Fix image post-processing for OWLv2 (#30686)

* feat: add note about owlv2

* fix: post processing coordinates

* remove: workaround document

* fix: extra quotes

* update: owlv2 docstrings

* fix: copies check

* feat: add unit test for resize

* Update tests/models/owlv2/test_image_processor_owlv2.py

Co-authored-by: amyeroberts <[email protected]>

---------

Co-authored-by: amyeroberts <[email protected]>

Author: jla524

src/transformers/models/owlv2/image_processing_owlv2.py

@@ -481,7 +481,6 @@ def preprocess(
         data = {"pixel_values": images}
         return BatchFeature(data=data, tensor_type=return_tensors)
 
-    # Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_object_detection
     def post_process_object_detection(
         self, outputs, threshold: float = 0.1, target_sizes: Union[TensorType, List[Tuple]] = None
     ):
@@ -525,6 +524,18 @@ def post_process_object_detection(
             else:
                 img_h, img_w = target_sizes.unbind(1)
 
+            # rescale coordinates
+            width_ratio = 1
+            height_ratio = 1
+
+            if img_w < img_h:
+                width_ratio = img_w / img_h
+            elif img_h < img_w:
+                height_ratio = img_h / img_w
+
+            img_w = img_w / width_ratio
+            img_h = img_h / height_ratio
+
             scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
             boxes = boxes * scale_fct[:, None, :]
 

src/transformers/models/owlv2/modeling_owlv2.py

@@ -1540,9 +1540,7 @@ def image_guided_detection(
         >>> import requests
         >>> from PIL import Image
         >>> import torch
-        >>> import numpy as np
         >>> from transformers import AutoProcessor, Owlv2ForObjectDetection
-        >>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
 
         >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
         >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
@@ -1557,20 +1555,7 @@ def image_guided_detection(
         >>> with torch.no_grad():
         ...     outputs = model.image_guided_detection(**inputs)
 
-        >>> # Note: boxes need to be visualized on the padded, unnormalized image
-        >>> # hence we'll set the target image sizes (height, width) based on that
-
-        >>> def get_preprocessed_image(pixel_values):
-        ...     pixel_values = pixel_values.squeeze().numpy()
-        ...     unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
-        ...     unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
-        ...     unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
-        ...     unnormalized_image = Image.fromarray(unnormalized_image)
-        ...     return unnormalized_image
-
-        >>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
-
-        >>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
+        >>> target_sizes = torch.Tensor([image.size[::-1]])
 
         >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
         >>> results = processor.post_process_image_guided_detection(
@@ -1581,19 +1566,19 @@ def image_guided_detection(
         >>> for box, score in zip(boxes, scores):
         ...     box = [round(i, 2) for i in box.tolist()]
         ...     print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}")
-        Detected similar object with confidence 0.938 at location [490.96, 109.89, 821.09, 536.11]
-        Detected similar object with confidence 0.959 at location [8.67, 721.29, 928.68, 732.78]
-        Detected similar object with confidence 0.902 at location [4.27, 720.02, 941.45, 761.59]
-        Detected similar object with confidence 0.985 at location [265.46, -58.9, 1009.04, 365.66]
-        Detected similar object with confidence 1.0 at location [9.79, 28.69, 937.31, 941.64]
-        Detected similar object with confidence 0.998 at location [869.97, 58.28, 923.23, 978.1]
-        Detected similar object with confidence 0.985 at location [309.23, 21.07, 371.61, 932.02]
-        Detected similar object with confidence 0.947 at location [27.93, 859.45, 969.75, 915.44]
-        Detected similar object with confidence 0.996 at location [785.82, 41.38, 880.26, 966.37]
-        Detected similar object with confidence 0.998 at location [5.08, 721.17, 925.93, 998.41]
-        Detected similar object with confidence 0.969 at location [6.7, 898.1, 921.75, 949.51]
-        Detected similar object with confidence 0.966 at location [47.16, 927.29, 981.99, 942.14]
-        Detected similar object with confidence 0.924 at location [46.4, 936.13, 953.02, 950.78]
+        Detected similar object with confidence 0.938 at location [327.31, 54.94, 547.39, 268.06]
+        Detected similar object with confidence 0.959 at location [5.78, 360.65, 619.12, 366.39]
+        Detected similar object with confidence 0.902 at location [2.85, 360.01, 627.63, 380.8]
+        Detected similar object with confidence 0.985 at location [176.98, -29.45, 672.69, 182.83]
+        Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82]
+        Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05]
+        Detected similar object with confidence 0.985 at location [206.15, 10.53, 247.74, 466.01]
+        Detected similar object with confidence 0.947 at location [18.62, 429.72, 646.5, 457.72]
+        Detected similar object with confidence 0.996 at location [523.88, 20.69, 586.84, 483.18]
+        Detected similar object with confidence 0.998 at location [3.39, 360.59, 617.29, 499.21]
+        Detected similar object with confidence 0.969 at location [4.47, 449.05, 614.5, 474.76]
+        Detected similar object with confidence 0.966 at location [31.44, 463.65, 654.66, 471.07]
+        Detected similar object with confidence 0.924 at location [30.93, 468.07, 635.35, 475.39]
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1665,10 +1650,8 @@ def forward(
         ```python
         >>> import requests
         >>> from PIL import Image
-        >>> import numpy as np
         >>> import torch
         >>> from transformers import AutoProcessor, Owlv2ForObjectDetection
-        >>> from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
 
