Merge pull request #8 from pytorch/lambda

adding lambda transform

README.md

@@ -194,7 +194,6 @@ This is popularly used to train the Inception networks
 - size: size of the smaller edge
 - interpolation: Default: PIL.Image.BILINEAR
 
-
 ### `Pad(padding, fill=0)`
 Pads the given image on each side with `padding` number of pixels, and the padding pixels are filled with
 pixel value `fill`.
@@ -209,6 +208,14 @@ Given mean: (R, G, B) and std: (R, G, B), will normalize each channel of the tor
 - `ToTensor()` - Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
 - `ToPILImage()` - Converts a torch.*Tensor of range [0, 1] and shape C x H x W or numpy ndarray of dtype=uint8, range[0, 255] and shape H x W x C to a PIL.Image of range [0, 255]
 
+## Generic Transofrms
+### `Lambda(lambda)`
+Given a Python lambda, applies it to the input `img` and returns it.
+For example:
+
+```python
+transforms.Lambda(lambda x: x.add(10))
+```
 
 # Utils
 

test/test_transforms.py

@@ -100,10 +100,19 @@ class Tester(unittest.TestCase):
             transforms.Pad(padding),
             transforms.ToTensor(),
         ])(img)
-        print(height, width, padding)
-        print(result.size(1), result.size(2))
         assert result.size(1) == height + 2*padding
         assert result.size(2) == width + 2*padding
+
+    def test_lambda(self):
+        trans = transforms.Lambda(lambda x: x.add(10))
+        x = torch.randn(10)
+        y = trans(x)
+        assert(y.equal(torch.add(x, 10)))
+
+        trans = transforms.Lambda(lambda x: x.add_(10))
+        x = torch.randn(10)
+        y = trans(x)
+        assert(y.equal(x))
         
 
 if __name__ == '__main__':

torchvision/transforms.py

@@ -5,6 +5,7 @@ import random
 from PIL import Image, ImageOps
 import numpy as np
 import numbers
+import types
 
 class Compose(object):
     """ Composes several transforms together.
@@ -126,6 +127,15 @@ class Pad(object):
     def __call__(self, img):
         return ImageOps.expand(img, border=self.padding, fill=self.fill)
 
+class Lambda(object):
+    """Applies a lambda as a transform"""
+    def __init__(self, lambd):
+        assert type(lambd) is types.LambdaType
+        self.lambd = lambd
+
+    def __call__(self, img):
+        return self.lambd(img)
+
 
 class RandomCrop(object):
     """Crops the given PIL.Image at a random location to have a region of