diff --git a/autoaugment.py b/autoaugment.py
index 76c6bc4..282a4c8 100644
--- a/autoaugment.py
+++ b/autoaugment.py
@@ -2,174 +2,479 @@
 import numpy as np
 import random
 
+class MyPolicy1(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
+        self.policies = [
+            policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            #policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
+        ]
 
-class ImageNetPolicy(object):
-    """ Randomly choose one of the best 24 Sub-policies on ImageNet.
 
+    def __call__(self, img, mask=None):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
+
+class MyPolicy2(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
         Example:
         >>> policy = ImageNetPolicy()
-        >>> transformed = policy(image)
+        >>> transformed_img, transformed_mask = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
+        self.policies = [
+            policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            #policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            #policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            #policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            #policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
+        ]
 
+
+    def __call__(self, img, mask=None):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
+
+class MyPolicy3(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image)
         Example as a PyTorch Transform:
         >>> transform=transforms.Compose([
         >>>     transforms.Resize(256),
         >>>     ImageNetPolicy(),
         >>>     transforms.ToTensor()])
     """
-    def __init__(self, fillcolor=(128, 128, 128)):
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
         self.policies = [
-            SubPolicy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor),
-            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
-            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor),
-            SubPolicy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor),
-            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
-
-            SubPolicy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor),
-            SubPolicy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor),
-            SubPolicy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor),
-            SubPolicy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor),
-            SubPolicy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor),
-
-            SubPolicy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor),
-            SubPolicy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor),
-            SubPolicy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor),
-            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
-            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
-
-            SubPolicy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor),
-            SubPolicy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor),
-            SubPolicy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor),
-            SubPolicy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor),
-            SubPolicy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor),
-
-            SubPolicy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor),
-            SubPolicy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor),
-            SubPolicy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor),
-            SubPolicy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor),
-            SubPolicy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor)
+            #policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            #policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
         ]
 
 
-    def __call__(self, img):
+    def __call__(self, img, mask=None):
         policy_idx = random.randint(0, len(self.policies) - 1)
-        return self.policies[policy_idx](img)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
 
     def __repr__(self):
-        return "AutoAugment ImageNet Policy"
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
 
+class MyPolicy4(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
+        self.policies = [
+            policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
+        ]
 
-class CIFAR10Policy(object):
-    """ Randomly choose one of the best 25 Sub-policies on CIFAR10.
 
