Fix owlv2 code snippet (#27698)

* Fix code snippet

* Improve code snippet

src/transformers/models/owlv2/modeling_owlv2.py

@@ -1544,19 +1544,38 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
         >>> 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")
+
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
         >>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg"
         >>> query_image = Image.open(requests.get(query_url, stream=True).raw)
         >>> inputs = processor(images=image, query_images=query_image, return_tensors="pt")
+
+        >>> # forward pass
         >>> with torch.no_grad():
         ...     outputs = model.image_guided_detection(**inputs)
-        >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
-        >>> target_sizes = torch.Tensor([image.size[::-1]])
+
+        >>> # 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]])
+
         >>> # Convert outputs (bounding boxes and class logits) to COCO API
         >>> results = processor.post_process_image_guided_detection(
         ...     outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes
@@ -1566,19 +1585,19 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
         >>> 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 [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, 380.79]
-        Detected similar object with confidence 0.985 at location [176.97, -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]
+        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]
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1650,8 +1669,10 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
         ```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")
@@ -1660,10 +1681,25 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
         >>> image = Image.open(requests.get(url, stream=True).raw)
         >>> texts = [["a photo of a cat", "a photo of a dog"]]
         >>> inputs = processor(text=texts, images=image, return_tensors="pt")
-        >>> outputs = model(**inputs)
 
-        >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
-        >>> target_sizes = torch.Tensor([image.size[::-1]])
+        >>> # forward pass
+        >>> 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]])
         >>> # 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
@@ -1676,8 +1712,8 @@ class Owlv2ForObjectDetection(Owlv2PreTrainedModel):
         >>> 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 [341.67, 17.54, 642.32, 278.51]
-        Detected a photo of a cat with confidence 0.665 at location [6.75, 38.97, 326.62, 354.85]
+        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]
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (