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Add top_k argument to post-process of conditional/deformable-DETR (#22787)

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* update min k_value of conditional detr post-processing

* feat: add top_k arg to post processing of deformable and conditional detr

* refactor: revert changes to deprecated methods

* refactor: move prob reshape to improve code clarity and reduce repetition
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Alexander Brokking 2023-05-11 11:07:43 +02:00 committed by GitHub
parent f82ee109e6
commit b92abfa6e0
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2 changed files with 12 additions and 4 deletions
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8
src/transformers/models/conditional_detr/image_processing_conditional_detr.py
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@ -1328,7 +1328,7 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
# Copied from transformers.models.deformable_detr.image_processing_deformable_detr.DeformableDetrImageProcessor.post_process_object_detection with DeformableDetr->ConditionalDetr
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100
):
"""
Converts the raw output of [`ConditionalDetrForObjectDetection`] into final bounding boxes in (top_left_x,
@ -1342,6 +1342,8 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
top_k (`int`, *optional*, defaults to 100):
Keep only top k bounding boxes before filtering by thresholding.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
@ -1356,7 +1358,9 @@ class ConditionalDetrImageProcessor(BaseImageProcessor):
)
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
prob = prob.view(out_logits.shape[0], -1)
k_value = min(top_k, prob.size(1))
topk_values, topk_indexes = torch.topk(prob, k_value, dim=1)
scores = topk_values
topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
labels = topk_indexes % out_logits.shape[2]

8
src/transformers/models/deformable_detr/image_processing_deformable_detr.py
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@ -1325,7 +1325,7 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
return results
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100
):
"""
Converts the raw output of [`DeformableDetrForObjectDetection`] into final bounding boxes in (top_left_x,
@ -1339,6 +1339,8 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
(height, width) of each image in the batch. If left to None, predictions will not be resized.
top_k (`int`, *optional*, defaults to 100):
Keep only top k bounding boxes before filtering by thresholding.
Returns:
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
@ -1353,7 +1355,9 @@ class DeformableDetrImageProcessor(BaseImageProcessor):
)
prob = out_logits.sigmoid()
topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
prob = prob.view(out_logits.shape[0], -1)
k_value = min(top_k, prob.size(1))
topk_values, topk_indexes = torch.topk(prob, k_value, dim=1)
scores = topk_values
topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
labels = topk_indexes % out_logits.shape[2]