+    def __call__(self, img, mask=None):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
+
+class MyPolicy5(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
+        self.policies = [
+            #policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            #policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            #policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            #policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            #policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            #policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            #policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            #policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
+        ]
+
+
+    def __call__(self, img, mask=None):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
+
+class ImageNetPolicy(object):
+    """ Randomly choose one of the best 24 Sub-policies on ImageNet. Adapted to segmentation tasks too.
+        Example:
+        >>> policy = ImageNetPolicy()
+        >>> transformed_img, transformed_mask = policy(image)
+        Example as a PyTorch Transform:
+        >>> transform=transforms.Compose([
+        >>>     transforms.Resize(256),
+        >>>     ImageNetPolicy(),
+        >>>     transforms.ToTensor()])
+    """
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+        
+        self.policies = [
+            policy(0.4, "posterize", 8, 0.6, "rotate", 9, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.6, "posterize", 7, 0.6, "posterize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 4, 0.8, "rotate", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 3, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.8, "posterize", 5, 1.0, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.2, "rotate", 3, 0.6, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 8, 0.4, "posterize", 6, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 0.4, "color", 0, fillcolor_img, fillcolor_mask),
+            policy(0.4, "rotate", 9, 0.6, "equalize", 2, fillcolor_img, fillcolor_mask),
+            policy(0.0, "equalize", 7, 0.8, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "rotate", 8, 1.0, "color", 2, fillcolor_img, fillcolor_mask),
+            policy(0.8, "color", 8, 0.8, "solarize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.4, "sharpness", 7, 0.6, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "shearX", 5, 1.0, "equalize", 9, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 0, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "equalize", 7, 0.2, "solarize", 4, fillcolor_img, fillcolor_mask),
+            policy(0.6, "solarize", 5, 0.6, "autocontrast", 5, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 1.0, "equalize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "color", 4, 1.0, "contrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask)
+        ]
+
+
+    def __call__(self, img, mask=None):
+        policy_idx = random.randint(0, len(self.policies) - 1)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
+
+    def __repr__(self):
+        return "AutoAugment ImageNet Policy" if self.classification else "Segmentation AutoAugment ImageNet Policy"
+
+class CIFAR10Policy(object):
+    """ Randomly choose one of the best 25 Sub-policies on CIFAR10. Adapted to segmentation tasks too.
         Example:
         >>> policy = CIFAR10Policy()
         >>> transformed = policy(image)
-
         Example as a PyTorch Transform:
         >>> transform=transforms.Compose([
         >>>     transforms.Resize(256),
         >>>     CIFAR10Policy(),
         >>>     transforms.ToTensor()])
     """
-    def __init__(self, fillcolor=(128, 128, 128)):
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+
         self.policies = [
-            SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
-            SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
-            SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
-            SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
-            SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
-
-            SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
-            SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
-            SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
-            SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
-            SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
-
-            SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
-            SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
-            SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
-            SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
-            SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
-
-            SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
-            SubPolicy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor),
-            SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
-            SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
-            SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
-
-            SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
-            SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
-            SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
-            SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
-            SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
+            policy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor_img, fillcolor_mask),
+            policy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor_img, fillcolor_mask),
+            policy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor_img, fillcolor_mask),
+            policy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor_img, fillcolor_mask),
+            policy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor_img, fillcolor_mask),
+
+            policy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor_img, fillcolor_mask),
+            policy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor_img, fillcolor_mask),
+            policy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor_img, fillcolor_mask),
+
+            policy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor_img, fillcolor_mask),
+            policy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor_img, fillcolor_mask),
+            policy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor_img, fillcolor_mask),
+            policy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor_img, fillcolor_mask),
+            policy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor_img, fillcolor_mask),
+            policy(0.2, "equalize", 8, 0.6, "equalize", 4, fillcolor_img, fillcolor_mask),
+            policy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor_img, fillcolor_mask),
+
+            policy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor_img, fillcolor_mask),
+            policy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor_img, fillcolor_mask),
+            policy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor_img, fillcolor_mask),
+            policy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor_img, fillcolor_mask)
         ]
 
-
-    def __call__(self, img):
+    def __call__(self, img, mask=None):
         policy_idx = random.randint(0, len(self.policies) - 1)
-        return self.policies[policy_idx](img)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
 
     def __repr__(self):
-        return "AutoAugment CIFAR10 Policy"
+        return "AutoAugment CIFAR10 Policy" if self.classification else "Segmentation AutoAugment CIFAR10 Policy"
 
