Owlvit docs test

docs/source/en/model_doc/owlvit.mdx

@@ -39,19 +39,26 @@ OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CL
 
 >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 >>> image = Image.open(requests.get(url, stream=True).raw)
-
->>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt")
-
+>>> texts = [["a photo of a cat", "a photo of a dog"]]
+>>> inputs = processor(text=texts, images=image, return_tensors="pt")
 >>> outputs = model(**inputs)
->>> logits = outputs["logits"]  # Prediction logits of shape [batch_size, num_patches, num_max_text_queries]
->>> boxes = outputs["pred_boxes"]  # Object box boundaries of shape [batch_size, num_patches, 4]
-
->>> batch_size = boxes.shape[0]
->>> for i in range(batch_size):  # Loop over sets of images and text queries
-...     boxes = outputs["pred_boxes"][i]
-...     logits = torch.max(outputs["logits"][i], dim=-1)
-...     scores = torch.sigmoid(logits.values)
-...     labels = logits.indices
+
+>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
+>>> target_sizes = torch.Tensor([image.size[::-1]])
+>>> # Convert outputs (bounding boxes and class logits) to COCO API
+>>> results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
+
+>>> i = 0  # Retrieve predictions for the first image for the corresponding text queries
+>>> text = texts[i]
+>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
+
+>>> score_threshold = 0.1
+>>> for box, score, label in zip(boxes, scores, labels):
+...     box = [round(i, 2) for i in box.tolist()]
+...     if score >= score_threshold:
+...         print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
+Detected a photo of a cat with confidence 0.243 at location [1.42, 50.69, 308.58, 370.48]
+Detected a photo of a cat with confidence 0.298 at location [348.06, 20.56, 642.33, 372.61]
 ```
 
 This model was contributed by [adirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).

src/transformers/models/owlvit/feature_extraction_owlvit.py

@@ -26,7 +26,6 @@
 
 if is_torch_available():
     import torch
-    from torch import nn
 
 logger = logging.get_logger(__name__)
 
@@ -109,18 +108,19 @@ def post_process(self, outputs, target_sizes):
             `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
             in the batch as predicted by the model.
         """
-        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
+        logits, boxes = outputs.logits, outputs.pred_boxes
 
-        if len(out_logits) != len(target_sizes):
+        if len(logits) != len(target_sizes):
             raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
         if target_sizes.shape[1] != 2:
             raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
 
-        prob = nn.functional.softmax(out_logits, -1)
-        scores, labels = prob[..., :-1].max(-1)
+        probs = torch.max(logits, dim=-1)
+        scores = torch.sigmoid(probs.values)
+        labels = probs.indices
 
         # Convert to [x0, y0, x1, y1] format
-        boxes = center_to_corners_format(out_bbox)
+        boxes = center_to_corners_format(boxes)
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
         img_h, img_w = target_sizes.unbind(1)

src/transformers/models/owlvit/modeling_owlvit.py

@@ -1300,23 +1300,31 @@ def forward(
         >>> import torch
         >>> from transformers import OwlViTProcessor, OwlViTForObjectDetection
 
-        >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
         >>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
+        >>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
 
         >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
         >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt")
+        >>> texts = [["a photo of a cat", "a photo of a dog"]]
+        >>> inputs = processor(text=texts, images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
-        >>> logits = outputs["logits"]  # Prediction logits of shape [batch_size, num_patches, num_max_text_queries]
-        >>> boxes = outputs["pred_boxes"]  # Object box boundaries of shape # [batch_size, num_patches, 4]
-
-        >>> batch_size = boxes.shape[0]
-        >>> for i in range(batch_size):  # Loop over sets of images and text queries
-        ...     boxes = outputs["pred_boxes"][i]
-        ...     logits = torch.max(outputs["logits"][i], dim=-1)
-        ...     scores = torch.sigmoid(logits.values)
-        ...     labels = logits.indices
+
+        >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
+        >>> target_sizes = torch.Tensor([image.size[::-1]])
+        >>> # Convert outputs (bounding boxes and class logits) to COCO API
+        >>> results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
+
+        >>> i = 0  # Retrieve predictions for the first image for the corresponding text queries
+        >>> text = texts[i]
+        >>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
+
+        >>> score_threshold = 0.1
+        >>> for box, score, label in zip(boxes, scores, labels):
+        ...     box = [round(i, 2) for i in box.tolist()]
+        ...     if score >= score_threshold:
+        ...         print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
+        Detected a photo of a cat with confidence 0.243 at location [1.42, 50.69, 308.58, 370.48]
+        Detected a photo of a cat with confidence 0.298 at location [348.06, 20.56, 642.33, 372.61]
         ```"""
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/owlvit/processing_owlvit.py

@@ -139,6 +139,13 @@ def __call__(self, text=None, images=None, padding="max_length", return_tensors=
         else:
             return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
 
+    def post_process(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to [`OwlViTFeatureExtractor.post_process`]. Please refer to the
+        docstring of this method for more information.
+        """
+        return self.feature_extractor.post_process(*args, **kwargs)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

utils/documentation_tests.txt

@@ -48,6 +48,7 @@ src/transformers/models/mobilevit/modeling_mobilevit.py
 src/transformers/models/opt/modeling_opt.py
 src/transformers/models/opt/modeling_tf_opt.py
 src/transformers/models/opt/modeling_flax_opt.py
+src/transformers/models/owlvit/modeling_owlvit.py
 src/transformers/models/pegasus/modeling_pegasus.py
 src/transformers/models/plbart/modeling_plbart.py
 src/transformers/models/poolformer/modeling_poolformer.py