Add Swinv2 backbone (#27742)

* First draft

* More improvements

* More improvements

* Make all tests pass

* Remove script

* Update image processor

* Address comments

* Use new gradient checkpointing method

* Convert checkpoints, add integration test

* Do not keep aspect ratio for now

* Set keep_aspect_ratio=False for beit, add integration test

* Remove print statement

src/transformers/__init__.py

@@ -3213,6 +3213,7 @@ else:
     _import_structure["models.swinv2"].extend(
         [
             "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "Swinv2Backbone",
             "Swinv2ForImageClassification",
             "Swinv2ForMaskedImageModeling",
             "Swinv2Model",
@@ -7541,6 +7542,7 @@ if TYPE_CHECKING:
         )
         from .models.swinv2 import (
             SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Swinv2Backbone,
             Swinv2ForImageClassification,
             Swinv2ForMaskedImageModeling,
             Swinv2Model,

src/transformers/models/auto/modeling_auto.py

@@ -1116,6 +1116,7 @@ MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict(
         ("nat", "NatBackbone"),
         ("resnet", "ResNetBackbone"),
         ("swin", "SwinBackbone"),
+        ("swinv2", "Swinv2Backbone"),
         ("timm_backbone", "TimmBackbone"),
         ("vitdet", "VitDetBackbone"),
     ]

src/transformers/models/dpt/convert_dpt_beit_to_hf.py

@@ -207,8 +207,9 @@ def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
     model.eval()
 
     # Check outputs on an image
+    # We set `keep_aspect_ratio=False` as our current BEiT does not support arbitrary window sizes
     processor = DPTImageProcessor(
-        size={"height": image_size, "width": image_size}, keep_aspect_ratio=True, ensure_multiple_of=32
+        size={"height": image_size, "width": image_size}, keep_aspect_ratio=False, ensure_multiple_of=32
     )
 
     image = prepare_img()

