add ONNX support for swin transformer (#19390)

* swin transformer onnx support

* Updated image dimensions as dynamic
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>
Co-authored-by: lewtun <lewis.c.tunstall@gmail.com>

docs/source/en/serialization.mdx

@@ -94,6 +94,7 @@ Ready-made configurations include the following architectures:
 - RoFormer
 - SegFormer
 - SqueezeBERT
+- Swin Transformer
 - T5
 - ViT
 - XLM

src/transformers/models/swin/__init__.py

@@ -21,7 +21,7 @@ from typing import TYPE_CHECKING
 from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
-_import_structure = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"]}
+_import_structure = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
 
 
 try:
@@ -53,7 +53,7 @@ else:
     ]
 
 if TYPE_CHECKING:
-    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig
+    from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
 
     try:
         if not is_torch_available():

src/transformers/models/swin/configuration_swin.py

@@ -14,7 +14,13 @@
 # limitations under the License.
 """ Swin Transformer model configuration"""
 
+from collections import OrderedDict
+from typing import Mapping
+
+from packaging import version
+
 from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfig
 from ...utils import logging
 
 
@@ -145,3 +151,20 @@ class SwinConfig(PretrainedConfig):
         # 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))
+
+
+class SwinOnnxConfig(OnnxConfig):
+
+    torch_onnx_minimum_version = version.parse("1.11")
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        return OrderedDict(
+            [
+                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
+            ]
+        )
+
+    @property
+    def atol_for_validation(self) -> float:
+        return 1e-4

src/transformers/onnx/features.py

@@ -471,6 +471,9 @@ class FeaturesManager:
             "question-answering",
             onnx_config_cls="models.squeezebert.SqueezeBertOnnxConfig",
         ),
+        "swin": supported_features_mapping(
+            "default", "image-classification", "masked-im", onnx_config_cls="models.swin.SwinOnnxConfig"
+        ),
         "t5": supported_features_mapping(
             "default",
             "default-with-past",

tests/onnx/test_onnx_v2.py

@@ -217,6 +217,7 @@ PYTORCH_EXPORT_MODELS = {
     ("longformer", "allenai/longformer-base-4096"),
     ("yolos", "hustvl/yolos-tiny"),
     ("segformer", "nvidia/segformer-b0-finetuned-ade-512-512"),
+    ("swin", "microsoft/swin-tiny-patch4-window7-224"),
 }
 
 PYTORCH_EXPORT_WITH_PAST_MODELS = {