[Maskformer] Add MaskFormerSwin backbone (#20344)

* First draft

* Fix backwards compatibility

* More fixes

* More fixes

* Make backbone more general

* Improve backbone

* Improve test

* Fix config checkpoint

* Address comments

* Use model_type

* Address more comments

* Fix special model names

* Remove MaskFormerSwinModel and MaskFormerSwinPreTrainedModel from main init

* Fix typo

* Update backbone

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

docs/source/en/index.mdx

@@ -288,6 +288,7 @@ Flax), PyTorch, and/or TensorFlow.
 |            Marian             |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           MarkupLM            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          MaskFormer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        MaskFormerSwin         |       ❌       |       ❌       |       ❌        |         ❌         |      ❌      |
 |             mBART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |         Megatron-BERT         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          MobileBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |

src/transformers/__init__.py

@@ -295,7 +295,7 @@ _import_structure = {
         "MarkupLMProcessor",
         "MarkupLMTokenizer",
     ],
-    "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
+    "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig", "MaskFormerSwinConfig"],
     "models.mbart": ["MBartConfig"],
     "models.mbart50": [],
     "models.mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTProcessor"],
@@ -1622,6 +1622,7 @@ else:
             "MaskFormerForInstanceSegmentation",
             "MaskFormerModel",
             "MaskFormerPreTrainedModel",
+            "MaskFormerSwinBackbone",
         ]
     )
     _import_structure["models.markuplm"].extend(
@@ -3479,7 +3480,7 @@ if TYPE_CHECKING:
         MarkupLMProcessor,
         MarkupLMTokenizer,
     )
-    from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
+    from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig, MaskFormerSwinConfig
     from .models.mbart import MBartConfig
     from .models.mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTProcessor
     from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
@@ -4573,6 +4574,7 @@ if TYPE_CHECKING:
             MaskFormerForInstanceSegmentation,
             MaskFormerModel,
             MaskFormerPreTrainedModel,
+            MaskFormerSwinBackbone,
         )
         from .models.mbart import (
             MBartForCausalLM,

src/transformers/models/auto/configuration_auto.py

@@ -98,6 +98,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("marian", "MarianConfig"),
         ("markuplm", "MarkupLMConfig"),
         ("maskformer", "MaskFormerConfig"),
+        ("maskformer-swin", "MaskFormerSwinConfig"),
         ("mbart", "MBartConfig"),
         ("mctct", "MCTCTConfig"),
         ("megatron-bert", "MegatronBertConfig"),
@@ -395,6 +396,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("marian", "Marian"),
         ("markuplm", "MarkupLM"),
         ("maskformer", "MaskFormer"),
+        ("maskformer-swin", "MaskFormerSwin"),
         ("mbart", "mBART"),
         ("mbart50", "mBART-50"),
         ("mctct", "M-CTC-T"),
@@ -491,6 +493,7 @@ SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict(
         ("data2vec-text", "data2vec"),
         ("data2vec-vision", "data2vec"),
         ("donut-swin", "donut"),
+        ("maskformer-swin", "maskformer"),
         ("xclip", "x_clip"),
     ]
 )

src/transformers/models/auto/modeling_auto.py

@@ -97,6 +97,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("marian", "MarianModel"),
         ("markuplm", "MarkupLMModel"),
         ("maskformer", "MaskFormerModel"),
+        ("maskformer-swin", "MaskFormerSwinModel"),
         ("mbart", "MBartModel"),
         ("mctct", "MCTCTModel"),
         ("megatron-bert", "MegatronBertModel"),
@@ -847,6 +848,7 @@ _MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
 MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict(
     [
         # Backbone mapping
+        ("maskformer-swin", "MaskFormerSwinBackbone"),
         ("resnet", "ResNetBackbone"),
     ]
 )

src/transformers/models/maskformer/__init__.py

@@ -20,7 +20,10 @@ from typing import TYPE_CHECKING
 from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
 
 
-_import_structure = {"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"]}
+_import_structure = {
+    "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
+    "configuration_maskformer_swin": ["MaskFormerSwinConfig"],
+}
 
 try:
     if not is_vision_available():
@@ -43,9 +46,15 @@ else:
         "MaskFormerModel",
         "MaskFormerPreTrainedModel",
     ]
+    _import_structure["modeling_maskformer_swin"] = [
+        "MaskFormerSwinBackbone",
+        "MaskFormerSwinModel",
+        "MaskFormerSwinPreTrainedModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
+    from .configuration_maskformer_swin import MaskFormerSwinConfig
 
     try:
         if not is_vision_available():
@@ -66,6 +75,11 @@ if TYPE_CHECKING:
             MaskFormerModel,
             MaskFormerPreTrainedModel,
         )
+        from .modeling_maskformer_swin import (
+            MaskFormerSwinBackbone,
+            MaskFormerSwinModel,
+            MaskFormerSwinPreTrainedModel,
+        )
 
 
 else:

