Timm wrapper label names (#35553)

* Add timm wrapper label names mapping

* Add index to classification pipeline

* Revert adding index for pipelines

* Add custom model check for loading timm labels

* Add tests for labels

* [run-slow] timm_wrapper

* Add note regarding label2id mapping

src/transformers/models/timm_wrapper/configuration_timm_wrapper.py

@@ -18,7 +18,11 @@
 from typing import Any, Dict
 
 from ...configuration_utils import PretrainedConfig
-from ...utils import logging
+from ...utils import is_timm_available, logging
+
+
+if is_timm_available():
+    from timm.data import ImageNetInfo, infer_imagenet_subset
 
 
 logger = logging.get_logger(__name__)
@@ -33,6 +37,9 @@ class TimmWrapperConfig(PretrainedConfig):
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
 
+    Config loads imagenet label descriptions and stores them in `id2label` attribute, `label2id` attribute for default
+    imagenet models is set to `None` due to occlusions in the label descriptions.
+
     Args:
         initializer_range (`float`, *optional*, defaults to 0.02):
             The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
@@ -60,10 +67,30 @@ def __init__(self, initializer_range: float = 0.02, do_pooling: bool = True, **k
 
     @classmethod
     def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
+        label_names = config_dict.get("label_names", None)
+        is_custom_model = "num_labels" in kwargs or "id2label" in kwargs
+
+        # if no labels added to config, use imagenet labeller in timm
+        if label_names is None and not is_custom_model:
+            imagenet_subset = infer_imagenet_subset(config_dict)
+            if imagenet_subset:
+                dataset_info = ImageNetInfo(imagenet_subset)
+                synsets = dataset_info.label_names()
+                label_descriptions = dataset_info.label_descriptions(as_dict=True)
+                label_names = [label_descriptions[synset] for synset in synsets]
+
+        if label_names is not None and not is_custom_model:
+            kwargs["id2label"] = dict(enumerate(label_names))
+
+            # if all label names are unique, create label2id mapping as well
+            if len(set(label_names)) == len(label_names):
+                kwargs["label2id"] = {name: i for i, name in enumerate(label_names)}
+            else:
+                kwargs["label2id"] = None
+
         # timm config stores the `num_classes` attribute in both the root of config and in the "pretrained_cfg" dict.
         # We are removing these attributes in order to have the native `transformers` num_labels attribute in config
         # and to avoid duplicate attributes
-
         num_labels_in_kwargs = kwargs.pop("num_labels", None)
         num_labels_in_dict = config_dict.pop("num_classes", None)
 
@@ -80,6 +107,9 @@ def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
     def to_dict(self) -> Dict[str, Any]:
         output = super().to_dict()
         output["num_classes"] = self.num_labels
+        output["label_names"] = list(self.id2label.values())
+        output.pop("id2label", None)
+        output.pop("label2id", None)
         return output
 
 

tests/models/timm_wrapper/test_modeling_timm_wrapper.py

@@ -200,6 +200,54 @@ def test_do_pooling_option(self):
             output = model(**inputs_dict, do_pooling=True)
         self.assertIsNotNone(output.pooler_output)
 
+    def test_timm_config_labels(self):
+        # test timm config with no labels
+        checkpoint = "timm/resnet18.a1_in1k"
+        config = TimmWrapperConfig.from_pretrained(checkpoint)
+        self.assertIsNone(config.label2id)
+        self.assertIsInstance(config.id2label, dict)
+        self.assertEqual(len(config.id2label), 1000)
+        self.assertEqual(config.id2label[1], "goldfish, Carassius auratus")
+
+        # test timm config with labels in config
+        checkpoint = "timm/eva02_large_patch14_clip_336.merged2b_ft_inat21"
+        config = TimmWrapperConfig.from_pretrained(checkpoint)
+
+        self.assertIsInstance(config.id2label, dict)
+        self.assertEqual(len(config.id2label), 10000)
+        self.assertEqual(config.id2label[1], "Sabella spallanzanii")
+
+        self.assertIsInstance(config.label2id, dict)
+        self.assertEqual(len(config.label2id), 10000)
+        self.assertEqual(config.label2id["Sabella spallanzanii"], 1)
+
+        # test custom labels are provided
+        checkpoint = "timm/resnet18.a1_in1k"
+        config = TimmWrapperConfig.from_pretrained(checkpoint, num_labels=2)
+        self.assertEqual(config.num_labels, 2)
+        self.assertEqual(config.id2label, {0: "LABEL_0", 1: "LABEL_1"})
+        self.assertEqual(config.label2id, {"LABEL_0": 0, "LABEL_1": 1})
+
+        # test with provided id2label and label2id
+        checkpoint = "timm/resnet18.a1_in1k"
+        config = TimmWrapperConfig.from_pretrained(
+            checkpoint, num_labels=2, id2label={0: "LABEL_0", 1: "LABEL_1"}, label2id={"LABEL_0": 0, "LABEL_1": 1}
+        )
+        self.assertEqual(config.num_labels, 2)
+        self.assertEqual(config.id2label, {0: "LABEL_0", 1: "LABEL_1"})
+        self.assertEqual(config.label2id, {"LABEL_0": 0, "LABEL_1": 1})
+
+        # test save load
+        checkpoint = "timm/resnet18.a1_in1k"
+        config = TimmWrapperConfig.from_pretrained(checkpoint)
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            config.save_pretrained(tmpdirname)
+            restored_config = TimmWrapperConfig.from_pretrained(tmpdirname)
+
+        self.assertEqual(config.num_labels, restored_config.num_labels)
+        self.assertEqual(config.id2label, restored_config.id2label)
+        self.assertEqual(config.label2id, restored_config.label2id)
+
 
 # We will verify our results on an image of cute cats
 def prepare_img():