Add SiglipForImageClassification and CLIPForImageClassification (#28952)

* First draft

* Add CLIPForImageClassification

* Remove scripts

* Fix doctests

tests/models/clip/test_modeling_clip.py

@@ -51,6 +51,7 @@ if is_torch_available():
     from torch import nn
 
     from transformers import (
+        CLIPForImageClassification,
         CLIPModel,
         CLIPTextModel,
         CLIPTextModelWithProjection,
@@ -744,6 +745,65 @@ class CLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             self.assertIsNotNone(model)
 
 
+class CLIPForImageClassificationModelTester(CLIPModelTester):
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.batch_size = self.vision_model_tester.batch_size
+        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
+        self.hidden_size = self.vision_model_tester.hidden_size
+        self.seq_length = self.vision_model_tester.seq_length
+
+    def prepare_config_and_inputs(self):
+        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class CLIPForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (CLIPForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": CLIPForImageClassification} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = CLIPForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="CLIPForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support inputs_embeds")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="CLIP uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+
 # We will verify our results on an image of cute cats
 def prepare_img():
     url = "http://images.cocodataset.org/val2017/000000039769.jpg"

tests/models/siglip/test_modeling_siglip.py

@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Testing suite for the PyTorch Siglip model. """
+""" Testing suite for the PyTorch SigLIP model. """
 
 
 import inspect
@@ -47,7 +47,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import SiglipModel, SiglipTextModel, SiglipVisionModel
+    from transformers import SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel
     from transformers.models.siglip.modeling_siglip import SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
@@ -584,6 +584,65 @@ class SiglipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             self.assertIsNotNone(model)
 
 
+class SiglipForImageClassificationModelTester(SiglipModelTester):
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.batch_size = self.vision_model_tester.batch_size
+        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
+        self.hidden_size = self.vision_model_tester.hidden_size
+        self.seq_length = self.vision_model_tester.seq_length
+
+    def prepare_config_and_inputs(self):
+        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SiglipForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (SiglipForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = SiglipForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+
 # We will verify our results on an image of cute cats
 def prepare_img():
     url = "http://images.cocodataset.org/val2017/000000039769.jpg"