[NAT, DiNAT] Add backbone class (#20654)

* Add first draft

* Add out_features attribute to config

* Add corresponding test

* Add Dinat backbone

* Add BackboneMixin

* Add Backbone mixin, improve tests

* Fix embeddings

* Fix bug

* Improve backbones

* Fix Nat backbone tests

* Fix Dinat backbone tests

* Apply suggestions
Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

src/transformers/__init__.py

@@ -1274,6 +1274,7 @@ else:
     _import_structure["models.dinat"].extend(
         [
             "DINAT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "DinatBackbone",
             "DinatForImageClassification",
             "DinatModel",
             "DinatPreTrainedModel",
@@ -1769,6 +1770,7 @@ else:
     _import_structure["models.nat"].extend(
         [
             "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "NatBackbone",
             "NatForImageClassification",
             "NatModel",
             "NatPreTrainedModel",
@@ -4388,6 +4390,7 @@ if TYPE_CHECKING:
         )
         from .models.dinat import (
             DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            DinatBackbone,
             DinatForImageClassification,
             DinatModel,
             DinatPreTrainedModel,
@@ -4784,6 +4787,7 @@ if TYPE_CHECKING:
         )
         from .models.nat import (
             NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            NatBackbone,
             NatForImageClassification,
             NatModel,
             NatPreTrainedModel,

src/transformers/models/auto/modeling_auto.py

@@ -865,7 +865,9 @@ MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict(
     [
         # Backbone mapping
         ("bit", "BitBackbone"),
+        ("dinat", "DinatBackbone"),
         ("maskformer-swin", "MaskFormerSwinBackbone"),
+        ("nat", "NatBackbone"),
         ("resnet", "ResNetBackbone"),
     ]
 )

src/transformers/models/dinat/__init__.py

@@ -35,6 +35,7 @@ else:
         "DinatForImageClassification",
         "DinatModel",
         "DinatPreTrainedModel",
+        "DinatBackbone",
     ]
 
 if TYPE_CHECKING:
@@ -48,6 +49,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_dinat import (
             DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            DinatBackbone,
             DinatForImageClassification,
             DinatModel,
             DinatPreTrainedModel,

src/transformers/models/dinat/configuration_dinat.py

@@ -72,6 +72,9 @@ class DinatConfig(PretrainedConfig):
             The epsilon used by the layer normalization layers.
         layer_scale_init_value (`float`, *optional*, defaults to 0.0):
             The initial value for the layer scale. Disabled if <=0.
+        out_features (`List[str]`, *optional*):
+            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
+            (depending on how many stages the model has). Will default to the last stage if unset.
 
     Example:
 
@@ -113,6 +116,7 @@ class DinatConfig(PretrainedConfig):
         initializer_range=0.02,
         layer_norm_eps=1e-5,
         layer_scale_init_value=0.0,
+        out_features=None,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -138,3 +142,13 @@ class DinatConfig(PretrainedConfig):
         # this indicates the channel dimension after the last stage of the model
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
         self.layer_scale_init_value = layer_scale_init_value
+        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
+        if out_features is not None:
+            if not isinstance(out_features, list):
+                raise ValueError("out_features should be a list")
+            for feature in out_features:
+                if feature not in self.stage_names:
+                    raise ValueError(
+                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
+                    )
+        self.out_features = out_features

src/transformers/models/dinat/modeling_dinat.py

@@ -25,7 +25,8 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_utils import PreTrainedModel
+from ...modeling_outputs import BackboneOutput
+from ...modeling_utils import BackboneMixin, PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     ModelOutput,
@@ -35,6 +36,7 @@ from ...utils import (
     add_start_docstrings_to_model_forward,
     is_natten_available,
     logging,
+    replace_return_docstrings,
     requires_backends,
 )
 from .configuration_dinat import DinatConfig
@@ -555,14 +557,11 @@ class DinatStage(nn.Module):
             layer_outputs = layer_module(hidden_states, output_attentions)
             hidden_states = layer_outputs[0]
 