 
 class SVHNPolicy(object):
-    """ Randomly choose one of the best 25 Sub-policies on SVHN.
-
+    """ Randomly choose one of the best 25 Sub-policies on SVHN. Adapted to segmentation tasks too.
         Example:
         >>> policy = SVHNPolicy()
         >>> transformed = policy(image)
-
         Example as a PyTorch Transform:
         >>> transform=transforms.Compose([
         >>>     transforms.Resize(256),
         >>>     SVHNPolicy(),
         >>>     transforms.ToTensor()])
     """
-    def __init__(self, fillcolor=(128, 128, 128)):
+    def __init__(self, fillcolor_img=(0, 0, 0), fillcolor_mask=0, task="classification"):
+        self.classification = False
+        if task == "classification":
+            self.classification = True
+            policy = SubPolicy
+        elif task == "segmentation":
+            policy = SegmentationSubPolicy
+
         self.policies = [
-            SubPolicy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor),
-            SubPolicy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor),
-            SubPolicy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor),
-            SubPolicy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor),
-            SubPolicy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor),
-
-            SubPolicy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor),
-            SubPolicy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor),
-            SubPolicy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor),
-            SubPolicy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor),
-            SubPolicy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor),
-
-            SubPolicy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor),
-            SubPolicy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor),
-            SubPolicy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor),
-            SubPolicy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor),
-            SubPolicy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor),
-
-            SubPolicy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor),
-            SubPolicy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor),
-            SubPolicy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor),
-            SubPolicy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor),
-            SubPolicy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor),
-
-            SubPolicy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor),
-            SubPolicy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor),
-            SubPolicy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor),
-            SubPolicy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor),
-            SubPolicy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor)
+            policy(0.9, "shearX", 4, 0.2, "invert", 3, fillcolor_img, fillcolor_mask),
+            policy(0.9, "shearY", 8, 0.7, "invert", 5, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 5, 0.6, "solarize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.9, "invert", 3, 0.6, "equalize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 1, 0.9, "rotate", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.9, "shearX", 4, 0.8, "autocontrast", 3, fillcolor_img, fillcolor_mask),
+            policy(0.9, "shearY", 8, 0.4, "invert", 5, fillcolor_img, fillcolor_mask),
+            policy(0.9, "shearY", 5, 0.2, "solarize", 6, fillcolor_img, fillcolor_mask),
+            policy(0.9, "invert", 6, 0.8, "autocontrast", 1, fillcolor_img, fillcolor_mask),
+            policy(0.6, "equalize", 3, 0.9, "rotate", 3, fillcolor_img, fillcolor_mask),
+
+            policy(0.9, "shearX", 4, 0.3, "solarize", 3, fillcolor_img, fillcolor_mask),
+            policy(0.8, "shearY", 8, 0.7, "invert", 4, fillcolor_img, fillcolor_mask),
+            policy(0.9, "equalize", 5, 0.6, "translateY", 6, fillcolor_img, fillcolor_mask),
+            policy(0.9, "invert", 4, 0.6, "equalize", 7, fillcolor_img, fillcolor_mask),
+            policy(0.3, "contrast", 3, 0.8, "rotate", 4, fillcolor_img, fillcolor_mask),
+
+            policy(0.8, "invert", 5, 0.0, "translateY", 2, fillcolor_img, fillcolor_mask),
+            policy(0.7, "shearY", 6, 0.4, "solarize", 8, fillcolor_img, fillcolor_mask),
+            policy(0.6, "invert", 4, 0.8, "rotate", 4, fillcolor_img, fillcolor_mask),
+            policy(0.3, "shearY", 7, 0.9, "translateX", 3, fillcolor_img, fillcolor_mask),
+            policy(0.1, "shearX", 6, 0.6, "invert", 5, fillcolor_img, fillcolor_mask),
+
+            policy(0.7, "solarize", 2, 0.6, "translateY", 7, fillcolor_img, fillcolor_mask),
+            policy(0.8, "shearY", 4, 0.8, "invert", 8, fillcolor_img, fillcolor_mask),
+            policy(0.7, "shearX", 9, 0.8, "translateY", 3, fillcolor_img, fillcolor_mask),
+            policy(0.8, "shearY", 5, 0.7, "autocontrast", 3, fillcolor_img, fillcolor_mask),
+            policy(0.7, "shearX", 2, 0.1, "invert", 5, fillcolor_img, fillcolor_mask)
         ]
 
-
-    def __call__(self, img):
+    def __call__(self, img, mask=None):
         policy_idx = random.randint(0, len(self.policies) - 1)
-        return self.policies[policy_idx](img)
+        return self.policies[policy_idx](img) if self.classification else self.policies[policy_idx](img, mask)
 
     def __repr__(self):
-        return "AutoAugment SVHN Policy"
-
+        return "AutoAugment SVHN Policy" if self.classification else "Segmentation AutoAugment SVHN Policy"
 
