
-
-