src/transformers/models/dpt/convert_dpt_swinv2_to_hf.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Convert DPT 3.1 checkpoints from the MiDaS repository. URL: https://github.com/isl-org/MiDaS"""
+
+
+import argparse
+from pathlib import Path
+
+import requests
+import torch
+from PIL import Image
+
+from transformers import DPTConfig, DPTForDepthEstimation, DPTImageProcessor, Swinv2Config
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_dpt_config(model_name):
+    if "tiny" in model_name:
+        embed_dim = 96
+        depths = (2, 2, 6, 2)
+        num_heads = (3, 6, 12, 24)
+        window_size = 16
+        # note: for Swinv2-tiny authors used the window_size = 16 variant
+        # as seen here: https://github.com/isl-org/MiDaS/blob/bdc4ed64c095e026dc0a2f17cabb14d58263decb/midas/backbones/swin2.py#L26
+        pretrained_window_sizes = (0, 0, 0, 0)
+    elif "base" in model_name:
+        embed_dim = 128
+        depths = (2, 2, 18, 2)
+        num_heads = (4, 8, 16, 32)
+        window_size = 24
+        pretrained_window_sizes = (12, 12, 12, 6)
+    elif "large" in model_name:
+        embed_dim = 192
+        depths = (2, 2, 18, 2)
+        num_heads = (6, 12, 24, 48)
+        window_size = 24
+        pretrained_window_sizes = (12, 12, 12, 6)
+
+    if "384" in model_name:
+        image_size = 384
+    elif "256" in model_name:
+        image_size = 256
+    else:
+        raise ValueError("Model not supported, to do")
+
+    backbone_config = Swinv2Config(
+        image_size=image_size,
+        embed_dim=embed_dim,
+        depths=depths,
+        window_size=window_size,
+        pretrained_window_sizes=pretrained_window_sizes,
+        num_heads=num_heads,
+        out_features=["stage1", "stage2", "stage3", "stage4"],
+    )
+
+    if model_name == "dpt-swinv2-tiny-256":
+        neck_hidden_sizes = [96, 192, 384, 768]
+    elif model_name == "dpt-swinv2-base-384":
+        neck_hidden_sizes = [128, 256, 512, 1024]
+    elif model_name == "dpt-swinv2-large-384":
+        neck_hidden_sizes = [192, 384, 768, 1536]
+
+    config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes)
+
+    return config, image_size
+
+
+# here we list all keys to be renamed (original name on the left, our name on the right)
+def create_rename_keys(config):
+    rename_keys = []
+
+    # fmt: off
+    # stem
+    rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
+    rename_keys.append(("pretrained.model.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
+    rename_keys.append(("pretrained.model.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
+    rename_keys.append(("pretrained.model.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
+
+    # transformer encoder
+    for i in range(len(config.backbone_config.depths)):
+        for j in range(config.backbone_config.depths[i]):
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.logit_scale", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.logit_scale"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.2.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.q_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.v_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
+
+        # downsample parameters
+        if i in [0,1,2]:
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias"))
+
+    # note: non-Transformer backbones like Swinv2, LeViT et al don't require activation postprocessing (readout projections + resize blocks)
+
+    # refinenet (tricky here)
+    mapping = {1:3, 2:2, 3:1, 4:0}
+
+    for i in range(1, 5):
+        j = mapping[i]
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias"))
+
+    # scratch convolutions
+    for i in range(4):
+        rename_keys.append((f"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight"))
+
+    # head
+    for i in range(0, 5, 2):
+        rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight"))
+        rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias"))
+
+    return rename_keys
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v(state_dict, config, model):
+    for i in range(len(config.backbone_config.depths)):
+        for j in range(config.backbone_config.depths[i]):
+            dim = model.backbone.encoder.layers[i].blocks[j].attention.self.all_head_size
+            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+            in_proj_weight = state_dict.pop(f"pretrained.model.layers.{i}.blocks.{j}.attn.qkv.weight")
+            # next, add query, keys and values (in that order) to the state dict
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
+                dim : dim * 2, :
+            ]
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
+                -dim:, :
+            ]
+
+
+def rename_key(dct, old, new):
+    val = dct.pop(old)
+    dct[new] = val
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@torch.no_grad()
+def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, verify_logits, push_to_hub):
+    """
+    Copy/paste/tweak model's weights to our DPT structure.
+    """
+
+    name_to_url = {
+        "dpt-swinv2-tiny-256": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt",
+        "dpt-swinv2-base-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt",
+        "dpt-swinv2-large-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt",
+    }
+
+    # define DPT configuration based on URL
+    checkpoint_url = name_to_url[model_name]
+    config, image_size = get_dpt_config(model_name)
+    # load original state_dict from URL
+    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+
+    # load HuggingFace model
+    model = DPTForDepthEstimation(config)
+
+    # remove certain keys
+    remove_ignore_keys_(state_dict)
+    # rename keys
+    rename_keys = create_rename_keys(config)
+    for src, dest in rename_keys:
+        rename_key(state_dict, src, dest)
+    # read in qkv matrices
+    read_in_q_k_v(state_dict, config, model)
+
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+    print("Missing keys:", missing_keys)
+    print("Unexpected keys:", unexpected_keys)
+    model.eval()
+
+    # Check outputs on an image
+    processor = DPTImageProcessor(size={"height": image_size, "width": image_size})
+
+    image = prepare_img()
+    processor(image, return_tensors="pt")
+
+    if verify_logits:
+        from torchvision import transforms
+
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        transforms = transforms.Compose(
+            [
+                transforms.Resize((image_size, image_size)),
+                transforms.ToTensor(),
+            ]
+        )
+        pixel_values = transforms(image).unsqueeze(0)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        predicted_depth = outputs.predicted_depth
+
+        print("Shape of predicted depth:", predicted_depth.shape)
+        print("First values of predicted depth:", predicted_depth[0, :3, :3])
+
+        # assert logits
+        if model_name == "dpt-swinv2-base-384":
+            # OK, checked
+            expected_shape = torch.Size([1, 384, 384])
+            expected_slice = torch.tensor(
+                [
+                    [1998.5575, 1997.3887, 2009.2981],
+                    [1952.8607, 1979.6488, 2001.0854],
+                    [1953.7697, 1961.7711, 1968.8904],
+                ],
+            )
+        elif model_name == "dpt-swinv2-tiny-256":
+            # OK, checked
+            expected_shape = torch.Size([1, 256, 256])
+            expected_slice = torch.tensor(
+                [[978.9163, 976.5215, 978.5349], [974.1859, 971.7249, 975.8046], [971.3419, 970.3118, 971.6830]],
+            )
+        elif model_name == "dpt-swinv2-large-384":
+            # OK, checked
+            expected_shape = torch.Size([1, 384, 384])
+            expected_slice = torch.tensor(
+                [
+                    [1203.7206, 1200.1495, 1197.8234],
+                    [1196.2484, 1183.5033, 1186.4640],
+                    [1178.8131, 1182.3260, 1174.3975],
+                ],
+            )
+
+        assert predicted_depth.shape == torch.Size(expected_shape)
+        assert torch.allclose(predicted_depth[0, :3, :3], expected_slice)
+        print("Looks ok!")
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model and processor to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        print("Pushing model and processor to hub...")
+        model.push_to_hub(repo_id=f"Intel/{model_name}")
+        processor.push_to_hub(repo_id=f"Intel/{model_name}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--model_name",
+        default="dpt-swinv2-base-384",
+        type=str,
+        choices=["dpt-swinv2-tiny-256", "dpt-swinv2-base-384", "dpt-swinv2-large-384"],
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--verify_logits",
+        action="store_true",
+        help="Whether to verify logits after conversion.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the hub after conversion.",
+    )
+
+    args = parser.parse_args()
+    convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)

