Improve support for custom dataset label name/description through HF hub export, via pretrained_cfg

timm/data/__init__.py

@@ -4,7 +4,7 @@ from .config import resolve_data_config, resolve_model_data_config
 from .constants import *
 from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
 from .dataset_factory import create_dataset
-from .dataset_info import DatasetInfo
+from .dataset_info import DatasetInfo, CustomDatasetInfo
 from .imagenet_info import ImageNetInfo, infer_imagenet_subset
 from .loader import create_loader
 from .mixup import Mixup, FastCollateMixup

timm/data/dataset_info.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict, List, Union
+from typing import Dict, List, Optional, Union
 
 
 class DatasetInfo(ABC):
@@ -30,3 +30,44 @@ class DatasetInfo(ABC):
     @abstractmethod
     def label_name_to_description(self, label: str, detailed: bool = False) -> str:
         pass
+
+
+class CustomDatasetInfo(DatasetInfo):
+    """ DatasetInfo that wraps passed values for custom datasets."""
+
+    def __init__(
+            self,
+            label_names: Union[List[str], Dict[int, str]],
+            label_descriptions: Optional[Dict[str, str]] = None
+    ):
+        super().__init__()
+        assert len(label_names) > 0
+        self._label_names = label_names  # label index => label name mapping
+        self._label_descriptions = label_descriptions  # label name => label description mapping
+        if self._label_descriptions is not None:
+            # validate descriptions (label names required)
+            assert isinstance(self._label_descriptions, dict)
+            for n in self._label_names:
+                assert n in self._label_descriptions
+
+    def num_classes(self):
+        return len(self._label_names)
+
+    def label_names(self):
+        return self._label_names
+
+    def label_descriptions(self, detailed: bool = False, as_dict: bool = False) -> Union[List[str], Dict[str, str]]:
+        return self._label_descriptions
+
+    def label_name_to_description(self, label: str, detailed: bool = False) -> str:
+        if self._label_descriptions:
+            return self._label_descriptions[label]
+        return label  # return label name itself if a descriptions is not present
+
+    def index_to_label_name(self, index) -> str:
+        assert 0 <= index < len(self._label_names)
+        return self._label_names[index]
+
+    def index_to_description(self, index: int, detailed: bool = False) -> str:
+        label = self.index_to_label_name(index)
+        return self.label_name_to_description(label, detailed=detailed)

timm/models/_hub.py

@@ -16,6 +16,7 @@ except ImportError:
     from torch.hub import _get_torch_home as get_dir
 
 from timm import __version__
+from timm.layers import ClassifierHead, NormMlpClassifierHead
 from timm.models._pretrained import filter_pretrained_cfg
 
 try:
@@ -96,7 +97,7 @@ def has_hf_hub(necessary=False):
     return _has_hf_hub
 
 
-def hf_split(hf_id):
+def hf_split(hf_id: str):
     # FIXME I may change @ -> # and be parsed as fragment in a URI model name scheme
     rev_split = hf_id.split('@')
     assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.'
@@ -127,19 +128,26 @@ def load_model_config_from_hf(model_id: str):
         hf_config = {}
         hf_config['architecture'] = pretrained_cfg.pop('architecture')
         hf_config['num_features'] = pretrained_cfg.pop('num_features', None)
-        if 'labels' in pretrained_cfg:
+        if 'labels' in pretrained_cfg:  # deprecated name for 'label_names'
-            hf_config['label_name'] = pretrained_cfg.pop('labels')
+            pretrained_cfg['label_names'] = pretrained_cfg.pop('labels')
         hf_config['pretrained_cfg'] = pretrained_cfg
 
     # NOTE currently discarding parent config as only arch name and pretrained_cfg used in timm right now
     pretrained_cfg = hf_config['pretrained_cfg']
     pretrained_cfg['hf_hub_id'] = model_id  # insert hf_hub id for pretrained weight load during model creation
     pretrained_cfg['source'] = 'hf-hub'
+
+    # model should be created with base config num_classes if its exist
     if 'num_classes' in hf_config:
-        # model should be created with parent num_classes if they exist
         pretrained_cfg['num_classes'] = hf_config['num_classes']
-    model_name = hf_config['architecture']
 