         >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble")
         >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
@@ -1682,20 +1665,7 @@ def forward(
         >>> with torch.no_grad():
         ...     outputs = model(**inputs)
 
-        >>> # Note: boxes need to be visualized on the padded, unnormalized image
-        >>> # hence we'll set the target image sizes (height, width) based on that
-
-        >>> def get_preprocessed_image(pixel_values):
-        ...     pixel_values = pixel_values.squeeze().numpy()
-        ...     unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
-        ...     unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
-        ...     unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
-        ...     unnormalized_image = Image.fromarray(unnormalized_image)
-        ...     return unnormalized_image
-
-        >>> unnormalized_image = get_preprocessed_image(inputs.pixel_values)
-
-        >>> target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
+        >>> target_sizes = torch.Tensor([image.size[::-1]])
         >>> # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores
         >>> results = processor.post_process_object_detection(
         ...     outputs=outputs, threshold=0.2, target_sizes=target_sizes
@@ -1708,8 +1678,8 @@ def forward(
         >>> for box, score, label in zip(boxes, scores, labels):
         ...     box = [round(i, 2) for i in box.tolist()]
         ...     print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
-        Detected a photo of a cat with confidence 0.614 at location [512.5, 35.08, 963.48, 557.02]
-        Detected a photo of a cat with confidence 0.665 at location [10.13, 77.94, 489.93, 709.69]
+        Detected a photo of a cat with confidence 0.614 at location [341.67, 23.39, 642.32, 371.35]
+        Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13]
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

tests/models/owlv2/test_image_processor_owlv2.py

@@ -17,15 +17,18 @@
 import unittest
 
 from transformers.testing_utils import require_torch, require_vision, slow
-from transformers.utils import is_vision_available
+from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
 
 
 if is_vision_available():
     from PIL import Image
 
-    from transformers import Owlv2ImageProcessor
+    from transformers import AutoProcessor, Owlv2ForObjectDetection, Owlv2ImageProcessor
+
+if is_torch_available():
+    import torch
 
 
 class Owlv2ImageProcessingTester(unittest.TestCase):
@@ -120,6 +123,25 @@ def test_image_processor_integration_test(self):
         mean_value = round(pixel_values.mean().item(), 4)
         self.assertEqual(mean_value, 0.2353)
 
+    @slow
+    def test_image_processor_integration_test_resize(self):
+        checkpoint = "google/owlv2-base-patch16-ensemble"
+        processor = AutoProcessor.from_pretrained(checkpoint)
+        model = Owlv2ForObjectDetection.from_pretrained(checkpoint)
+
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        inputs = processor(text=["cat"], images=image, return_tensors="pt")
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = processor.post_process_object_detection(outputs, threshold=0.2, target_sizes=target_sizes)[0]
+
+        boxes = results["boxes"].tolist()
+        self.assertEqual(boxes[0], [341.66656494140625, 23.38756561279297, 642.321044921875, 371.3482971191406])
+        self.assertEqual(boxes[1], [6.753320693969727, 51.96149826049805, 326.61810302734375, 473.12982177734375])
+
     @unittest.skip("OWLv2 doesn't treat 4 channel PIL and numpy consistently yet")  # FIXME Amy
     def test_call_numpy_4_channels(self):
         pass