src/transformers/models/dpt/image_processing_dpt.py

@@ -100,7 +100,7 @@ class DPTImageProcessor(BaseImageProcessor):
             Whether to resize the image's (height, width) dimensions. Can be overidden by `do_resize` in `preprocess`.
         size (`Dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`):
             Size of the image after resizing. Can be overidden by `size` in `preprocess`.
-        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
             Defines the resampling filter to use if resizing the image. Can be overidden by `resample` in `preprocess`.
         keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
             If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
@@ -136,7 +136,7 @@ class DPTImageProcessor(BaseImageProcessor):
         self,
         do_resize: bool = True,
         size: Dict[str, int] = None,
-        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
         keep_aspect_ratio: bool = False,
         ensure_multiple_of: int = 1,
         do_rescale: bool = True,
@@ -202,6 +202,7 @@ class DPTImageProcessor(BaseImageProcessor):
         size = get_size_dict(size)
         if "height" not in size or "width" not in size:
             raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
+
         output_size = get_resize_output_image_size(
             image,
             output_size=(size["height"], size["width"]),
@@ -381,7 +382,14 @@ class DPTImageProcessor(BaseImageProcessor):
 
         if do_resize:
             images = [
-                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
+                self.resize(
+                    image=image,
+                    size=size,
+                    resample=resample,
+                    keep_aspect_ratio=keep_aspect_ratio,
+                    ensure_multiple_of=ensure_multiple_of,
+                    input_data_format=input_data_format,
+                )
                 for image in images
             ]
 

src/transformers/models/swin2sr/modeling_swin2sr.py

@@ -489,11 +489,12 @@ class Swin2SROutput(nn.Module):
 class Swin2SRLayer(nn.Module):
     def __init__(self, config, dim, input_resolution, num_heads, shift_size=0, pretrained_window_size=0):
         super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.shift_size = shift_size
-        self.window_size = config.window_size
         self.input_resolution = input_resolution
-        self.set_shift_and_window_size(input_resolution)
+        window_size, shift_size = self._compute_window_shift(
+            (config.window_size, config.window_size), (shift_size, shift_size)
+        )
+        self.window_size = window_size[0]
+        self.shift_size = shift_size[0]
         self.attention = Swin2SRAttention(
             config=config,
             dim=dim,
@@ -509,29 +510,10 @@ class Swin2SRLayer(nn.Module):
         self.output = Swin2SROutput(config, dim)
         self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
 
-    def set_shift_and_window_size(self, input_resolution):
-        target_window_size = (
-            self.window_size
-            if isinstance(self.window_size, collections.abc.Iterable)
-            else (self.window_size, self.window_size)
-        )
-        target_shift_size = (
-            self.shift_size
-            if isinstance(self.shift_size, collections.abc.Iterable)
-            else (self.shift_size, self.shift_size)
-        )
-        window_dim = input_resolution[0].item() if torch.is_tensor(input_resolution[0]) else input_resolution[0]
-        self.window_size = window_dim if window_dim <= target_window_size[0] else target_window_size[0]
-        self.shift_size = (
-            0
-            if input_resolution
-            <= (
-                self.window_size
-                if isinstance(self.window_size, collections.abc.Iterable)
-                else (self.window_size, self.window_size)
-            )
-            else target_shift_size[0]
-        )
+    def _compute_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
+        window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
+        shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
+        return window_size, shift_size
 
     def get_attn_mask(self, height, width, dtype):
         if self.shift_size > 0:
@@ -574,12 +556,7 @@ class Swin2SRLayer(nn.Module):
         input_dimensions: Tuple[int, int],
         head_mask: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = False,
-        always_partition: Optional[bool] = False,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        if not always_partition:
-            self.set_shift_and_window_size(input_dimensions)
-        else:
-            pass
         height, width = input_dimensions
         batch_size, _, channels = hidden_states.size()
         shortcut = hidden_states

src/transformers/models/swinv2/__init__.py

@@ -33,6 +33,7 @@ else:
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
+        "Swinv2Backbone",
     ]
 
