update

app.py

@@ -188,18 +188,19 @@ if __name__ == "__main__":
     - The adversarial example and repaired adversarial example may be unnatural to read, while it is because the attackers usually generate unnatural perturbations. RPD does not introduce additional unnatural perturbations.
     - To our best knowledge, Reactive Perturbation Defocusing is a novel approach in adversarial defense. RPD significantly (>10% defense accuracy improvement) outperforms the state-of-the-art methods.
     - The DeepWordBug is an unknown attacker to the adversarial detector and reactive defense module. DeepWordBug has different attacking patterns from other attackers and shows the generalizability and robustness of RPD.
+    - To help the review & evaluation of ACL2023, we will host this demo on a GPU device to speed up the inference process in the next month. Then it will be deployed on a CPU device in the future.
     """)
         gr.Markdown("<h2 align='center'>Natural Example Input</h2>")
         with gr.Group():
             with gr.Row():
                 input_dataset = gr.Radio(
                     choices=["SST2", "AGNews10K", "Amazon"],
-                    value="Amazon",
+                    value="SST2",
                     label="Select a testing dataset and an adversarial attacker to generate an adversarial example.",
                 )
                 input_attacker = gr.Radio(
                     choices=["BAE", "PWWS", "TextFooler", "DeepWordBug"],
-                    value="TextFooler",
+                    value="PWWS",
                     label="Choose an Adversarial Attacker for generating an adversarial example to attack the model.",
                 )
             with gr.Group():
@@ -213,7 +214,7 @@ if __name__ == "__main__":
                     )
 
         button_gen = gr.Button(
-            "Generate an adversarial example to repair using RPD (it will takes 1-10 minutes because no GPU is available)",
+            "Generate an adversarial example to repair using RPD (GPU: < 1 minute, CPU: 1-10 minutes)",
             variant="primary",
         )
 

textattack/constraints/semantics/sentence_encoders/universal_sentence_encoder/multilingual_universal_sentence_encoder.py

@@ -20,14 +20,19 @@ class MultilingualUniversalSentenceEncoder(SentenceEncoder):
         tensorflow_text._load()
         if large:
             tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3"
+            mirror_tfhub_url = "https://hub.tensorflow.google.cn/google/universal-sentence-encoder-multilingual-large/3"
         else:
-            tfhub_url = (
-                "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
-            )
+            tfhub_url = "https://https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
+            mirror_tfhub_url = "https://hub.tensorflow.google.cn/google/universal-sentence-encoder-multilingual/3"
 
-        # TODO add QA SET. Details at: https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3
+        # TODO add QA SET. Details at: https://hub.tensorflow.google.cn/google/universal-sentence-encoder-multilingual-qa/3
         self._tfhub_url = tfhub_url
-        self.model = hub.load(tfhub_url)
+        self.mirror_tfhub_url = mirror_tfhub_url
+        try:
+            self.model = hub.load(self._tfhub_url)
+        except Exception as e:
+            print('Error loading model from tfhub, trying mirror url')
+            self.model = hub.load(self.mirror_tfhub_url)
 
     def encode(self, sentences):
         return self.model(sentences).numpy()
@@ -39,4 +44,8 @@ class MultilingualUniversalSentenceEncoder(SentenceEncoder):
 
     def __setstate__(self, state):
         self.__dict__ = state
-        self.model = hub.load(self._tfhub_url)
+        try:
+            self.model = hub.load(self._tfhub_url)
+        except Exception as e:
+            print('Error loading model from tfhub, trying mirror url')
+            self.model = hub.load(self.mirror_tfhub_url)

textattack/constraints/semantics/sentence_encoders/universal_sentence_encoder/universal_sentence_encoder.py

@@ -18,22 +18,26 @@ class UniversalSentenceEncoder(SentenceEncoder):
         super().__init__(threshold=threshold, metric=metric, **kwargs)
         if large:
             tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder-large/5"
+            mirror_tfhub_url = "https://hub.tensorflow.google.cn/google/universal-sentence-encoder-large/5"
         else:
-            tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder/3"
+            tfhub_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
+            mirror_tfhub_url = (
+                "https://hub.tensorflow.google.cn/google/universal-sentence-encoder/4"
+            )
 
         self._tfhub_url = tfhub_url
+        self.mirror_tfhub_url = mirror_tfhub_url
         # Lazily load the model
         self.model = None
 
     def encode(self, sentences):
         if not self.model:
-            self.model = hub.load(self._tfhub_url)
-        encoding = self.model(sentences)
-
-        if isinstance(encoding, dict):
-            encoding = encoding["outputs"]
-
-        return encoding.numpy()
+            try:
+                self.model = hub.load(self._tfhub_url)
+            except Exception as e:
+                print('Error loading model from tfhub, trying mirror url')
+                self.model = hub.load(self.mirror_tfhub_url)
+        return self.model(sentences).numpy()
 
     def __getstate__(self):
         state = self.__dict__.copy()
@@ -42,4 +46,8 @@ class UniversalSentenceEncoder(SentenceEncoder):
 
     def __setstate__(self, state):
         self.__dict__ = state
-        self.model = None
+        try:
+            self.model = hub.load(self._tfhub_url)
+        except Exception as e:
+            print('Error loading model from tfhub, trying mirror url')
+            self.model = hub.load(self.mirror_tfhub_url)

utils.py

@@ -99,7 +99,7 @@ def get_sst2_example():
                     label = int(label.strip())
                     data.append((text, label))
                     label_set.add(label)
-        return data[random.randint(0, len(data))]
+        return random.choice(data)
 
 
 def get_agnews_example():
@@ -142,7 +142,7 @@ def get_agnews_example():
                     label = int(label.strip())
                     data.append((text, label))
                     label_set.add(label)
-        return data[random.randint(0, len(data))]
+        return random.choice(data)
 
 
 def get_amazon_example():
@@ -186,7 +186,7 @@ def get_amazon_example():
                     label = int(label.strip())
                     data.append((text, label))
                     label_set.add(label)
-        return data[random.randint(0, len(data))]
+        return random.choice(data)
 
 
 def get_imdb_example():
@@ -230,5 +230,5 @@ def get_imdb_example():
                     label = int(label.strip())
                     data.append((text, label))
                     label_set.add(label)
-        return data[random.randint(0, len(data))]
+        return random.choice(data)