[RFC] Add support for joint transformations in VisionDataset

torchvision/datasets/coco.py

@@ -43,10 +43,8 @@ class CocoCaptions(VisionDataset):
 
     """
 
-    def __init__(self, root, annFile, transform=None, target_transform=None):
-        super(CocoCaptions, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None):
+        super(CocoCaptions, self).__init__(root, transforms, transform, target_transform)
         from pycocotools.coco import COCO
         self.coco = COCO(annFile)
         self.ids = list(sorted(self.coco.imgs.keys()))
@@ -68,11 +66,9 @@ def __getitem__(self, index):
         path = coco.loadImgs(img_id)[0]['file_name']
 
         img = Image.open(os.path.join(self.root, path)).convert('RGB')
-        if self.transform is not None:
-            img = self.transform(img)
 
-        if self.target_transform is not None:
-            target = self.target_transform(target)
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
 
         return img, target
 
@@ -92,10 +88,8 @@ class CocoDetection(VisionDataset):
             target and transforms it.
     """
 
-    def __init__(self, root, annFile, transform=None, target_transform=None):
-        super(CocoDetection, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None):
+        super(CocoDetection, self).__init__(root, transforms, transform, target_transform)
         from pycocotools.coco import COCO
         self.coco = COCO(annFile)
         self.ids = list(sorted(self.coco.imgs.keys()))
@@ -116,11 +110,8 @@ def __getitem__(self, index):
         path = coco.loadImgs(img_id)[0]['file_name']
 
         img = Image.open(os.path.join(self.root, path)).convert('RGB')
-        if self.transform is not None:
-            img = self.transform(img)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
 
         return img, target
 

torchvision/datasets/sbd.py

@@ -54,7 +54,7 @@ def __init__(self,
                  image_set='train',
                  mode='boundaries',
                  download=False,
-                 xy_transform=None, **kwargs):
+                 transforms=None):
 
         try:
             from scipy.io import loadmat
@@ -63,12 +63,11 @@ def __init__(self,
             raise RuntimeError("Scipy is not found. This dataset needs to have scipy installed: "
                                "pip install scipy")
 
-        super(SBDataset, self).__init__(root)
+        super(SBDataset, self).__init__(root, transforms)
 
         if mode not in ("segmentation", "boundaries"):
             raise ValueError("Argument mode should be 'segmentation' or 'boundaries'")
 
-        self.xy_transform = xy_transform
         self.image_set = image_set
         self.mode = mode
         self.num_classes = 20
@@ -120,8 +119,8 @@ def __getitem__(self, index):
         img = Image.open(self.images[index]).convert('RGB')
         target = self._get_target(self.masks[index])
 
-        if self.xy_transform is not None:
-            img, target = self.xy_transform(img, target)
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
 
         return img, target
 

torchvision/datasets/vision.py

@@ -6,11 +6,25 @@
 class VisionDataset(data.Dataset):
     _repr_indent = 4
 
-    def __init__(self, root):
+    def __init__(self, root, transforms=None, transform=None, target_transform=None):
         if isinstance(root, torch._six.string_classes):
             root = os.path.expanduser(root)
         self.root = root
 
+        has_transforms = transforms is not None
+        has_separate_transform = transform is not None or target_transform is not None
+        if has_transforms and has_separate_transform:
+            raise ValueError("Only transforms or transform/target_transform can "
+                             "be passed as argument")
+
+        # for backwards-compatibility
+        self.transform = transform
+        self.target_transform = target_transform
+
+        if has_separate_transform:
+            transforms = StandardTransform(transform, target_transform)
+        self.transforms = transforms
+
     def __getitem__(self, index):
         raise NotImplementedError
 
@@ -23,12 +37,8 @@ def __repr__(self):
         if self.root is not None:
             body.append("Root location: {}".format(self.root))
         body += self.extra_repr().splitlines()
-        if hasattr(self, 'transform') and self.transform is not None:
-            body += self._format_transform_repr(self.transform,
-                                                "Transforms: ")
-        if hasattr(self, 'target_transform') and self.target_transform is not None:
-            body += self._format_transform_repr(self.target_transform,
-                                                "Target transforms: ")
+        if self.transforms is not None:
+            body += [repr(self.transforms)]
         lines = [head] + [" " * self._repr_indent + line for line in body]
         return '\n'.join(lines)
 
@@ -39,3 +49,32 @@ def _format_transform_repr(self, transform, head):
 
     def extra_repr(self):
         return ""
+
+
+class StandardTransform(object):
+    def __init__(self, transform=None, target_transform=None):
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __call__(self, input, target):
+        if self.transform is not None:
+            input = self.transform(input)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+        return input, target
+
+    def _format_transform_repr(self, transform, head):
+        lines = transform.__repr__().splitlines()
+        return (["{}{}".format(head, lines[0])] +
+                ["{}{}".format(" " * len(head), line) for line in lines[1:]])
+
+    def __repr__(self):
+        body = [self.__class__.__name__]
+        if self.transform is not None:
+            body += self._format_transform_repr(self.transform,
+                                                "Transform: ")
+        if self.target_transform is not None:
+            body += self._format_transform_repr(self.target_transform,
+                                                "Target transform: ")
+
+        return '\n'.join(body)

torchvision/datasets/voc.py

@@ -74,10 +74,9 @@ def __init__(self,
                  image_set='train',
                  download=False,
                  transform=None,
-                 target_transform=None):
-        super(VOCSegmentation, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+                 target_transform=None,
+                 transforms=None):
+        super(VOCSegmentation, self).__init__(root, transforms, transform, target_transform)
         self.year = year
         self.url = DATASET_YEAR_DICT[year]['url']
         self.filename = DATASET_YEAR_DICT[year]['filename']
@@ -122,11 +121,8 @@ def __getitem__(self, index):
         img = Image.open(self.images[index]).convert('RGB')
         target = Image.open(self.masks[index])
 
-        if self.transform is not None:
-            img = self.transform(img)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
 
         return img, target
 
@@ -157,10 +153,9 @@ def __init__(self,
                  image_set='train',
                  download=False,
                  transform=None,
-                 target_transform=None):
-        super(VOCDetection, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+                 target_transform=None,
+                 transforms=None):
+        super(VOCDetection, self).__init__(root, transforms, transform, target_transform)
         self.year = year
         self.url = DATASET_YEAR_DICT[year]['url']
         self.filename = DATASET_YEAR_DICT[year]['filename']
@@ -208,11 +203,8 @@ def __getitem__(self, index):
         target = self.parse_voc_xml(
             ET.parse(self.annotations[index]).getroot())
 
-        if self.transform is not None:
-            img = self.transform(img)
-
-        if self.target_transform is not None:
-            target = self.target_transform(target)
+        if self.transforms is not None:
+            img, target = self.transforms(img, target)
 
         return img, target