 
@@ -47,6 +48,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_swinv2 import (
             SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
+            Swinv2Backbone,
             Swinv2ForImageClassification,
             Swinv2ForMaskedImageModeling,
             Swinv2Model,

src/transformers/models/swinv2/configuration_swinv2.py

@@ -16,6 +16,7 @@
 
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
+from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
 
 
 logger = logging.get_logger(__name__)
@@ -27,7 +28,7 @@ SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 }
 
 
-class Swinv2Config(PretrainedConfig):
+class Swinv2Config(BackboneConfigMixin, PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`Swinv2Model`]. It is used to instantiate a Swin
     Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a
@@ -53,6 +54,8 @@ class Swinv2Config(PretrainedConfig):
             Number of attention heads in each layer of the Transformer encoder.
         window_size (`int`, *optional*, defaults to 7):
             Size of windows.
+        pretrained_window_sizes (`list(int)`, *optional*, defaults to `[0, 0, 0, 0]`):
+            Size of windows during pretraining.
         mlp_ratio (`float`, *optional*, defaults to 4.0):
             Ratio of MLP hidden dimensionality to embedding dimensionality.
         qkv_bias (`bool`, *optional*, defaults to `True`):
@@ -74,6 +77,14 @@ class Swinv2Config(PretrainedConfig):
             The epsilon used by the layer normalization layers.
         encoder_stride (`int`, *optional*, defaults to 32):
             Factor to increase the spatial resolution by in the decoder head for masked image modeling.
+        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). If unset and `out_indices` is set, will default to the
+            corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
+        out_indices (`List[int]`, *optional*):
+            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
+            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
+            If unset and `out_features` is unset, will default to the last stage.
 
     Example:
 
@@ -106,6 +117,7 @@ class Swinv2Config(PretrainedConfig):
         depths=[2, 2, 6, 2],
         num_heads=[3, 6, 12, 24],
         window_size=7,
+        pretrained_window_sizes=[0, 0, 0, 0],
         mlp_ratio=4.0,
         qkv_bias=True,
         hidden_dropout_prob=0.0,
@@ -116,6 +128,8 @@ class Swinv2Config(PretrainedConfig):
         initializer_range=0.02,
         layer_norm_eps=1e-5,
         encoder_stride=32,
+        out_features=None,
+        out_indices=None,
         **kwargs,
     ):
         super().__init__(**kwargs)
@@ -128,6 +142,7 @@ class Swinv2Config(PretrainedConfig):
         self.num_layers = len(depths)
         self.num_heads = num_heads
         self.window_size = window_size
+        self.pretrained_window_sizes = pretrained_window_sizes
         self.mlp_ratio = mlp_ratio
         self.qkv_bias = qkv_bias
         self.hidden_dropout_prob = hidden_dropout_prob
@@ -138,7 +153,10 @@ class Swinv2Config(PretrainedConfig):
         self.layer_norm_eps = layer_norm_eps
         self.initializer_range = initializer_range
         self.encoder_stride = encoder_stride
+        self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )
         # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
-        self.pretrained_window_sizes = (0, 0, 0, 0)

src/transformers/models/swinv2/modeling_swinv2.py

@@ -23,10 +23,11 @@ from typing import Optional, Tuple, Union
 
 import torch
 import torch.utils.checkpoint
-from torch import nn
+from torch import Tensor, nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
+from ...modeling_outputs import BackboneOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import find_pruneable_heads_and_indices, meshgrid, prune_linear_layer
 from ...utils import (
@@ -37,6 +38,7 @@ from ...utils import (
     logging,
     replace_return_docstrings,
 )
+from ...utils.backbone_utils import BackboneMixin
 from .configuration_swinv2 import Swinv2Config
 
