Make get_params static method

torchvision/transforms.py

@@ -67,7 +67,7 @@ def to_tensor(pic):
         return img
 
 
-def to_pilimage(pic):
+def to_pil_image(pic):
     if not(_is_numpy_image(pic) or _is_tensor_image(pic)):
         raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))
 
@@ -219,7 +219,7 @@ class ToPILImage(object):
             PIL.Image: Image converted to PIL.Image.
 
         """
-        return to_pilimage(pic)
+        return to_pil_image(pic)
 
 
 class Normalize(object):
@@ -294,9 +294,10 @@ class CenterCrop(object):
         else:
             self.size = size
 
-    def get_params(self, img):
+    @staticmethod
+    def get_params(img, output_size):
         w, h = img.size
-        th, tw = self.size
+        th, tw = output_size
         x1 = int(round((w - tw) / 2.))
         y1 = int(round((h - th) / 2.))
         return x1, y1, tw, th
@@ -309,7 +310,7 @@ class CenterCrop(object):
         Returns:
             PIL.Image: Cropped image.
         """
-        x1, y1, tw, th = self.get_params(img)
+        x1, y1, tw, th = self.get_params(img, self.size)
         return crop(img, x1, y1, tw, th)
 
 
@@ -382,9 +383,10 @@ class RandomCrop(object):
             self.size = size
         self.padding = padding
 
-    def get_params(self, img):
+    @staticmethod
+    def get_params(img, output_size):
         w, h = img.size
-        th, tw = self.size
+        th, tw = output_size
         if w == tw and h == th:
             return img
 
@@ -403,7 +405,7 @@ class RandomCrop(object):
         if self.padding > 0:
             img = pad(img, self.padding)
 
-        x1, y1, tw, th = self.get_params(img)
+        x1, y1, tw, th = self.get_params(img, self.size)
 
         return crop(img, x1, y1, tw, th)
 
@@ -441,7 +443,8 @@ class RandomSizedCrop(object):
         self.size = size
         self.interpolation = interpolation
 
-    def get_params(self, img):
+    @staticmethod
+    def get_params(img):
         for attempt in range(10):
             area = img.size[0] * img.size[1]
             target_area = random.uniform(0.08, 1.0) * area