 class SubPolicy(object):
-    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
+    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128), fillcolor_mask=None):
         ranges = {
             "shearX": np.linspace(0, 0.3, 10),
             "shearY": np.linspace(0, 0.3, 10),
@@ -231,4 +536,80 @@ def rotate_with_fill(img, magnitude):
     def __call__(self, img):
         if random.random() < self.p1: img = self.operation1(img, self.magnitude1)
         if random.random() < self.p2: img = self.operation2(img, self.magnitude2)
-        return img
\ No newline at end of file
+        return img
+
+class SegmentationSubPolicy(object):
+    def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor_img=(0,0,0), fillcolor_mask=0):
+        ranges = {
+            "shearX": np.linspace(0, 0.3, 10),
+            "shearY": np.linspace(0, 0.3, 10),
+            "translateX": np.linspace(0, 150 / 331, 10),
+            "translateY": np.linspace(0, 150 / 331, 10),
+            "rotate": np.linspace(0, 30, 10),
+            "color": np.linspace(0.0, 0.9, 10),
+            "posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int),
+            "solarize": np.linspace(256, 0, 10),
+            "contrast": np.linspace(0.0, 0.9, 10),
+            "sharpness": np.linspace(0.0, 0.9, 10),
+            "brightness": np.linspace(0.0, 0.9, 10),
+            "autocontrast": [0] * 10,
+            "equalize": [0] * 10,
+            "invert": [0] * 10
+        }
+
+        # from https://stackoverflow.com/questions/5252170/specify-image-filling-color-when-rotating-in-python-with-pil-and-setting-expand
+        def rotate_with_fill(img, magnitude):
+            rot = img.convert("RGBA").rotate(magnitude)
+            return Image.composite(rot, Image.new("RGBA", rot.size, (0,) * 4), rot).convert(img.mode)
+
+        func = {
+            "shearX": lambda img, mask, magnitude: (
+                img.transform(img.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor_img), 
+                mask.transform(mask.size, Image.AFFINE, (1, magnitude * random.choice([-1, 1]), 0, 0, 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor_mask)
+                ),
+            "shearY": lambda img, mask, magnitude: (img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor_img),
+                mask.transform(
+                mask.size, Image.AFFINE, (1, 0, 0, magnitude * random.choice([-1, 1]), 1, 0),
+                Image.BICUBIC, fillcolor=fillcolor_mask)),
+            "translateX": lambda img, mask, magnitude: (img.transform(
+                img.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor_img),
+                mask.transform(
+                mask.size, Image.AFFINE, (1, 0, magnitude * img.size[0] * random.choice([-1, 1]), 0, 1, 0),
+                fillcolor=fillcolor_mask)),
+            "translateY": lambda img, mask, magnitude: (img.transform(
+                img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
+                fillcolor=fillcolor_img),
+                mask.transform(
+                mask.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude * img.size[1] * random.choice([-1, 1])),
+                fillcolor=fillcolor_mask)),
+            "rotate": lambda img, mask, magnitude: (rotate_with_fill(img, magnitude), rotate_with_fill(mask, magnitude)),
+            "color": lambda img, mask, magnitude: (ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])), mask),
+            "posterize": lambda img, mask, magnitude: (ImageOps.posterize(img, magnitude), mask),
+            "solarize": lambda img, mask, magnitude: (ImageOps.solarize(img, magnitude), mask),
+            "contrast": lambda img, mask, magnitude: (ImageEnhance.Contrast(img).enhance(
+                1 + magnitude * random.choice([-1, 1])), mask),
+            "sharpness": lambda img, mask, magnitude: (ImageEnhance.Sharpness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])), mask),
+            "brightness": lambda img, mask, magnitude: (ImageEnhance.Brightness(img).enhance(
+                1 + magnitude * random.choice([-1, 1])), mask),
+            "autocontrast": lambda img, mask, magnitude: (ImageOps.autocontrast(img), mask),
+            "equalize": lambda img, mask, magnitude: (ImageOps.equalize(img), mask),
+            "invert": lambda img, mask, magnitude: (ImageOps.invert(img), mask)
+        }
+
+        self.p1 = p1
+        self.operation1 = func[operation1]
+        self.magnitude1 = ranges[operation1][magnitude_idx1]
+        self.p2 = p2
+        self.operation2 = func[operation2]
+        self.magnitude2 = ranges[operation2][magnitude_idx2]
+
+    def __call__(self, img, mask):
+        if random.random() < self.p1: img, mask = self.operation1(img, mask, self.magnitude1)
+        if random.random() < self.p2: img, mask = self.operation2(img, mask, self.magnitude2)
+        return img, mask
\ No newline at end of file