 
@@ -641,11 +643,12 @@ class Swinv2Output(nn.Module):
 class Swinv2Layer(nn.Module):
     def __init__(self, config, dim, input_resolution, num_heads, shift_size=0, pretrained_window_size=0):
         super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.shift_size = shift_size
-        self.window_size = config.window_size
         self.input_resolution = input_resolution
-        self.set_shift_and_window_size(input_resolution)
+        window_size, shift_size = self._compute_window_shift(
+            (config.window_size, config.window_size), (shift_size, shift_size)
+        )
+        self.window_size = window_size[0]
+        self.shift_size = shift_size[0]
         self.attention = Swinv2Attention(
             config=config,
             dim=dim,
@@ -661,29 +664,10 @@ class Swinv2Layer(nn.Module):
         self.output = Swinv2Output(config, dim)
         self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps)
 
-    def set_shift_and_window_size(self, input_resolution):
-        target_window_size = (
-            self.window_size
-            if isinstance(self.window_size, collections.abc.Iterable)
-            else (self.window_size, self.window_size)
-        )
-        target_shift_size = (
-            self.shift_size
-            if isinstance(self.shift_size, collections.abc.Iterable)
-            else (self.shift_size, self.shift_size)
-        )
-        window_dim = input_resolution[0].item() if torch.is_tensor(input_resolution[0]) else input_resolution[0]
-        self.window_size = window_dim if window_dim <= target_window_size[0] else target_window_size[0]
-        self.shift_size = (
-            0
-            if input_resolution
-            <= (
-                self.window_size
-                if isinstance(self.window_size, collections.abc.Iterable)
-                else (self.window_size, self.window_size)
-            )
-            else target_shift_size[0]
-        )
+    def _compute_window_shift(self, target_window_size, target_shift_size) -> Tuple[Tuple[int, int], Tuple[int, int]]:
+        window_size = [r if r <= w else w for r, w in zip(self.input_resolution, target_window_size)]
+        shift_size = [0 if r <= w else s for r, w, s in zip(self.input_resolution, window_size, target_shift_size)]
+        return window_size, shift_size
 
     def get_attn_mask(self, height, width, dtype):
         if self.shift_size > 0:
@@ -726,12 +710,7 @@ class Swinv2Layer(nn.Module):
         input_dimensions: Tuple[int, int],
         head_mask: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = False,
-        always_partition: Optional[bool] = False,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        if not always_partition:
-            self.set_shift_and_window_size(input_dimensions)
-        else:
-            pass
         height, width = input_dimensions
         batch_size, _, channels = hidden_states.size()
         shortcut = hidden_states
@@ -791,24 +770,18 @@ class Swinv2Stage(nn.Module):
         super().__init__()
         self.config = config
         self.dim = dim
-        window_size = (
-            config.window_size
-            if isinstance(config.window_size, collections.abc.Iterable)
-            else (config.window_size, config.window_size)
-        )
-        self.blocks = nn.ModuleList(
-            [
-                Swinv2Layer(
-                    config=config,
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    shift_size=[0, 0] if (i % 2 == 0) else [window_size[0] // 2, window_size[1] // 2],
-                    pretrained_window_size=pretrained_window_size,
-                )
-                for i in range(depth)
-            ]
-        )
+        blocks = []
+        for i in range(depth):
+            block = Swinv2Layer(
+                config=config,
+                dim=dim,
+                input_resolution=input_resolution,
+                num_heads=num_heads,
+                shift_size=0 if (i % 2 == 0) else config.window_size // 2,
+                pretrained_window_size=pretrained_window_size,
+            )
+            blocks.append(block)
+        self.blocks = nn.ModuleList(blocks)
 
         # patch merging layer
         if downsample is not None:
@@ -818,21 +791,22 @@ class Swinv2Stage(nn.Module):
 
         self.pointing = False
 
-    # Copied from transformers.models.swin.modeling_swin.SwinStage.forward
     def forward(
         self,
         hidden_states: torch.Tensor,
         input_dimensions: Tuple[int, int],
         head_mask: Optional[torch.FloatTensor] = None,
         output_attentions: Optional[bool] = False,
-        always_partition: Optional[bool] = False,
     ) -> Tuple[torch.Tensor]:
         height, width = input_dimensions
         for i, layer_module in enumerate(self.blocks):
             layer_head_mask = head_mask[i] if head_mask is not None else None
 
             layer_outputs = layer_module(
-                hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
+                hidden_states,
+                input_dimensions,
+                layer_head_mask,
+                output_attentions,
             )
 