+        hidden_states_before_downsampling = hidden_states
         if self.downsample is not None:
-            height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
-            output_dimensions = (height, width, height_downsampled, width_downsampled)
-            hidden_states = self.downsample(layer_outputs[0])
-        else:
-            output_dimensions = (height, width, height, width)
+            hidden_states = self.downsample(hidden_states_before_downsampling)
 
-        stage_outputs = (hidden_states, output_dimensions)
+        stage_outputs = (hidden_states, hidden_states_before_downsampling)
 
         if output_attentions:
             stage_outputs += layer_outputs[1:]
@@ -596,6 +595,7 @@ class DinatEncoder(nn.Module):
         hidden_states: torch.Tensor,
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
+        output_hidden_states_before_downsampling: Optional[bool] = False,
         return_dict: Optional[bool] = True,
     ) -> Union[Tuple, DinatEncoderOutput]:
         all_hidden_states = () if output_hidden_states else None
@@ -612,8 +612,14 @@ class DinatEncoder(nn.Module):
             layer_outputs = layer_module(hidden_states, output_attentions)
 
             hidden_states = layer_outputs[0]
+            hidden_states_before_downsampling = layer_outputs[1]
 
-            if output_hidden_states:
+            if output_hidden_states and output_hidden_states_before_downsampling:
+                # rearrange b h w c -> b c h w
+                reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2)
+                all_hidden_states += (hidden_states_before_downsampling,)
+                all_reshaped_hidden_states += (reshaped_hidden_state,)
+            elif output_hidden_states and not output_hidden_states_before_downsampling:
                 # rearrange b h w c -> b c h w
                 reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
                 all_hidden_states += (hidden_states,)
@@ -871,3 +877,120 @@ class DinatForImageClassification(DinatPreTrainedModel):
             attentions=outputs.attentions,
             reshaped_hidden_states=outputs.reshaped_hidden_states,
         )
+
+
+@add_start_docstrings(
+    "NAT backbone, to be used with frameworks like DETR and MaskFormer.",
+    DINAT_START_DOCSTRING,
+)
+class DinatBackbone(DinatPreTrainedModel, BackboneMixin):
+    def __init__(self, config):
+        super().__init__(config)
+
+        requires_backends(self, ["natten"])
+
+        self.stage_names = config.stage_names
+
+        self.embeddings = DinatEmbeddings(config)
+        self.encoder = DinatEncoder(config)
+
+        self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
+
+        num_features = [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
+        self.out_feature_channels = {}
+        self.out_feature_channels["stem"] = config.embed_dim
+        for i, stage in enumerate(self.stage_names[1:]):
+            self.out_feature_channels[stage] = num_features[i]
+
+        # Add layer norms to hidden states of out_features
+        hidden_states_norms = dict()
+        for stage, num_channels in zip(self.out_features, self.channels):
+            hidden_states_norms[stage] = nn.LayerNorm(num_channels)
+        self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    @property
+    def channels(self):
+        return [self.out_feature_channels[name] for name in self.out_features]
+
+    @add_start_docstrings_to_model_forward(DINAT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> BackboneOutput:
+        """
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoImageProcessor, AutoBackbone
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224")
+        >>> model = AutoBackbone.from_pretrained(
+        ...     "shi-labs/nat-mini-in1k-2240", out_features=["stage1", "stage2", "stage3", "stage4"]
+        ... )
+
+        >>> inputs = processor(image, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+
+        >>> feature_maps = outputs.feature_maps
+        >>> list(feature_maps[-1].shape)
+        [1, 2048, 7, 7]
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        embedding_output = self.embeddings(pixel_values)
+
+        outputs = self.encoder(
+            embedding_output,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            output_hidden_states_before_downsampling=True,
+            return_dict=True,
+        )
+
+        hidden_states = outputs.reshaped_hidden_states
+
+        feature_maps = ()
+        for stage, hidden_state in zip(self.stage_names, hidden_states):
+            if stage in self.out_features:
+                batch_size, num_channels, height, width = hidden_state.shape
+                hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
+                hidden_state = hidden_state.view(batch_size, height * width, num_channels)
+                hidden_state = self.hidden_states_norms[stage](hidden_state)
+                hidden_state = hidden_state.view(batch_size, height, width, num_channels)
+                hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
+                feature_maps += (hidden_state,)
+
+        if not return_dict:
+            output = (feature_maps,)
+            if output_hidden_states:
+                output += (outputs.hidden_states,)
+            return output
+
+        return BackboneOutput(
+            feature_maps=feature_maps,
+            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            attentions=outputs.attentions,
+        )

