add doctests for DETR (#17786)

* add: check labels for detr object detection doctests

* add: check shapes

* add: add detr to documentation_tests.py

* fix: make fixup output

* fix: add a comment

src/transformers/models/detr/modeling_detr.py

@@ -1240,6 +1240,8 @@ class DetrModel(DetrPreTrainedModel):
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
         >>> last_hidden_states = outputs.last_hidden_state
+        >>> list(last_hidden_states.shape)
+        [1, 100, 256]
         ```"""
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1399,8 +1401,16 @@ class DetrForObjectDetection(DetrPreTrainedModel):
         >>> inputs = feature_extractor(images=image, return_tensors="pt")
         >>> outputs = model(**inputs)
         >>> # model predicts bounding boxes and corresponding COCO classes
-        >>> logits = outputs.logits
+        >>> bboxes, logits = outputs.pred_boxes, outputs.logits
-        >>> bboxes = outputs.pred_boxes
+
+        >>> # get probability per object class and remove the no-object class
+        >>> probas_per_class = outputs.logits.softmax(-1)[:, :, :-1]
+        >>> objects_to_keep = probas_per_class.max(-1).values > 0.9
+
+        >>> ids, _ = probas_per_class.max(-1).indices[objects_to_keep].sort()
+        >>> labels = [model.config.id2label[id.item()] for id in ids]
+        >>> labels
+        ['cat', 'cat', 'couch', 'remote', 'remote']
         ```"""
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
@@ -1556,8 +1566,16 @@ class DetrForSegmentation(DetrPreTrainedModel):
         >>> outputs = model(**inputs)
         >>> # model predicts COCO classes, bounding boxes, and masks
         >>> logits = outputs.logits
+        >>> list(logits.shape)
+        [1, 100, 251]
+
         >>> bboxes = outputs.pred_boxes
+        >>> list(bboxes.shape)
+        [1, 100, 4]
+
         >>> masks = outputs.pred_masks
+        >>> list(masks.shape)
+        [1, 100, 200, 267]
         ```"""
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict

utils/documentation_tests.txt

@@ -25,6 +25,7 @@ src/transformers/models/cvt/modeling_cvt.py
 src/transformers/models/data2vec/modeling_data2vec_audio.py
 src/transformers/models/data2vec/modeling_data2vec_vision.py
 src/transformers/models/deit/modeling_deit.py
+src/transformers/models/detr/modeling_detr.py
 src/transformers/models/dpt/modeling_dpt.py
 src/transformers/models/electra/modeling_electra.py
 src/transformers/models/electra/modeling_tf_electra.py