             hidden_states = layer_outputs[0]
@@ -860,25 +834,24 @@ class Swinv2Encoder(nn.Module):
         if self.config.pretrained_window_sizes is not None:
             pretrained_window_sizes = config.pretrained_window_sizes
         dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
-        self.layers = nn.ModuleList(
-            [
-                Swinv2Stage(
-                    config=config,
-                    dim=int(config.embed_dim * 2**i_layer),
-                    input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
-                    depth=config.depths[i_layer],
-                    num_heads=config.num_heads[i_layer],
-                    drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
-                    downsample=Swinv2PatchMerging if (i_layer < self.num_layers - 1) else None,
-                    pretrained_window_size=pretrained_window_sizes[i_layer],
-                )
-                for i_layer in range(self.num_layers)
-            ]
-        )
+
+        layers = []
+        for i_layer in range(self.num_layers):
+            stage = Swinv2Stage(
+                config=config,
+                dim=int(config.embed_dim * 2**i_layer),
+                input_resolution=(grid_size[0] // (2**i_layer), grid_size[1] // (2**i_layer)),
+                depth=config.depths[i_layer],
+                num_heads=config.num_heads[i_layer],
+                drop_path=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])],
+                downsample=Swinv2PatchMerging if (i_layer < self.num_layers - 1) else None,
+                pretrained_window_size=pretrained_window_sizes[i_layer],
+            )
+            layers.append(stage)
+        self.layers = nn.ModuleList(layers)
 
         self.gradient_checkpointing = False
 
-    # Copied from transformers.models.swin.modeling_swin.SwinEncoder.forward with SwinEncoderOutput->Swinv2EncoderOutput
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -887,7 +860,6 @@ class Swinv2Encoder(nn.Module):
         output_attentions: Optional[bool] = False,
         output_hidden_states: Optional[bool] = False,
         output_hidden_states_before_downsampling: Optional[bool] = False,
-        always_partition: Optional[bool] = False,
         return_dict: Optional[bool] = True,
     ) -> Union[Tuple, Swinv2EncoderOutput]:
         all_hidden_states = () if output_hidden_states else None
@@ -907,11 +879,14 @@ class Swinv2Encoder(nn.Module):
 
             if self.gradient_checkpointing and self.training:
                 layer_outputs = self._gradient_checkpointing_func(
-                    layer_module.__call__, hidden_states, input_dimensions, layer_head_mask, output_attentions
+                    layer_module.__call__, hidden_states, input_dimensions, layer_head_mask
                 )
             else:
                 layer_outputs = layer_module(
-                    hidden_states, input_dimensions, layer_head_mask, output_attentions, always_partition
+                    hidden_states,
+                    input_dimensions,
+                    layer_head_mask,
+                    output_attentions,
                 )
 
             hidden_states = layer_outputs[0]
@@ -942,7 +917,11 @@ class Swinv2Encoder(nn.Module):
                 all_self_attentions += layer_outputs[3:]
 
         if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
+            return tuple(
+                v
+                for v in [hidden_states, all_hidden_states, all_self_attentions, all_reshaped_hidden_states]
+                if v is not None
+            )
 
         return Swinv2EncoderOutput(
             last_hidden_state=hidden_states,
@@ -1323,3 +1302,99 @@ class Swinv2ForImageClassification(Swinv2PreTrainedModel):
             attentions=outputs.attentions,
             reshaped_hidden_states=outputs.reshaped_hidden_states,
         )
+
+
+@add_start_docstrings(
+    """
+    Swinv2 backbone, to be used with frameworks like DETR and MaskFormer.
+    """,
+    SWINV2_START_DOCSTRING,
+)
+class Swinv2Backbone(Swinv2PreTrainedModel, BackboneMixin):
+    def __init__(self, config):
+        super().__init__(config)
+        super()._init_backbone(config)
+
+        self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
+        self.embeddings = Swinv2Embeddings(config)
+        self.encoder = Swinv2Encoder(config, self.embeddings.patch_grid)
+
+        # initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    @add_start_docstrings_to_model_forward(SWINV2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        pixel_values: Tensor,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: 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("microsoft/swinv2-tiny-patch4-window8-256")
+        >>> model = AutoBackbone.from_pretrained(
+        ...     "microsoft/swinv2-tiny-patch4-window8-256", 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, input_dimensions = self.embeddings(pixel_values)
+
+        outputs = self.encoder(
+            embedding_output,
+            input_dimensions,
+            head_mask=None,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            output_hidden_states_before_downsampling=True,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs.reshaped_hidden_states if return_dict else outputs[-1]
+
+        feature_maps = ()
+        for stage, hidden_state in zip(self.stage_names, hidden_states):
+            if stage in self.out_features:
+                feature_maps += (hidden_state,)
+
+        if not return_dict:
+            output = (feature_maps,)
+            if output_hidden_states:
+                output += (outputs[1],)
+            if output_attentions:
+                output += (outputs[2],)
+            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