src/transformers/models/nat/__init__.py

@@ -35,6 +35,7 @@ else:
         "NatForImageClassification",
         "NatModel",
         "NatPreTrainedModel",
+        "NatBackbone",
     ]
 
 if TYPE_CHECKING:
@@ -48,6 +49,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_nat import (
             NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            NatBackbone,
             NatForImageClassification,
             NatModel,
             NatPreTrainedModel,

src/transformers/models/nat/configuration_nat.py

@@ -70,6 +70,9 @@ class NatConfig(PretrainedConfig):
             The epsilon used by the layer normalization layers.
         layer_scale_init_value (`float`, *optional*, defaults to 0.0):
             The initial value for the layer scale. Disabled if <=0.
+        out_features (`List[str]`, *optional*):
+            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
+            (depending on how many stages the model has). Will default to the last stage if unset.
 
     Example:
 
@@ -110,6 +113,7 @@ class NatConfig(PretrainedConfig):
         initializer_range=0.02,
         layer_norm_eps=1e-5,
         layer_scale_init_value=0.0,
+        out_features=None,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -134,3 +138,13 @@ class NatConfig(PretrainedConfig):
         # this indicates the channel dimension after the last stage of the model
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
         self.layer_scale_init_value = layer_scale_init_value
+        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
+        if out_features is not None:
+            if not isinstance(out_features, list):
+                raise ValueError("out_features should be a list")
+            for feature in out_features:
+                if feature not in self.stage_names:
+                    raise ValueError(
+                        f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}"
+                    )
+        self.out_features = out_features

src/transformers/models/nat/modeling_nat.py

@@ -25,7 +25,8 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
-from ...modeling_utils import PreTrainedModel
+from ...modeling_outputs import BackboneOutput
+from ...modeling_utils import BackboneMixin, PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
 from ...utils import (
     ModelOutput,
@@ -35,6 +36,7 @@ from ...utils import (
     add_start_docstrings_to_model_forward,
     is_natten_available,
     logging,
+    replace_return_docstrings,
     requires_backends,
 )
 from .configuration_nat import NatConfig
@@ -536,14 +538,11 @@ class NatStage(nn.Module):
             layer_outputs = layer_module(hidden_states, output_attentions)
             hidden_states = layer_outputs[0]
 
+        hidden_states_before_downsampling = hidden_states
         if self.downsample is not None:
-            height_downsampled, width_downsampled = (height + 1) // 2, (width + 1) // 2
-            output_dimensions = (height, width, height_downsampled, width_downsampled)
-            hidden_states = self.downsample(layer_outputs[0])
-        else:
-            output_dimensions = (height, width, height, width)
+            hidden_states = self.downsample(hidden_states_before_downsampling)
 
-        stage_outputs = (hidden_states, output_dimensions)
+        stage_outputs = (hidden_states, hidden_states_before_downsampling)
 
         if output_attentions:
             stage_outputs += layer_outputs[1:]
@@ -575,6 +574,7 @@ class NatEncoder(nn.Module):
         hidden_states: torch.Tensor,
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
+        output_hidden_states_before_downsampling: Optional[bool] = False,
         return_dict: Optional[bool] = True,
     ) -> Union[Tuple, NatEncoderOutput]:
         all_hidden_states = () if output_hidden_states else None
@@ -591,8 +591,14 @@ class NatEncoder(nn.Module):
             layer_outputs = layer_module(hidden_states, output_attentions)
 
             hidden_states = layer_outputs[0]
+            hidden_states_before_downsampling = layer_outputs[1]
 