src/transformers/models/maskformer/configuration_maskformer_swin.py

+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+""" MaskFormer Swin Transformer model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MaskFormerSwinConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MaskFormerSwinModel`]. It is used to instantiate
+    a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the Swin
+    [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224)
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 4):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        embed_dim (`int`, *optional*, defaults to 96):
+            Dimensionality of patch embedding.
+        depths (`List[int]`, *optional*, defaults to `[2, 2, 6, 2]`):
+            Depth of each layer in the Transformer encoder.
+        num_heads (`List[int]`, *optional*, defaults to `[3, 6, 12, 24]`):
+            Number of attention heads in each layer of the Transformer encoder.
+        window_size (`int`, *optional*, defaults to 7):
+            Size of windows.
+        mlp_ratio (`float`, *optional*, defaults to 4.0):
+            Ratio of MLP hidden dimensionality to embedding dimensionality.
+        qkv_bias (`bool`, *optional*, defaults to True):
+            Whether or not a learnable bias should be added to the queries, keys and values.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings and encoder.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        drop_path_rate (`float`, *optional*, defaults to 0.1):
+            Stochastic depth rate.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
+            `"selu"` and `"gelu_new"` are supported.
+        use_absolute_embeddings (`bool`, *optional*, defaults to False):
+            Whether or not to add absolute position embeddings to the patch embeddings.
+        patch_norm (`bool`, *optional*, defaults to True):
+            Whether or not to add layer normalization after patch embedding.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        out_features (`List[str]`, *optional*):
+            If used as a backbone, list of feature names to output, e.g. `["stem", "stage1"]`.
+
+    Example:
+
+    ```python
+    >>> from transformers import MaskFormerSwinConfig, MaskFormerSwinModel
+
+    >>> # Initializing a microsoft/swin-tiny-patch4-window7-224 style configuration
+    >>> configuration = MaskFormerSwinConfig()
+
+    >>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style configuration
+    >>> model = MaskFormerSwinModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "maskformer-swin"
+
+    attribute_map = {
+        "num_attention_heads": "num_heads",
+        "num_hidden_layers": "num_layers",
+    }
+
+    def __init__(
+        self,
+        image_size=224,
+        patch_size=4,
+        num_channels=3,
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        drop_path_rate=0.1,
+        hidden_act="gelu",
+        use_absolute_embeddings=False,
+        patch_norm=True,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        out_features=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.embed_dim = embed_dim
+        self.depths = depths
+        self.num_layers = len(depths)
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.mlp_ratio = mlp_ratio
+        self.qkv_bias = qkv_bias
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.drop_path_rate = drop_path_rate
+        self.hidden_act = hidden_act
+        self.use_absolute_embeddings = use_absolute_embeddings
+        self.path_norm = patch_norm
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        # we set the hidden_size attribute in order to make Swin 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.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/utils/dummy_pt_objects.py

@@ -3361,6 +3361,13 @@ class MaskFormerPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class MaskFormerSwinBackbone(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class MBartForCausalLM(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/maskformer/test_modeling_maskformer_swin.py

+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+""" Testing suite for the PyTorch MaskFormer Swin model. """
+
+import collections
+import inspect
+import unittest
+from typing import Dict, List, Tuple
+
+from transformers import MaskFormerSwinConfig
+from transformers.testing_utils import require_torch, torch_device
+from transformers.utils import is_torch_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import MaskFormerSwinBackbone
+    from transformers.models.maskformer import MaskFormerSwinModel
+
+
+class MaskFormerSwinModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=32,
+        patch_size=2,
+        num_channels=3,
+        embed_dim=16,
+        depths=[1, 2, 1],
+        num_heads=[2, 2, 4],
+        window_size=2,
+        mlp_ratio=2.0,
+        qkv_bias=True,
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        drop_path_rate=0.1,
+        hidden_act="gelu",
+        use_absolute_embeddings=False,
+        patch_norm=True,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        is_training=True,
+        scope=None,
+        use_labels=True,
+        type_sequence_label_size=10,
+        encoder_stride=8,
+        out_features=["stage1", "stage2", "stage3"],
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.embed_dim = embed_dim
+        self.depths = depths
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.mlp_ratio = mlp_ratio
+        self.qkv_bias = qkv_bias
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.drop_path_rate = drop_path_rate
+        self.hidden_act = hidden_act
+        self.use_absolute_embeddings = use_absolute_embeddings
+        self.patch_norm = patch_norm
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        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.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)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return MaskFormerSwinConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            embed_dim=self.embed_dim,
+            depths=self.depths,
+            num_heads=self.num_heads,
+            window_size=self.window_size,
+            mlp_ratio=self.mlp_ratio,
+            qkv_bias=self.qkv_bias,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            drop_path_rate=self.drop_path_rate,
+            hidden_act=self.hidden_act,
+            use_absolute_embeddings=self.use_absolute_embeddings,
+            path_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):
+        model = MaskFormerSwinModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
+        expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
+
+        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 = MaskFormerSwinBackbone(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+
+        # verify feature maps