@@ -7759,6 +7759,13 @@ class Swin2SRPreTrainedModel(metaclass=DummyObject):
 SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class Swinv2Backbone(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class Swinv2ForImageClassification(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/dpt/test_image_processing_dpt.py

@@ -126,3 +126,13 @@ class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
         ).pixel_values
         self.assertTrue(pixel_values.shape[2] % 4 == 0)
         self.assertTrue(pixel_values.shape[3] % 4 == 0)
+
+    def test_keep_aspect_ratio(self):
+        size = {"height": 512, "width": 512}
+        image_processor = DPTImageProcessor(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
+
+        image = np.zeros((489, 640, 3))
+
+        pixel_values = image_processor(image, return_tensors="pt").pixel_values
+
+        self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])

tests/models/dpt/test_modeling_dpt_auto_backbone.py

@@ -258,7 +258,7 @@ def prepare_img():
 @require_vision
 @slow
 class DPTModelIntegrationTest(unittest.TestCase):
-    def test_inference_depth_estimation(self):
+    def test_inference_depth_estimation_dinov2(self):
         image_processor = DPTImageProcessor.from_pretrained("facebook/dpt-dinov2-small-kitti")
         model = DPTForDepthEstimation.from_pretrained("facebook/dpt-dinov2-small-kitti").to(torch_device)
 
@@ -279,3 +279,47 @@ class DPTModelIntegrationTest(unittest.TestCase):
         ).to(torch_device)
 
         self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
+
+    def test_inference_depth_estimation_beit(self):
+        image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-base-384")
+        model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-base-384").to(torch_device)
+
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predicted_depth = outputs.predicted_depth
+
+        # verify the predicted depth
+        expected_shape = torch.Size((1, 384, 384))
+        self.assertEqual(predicted_depth.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[2669.7061, 2663.7144, 2674.9399], [2633.9326, 2650.9092, 2665.4270], [2621.8271, 2632.0129, 2637.2290]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
+
+    def test_inference_depth_estimation_swinv2(self):
+        image_processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
+        model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256").to(torch_device)
+
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+            predicted_depth = outputs.predicted_depth
+
+        # verify the predicted depth
+        expected_shape = torch.Size((1, 256, 256))
+        self.assertEqual(predicted_depth.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[1032.7719, 1025.1886, 1030.2661], [1023.7619, 1021.0075, 1024.9121], [1022.5667, 1018.8522, 1021.4145]]
+        ).to(torch_device)
+
+        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))

tests/models/swinv2/test_modeling_swinv2.py

@@ -14,12 +14,14 @@
 # limitations under the License.
 """ Testing suite for the PyTorch Swinv2 model. """
 import collections
+import inspect
 import unittest
 
 from transformers import Swinv2Config
 from transformers.testing_utils import require_torch, require_vision, slow, torch_device
 from transformers.utils import cached_property, is_torch_available, is_vision_available
 
+from ...test_backbone_common import BackboneTesterMixin
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
 from ...test_pipeline_mixin import PipelineTesterMixin
@@ -29,7 +31,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import Swinv2ForImageClassification, Swinv2ForMaskedImageModeling, Swinv2Model
+    from transformers import Swinv2Backbone, Swinv2ForImageClassification, Swinv2ForMaskedImageModeling, Swinv2Model
     from transformers.models.swinv2.modeling_swinv2 import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
 
 if is_vision_available():
@@ -65,6 +67,8 @@ class Swinv2ModelTester:
         use_labels=True,
         type_sequence_label_size=10,
         encoder_stride=8,
+        out_features=["stage1", "stage2"],
+        out_indices=[1, 2],
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -90,6 +94,8 @@ class Swinv2ModelTester:
         self.use_labels = use_labels
         self.type_sequence_label_size = type_sequence_label_size
         self.encoder_stride = encoder_stride
+        self.out_features = out_features
+        self.out_indices = out_indices
 
     def prepare_config_and_inputs(self):
         pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
@@ -122,6 +128,8 @@ class Swinv2ModelTester:
             layer_norm_eps=self.layer_norm_eps,
             initializer_range=self.initializer_range,
             encoder_stride=self.encoder_stride,
+            out_features=self.out_features,
+            out_indices=self.out_indices,
         )
 
     def create_and_check_model(self, config, pixel_values, labels):
@@ -135,6 +143,33 @@ class Swinv2ModelTester:
 