+    # label meta-data in base config overrides saved pretrained_cfg on load
+    if 'label_names' in hf_config:
+        pretrained_cfg['label_names'] = hf_config.pop('label_names')
+    if 'label_descriptions' in hf_config:
+        pretrained_cfg['label_descriptions'] = hf_config.pop('label_descriptions')
+
+    model_name = hf_config['architecture']
     return pretrained_cfg, model_name
 
 
@@ -150,7 +158,7 @@ def load_state_dict_from_hf(model_id: str, filename: str = 'pytorch_model.bin'):
     return state_dict
 
 
-def save_config_for_hf(model, config_path, model_config=None):
+def save_config_for_hf(model, config_path: str, model_config: Optional[dict] = None):
     model_config = model_config or {}
     hf_config = {}
     pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True)
@@ -164,22 +172,22 @@ def save_config_for_hf(model, config_path, model_config=None):
 
     if 'labels' in model_config:
         _logger.warning(
-            "'labels' as a config field for timm models is deprecated. Please use 'label_name' and 'display_name'. "
+            "'labels' as a config field for is deprecated. Please use 'label_names' and 'label_descriptions'."
-            "Using provided 'label' field as 'label_name'.")
+            " Renaming provided 'labels' field to 'label_names'.")
-        model_config['label_name'] = model_config.pop('labels')
+        model_config.setdefault('label_names', model_config.pop('labels'))
 
-    label_name = model_config.pop('label_name', None)
+    label_names = model_config.pop('label_names', None)
-    if label_name:
+    if label_names:
-        assert isinstance(label_name, (dict, list, tuple))
+        assert isinstance(label_names, (dict, list, tuple))
         # map label id (classifier index) -> unique label name (ie synset for ImageNet, MID for OpenImages)
         # can be a dict id: name if there are id gaps, or tuple/list if no gaps.
-        hf_config['label_name'] = model_config['label_name']
+        hf_config['label_names'] = label_names
 
-    display_name = model_config.pop('display_name', None)
+    label_descriptions = model_config.pop('label_descriptions', None)
-    if display_name:
+    if label_descriptions:
-        assert isinstance(display_name, dict)
+        assert isinstance(label_descriptions, dict)
-        # map label_name -> user interface display name
+        # maps label names -> descriptions
-        hf_config['display_name'] = model_config['display_name']
+        hf_config['label_descriptions'] = label_descriptions
 
     hf_config['pretrained_cfg'] = pretrained_cfg
     hf_config.update(model_config)
@@ -188,7 +196,7 @@ def save_config_for_hf(model, config_path, model_config=None):
         json.dump(hf_config, f, indent=2)
 
 
-def save_for_hf(model, save_directory, model_config=None):
+def save_for_hf(model, save_directory: str, model_config: Optional[dict] = None):
     assert has_hf_hub(True)
     save_directory = Path(save_directory)
     save_directory.mkdir(exist_ok=True, parents=True)
@@ -249,7 +257,7 @@ def push_to_hf_hub(
         )
 
 
-def generate_readme(model_card, model_name):
+def generate_readme(model_card: dict, model_name: str):
     readme_text = "---\n"
     readme_text += "tags:\n- image-classification\n- timm\n"
     readme_text += "library_tag: timm\n"

timm/models/_pretrained.py

@@ -34,9 +34,11 @@ class PretrainedCfg:
     mean: Tuple[float, ...] = (0.485, 0.456, 0.406)
     std: Tuple[float, ...] = (0.229, 0.224, 0.225)
 
-    # head config
+    # head / classifier config and meta-data
     num_classes: int = 1000
     label_offset: Optional[int] = None
+    label_names: Optional[Tuple[str]] = None
+    label_descriptions: Optional[Dict[str, str]] = None
 
     # model attributes that vary with above or required for pretrained adaptation
     pool_size: Optional[Tuple[int, ...]] = None