@@ -0,0 +1,50 @@
+import copy
+import numpy as np
+from collections import Iterable
+from scipy.stats import truncnorm
+
+import torch
+import torch.nn as nn
+
+class LinfPGDAttack(object):
+    def __init__(self, model=None, epsilon=0.05, k=1, a=0.05):
+        self.model = model
+        self.epsilon = epsilon
+        self.k = k
+        self.a = a
+        self.loss_fn = nn.MSELoss()
+
+    def perturb(self, X_nat, y):
+        """
+        Given examples (X_nat, y), returns adversarial
+        examples within epsilon of X_nat in l_infinity norm.
+        """
+        X = X_nat.clone().detach_()
+
+        for i in range(self.k):
+            print('test', i)
+            X.requires_grad = True
+            output = self.model(X)
+
+            self.model.zero_grad()
+            loss = -self.loss_fn(output, y)
+            loss.backward()
+            grad = X.grad
+
+            X_adv = X + self.a * grad.sign()
+
+            eta = torch.clamp(X_adv - X_nat, min=-self.epsilon, max=self.epsilon)
+            X = torch.clamp(X_nat + eta, min=-1, max=1).detach_()
+            eta = None
+            X_adv = None
+
+        return X, X - X_nat
+
+def clip_tensor(X, Y, Z):
+    # Clip X with Y min and Z max
+    X_np = X.data.cpu().numpy()
+    Y_np = Y.data.cpu().numpy()
+    Z_np = Z.data.cpu().numpy()
+    X_clipped = np.clip(X_np, Y_np, Z_np)
+    X_res = torch.FloatTensor(X_clipped)
+    return X_res