-            if output_hidden_states:
+            if output_hidden_states and output_hidden_states_before_downsampling:
+                # rearrange b h w c -> b c h w
+                reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2)
+                all_hidden_states += (hidden_states_before_downsampling,)
+                all_reshaped_hidden_states += (reshaped_hidden_state,)
+            elif output_hidden_states and not output_hidden_states_before_downsampling:
                 # rearrange b h w c -> b c h w
                 reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2)
                 all_hidden_states += (hidden_states,)
@@ -849,3 +855,121 @@ class NatForImageClassification(NatPreTrainedModel):
             attentions=outputs.attentions,
             reshaped_hidden_states=outputs.reshaped_hidden_states,
         )
+
+
+@add_start_docstrings(
+    "NAT backbone, to be used with frameworks like DETR and MaskFormer.",
+    NAT_START_DOCSTRING,
+)
+class NatBackbone(NatPreTrainedModel, BackboneMixin):
+    def __init__(self, config):
+        super().__init__(config)
+
+        requires_backends(self, ["natten"])
+
+        self.stage_names = config.stage_names
+
+        self.embeddings = NatEmbeddings(config)
+        self.encoder = NatEncoder(config)
+
+        self.out_features = config.out_features if config.out_features is not None else [self.stage_names[-1]]
+
+        num_features = [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
+        self.out_feature_channels = {}
+        self.out_feature_channels["stem"] = config.embed_dim
+        for i, stage in enumerate(self.stage_names[1:]):
+            self.out_feature_channels[stage] = num_features[i]
+
+        # Add layer norms to hidden states of out_features
+        hidden_states_norms = dict()
+        for stage, num_channels in zip(self.out_features, self.channels):
+            hidden_states_norms[stage] = nn.LayerNorm(num_channels)
+        self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    @property
+    def channels(self):
+        return [self.out_feature_channels[name] for name in self.out_features]
+
+    @add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> BackboneOutput:
+        """
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import AutoImageProcessor, AutoBackbone
+        >>> import torch
+        >>> from PIL import Image
+        >>> import requests
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224")
+        >>> model = AutoBackbone.from_pretrained(
+        ...     "shi-labs/nat-mini-in1k-2240", out_features=["stage1", "stage2", "stage3", "stage4"]
+        ... )
+
+        >>> inputs = processor(image, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+
+        >>> feature_maps = outputs.feature_maps
+        >>> list(feature_maps[-1].shape)
+        [1, 2048, 7, 7]
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        embedding_output = self.embeddings(pixel_values)
+
+        outputs = self.encoder(
+            embedding_output,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            output_hidden_states_before_downsampling=True,
+            return_dict=True,
+        )
+
+        hidden_states = outputs.reshaped_hidden_states
+
+        feature_maps = ()
+        for stage, hidden_state in zip(self.stage_names, hidden_states):
+            if stage in self.out_features:
+                # TODO can we simplify this?
+                batch_size, num_channels, height, width = hidden_state.shape
+                hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous()
+                hidden_state = hidden_state.view(batch_size, height * width, num_channels)
+                hidden_state = self.hidden_states_norms[stage](hidden_state)
+                hidden_state = hidden_state.view(batch_size, height, width, num_channels)
+                hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
+                feature_maps += (hidden_state,)
+
+        if not return_dict:
+            output = (feature_maps,)
+            if output_hidden_states:
+                output += (outputs.hidden_states,)
+            return output
+
+        return BackboneOutput(
+            feature_maps=feature_maps,
+            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1856,6 +1856,13 @@ class DeiTPreTrainedModel(metaclass=DummyObject):
 DINAT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class DinatBackbone(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class DinatForImageClassification(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -3913,6 +3920,13 @@ class MvpPreTrainedModel(metaclass=DummyObject):
 NAT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class NatBackbone(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class NatForImageClassification(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/dinat/test_modeling_dinat.py

