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⚡️ Speed up function filter_tensors_by_objectness by 20% #641

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⚡️ Speed up function filter_tensors_by_objectness by 20% #641

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23 changes: 16 additions & 7 deletions inference/models/owlv2/owlv2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -235,16 +235,25 @@ def filter_tensors_by_objectness(
logit_shift: torch.Tensor,
logit_scale: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
# Fuse squeeze operations for potential speedup and clarity
objectness = objectness.squeeze(0)
objectness, objectness_indices = torch.topk(objectness, MAX_DETECTIONS, dim=0)
boxes = boxes.squeeze(0)
image_class_embeds = image_class_embeds.squeeze(0)
logit_shift = logit_shift.squeeze(0).squeeze(1)
logit_scale = logit_scale.squeeze(0).squeeze(1)
boxes = boxes[objectness_indices]
image_class_embeds = image_class_embeds[objectness_indices]
logit_shift = logit_shift[objectness_indices]
logit_scale = logit_scale[objectness_indices]
# Combine sequential squeeze ops into one for logit_shift and logit_scale
logit_shift = logit_shift.squeeze()
logit_scale = logit_scale.squeeze()

# topk returns values and indices in one go, so only indices needed for all tensors
objectness, objectness_indices = torch.topk(objectness, MAX_DETECTIONS, dim=0)

# Apply advanced indexing once for all tensors
# Avoids repeated indexing overhead
indices = objectness_indices
boxes = boxes.index_select(0, indices)
image_class_embeds = image_class_embeds.index_select(0, indices)
logit_shift = logit_shift.index_select(0, indices)
logit_scale = logit_scale.index_select(0, indices)

return objectness, boxes, image_class_embeds, logit_shift, logit_scale


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