+        self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
+        self.parent.assertListEqual(list(result.feature_maps[0].shape), [13, 16, 16, 16])
+
+        # verify channels
+        self.parent.assertEqual(len(model.channels), len(config.out_features))
+        self.parent.assertListEqual(model.channels, [16, 32, 64])
+
+        # verify ValueError
+        with self.parent.assertRaises(ValueError):
+            config.out_features = ["stem"]
+            model = MaskFormerSwinBackbone(config=config)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class MaskFormerSwinModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            MaskFormerSwinModel,
+            MaskFormerSwinBackbone,
+        )
+        if is_torch_available()
+        else ()
+    )
+    fx_compatible = False
+    test_torchscript = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = MaskFormerSwinModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=MaskFormerSwinConfig, embed_dim=37)
+
+    def test_config(self):
+        self.create_and_test_config_common_properties()
+        self.config_tester.create_and_test_config_to_json_string()
+        self.config_tester.create_and_test_config_to_json_file()
+        self.config_tester.create_and_test_config_from_and_save_pretrained()
+        self.config_tester.create_and_test_config_with_num_labels()
+        self.config_tester.check_config_can_be_init_without_params()
+        self.config_tester.check_config_arguments_init()
+
+    def create_and_test_config_common_properties(self):
+        return
+
+    def test_model(self):
+        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)
+
+    @unittest.skip("Swin does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip("Swin does not support feedforward chunking")
+    def test_feed_forward_chunking(self):
+        pass
+
+    def test_model_common_attributes(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            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)
+
+    @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
+        model = model_class(config)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+        hidden_states = outputs.hidden_states
+
+        expected_num_layers = getattr(
+            self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
+        )
+        self.assertEqual(len(hidden_states), expected_num_layers)
+
+        # Swin has a different seq_length
+        patch_size = (
+            config.patch_size
+            if isinstance(config.patch_size, collections.abc.Iterable)
+            else (config.patch_size, config.patch_size)
+        )
+
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+
+        self.assertListEqual(
+            list(hidden_states[0].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()
+
+        image_size = (
+            self.model_tester.image_size
+            if isinstance(self.model_tester.image_size, collections.abc.Iterable)
+            else (self.model_tester.image_size, self.model_tester.image_size)
+        )
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
+
+    def test_hidden_states_output_with_padding(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.patch_size = 3
+
+        image_size = (
+            self.model_tester.image_size
+            if isinstance(self.model_tester.image_size, collections.abc.Iterable)
+            else (self.model_tester.image_size, self.model_tester.image_size)
+        )
+        patch_size = (
+            config.patch_size
+            if isinstance(config.patch_size, collections.abc.Iterable)
+            else (config.patch_size, config.patch_size)
+        )
+
+        padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
+        padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
+
+    @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints")
+    def test_model_from_pretrained(self):
+        pass
+
+    @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
+    def test_initialization(self):
+        pass
+
+    @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
+    def test_gradient_checkpointing_backward_compatibility(self):
+        pass
+
+    def test_model_outputs_equivalence(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def set_nan_tensor_to_zero(t):
+            t[t != t] = 0
+            return t
+
+        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
+            with torch.no_grad():
+                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
+                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
+
+                def recursive_check(tuple_object, dict_object):
+                    if isinstance(tuple_object, (List, Tuple)):
+                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif isinstance(tuple_object, Dict):
+                        for tuple_iterable_value, dict_iterable_value in zip(
+                            tuple_object.values(), dict_object.values()
+                        ):
+                            recursive_check(tuple_iterable_value, dict_iterable_value)
+                    elif tuple_object is None:
+                        return
+                    else:
+                        self.assertTrue(
+                            torch.allclose(
+                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
+                            ),
+                            msg=(
+                                "Tuple and dict output are not equal. Difference:"
+                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
+                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
+                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
+                            ),
+                        )
+
+                recursive_check(tuple_output, dict_output)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

utils/check_copies.py

@@ -492,8 +492,9 @@ SPECIAL_MODEL_NAMES = {
     "Data2VecAudio": "Data2Vec",
     "Data2VecText": "Data2Vec",
     "Data2VecVision": "Data2Vec",
-    "DonutSwin": "Donut",
+    "DonutSwin": "Swin Transformer",
     "Marian": "MarianMT",
+    "MaskFormerSwin": "Swin Transformer",
     "OpenAI GPT-2": "GPT-2",
     "OpenAI GPT": "GPT",
     "Perceiver": "Perceiver IO",

utils/check_repo.py

@@ -41,6 +41,8 @@ PRIVATE_MODELS = [
     "T5Stack",
     "SwitchTransformersStack",
     "TFDPRSpanPredictor",
+    "MaskFormerSwinModel",
+    "MaskFormerSwinPreTrainedModel",
 ]
 
 # Update this list for models that are not tested with a comment explaining the reason it should not be.
@@ -668,8 +670,11 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "PyTorchBenchmarkArguments",
     "TensorFlowBenchmark",
     "TensorFlowBenchmarkArguments",
+    "MaskFormerSwinBackbone",
     "ResNetBackbone",
     "AutoBackbone",
+    "MaskFormerSwinConfig",
+    "MaskFormerSwinModel",
 ]