@@ -30,7 +30,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import DinatForImageClassification, DinatModel
+    from transformers import DinatBackbone, DinatForImageClassification, DinatModel
     from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST
 
 if is_vision_available():
@@ -64,8 +64,8 @@ class DinatModelTester:
         is_training=True,
         scope=None,
         use_labels=True,
-        type_sequence_label_size=10,
-        encoder_stride=8,
+        num_labels=10,
+        out_features=["stage1", "stage2"],
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -89,15 +89,15 @@ class DinatModelTester:
         self.is_training = is_training
         self.scope = scope
         self.use_labels = use_labels
-        self.type_sequence_label_size = type_sequence_label_size
-        self.encoder_stride = encoder_stride
+        self.num_labels = num_labels
+        self.out_features = out_features
 
     def prepare_config_and_inputs(self):
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
 
         labels = None
         if self.use_labels:
-            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            labels = ids_tensor([self.batch_size], self.num_labels)
 
         config = self.get_config()
 
@@ -105,6 +105,7 @@ class DinatModelTester:
 
     def get_config(self):
         return DinatConfig(
+            num_labels=self.num_labels,
             image_size=self.image_size,
             patch_size=self.patch_size,
             num_channels=self.num_channels,
@@ -122,7 +123,7 @@ class DinatModelTester:
             patch_norm=self.patch_norm,
             layer_norm_eps=self.layer_norm_eps,
             initializer_range=self.initializer_range,
-            encoder_stride=self.encoder_stride,
+            out_features=self.out_features,
         )
 
     def create_and_check_model(self, config, pixel_values, labels):
@@ -139,12 +140,11 @@ class DinatModelTester:
         )
 
     def create_and_check_for_image_classification(self, config, pixel_values, labels):
-        config.num_labels = self.type_sequence_label_size
         model = DinatForImageClassification(config)
         model.to(torch_device)
         model.eval()
         result = model(pixel_values, labels=labels)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 
         # test greyscale images
         config.num_channels = 1
@@ -154,7 +154,34 @@ class DinatModelTester:
 
         pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         result = model(pixel_values)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_backbone(self, config, pixel_values, labels):
+        model = DinatBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify hidden states
+        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), len(config.out_features))
+
+        # verify backbone works with out_features=None
+        config.out_features = None
+        model = DinatBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify feature maps
+        self.parent.assertEqual(len(result.feature_maps), 1)
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), 1)
 
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
@@ -167,7 +194,15 @@ class DinatModelTester:
 @require_torch
 class DinatModelTest(ModelTesterMixin, unittest.TestCase):
 
-    all_model_classes = (DinatModel, DinatForImageClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (
+            DinatModel,
+            DinatForImageClassification,
+            DinatBackbone,
+        )
+        if is_torch_available()
+        else ()
+    )
     fx_compatible = False
 
     test_torchscript = False
@@ -199,8 +234,16 @@ class DinatModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
 
+    def test_backbone(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_backbone(*config_and_inputs)
+
+    @unittest.skip(reason="Dinat does not use inputs_embeds")
     def test_inputs_embeds(self):
-        # Dinat does not use inputs_embeds
+        pass
+
+    @unittest.skip(reason="Dinat does not use feedforward chunking")
+    def test_feed_forward_chunking(self):
         pass
 
     def test_model_common_attributes(self):
@@ -257,6 +300,7 @@ class DinatModelTest(ModelTesterMixin, unittest.TestCase):
             [height, width, self.model_tester.embed_dim],
         )
 