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))
 
+    def create_and_check_backbone(self, config, pixel_values, labels):
+        model = Swinv2Backbone(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 = Swinv2Backbone(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 create_and_check_for_masked_image_modeling(self, config, pixel_values, labels):
         model = Swinv2ForMaskedImageModeling(config=config)
         model.to(torch_device)
@@ -172,7 +207,14 @@ class Swinv2ModelTester:
 @require_torch
 class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (
-        (Swinv2Model, Swinv2ForImageClassification, Swinv2ForMaskedImageModeling) if is_torch_available() else ()
+        (
+            Swinv2Model,
+            Swinv2ForImageClassification,
+            Swinv2ForMaskedImageModeling,
+            Swinv2Backbone,
+        )
+        if is_torch_available()
+        else ()
     )
     pipeline_model_mapping = (
         {"feature-extraction": Swinv2Model, "image-classification": Swinv2ForImageClassification}
@@ -201,6 +243,10 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*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)
+
     # TODO: check if this works again for PyTorch 2.x.y
     @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
     def test_multi_gpu_data_parallel_forward(self):
@@ -219,6 +265,18 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             x = model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(x, nn.Linear))
 
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
     def test_attention_outputs(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.return_dict = True
@@ -263,11 +321,8 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             with torch.no_grad():
                 outputs = model(**self._prepare_for_class(inputs_dict, model_class))
 
-            if hasattr(self.model_tester, "num_hidden_states_types"):
-                added_hidden_states = self.model_tester.num_hidden_states_types
-            else:
-                # also another +1 for reshaped_hidden_states
-                added_hidden_states = 2
+            # also another +1 for reshaped_hidden_states
+            added_hidden_states = 1 if model_class.__name__ == "Swinv2Backbone" else 2
             self.assertEqual(out_len + added_hidden_states, len(outputs))
 
             self_attentions = outputs.attentions
@@ -308,17 +363,18 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             [num_patches, self.model_tester.embed_dim],
         )
 
-        reshaped_hidden_states = outputs.reshaped_hidden_states
-        self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
+        if not model_class.__name__ == "Swinv2Backbone":
+            reshaped_hidden_states = outputs.reshaped_hidden_states
+            self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
 
-        batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
-        reshaped_hidden_states = (
-            reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1)
-        )
-        self.assertListEqual(
-            list(reshaped_hidden_states.shape[-2:]),
-            [num_patches, self.model_tester.embed_dim],
-        )
+            batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
+            reshaped_hidden_states = (
+                reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1)
+            )
+            self.assertListEqual(
+                list(reshaped_hidden_states.shape[-2:]),
+                [num_patches, self.model_tester.embed_dim],
+            )
 
     def test_hidden_states_output(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -380,6 +436,10 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             model = Swinv2Model.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    @unittest.skip(reason="Swinv2 does not support feedforward chunking yet")
+    def test_feed_forward_chunking(self):
+        pass
+
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -425,3 +485,12 @@ class Swinv2ModelIntegrationTest(unittest.TestCase):
         self.assertEqual(outputs.logits.shape, expected_shape)
         expected_slice = torch.tensor([-0.3947, -0.4306, 0.0026]).to(torch_device)
         self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
+
+
+@require_torch
+class Swinv2BackboneTest(unittest.TestCase, BackboneTesterMixin):
+    all_model_classes = (Swinv2Backbone,) if is_torch_available() else ()
+    config_class = Swinv2Config
+
+    def setUp(self):
+        self.model_tester = Swinv2ModelTester(self)

utils/check_repo.py

@@ -988,6 +988,7 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "NatBackbone",
     "ResNetBackbone",
     "SwinBackbone",
+    "Swinv2Backbone",
     "TimmBackbone",
     "TimmBackboneConfig",
     "VitDetBackbone",