Adding support for LayoutLMvX variants for object-detection. (#20143)

* Adding support for LayoutLMvX variants for `object-detection`.

* Revert bogs `layoutlm` feature extractor which does not exist (it was a
V2 model) .

* Updated condition.

* Handling the comments.

src/transformers/pipelines/__init__.py

@@ -345,7 +345,7 @@ SUPPORTED_TASKS = {
         "tf": (),
         "pt": (AutoModelForObjectDetection,) if is_torch_available() else (),
         "default": {"model": {"pt": ("facebook/detr-resnet-50", "2729413")}},
-        "type": "image",
+        "type": "multimodal",
     },
     "zero-shot-object-detection": {
         "impl": ZeroShotObjectDetectionPipeline,

src/transformers/pipelines/object_detection.py

@@ -11,7 +11,7 @@ if is_vision_available():
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
 
 logger = logging.get_logger(__name__)
 
@@ -39,7 +39,9 @@ class ObjectDetectionPipeline(Pipeline):
             raise ValueError(f"The {self.__class__} is only available in PyTorch.")
 
         requires_backends(self, "vision")
-        self.check_model_type(MODEL_FOR_OBJECT_DETECTION_MAPPING)
+        self.check_model_type(
+            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items())
+        )
 
     def _sanitize_parameters(self, **kwargs):
         postprocess_kwargs = {}
@@ -82,6 +84,8 @@ class ObjectDetectionPipeline(Pipeline):
         image = load_image(image)
         target_size = torch.IntTensor([[image.height, image.width]])
         inputs = self.feature_extractor(images=[image], return_tensors="pt")
+        if self.tokenizer is not None:
+            inputs = self.tokenizer(text=inputs["words"], boxes=inputs["boxes"], return_tensors="pt")
         inputs["target_size"] = target_size
         return inputs
 
@@ -89,26 +93,54 @@ class ObjectDetectionPipeline(Pipeline):
         target_size = model_inputs.pop("target_size")
         outputs = self.model(**model_inputs)
         model_outputs = outputs.__class__({"target_size": target_size, **outputs})
+        if self.tokenizer is not None:
+            model_outputs["bbox"] = model_inputs["bbox"]
         return model_outputs
 
     def postprocess(self, model_outputs, threshold=0.9):
         target_size = model_outputs["target_size"]
-        raw_annotations = self.feature_extractor.post_process_object_detection(model_outputs, threshold, target_size)
-        raw_annotation = raw_annotations[0]
-        scores = raw_annotation["scores"]
-        labels = raw_annotation["labels"]
-        boxes = raw_annotation["boxes"]
+        if self.tokenizer is not None:
+            # This is a LayoutLMForTokenClassification variant.
+            # The OCR got the boxes and the model classified the words.
+            width, height = target_size[0].tolist()
 
-        raw_annotation["scores"] = scores.tolist()
-        raw_annotation["labels"] = [self.model.config.id2label[label.item()] for label in labels]
-        raw_annotation["boxes"] = [self._get_bounding_box(box) for box in boxes]
+            def unnormalize(bbox):
+                return self._get_bounding_box(
+                    torch.Tensor(
+                        [
+                            (width * bbox[0] / 1000),
+                            (height * bbox[1] / 1000),
+                            (width * bbox[2] / 1000),
+                            (height * bbox[3] / 1000),
+                        ]
+                    )
+                )
 
-        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
-        keys = ["score", "label", "box"]
-        annotation = [
-            dict(zip(keys, vals))
-            for vals in zip(raw_annotation["scores"], raw_annotation["labels"], raw_annotation["boxes"])
-        ]
+            scores, classes = model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
+            labels = [self.model.config.id2label[prediction] for prediction in classes.tolist()]
+            boxes = [unnormalize(bbox) for bbox in model_outputs["bbox"].squeeze(0)]
+            keys = ["score", "label", "box"]
+            annotation = [dict(zip(keys, vals)) for vals in zip(scores.tolist(), labels, boxes) if vals[0] > threshold]
+        else:
+            # This is a regular ForObjectDetectionModel
+            raw_annotations = self.feature_extractor.post_process_object_detection(
+                model_outputs, threshold, target_size
+            )
+            raw_annotation = raw_annotations[0]
+            scores = raw_annotation["scores"]
+            labels = raw_annotation["labels"]
+            boxes = raw_annotation["boxes"]
+
+            raw_annotation["scores"] = scores.tolist()
+            raw_annotation["labels"] = [self.model.config.id2label[label.item()] for label in labels]
+            raw_annotation["boxes"] = [self._get_bounding_box(box) for box in boxes]
+
+            # {"scores": [...], ...} --> [{"score":x, ...}, ...]
+            keys = ["score", "label", "box"]
+            annotation = [
+                dict(zip(keys, vals))
+                for vals in zip(raw_annotation["scores"], raw_annotation["labels"], raw_annotation["boxes"])
+            ]
 
         return annotation
 

tests/pipelines/test_pipelines_object_detection.py

@@ -243,3 +243,30 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
                 {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )
+
+    @require_torch
+    @slow
+    def test_layoutlm(self):
+        model_id = "philschmid/layoutlm-funsd"
+        threshold = 0.998
+
+        object_detector = pipeline("object-detection", model=model_id, threshold=threshold)
+
+        outputs = object_detector(
+            "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {
+                    "score": 0.9982,
+                    "label": "B-QUESTION",
+                    "box": {"xmin": 654, "ymin": 165, "xmax": 719, "ymax": 719},
+                },
+                {
+                    "score": 0.9982,
+                    "label": "I-QUESTION",
+                    "box": {"xmin": 691, "ymin": 202, "xmax": 735, "ymax": 735},
+                },
+            ],
+        )