+        if model_class.__name__ != "DinatBackbone":
             reshaped_hidden_states = outputs.reshaped_hidden_states
             self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
 

tests/models/nat/test_modeling_nat.py

@@ -30,7 +30,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import NatForImageClassification, NatModel
+    from transformers import NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST
 
 if is_vision_available():
@@ -63,8 +63,8 @@ class NatModelTester:
         is_training=True,
         scope=None,
         use_labels=True,
-        type_sequence_label_size=10,
-        encoder_stride=8,
+        num_labels=10,
+        out_features=["stage1", "stage2"],
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -87,15 +87,15 @@ class NatModelTester:
         self.is_training = is_training
         self.scope = scope
         self.use_labels = use_labels
-        self.type_sequence_label_size = type_sequence_label_size
-        self.encoder_stride = encoder_stride
+        self.num_labels = num_labels
+        self.out_features = out_features
 
     def prepare_config_and_inputs(self):
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
 
         labels = None
         if self.use_labels:
-            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            labels = ids_tensor([self.batch_size], self.num_labels)
 
         config = self.get_config()
 
@@ -103,6 +103,7 @@ class NatModelTester:
 
     def get_config(self):
         return NatConfig(
+            num_labels=self.num_labels,
             image_size=self.image_size,
             patch_size=self.patch_size,
             num_channels=self.num_channels,
@@ -119,7 +120,7 @@ class NatModelTester:
             patch_norm=self.patch_norm,
             layer_norm_eps=self.layer_norm_eps,
             initializer_range=self.initializer_range,
-            encoder_stride=self.encoder_stride,
+            out_features=self.out_features,
         )
 
     def create_and_check_model(self, config, pixel_values, labels):
@@ -136,12 +137,11 @@ class NatModelTester:
         )
 
     def create_and_check_for_image_classification(self, config, pixel_values, labels):
-        config.num_labels = self.type_sequence_label_size
         model = NatForImageClassification(config)
         model.to(torch_device)
         model.eval()
         result = model(pixel_values, labels=labels)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
 
         # test greyscale images
         config.num_channels = 1
@@ -151,7 +151,34 @@ class NatModelTester:
 
         pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         result = model(pixel_values)
-        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_backbone(self, config, pixel_values, labels):
+        model = NatBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify hidden states
+        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), len(config.out_features))
+
+        # verify backbone works with out_features=None
+        config.out_features = None
+        model = NatBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify feature maps
+        self.parent.assertEqual(len(result.feature_maps), 1)
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), 1)
 
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
@@ -164,7 +191,15 @@ class NatModelTester:
 @require_torch
 class NatModelTest(ModelTesterMixin, unittest.TestCase):
 
-    all_model_classes = (NatModel, NatForImageClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (
+            NatModel,
+            NatForImageClassification,
+            NatBackbone,
+        )
+        if is_torch_available()
+        else ()
+    )
     fx_compatible = False
 
     test_torchscript = False
@@ -196,8 +231,16 @@ class NatModelTest(ModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
 
+    def test_backbone(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_backbone(*config_and_inputs)
+
+    @unittest.skip(reason="Nat does not use inputs_embeds")
     def test_inputs_embeds(self):
-        # Nat does not use inputs_embeds
+        pass
+
+    @unittest.skip(reason="Nat does not use feedforward chunking")
+    def test_feed_forward_chunking(self):
         pass
 
     def test_model_common_attributes(self):
@@ -254,6 +297,7 @@ class NatModelTest(ModelTesterMixin, unittest.TestCase):
             [height, width, self.model_tester.embed_dim],
         )
 
+        if model_class.__name__ != "NatBackbone":
             reshaped_hidden_states = outputs.reshaped_hidden_states
             self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
 

utils/check_repo.py

@@ -677,6 +677,8 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "MaskFormerSwinBackbone",
     "ResNetBackbone",
     "AutoBackbone",
+    "DinatBackbone",
+    "NatBackbone",
     "MaskFormerSwinConfig",
     "MaskFormerSwinModel",
 ]