consistancy in naming

Signed-off-by: GiulioZizzo <[email protected]>

art/experimental/attacks/evasion/fast_gradient.py

@@ -27,7 +27,7 @@
 from art.attacks.evasion.fast_gradient import FastGradientMethod
 from art.attacks.attack import EvasionAttack
 from art.estimators.estimator import BaseEstimator, LossGradientsMixin
-from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalInput
+from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalInput
 
 from art.summary_writer import SummaryWriter
 from art.config import ART_NUMPY_DTYPE

art/experimental/estimators/__init__.py

@@ -1,5 +1,5 @@
 """
 Experimental Estimator API
 """
-from art.experimental.estimators import huggingface_multimodal
+from art.experimental.estimators import hugging_face_multimodal
 from art.experimental.estimators.jax import JaxEstimator

art/experimental/estimators/hugging_face_multimodal/__init__.py

@@ -0,0 +1,5 @@
+"""
+Module containing estimators for CLIP.
+"""
+from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm import HuggingFaceMultiModalPyTorch
+from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput

art/experimental/estimators/huggingface_multimodal/hugging_face_mm.py → art/experimental/estimators/hugging_face_multimodal/hugging_face_mm.py

@@ -36,7 +36,7 @@
     from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
     from art.defences.preprocessor.preprocessor import Preprocessor
     from art.defences.postprocessor.postprocessor import Postprocessor
-    from art.experimental.estimators.huggingface_multimodal.huggingface_mm_inputs import HuggingFaceMultiModalInput
+    from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalInput
 
 logger = logging.getLogger(__name__)
 
@@ -245,7 +245,9 @@ def predict(
                        and providing it takes no effect.
         :return: Predictions over the supplied data.
         """
-        from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput
+        from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import (
+            HuggingFaceMultiModalInput,
+        )
 
         # Set model to evaluation mode
         self._model.eval()
@@ -289,7 +291,9 @@ def fit(  # pylint: disable=W0221
                        and providing it takes no effect.
         """
         import torch
-        from art.experimental.estimators.huggingface_multimodal.hugging_face_mm_inputs import HuggingFaceMultiModalInput
+        from art.experimental.estimators.hugging_face_multimodal.hugging_face_mm_inputs import (
+            HuggingFaceMultiModalInput,
+        )
 
         self._model.train()
         if self._optimizer is None:

notebooks/clip_attack.ipynb

@@ -22,7 +22,7 @@
     "import numpy as np\n",
     "import torch\n",
     "\n",
-    "from art.experimental.estimators.huggingface_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n",
+    "from art.experimental.estimators.hugging_face_multimodal import HuggingFaceMultiModalPyTorch, HuggingFaceMultiModalInput\n",
     "from art.experimental.attacks.evasion import CLIPProjectedGradientDescentNumpy\n",
     "\n",
     "# Image normalization numbers\n",
@@ -150,7 +150,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023-11-30 09:48:11.240678: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "2023-11-30 14:58:29.132186: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
       "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
      ]
     },
@@ -168,7 +168,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.70it/s]"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.60it/s]"
      ]
     },
     {
@@ -188,7 +188,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d733d074704040ec8eb12855556f4922",
+       "model_id": "04de95b8f8e244eea99333f888cdd862",
        "version_major": 2,
        "version_minor": 0
       },
@@ -203,7 +203,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  1.95it/s]"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.15it/s]\n"
      ]
     },
     {
@@ -212,13 +212,6 @@
      "text": [
       "The adversarial accuracy is  0.0\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
     }
    ],
    "source": [

tests/attacks/evasion/test_multimodal_attack.py

@@ -46,7 +46,7 @@ def test_grad_equivalence(max_iter, art_warning):
 
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         from transformers import CLIPModel
-        from art.experimental.estimators.huggingface_multimodal import (
+        from art.experimental.estimators.hugging_face_multimodal import (
             HuggingFaceMultiModalPyTorch,
             HuggingFaceMultiModalInput,
         )
@@ -103,7 +103,7 @@ def test_perturbation_equivalence(to_batch, art_warning):
 
         from transformers import CLIPModel
 
-        from art.experimental.estimators.huggingface_multimodal import (
+        from art.experimental.estimators.hugging_face_multimodal import (
             HuggingFaceMultiModalPyTorch,
             HuggingFaceMultiModalInput,
         )
@@ -183,7 +183,7 @@ def test_attack_functionality(art_warning, to_one_hot):
 
         from transformers import CLIPProcessor, CLIPModel
 
-        from art.experimental.estimators.huggingface_multimodal import (
+        from art.experimental.estimators.hugging_face_multimodal import (
             HuggingFaceMultiModalPyTorch,
             HuggingFaceMultiModalInput,
         )

tests/estimators/classification/test_multimodal.py

@@ -30,7 +30,7 @@ def test_predict(art_warning):
     try:
         import torch
         from transformers import CLIPModel, CLIPProcessor
-        from art.experimental.estimators.huggingface_multimodal import (
+        from art.experimental.estimators.hugging_face_multimodal import (
             HuggingFaceMultiModalPyTorch,
             HuggingFaceMultiModalInput,
         )
@@ -69,7 +69,7 @@ def test_predict(art_warning):
         )
         inputs = HuggingFaceMultiModalInput(**inputs)
         predictions = art_classifier.predict(inputs)
-        assert ((np.sum(np.argmax(predictions, axis=1) == labels) / len(labels)) == 1.0)
+        assert (np.sum(np.argmax(predictions, axis=1) == labels) / len(labels)) == 1.0
     except ARTTestException as e:
         art_warning(e)
 
@@ -82,7 +82,7 @@ def test_fit(art_warning, fix_get_cifar10_data):
     try:
         import torch
         from transformers import CLIPProcessor, CLIPModel
-        from art.experimental.estimators.huggingface_multimodal import (
+        from art.experimental.estimators.hugging_face_multimodal import (
             HuggingFaceMultiModalPyTorch,
             HuggingFaceMultiModalInput,
         )